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10.1101/004010

MixMir: microRNA motif discovery from gene expression data using mixed linear models

LIYANG Diao;Antoine Marcais;Scott Norton;Kevin C. Chen;

Kevin C. Chen

Rutgers, The State University of New Jersey

2014-04-09

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/04/09/004010.source.xml

microRNAs (miRNAs) are a class of [~]22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. miRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir. Our method corrects for 3' UTR background sequence similarity between transcripts, which is known to correlate with mRNA transcript abundance. We demonstrate that after accounting for kmer sequence similarities in 3 UTRs, a statistical linear model based on motif presence/absence can effectively discover active miRNAs in a sample. MixMir utilizes fast software implementations for solving mixed linear models which are widely-used in genome-wide association studies (GWAS). Essentially we use 3 UTR sequence similarity in place of population cryptic relatedness in the GWAS problem. Compared to similar methods such as miReduce, Sylamer and cWords, we found that MixMir performed better at discovering true miRNA motifs in three mouse Dicer knockout experiments from different tissues, two of which were collected by our group. We confirmed these results on protein and mRNA expression data obtained from miRNA transfection experiments in human cell lines. MixMir can be freely downloaded from https://github.com/ldiao/MixMir.

10.1093/nar/gku672

biorxiv

10.1101/004010

MixMir: microRNA motif discovery from gene expression data using mixed linear models

LIYANG Diao;Antoine Marcais;Scott Norton;Kevin C. Chen;

Kevin C. Chen

Rutgers, The State University of New Jersey

2014-04-10

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/04/10/004010.source.xml

microRNAs (miRNAs) are a class of [~]22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. miRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir. Our method corrects for 3' UTR background sequence similarity between transcripts, which is known to correlate with mRNA transcript abundance. We demonstrate that after accounting for kmer sequence similarities in 3 UTRs, a statistical linear model based on motif presence/absence can effectively discover active miRNAs in a sample. MixMir utilizes fast software implementations for solving mixed linear models which are widely-used in genome-wide association studies (GWAS). Essentially we use 3 UTR sequence similarity in place of population cryptic relatedness in the GWAS problem. Compared to similar methods such as miReduce, Sylamer and cWords, we found that MixMir performed better at discovering true miRNA motifs in three mouse Dicer knockout experiments from different tissues, two of which were collected by our group. We confirmed these results on protein and mRNA expression data obtained from miRNA transfection experiments in human cell lines. MixMir can be freely downloaded from https://github.com/ldiao/MixMir.

10.1093/nar/gku672

biorxiv

10.1101/004010

MixMir: microRNA motif discovery from gene expression data using mixed linear models

LIYANG Diao;Antoine Marcais;Scott Norton;Kevin C. Chen;

Kevin C. Chen

Rutgers, The State University of New Jersey

2014-06-12

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/06/12/004010.source.xml

microRNAs (miRNAs) are a class of [~]22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. miRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir. Our method corrects for 3' UTR background sequence similarity between transcripts, which is known to correlate with mRNA transcript abundance. We demonstrate that after accounting for kmer sequence similarities in 3 UTRs, a statistical linear model based on motif presence/absence can effectively discover active miRNAs in a sample. MixMir utilizes fast software implementations for solving mixed linear models which are widely-used in genome-wide association studies (GWAS). Essentially we use 3 UTR sequence similarity in place of population cryptic relatedness in the GWAS problem. Compared to similar methods such as miReduce, Sylamer and cWords, we found that MixMir performed better at discovering true miRNA motifs in three mouse Dicer knockout experiments from different tissues, two of which were collected by our group. We confirmed these results on protein and mRNA expression data obtained from miRNA transfection experiments in human cell lines. MixMir can be freely downloaded from https://github.com/ldiao/MixMir.

10.1093/nar/gku672

biorxiv

10.1101/004101

Whole Genome Bisulfite Sequencing of Cell Free DNA and its Cellular Contributors Uncovers Placenta Hypomethylated Domains

Taylor Jensen;Sung K Kim;Zhanyang Zhu;Christine Chin;Claudia Gebhard;Tim Lu;Cosmin Deciu;Dirk van den Boom;Mathias Ehrich;

Taylor Jensen

Sequenom Laboratories

2014-04-11

New Results

cc_no

Genomics

https://www.biorxiv.org/content/early/2014/04/11/004101.source.xml

BackgroundCirculating cell free fetal DNA has enabled non-invasive prenatal fetal aneuploidy testing without direct discrimination of the genetically distinct maternal and fetal DNA. Current testing may be improved by specifically enriching the sample material for fetal DNA. DNA methylation may allow for such a separation of DNA and thus support additional clinical opportunities; however, this depends on knowledge of the methylomes of ccf DNA and its cellular contributors.\n\nResultsWhole genome bisulfite sequencing was performed on a set of unmatched samples including ccf DNA from 8 non-pregnant (NP) and 7 pregnant female donors and genomic DNA from 7 maternal buffy coat and 5 placenta samples. We found CpG cytosines within longer fragments were more likely to be methylated, linking DNA methylation and fragment size in ccf DNA. Comparison of the methylomes of placenta and NP ccf DNA revealed many of the 51,259 identified differentially methylated regions (DMRs) were located in domains exhibiting consistent placenta hypomethylation across millions of consecutive bases, regions we termed placenta hypomethylated domains (PHDs). We found PHDs were consistently located within regions exhibiting low CpG and gene density. DMRs identified when comparing placenta to NP ccf DNA were recapitulated in pregnant ccf DNA, confirming the ability to detect differential methylation in ccf DNA mixtures.\n\nConclusionsWe generated methylome maps for four sample types at single base resolution, identified a link between DNA methylation and fragment length in ccf DNA, identified DMRs between sample groups, and uncovered the presence of megabase-size placenta hypomethylated domains. Furthermore, we anticipate these results to provide a foundation to which future studies using discriminatory DNA methylation may be compared.

10.1186/s13059-015-0645-x

biorxiv

10.1101/004119

Natural CMT2 variation is associated with genome-wide methylation changes and temperature seasonality

Xia Shen;Jennifer De Jonge;Simon Forsberg;Mats Pettersson;Zheya Sheng;Lars Hennig;Örjan Carlborg;

Örjan Carlborg

Swedish University of Agricultural Sciences

2014-04-10

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/04/10/004119.source.xml

As Arabidopsis thaliana has colonized a wide range of habitats across the world it is an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we used public data from two collections of A. thaliana accessions to associate genetic variability at individual loci with differences in climates at the sampling sites. We use a novel method to screen the genome for plastic alleles that tolerate a broader climate range than the major allele. This approach reduces confounding with population structure and increases power compared to standard genome-wide association methods. Sixteen novel loci were found, including an association between Chromomethylase 2 (CMT2) and temperature seasonality where the genome-wide CHH methylation was different for the group of accessions carrying the plastic allele. Cmt2 mutants were shown to be more tolerant to heat-stress, suggesting genetic regulation of epigenetic modifications as a likely mechanism underlying natural adaptation to variable temperatures, potentially through differential allelic plasticity to temperature-stress.\n\nAUTHOR SUMMARYA central problem when studying adaptation to a new environment is the interplay between genetic variation and phenotypic plasticity. Arabidopsis thaliana has colonized a wide range of habitats across the world and it is therefore an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we study two collections of A. thaliana accessions from across Eurasia to identify loci associated with differences in climates at the sampling sites. A new genome-wide association analysis method was developed to detect adaptive loci where the alleles tolerate different climate ranges. Sixteen novel such loci were found including a strong association between Chromomethylase 2 (CMT2) and temperature seasonality. The reference allele dominated in areas with less seasonal variability in temperature, and the alternative allele existed in both stable and variable regions. Our results thus link natural variation in CMT2 and epigenetic changes to temperature adaptation. We showed experimentally that plants with a defective CMT2 gene tolerate heat-stress better than plants with a functional gene. Together this strongly suggests a role for genetic regulation of epigenetic modifications in natural adaptation to temperature and illustrates the importance of re-analyses of existing data using new analytical methods to obtain deeper insights into the underlying biology from available data.

10.1371/journal.pgen.1004842

biorxiv

10.1101/004119

Natural CMT2 variation is associated with genome-wide methylation changes and temperature seasonality

Xia Shen;Jennifer De Jonge;Simon Forsberg;Mats Pettersson;Zheya Sheng;Lars Hennig;Örjan Carlborg;

Örjan Carlborg

Swedish University of Agricultural Sciences

2014-10-03

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/10/03/004119.source.xml

As Arabidopsis thaliana has colonized a wide range of habitats across the world it is an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we used public data from two collections of A. thaliana accessions to associate genetic variability at individual loci with differences in climates at the sampling sites. We use a novel method to screen the genome for plastic alleles that tolerate a broader climate range than the major allele. This approach reduces confounding with population structure and increases power compared to standard genome-wide association methods. Sixteen novel loci were found, including an association between Chromomethylase 2 (CMT2) and temperature seasonality where the genome-wide CHH methylation was different for the group of accessions carrying the plastic allele. Cmt2 mutants were shown to be more tolerant to heat-stress, suggesting genetic regulation of epigenetic modifications as a likely mechanism underlying natural adaptation to variable temperatures, potentially through differential allelic plasticity to temperature-stress.\n\nAUTHOR SUMMARYA central problem when studying adaptation to a new environment is the interplay between genetic variation and phenotypic plasticity. Arabidopsis thaliana has colonized a wide range of habitats across the world and it is therefore an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we study two collections of A. thaliana accessions from across Eurasia to identify loci associated with differences in climates at the sampling sites. A new genome-wide association analysis method was developed to detect adaptive loci where the alleles tolerate different climate ranges. Sixteen novel such loci were found including a strong association between Chromomethylase 2 (CMT2) and temperature seasonality. The reference allele dominated in areas with less seasonal variability in temperature, and the alternative allele existed in both stable and variable regions. Our results thus link natural variation in CMT2 and epigenetic changes to temperature adaptation. We showed experimentally that plants with a defective CMT2 gene tolerate heat-stress better than plants with a functional gene. Together this strongly suggests a role for genetic regulation of epigenetic modifications in natural adaptation to temperature and illustrates the importance of re-analyses of existing data using new analytical methods to obtain deeper insights into the underlying biology from available data.

10.1371/journal.pgen.1004842

biorxiv

10.1101/004168

Neural lineage induction reveals multi-scale dynamics of 3D chromatin organization

Aleksandra Pekowska;Bernd Klaus;Felix Alexander Klein;Simon Anders;Malgorzata Oles;Lars Michael Steinmetz;Paul Bertone;Wolfgang Huber;

Aleksandra Pekowska

European Molecular Biology Laboratory (EMBL)

2014-04-11

New Results

cc_by_nc_nd

Molecular Biology

https://www.biorxiv.org/content/early/2014/04/11/004168.source.xml

Regulation of gene expression underlies cell identity. Chromatin structure and gene activity are linked at multiple levels, via positioning of genomic loci to transcriptionally permissive or repressive environments and by connecting cis-regulatory elements such as promoters and enhancers. However, the genome-wide dynamics of these processes during cell differentiation has not been characterized. Using tethered chromatin conformation capture (TCC) sequencing we determined global three-dimensional chromatin structures in mouse embryonic stem (ES) and neural stem (NS) cell derivatives. We found that changes in the propensity of genomic regions to form inter-chromosomal contacts are pervasive in neural induction and are associated with the regulation of gene expression. Moreover, we found a pronounced contribution of euchromatic domains to the intra-chromosomal interaction network of pluripotent cells, indicating the existence of an ES cell-specific mode of chromatin organization. Mapping of promoter-enhancer interactions in pluripotent and differentiated cells revealed that spatial proximity without enhancer element activity is a common architectural feature in cells undergoing early developmental changes. Activity-independent formation of higher-order contacts between cis-regulatory elements, predominant at complex loci, may thus provide an additional layer of transcriptional control.

biorxiv

10.1101/004135

Direct Reciprocity Under Uncertainty Does Not Explain One-shot Cooperation, but Demonstrates the Benefits of a Norm Psychology

Matthew Zefferman;

Matthew Zefferman

National Institute for Mathematical and Biological Synthesis, University of Tennessee

2014-04-11

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/11/004135.source.xml

Humans in many societies cooperate in economic experiments at much higher levels than would be expected if their goal was maximizing economic returns, even when their interactions are anonymous and one-shot. This is a puzzle because paying a cost to benefit another in one-shot interactions gives no direct or indirect benefits to the cooperator. This paper explores the logic of two competing evolutionary hypotheses to explain this behavior. The \"norm psychology\" hypothesis holds that a player's choice of strategy reflects socially-learned cultural norms. Its premise is that over the course of human evolutionary history, cultural norms varied considerably across human societies and through a process of gene-culture co-evolution, humans evolved mechanisms to learn and adopt the norms that are successful in their particular society. The \"mismatch\" hypothesis holds that pro-social preferences evolved genetically in our hunter-gatherer past where one-shot anonymous interactions were rare and these preferences are misapplied in modern laboratory settings. I compare these hypotheses by adopting a well-known model of the mismatch hypothesis and show that selection for one-shot cooperation in the model is an artifact of agents being constrained to only two strategies: Tit-for-Tat and Always Defect. Allowing for repentant and forgiving strategies reverses selection away from one-shot cooperation under all environmental parameters. Direct reciprocity does not necessarily lead to cooperation, but instead generates many different normative equilibria depending on a groups idiosyncratic evolutionary history. Therefore, an agent whose behavior is evoked solely from non-cultural environmental cues will be disadvantaged relative to an agent who learns the locally successful norms. Cooperation in one-shot laboratory experiments is thus more easily explained as the result of a psychology evolved for learning social norms than as a genetic mismatch.

10.1016/j.evolhumbehav.2014.04.003

biorxiv

10.1101/004127

Average oxidation state of carbon in proteins

Jeffrey M. Dick;

Jeffrey M. Dick

Curtin University

2014-04-14

New Results

cc_by

Biochemistry

https://www.biorxiv.org/content/early/2014/04/14/004127.source.xml

The degree of oxidation of carbon atoms in organic molecules depends on the covalent structure. In proteins, the average oxidation state of carbon (ZC) can be calculated as an elemental ratio from the chemical formula. To investigate oxidation-reduction (redox) patterns, groups of proteins from different subcellular locations and phylogenetic divisions were selected for comparison. Extracellular proteins of yeast have a relatively high oxidation state of carbon, corresponding with oxidizing conditions outside of the cell. However, an inverse relationship between ZC and redox potential occurs between the endoplasmic reticulum and cytoplasm; this trend is interpreted as resulting from overall coupling of protein turnover to the formation of a lower glutathione redox potential in the cytoplasm. In Rubisco homologues, lower ZC tends to occur in organisms with higher optimal growth temperature, and there are broad changes in ZC in whole-genome protein compositions in microbes from different environments. Energetic costs calculated from thermodynamic models suggest that thermophilic organisms exhibit molecular adaptation to not only high temperature but also the reducing nature of many hydrothermal fluids. A view of protein metabolism that depends on the chemical conditions of cells and environments raises new questions linking biochemical processes to changes on evolutionary timescales.

10.1098/rsif.2013.1095

biorxiv

10.1101/004184

Flexible methods for estimating genetic distances from nucleotide data

Simon Joly;David J Bryant;Peter J Lockhart;

Simon Joly

Montreal Botanical Garden

2014-04-14

New Results

cc_by

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/14/004184.source.xml

O_LIWith the increasing use of massively parallel sequencing approaches in evolutionary biology, the need for fast and accurate methods suitable to investigate genetic structure and evolutionary history are more important than ever. We propose new distance measures for estimating genetic distances between individuals when allelic variation, gene dosage and recombination could compromise standard approaches.\nC_LIO_LIWe present four distance measures based on single nucleotide polymorphisms (SNP) and evaluate them against previously published measures using coalescent-based simulations. Simulations were used to test (i) whether the measures give unbiased and accurate distance estimates, (ii) if they can accurately identify the genomic mixture of hybrid individuals and (iii) if they give precise (low variance) estimates.\nC_LIO_LIThe results showed that the SNP-based GENPOFAD distance we propose appears to work well in the widest circumstances. It was the most accurate method for estimating genetic distances and is also relatively good at estimating the genomic mixture of hybrid individuals.\nC_LIO_LIOur simulations provide benchmarks to compare the performance of different distance measures in specific situations.\nC_LI

10.1111/2041-210X.12343

biorxiv

10.1101/004184

Flexible methods for estimating genetic distances from nucleotide data

Simon Joly;David J Bryant;Peter J Lockhart;

Simon Joly

Montreal Botanical Garden

2014-11-28

New Results

cc_by

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/11/28/004184.source.xml

O_LIWith the increasing use of massively parallel sequencing approaches in evolutionary biology, the need for fast and accurate methods suitable to investigate genetic structure and evolutionary history are more important than ever. We propose new distance measures for estimating genetic distances between individuals when allelic variation, gene dosage and recombination could compromise standard approaches.\nC_LIO_LIWe present four distance measures based on single nucleotide polymorphisms (SNP) and evaluate them against previously published measures using coalescent-based simulations. Simulations were used to test (i) whether the measures give unbiased and accurate distance estimates, (ii) if they can accurately identify the genomic mixture of hybrid individuals and (iii) if they give precise (low variance) estimates.\nC_LIO_LIThe results showed that the SNP-based GENPOFAD distance we propose appears to work well in the widest circumstances. It was the most accurate method for estimating genetic distances and is also relatively good at estimating the genomic mixture of hybrid individuals.\nC_LIO_LIOur simulations provide benchmarks to compare the performance of different distance measures in specific situations.\nC_LI

10.1111/2041-210X.12343

biorxiv

10.1101/004176

A novel paradigm for auditory discrimination training with social reinforcement in songbirds

Kirill Tokarev;Ofer Tchernichovski;

Kirill Tokarev

Hunter College, CUNY

2014-04-14

New Results

cc_by_nc

Neuroscience

https://www.biorxiv.org/content/early/2014/04/14/004176.source.xml

Zebra finches are a highly social, gregarious, species and eagerly engage in vocal communication. We have developed a training apparatus that allows training zebra finches to discriminate socially reinforced and aversive vocal stimuli. In our experiments, juvenile male zebra finches were trained to discriminate a song that was followed by a brief air puff (aversive) and a song that allowed them to stay in visual contact with another bird, audience (social song). During training, the birds learned quickly to avoid air puffs by escaping the aversive song within 2 sec. They escaped significantly more aversive songs than socially reinforced ones, and this effect grew stronger with the number of training sessions. Therefore, we propose this training procedure as an effective method to teach zebra finches to discriminate between different auditory stimuli, which may also be used as a broader paradigm for addressing social reinforcement learning. The apparatus can be built from commercially available parts, and we are sharing the controlling software on our website.

biorxiv

10.1101/004218

Bridging the Genotype and the Phenotype: Towards An Epigenetic Landscape Approach to Evolutionary Systems Biology

Jose Davila-Velderrain;Elena R Alvarez-Buylla;

Elena R Alvarez-Buylla

Universidad Nacional Autonoma de Mexico

2014-04-14

New Results

cc_by_nc_nd

Systems Biology

https://www.biorxiv.org/content/early/2014/04/14/004218.source.xml

Understanding the mapping of genotypes into phenotypes is a central challenge of current biological research. Such mapping, conceptually represents a developmental mechanism through which phenotypic variation can be generated. Given the nongenetic character of developmental dynamics, phenotypic variation to a great extent has been neglected in the study of evolution. What is the relevance of considering this generative process in the study of evolution? How can we study its evolutionary consequences? Despite an historical systematic bias towards linear causation schemes in biology; in the post-genomic era, a systems-view to biology based on nonlinear (network) thinking is increasingly being adopted. Within this view, evolutionary dynamics can be studied using simple dynamical models of gene regulatory networks (GRNs). Through the study of GRN dynamics, genotypes and phenotypes can be unambiguously defined. The orchestrating role of GRNs constitutes an operational non-linear genotype-phenotype map. Further extension of these GRN models in order to explore and characterize an associated Epigenetic Landscape enables the study of the evolutionary consequences of both genetic and non-genetic sources of phenotypic variation within the same coherent theoretical framework. The merging of conceptually clear theories, computational/mathematical tools, and molecular/genomic data into coherent frameworks could be the basis for a transformation of biological research from mainly a descriptive exercise into a truly mechanistic, explanatory endeavor.

biorxiv

10.1101/004192

Epigenetic Landscape Models: The Post-Genomic Era

Jose Davila-Velderrain;Juan Carlos Martinez-Garcia;Elena R Alvarez-Buylla;

Elena R Alvarez-Buylla

Universidad Nacional Autonoma de Mexico

2014-04-14

Confirmatory Results

cc_by_nc_nd

Systems Biology

https://www.biorxiv.org/content/early/2014/04/14/004192.source.xml

Complex networks of regulatory interactions orchestrate developmental processes in multicellular organisms. Such a complex internal structure intrinsically constrains cellular behavior allowing only a reduced set of attainable and observable cellular states or cell types. Thus, a multicellular system undergoes cell fate decisions in a robust manner in the course of its normal development. The epigenetic landscape (EL) model originally proposed by C.H. Waddington was an early attempt to integrate these processes in a universal conceptual model of development. Since then, a wealth of experimental data has accumulated, the general mechanisms of gene regulation have been uncovered, and the placement of specific molecular components within modular gene regulatory networks (GRN) has become a common practice. This has motivated the development of mathematical and computational models inspired by the EL aiming to integrate molecular data and gain a better understanding of development, and hopefully predict cell differentiation and reprogramming events. Both deterministic and stochastic dynamical models have been used to described cell state transitions. In this review, we describe recent EL models, emphasising that the construction of an explicit landscape from a GRN is not the only way to implement theoretical models consistent with the conceptual basis of the EL. Moreover, models based on the EL have been shown to be useful in the study of morphogenic processes and not just cell differentiation. Here we describe the distinct approaches, comparing their strengths and weaknesses and the kind of biological questions that they have been able to address. We also point to challenges ahead.

10.1186/s12918-015-0166-y

biorxiv

10.1101/004192

Epigenetic Landscape Models: The Post-Genomic Era

Jose Davila-Velderrain;Juan Carlos Martinez-Garcia;Elena R Alvarez-Buylla;

Elena R Alvarez-Buylla

Universidad Nacional Autonoma de Mexico

2015-02-28

Confirmatory Results

cc_by_nc_nd

Systems Biology

https://www.biorxiv.org/content/early/2015/02/28/004192.source.xml

Complex networks of regulatory interactions orchestrate developmental processes in multicellular organisms. Such a complex internal structure intrinsically constrains cellular behavior allowing only a reduced set of attainable and observable cellular states or cell types. Thus, a multicellular system undergoes cell fate decisions in a robust manner in the course of its normal development. The epigenetic landscape (EL) model originally proposed by C.H. Waddington was an early attempt to integrate these processes in a universal conceptual model of development. Since then, a wealth of experimental data has accumulated, the general mechanisms of gene regulation have been uncovered, and the placement of specific molecular components within modular gene regulatory networks (GRN) has become a common practice. This has motivated the development of mathematical and computational models inspired by the EL aiming to integrate molecular data and gain a better understanding of development, and hopefully predict cell differentiation and reprogramming events. Both deterministic and stochastic dynamical models have been used to described cell state transitions. In this review, we describe recent EL models, emphasising that the construction of an explicit landscape from a GRN is not the only way to implement theoretical models consistent with the conceptual basis of the EL. Moreover, models based on the EL have been shown to be useful in the study of morphogenic processes and not just cell differentiation. Here we describe the distinct approaches, comparing their strengths and weaknesses and the kind of biological questions that they have been able to address. We also point to challenges ahead.

10.1186/s12918-015-0166-y

biorxiv

10.1101/004226

Building a better frog trap: The benefits of mal-adaptive habitat choice for metapopulations with different life history strategies

Rosemary Hartman;Noam Ross;

Rosemary Hartman

University of California Davis

2014-04-15

New Results

cc_by_nc_nd

Ecology

https://www.biorxiv.org/content/early/2014/04/15/004226.source.xml

By spatially distributing offspring among several habitat patches in varying environments, an organism can \"hedge its bets\" to protect against bad conditions in any single patch. This strategy can maintain populations even when some or even all locations are, on average, population sinks. However, species may not have evolved this bet-hedging mechanism, especially when sink environments are anthropogenic \"traps\" - locations where traditional habitat cues have been altered. Using a model based on the life history of the Cascades frog (Rana cascadae), we examine the conditions that maximize growth in an environment with ecological traps created by the introduction of predators. In a temporally stochastic environment, maximum growth rates occur when some juveniles disperse to the trap. We then examine how different life histories and predation regimes affect the ability of an organism gain an advantage by bet-hedging, and find that bet-hedging can be less useful when the ecological trap drives adult, rather than juvenile, mortality.

biorxiv

10.1101/004234

Genetic Influences on Brain Gene Expression in Rats Selected for Tameness and Aggression

Henrike Heyne;Susann Lautenschlaeger;Ronald Nelson;François Besnier;Maxime Rotival;Alexander Cagan;Rimma Kozhemyakina;Irina Plyusnina;Lyudmila Trut;Orjan Carlborg;Enrico Petretto;Leonid Kruglyak;Svante Pääbo;Torsten Schoeneberg;Frank Albert;

Henrike Heyne

University of Leipzig

2014-04-17

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/04/17/004234.source.xml

Inter-individual differences in many behaviors are partly due to genetic differences, but the identification of the genes and variants that influence behavior remains challenging. Here, we studied an F2 intercross of two outbred lines of rats selected for tame and aggressive behavior towards humans for more than 64 generations. By using a mapping approach that is able to identify genetic loci segregating within the lines, we identified four times more loci influencing tameness and aggression than by an approach that assumes fixation of causative alleles, suggesting that many causative loci were not driven to fixation by the selection. We used RNA sequencing in 150 F2 animals to identify hundreds of loci that influence brain gene expression. Several of these loci colocalize with tameness loci and may reflect the same genetic variants. Through analyses of correlations between allele effects on behavior and gene expression, differential expression between the tame and aggressive rat selection lines, and correlations between gene expression and tameness in F2 animals, we identify the genes Gltscr2, Lgi4, Zfp40 and Slc17a7 as candidate contributors to the strikingly different behavior of the tame and aggressive animals.

10.1534/genetics.114.168948

biorxiv

10.1101/004267

Gradual divergence and diversification of mammalian duplicate gene functions

Raquel Assis;Doris Bachtrog;

Raquel Assis

Pennsylvania State University

2014-04-17

New Results

cc_no

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/17/004267.source.xml

Gene duplication provides raw material for the evolution of functional innovation. We recently developed a phylogenetic method to classify the evolutionary processes underlying the retention and functional evolution of duplicate genes by quantifying divergence of their gene expression profiles. Here, we apply our method to pairs of duplicate genes in eight mammalian genomes, using data from 11 distinct tissues to construct spatial gene expression profiles. We find that young mammalian duplicates are often functionally conserved, and that functional divergence gradually increases with evolutionary distance between species. Examination of expression patterns in genes with conserved and new functions supports the \"out-of-testes\" hypothesis, in which new genes arise with testis-specific functions and acquire functions in other tissues over time. While new functions tend to be tissue-specific, there is no bias toward expression in any particular tissue. Thus, duplicate genes acquire a diversity of functions outside of the testes, possibly contributing to the origin of a multitude of complex phenotypes during mammalian evolution.

biorxiv

10.1101/004283

Weight Loss in Response to Food Deprivation Predicts The Extent of Diet Induced Obesity in C57BL/6J Mice

Matthew J. Peloqiun;Dave Bridges;

Dave Bridges

UTHSC

2014-04-17

New Results

cc_by

Physiology

https://www.biorxiv.org/content/early/2014/04/17/004283.source.xml

Inbred C57BL/6J mice have been used to study diet-induced obesity and the detrimental physiological effects associated with it. Little is understood about predictive factors that predispose an animal to weight gain. To address this, mice were fed a high fat diet, control diet or normal chow diet. Several measurements including pre-diet serum hormone levels and pre-diet body weight were analyzed, but these had limited predictive value regarding weight gain. However, baseline measurements of weight loss in response to food deprivation showed a strong negative correlation with high fat diet-induced weight gain. These data suggest that fasting-induced weight loss in adolescent mice is a useful predictor of diet-induced weight gain.

biorxiv

10.1101/004283

Weight Loss in Response to Food Deprivation Predicts The Extent of Diet Induced Obesity in C57BL/6J Mice

Matthew J. Peloqiun;Dave Bridges;

Dave Bridges

UTHSC

2014-04-23

New Results

cc_by

Physiology

https://www.biorxiv.org/content/early/2014/04/23/004283.source.xml

Inbred C57BL/6J mice have been used to study diet-induced obesity and the detrimental physiological effects associated with it. Little is understood about predictive factors that predispose an animal to weight gain. To address this, mice were fed a high fat diet, control diet or normal chow diet. Several measurements including pre-diet serum hormone levels and pre-diet body weight were analyzed, but these had limited predictive value regarding weight gain. However, baseline measurements of weight loss in response to food deprivation showed a strong negative correlation with high fat diet-induced weight gain. These data suggest that fasting-induced weight loss in adolescent mice is a useful predictor of diet-induced weight gain.

biorxiv

10.1101/004283

Weight Loss in Response to Food Deprivation Predicts The Extent of Diet Induced Obesity in C57BL/6J Mice

Matthew J. Peloqiun;Dave Bridges;

Dave Bridges

UTHSC

2014-06-30

New Results

cc_by

Physiology

https://www.biorxiv.org/content/early/2014/06/30/004283.source.xml

Inbred C57BL/6J mice have been used to study diet-induced obesity and the detrimental physiological effects associated with it. Little is understood about predictive factors that predispose an animal to weight gain. To address this, mice were fed a high fat diet, control diet or normal chow diet. Several measurements including pre-diet serum hormone levels and pre-diet body weight were analyzed, but these had limited predictive value regarding weight gain. However, baseline measurements of weight loss in response to food deprivation showed a strong negative correlation with high fat diet-induced weight gain. These data suggest that fasting-induced weight loss in adolescent mice is a useful predictor of diet-induced weight gain.

biorxiv

10.1101/004291

Quantitative comparison of single-cell sequencing methods using hippocampal neurons

Luwen Ning;Guan Wang;Zhoufang Li;Wen Hu;Qingming Hou;Yin Tong;Meng Zhang;Li Qin;Xiaoping Chen;Hengye Man;Pinghua Liu;Jiankui He;

Jiankui He

South University of Science and Technology of China

2014-04-18

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/04/18/004291.source.xml

Single-cell genomic analysis has grown rapidly in recent years and will find widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. In this study, we amplified genomic DNA from individual hippocampal neurons using one of three single-cell DNA amplification methods (multiple annealing and looping-based amplification cycles (MALBAC), multiple displacement amplification (MDA), and GenomePlex whole genome amplification (WGA4)). We then systematically evaluated the genome coverage, GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. Chromosome-level and subchromosomal-level copy number variations among individual neurons can be detected.

biorxiv

10.1101/004317

Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes

Alexandre Melnikov;Peter Rogov;Li Wang;Andreas Gnirke;Tarjei S Mikkelsen;

Tarjei S Mikkelsen

Broad Institute

2014-04-18

New Results

cc_no

Molecular Biology

https://www.biorxiv.org/content/early/2014/04/18/004317.source.xml

Deep mutational scanning has emerged as a promising tool for mapping sequence-activity relationships in proteins1-4, RNA5 and DNA6-8. In this approach, diverse variants of a sequence of interest are first ranked according to their activities in a relevant pooled assay, and this ranking is then used to infer the shape of the fitness landscape around the wild-type sequence. Little is currently know, however, about the degree to which such fitness landscapes are dependent on the specific assay conditions from which they are inferred. To explore this issue, we performed deep mutational scanning of APH(3)II, a Tn5 transposon-derived kinase that confers resistance to aminoglycoside antibiotics9, in E. coli under selection with each of six structurally diverse antibiotics at a range of inhibitory concentrations. We found that the resulting fitness landscapes showed significant dependence on both antibiotic structure and concentration. This shows that the notion of essential amino acid residues is context-dependent, but also that this dependence can be exploited to guide protein engineering. Specifically, we found that differential analysis of fitness landscapes allowed us to generate synthetic APH(3)II variants with orthogonal substrate specificities.

10.1093/nar/gku511

biorxiv

10.1101/004317

Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes

Alexandre Melnikov;Peter Rogov;Li Wang;Andreas Gnirke;Tarjei S Mikkelsen;

Tarjei S Mikkelsen

Broad Institute

2014-04-21

New Results

cc_no

Molecular Biology

https://www.biorxiv.org/content/early/2014/04/21/004317.source.xml

Deep mutational scanning has emerged as a promising tool for mapping sequence-activity relationships in proteins1-4, RNA5 and DNA6-8. In this approach, diverse variants of a sequence of interest are first ranked according to their activities in a relevant pooled assay, and this ranking is then used to infer the shape of the fitness landscape around the wild-type sequence. Little is currently know, however, about the degree to which such fitness landscapes are dependent on the specific assay conditions from which they are inferred. To explore this issue, we performed deep mutational scanning of APH(3)II, a Tn5 transposon-derived kinase that confers resistance to aminoglycoside antibiotics9, in E. coli under selection with each of six structurally diverse antibiotics at a range of inhibitory concentrations. We found that the resulting fitness landscapes showed significant dependence on both antibiotic structure and concentration. This shows that the notion of essential amino acid residues is context-dependent, but also that this dependence can be exploited to guide protein engineering. Specifically, we found that differential analysis of fitness landscapes allowed us to generate synthetic APH(3)II variants with orthogonal substrate specificities.

10.1093/nar/gku511

biorxiv

10.1101/004317

Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes

Alexandre Melnikov;Peter Rogov;Li Wang;Andreas Gnirke;Tarjei S Mikkelsen;

Tarjei S Mikkelsen

Broad Institute

2014-06-10

New Results

cc_no

Molecular Biology

https://www.biorxiv.org/content/early/2014/06/10/004317.source.xml

Deep mutational scanning has emerged as a promising tool for mapping sequence-activity relationships in proteins1-4, RNA5 and DNA6-8. In this approach, diverse variants of a sequence of interest are first ranked according to their activities in a relevant pooled assay, and this ranking is then used to infer the shape of the fitness landscape around the wild-type sequence. Little is currently know, however, about the degree to which such fitness landscapes are dependent on the specific assay conditions from which they are inferred. To explore this issue, we performed deep mutational scanning of APH(3)II, a Tn5 transposon-derived kinase that confers resistance to aminoglycoside antibiotics9, in E. coli under selection with each of six structurally diverse antibiotics at a range of inhibitory concentrations. We found that the resulting fitness landscapes showed significant dependence on both antibiotic structure and concentration. This shows that the notion of essential amino acid residues is context-dependent, but also that this dependence can be exploited to guide protein engineering. Specifically, we found that differential analysis of fitness landscapes allowed us to generate synthetic APH(3)II variants with orthogonal substrate specificities.

10.1093/nar/gku511

biorxiv

10.1101/004242

Exact Reconstruction of Gene Regulatory Networks using Compressive Sensing

Young Hwan Chang;Joe W. Gray;Claire J. Tomlin;

Claire J. Tomlin

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley

2014-04-20

New Results

cc_no

Systems Biology

https://www.biorxiv.org/content/early/2014/04/20/004242.source.xml

BackgroundWe consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the networks sparseness.\n\nResultsFor the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. For each problem, a set of numerical examples is presented.\n\nConclusionsThe method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

10.1186/s12859-014-0400-4

biorxiv

10.1101/004325

Behavioral variation in Drosophila melanogaster: no evidence for common alleles of large- effect at the foraging gene in a population from North Carolina, USA

Thomas Turner;Christopher C Giauque;Daniel R Schrider;Andrew D Kern;

Thomas Turner

University of California, Santa Barbara

2014-04-21

New Results

cc_by

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/21/004325.source.xml

Thirty four years ago, it was postulated that natural populations of Drosophila melanogaster are comprised of two behavioral morphs termed \"rover\" and \"sitter\", and that this variation is caused mainly by large-effect alleles at a single locus. Since that time, considerable data has been amassed that compares the behavior and physiology of these morphs. Contrary to common assertions, however, published support for the existence of common large effect alleles in nature is quite limited. To further investigate, we quantified the foraging behavior of 36 natural strains, performed a genome-wide association study, and described patterns of molecular evolution at the foraging locus. Though there was significant variation in foraging behavior among genotypes, this variation was continuously distributed and not significantly associated with genetic variation at the foraging gene. Patterns of molecular population genetic variation at this gene also provide no support for the hypothesis that for is a target of long term balancing selection We propose that additional data is required to support a hypothesis of common alleles of large effect on foraging behavior in nature. Genome-wide association does support a role for natural variation at several other loci, including the sulfateless gene, though these associations should be considered preliminary until validated with a larger sample size.

biorxiv

10.1101/004325

Behavioral variation in Drosophila melanogaster: no evidence for common alleles of large- effect at the foraging gene in a population from North Carolina, USA

Thomas Turner;Christopher C Giauque;Daniel R Schrider;Andrew D Kern;

Thomas Turner

University of California, Santa Barbara

2015-01-16

New Results

cc_by

Evolutionary Biology

https://www.biorxiv.org/content/early/2015/01/16/004325.source.xml

Thirty four years ago, it was postulated that natural populations of Drosophila melanogaster are comprised of two behavioral morphs termed \"rover\" and \"sitter\", and that this variation is caused mainly by large-effect alleles at a single locus. Since that time, considerable data has been amassed that compares the behavior and physiology of these morphs. Contrary to common assertions, however, published support for the existence of common large effect alleles in nature is quite limited. To further investigate, we quantified the foraging behavior of 36 natural strains, performed a genome-wide association study, and described patterns of molecular evolution at the foraging locus. Though there was significant variation in foraging behavior among genotypes, this variation was continuously distributed and not significantly associated with genetic variation at the foraging gene. Patterns of molecular population genetic variation at this gene also provide no support for the hypothesis that for is a target of long term balancing selection We propose that additional data is required to support a hypothesis of common alleles of large effect on foraging behavior in nature. Genome-wide association does support a role for natural variation at several other loci, including the sulfateless gene, though these associations should be considered preliminary until validated with a larger sample size.

biorxiv

10.1101/004374

Phylogenetic ANOVA: The Expression Variance and Evolution (EVE) model for quantitative trait evolution

Rori Rohlfs;Rasmus Nielsen;

Rori Rohlfs

University of California, Berkeley

2014-04-21

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/21/004374.source.xml

A number of methods have been developed for modeling the evolution of a quantitative trait on a phylogeny. These methods have received renewed interest in the context of genome-wide studies of gene expression, in which the expression levels of many genes can be modeled as quantitative traits. We here develop a new method for joint analyses of quantitative traits within and between-species, the Expression Variance and Evolution (EVE) model. The model parameterizes the ratio of population to evolutionary expression variance, facilitating a wide variety of analyses, including a test for lineage-specific shifts in expression level, and a phylogenetic ANOVA that can detect genes with increased or decreased ratios of expression divergence to diversity, analogous to the famous HKA test used to detect selection at the DNA level. We use simulations to explore the properties of these tests under a variety of circumstances and show that the phylogenetic ANOVA is more accurate than the standard ANOVA (no accounting for phylogeny) sometimes used in transcriptomics. We then apply the EVE model to a mammalian phylogeny of 15 species typed for expression levels in liver tissue. We identify genes with high expression divergence between-species as candidates for expression level adaptation, and genes with high expression diversity within-species as candidates for expression level conservation and/or plasticity. Using the test for lineage-specific expression shifts, we identify several candidate genes for expression level adaptation on the catarrhine and human lineages, including genes putatively related to dietary changes in humans. We compare these results to those reported previously using a model which ignores expression variance within-species, uncovering important differences in performance. We demonstrate the necessity for a phylogenetic model in comparative expression studies and show the utility of the EVE model to detect expression divergence, diversity, and branch-specific shifts.

10.1093/sysbio/syv042

biorxiv

10.1101/004374

Phylogenetic ANOVA: The Expression Variance and Evolution (EVE) model for quantitative trait evolution

Rori Rohlfs;Rasmus Nielsen;

Rori Rohlfs

University of California, Berkeley

2015-06-02

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2015/06/02/004374.source.xml

A number of methods have been developed for modeling the evolution of a quantitative trait on a phylogeny. These methods have received renewed interest in the context of genome-wide studies of gene expression, in which the expression levels of many genes can be modeled as quantitative traits. We here develop a new method for joint analyses of quantitative traits within and between-species, the Expression Variance and Evolution (EVE) model. The model parameterizes the ratio of population to evolutionary expression variance, facilitating a wide variety of analyses, including a test for lineage-specific shifts in expression level, and a phylogenetic ANOVA that can detect genes with increased or decreased ratios of expression divergence to diversity, analogous to the famous HKA test used to detect selection at the DNA level. We use simulations to explore the properties of these tests under a variety of circumstances and show that the phylogenetic ANOVA is more accurate than the standard ANOVA (no accounting for phylogeny) sometimes used in transcriptomics. We then apply the EVE model to a mammalian phylogeny of 15 species typed for expression levels in liver tissue. We identify genes with high expression divergence between-species as candidates for expression level adaptation, and genes with high expression diversity within-species as candidates for expression level conservation and/or plasticity. Using the test for lineage-specific expression shifts, we identify several candidate genes for expression level adaptation on the catarrhine and human lineages, including genes putatively related to dietary changes in humans. We compare these results to those reported previously using a model which ignores expression variance within-species, uncovering important differences in performance. We demonstrate the necessity for a phylogenetic model in comparative expression studies and show the utility of the EVE model to detect expression divergence, diversity, and branch-specific shifts.

10.1093/sysbio/syv042

biorxiv

10.1101/004309

Regulatory variants explain much more heritability than coding variants across 11 common diseases

Alexander Gusev;S Hong Lee;Benjamin M Neale;Gosia Trynka;Bjarni J Vilhjalmsson;Hilary Finucane;Han Xu;Chongzhi Zang;Stephan Ripke;Eli Stahl;n/a Schizophrenia Working Group of the PGC;n/a SWE-SCZ Consortium;Anna K Kahler;Christina M Hultman;Shaun M Purcell;Steven A McCarroll;Mark Daly;Bogdan Pasaniuc;Patrick F Sullivan;Naomi R Wray;Soumya Raychaudhuri;Alkes L Price;

Alexander Gusev

Harvard School of Public Health

2014-04-21

New Results

cc_by

Genetics

https://www.biorxiv.org/content/early/2014/04/21/004309.source.xml

Common variants implicated by genome-wide association studies (GWAS) of complex diseases are known to be enriched for coding and regulatory variants. We applied methods to partition the heritability explained by genotyped SNPs [Formula] across functional categories (while accounting for shared variance due to linkage disequilibrium) to genotype and imputed data for 11 common diseases. DNaseI Hypersensitivity Sites (DHS) from 218 cell-types, spanning 16% of the genome, explained an average of 79% of [Formula] (5.1x enrichment; P < 10-20); further enrichment was observed at enhancer and cell-type specific DHS elements. The enrichments were much smaller in analyses that did not use imputed data or were restricted to GWAS-associated SNPs. In contrast, coding variants, spanning 1% of the genome, explained only 8% of [Formula] enrichment; P = 5 x 10-4). We replicated these findings but found no significant contribution from rare coding variants in an independent schizophrenia cohort genotyped on GWAS and exome chips.

biorxiv

10.1101/004341

Quantifying selection in immune receptor repertoires

Yuval Elhanati;Anand Murugan;Curtis G Callan Jr.;Thierry Mora;Aleksandra Walczak;

Thierry Mora

Ecole Normale Supérieure

2014-04-21

New Results

cc_no

Immunology

https://www.biorxiv.org/content/early/2014/04/21/004341.source.xml

The efficient recognition of pathogens by the adaptive immune system relies on the diversity of receptors displayed at the surface of immune cells. T-cell receptor diversity results from an initial random DNA editing process, called VDJ recombination, followed by functional selection of cells according to the interaction of their surface receptors with self and foreign antigenic peptides. To quantify the effect of selection on the highly variable elements of the receptor, we apply a probabilistic maximum likelihood approach to the analysis of high-throughput sequence data from the {beta}-chain of human T-cell receptors. We quantify selection factors for V and J gene choice, and for the length and amino-acid composition of the variable region. Our approach is necessary to disentangle the effects of selection from biases inherent in the recombination process. Inferred selection factors differ little between donors, or between naive and memory repertoires. The number of sequences shared between donors is well-predicted by the model, indicating a purely stochastic origin of such \"public\" sequences. We find a significant correlation between biases induced by VDJ recombination and our inferred selection factors, together with a reduction of diversity during selection. Both effects suggest that natural selection acting on the recombination process has anticipated the selection pressures experienced during somatic evolution.

10.1073/pnas.1409572111

biorxiv

10.1101/004366

Quantitative comparison of single-cell sequencing methods using hippocampal neurons

Luwen Ning;Guan Wang;Zhoufang Li;Wen Hu;Qingming Hou;Yin Tong;Meng Zhang;Li Qin;Xiaoping Chen;Heng-Ye Man;Pinghua Liu;Jiankui He;

Jiankui He

South University of Science and Technology of China

2014-04-21

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/04/21/004366.source.xml

Single-cell genomic analysis has grown rapidly in recent years and will find widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. In this study, we amplified genomic DNA from individual hippocampal neurons using one of three single-cell DNA amplification methods (multiple annealing and looping-based amplification cycles (MALBAC), multiple displacement amplification (MDA), and GenomePlex whole genome amplification (WGA4)). We then systematically evaluated the genome coverage, GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. Chromosome-level and subchromosomal-level copy number variations among individual neurons can be detected.

biorxiv

10.1101/004390

Notorious Novel Avian Influenza Viruses H10N8 and H7N9 in China in 2013 Co-originated from H9N2

Liang Chen;Feng Zhu;Chenglong Xiong;Zhijie Zhang;Lufang Jiang;Rui Li;Genming Zhao;Yue Chen;Qingwu Jiang;

Chenglong Xiong

Department of Sanitary Microbiology, School of Public Health, Fudan University

2014-04-21

New Results

cc_by

Pathology

https://www.biorxiv.org/content/early/2014/04/21/004390.source.xml

In 2013, two new avian influenza viruses (AIVs) H7N9 and H10N8 emerged in China caused worldwide concerns. Previous studies have studied their originations independently; this study is the first time to investigate their co-originating characteristics. Gene segments of assorted subtype influenza A viruses, as well as H10N8 and H7N9, were collected from public database. 26 With the help of series software, small and large-scale phylogenetic trees, mean evolutionary rates, and divergence years were obtained successionally. The results demonstrated the two AIVs co-originated from H9N2, and shared a spectrum of mutations in common on many key sites related to pathogenic, tropism and epidemiological characteristics. For a long time, H9N2 viruses had been circulated in eastern and southern China; poultry was the stable and lasting maintenance reservoir. High carrying rate of AIVs H9N2 in poultry had an extremely high risk of co-infections with other influenza viruses, which increased the risk of virus reassortment. It implied that novel AIVs reassortants based on H9N2 might appear and prevail at any time in China; therefore, surveillance of H9N2 AIVs should be given a high priority.

biorxiv

10.1101/004457

Cell size regulation in bacteria

Ariel Amir;

Ariel Amir

Harvard University

2014-04-23

New Results

cc_no

Biophysics

https://www.biorxiv.org/content/early/2014/04/23/004457.source.xml

Various bacteria such as the canonical gram negative Escherichia coli or the well-studied gram positive Bacillus subtilis divide symmetrically after they approximately double their volume. Their size at division is not constant, but is typically distributed over a narrow range. Here, we propose an analytically tractable model for cell size control, and calculate the cell size and inter-division time distributions, and the correlations between these variables. We suggest ways of extracting the model parameters from experimental data, and show that existing data for E. coli supports partial size control, and a particular explanation: a cell attempts to add a constant volume from the time of initiation of DNA replication to the next initiation event. This hypothesis accounts for the experimentally observed correlations between mother and daughter cells as well as the exponential dependence of size on growth rate.\n\nPACS numbers: 87.17.Ee, 87.17.Aa, 87.10.Mn, 87.81Tt

10.1103/PhysRevLett.112.208102

biorxiv

10.1101/004457

Cell size regulation in bacteria

Ariel Amir;

Ariel Amir

Harvard University

2014-04-24

New Results

cc_no

Biophysics

https://www.biorxiv.org/content/early/2014/04/24/004457.source.xml

Various bacteria such as the canonical gram negative Escherichia coli or the well-studied gram positive Bacillus subtilis divide symmetrically after they approximately double their volume. Their size at division is not constant, but is typically distributed over a narrow range. Here, we propose an analytically tractable model for cell size control, and calculate the cell size and inter-division time distributions, and the correlations between these variables. We suggest ways of extracting the model parameters from experimental data, and show that existing data for E. coli supports partial size control, and a particular explanation: a cell attempts to add a constant volume from the time of initiation of DNA replication to the next initiation event. This hypothesis accounts for the experimentally observed correlations between mother and daughter cells as well as the exponential dependence of size on growth rate.\n\nPACS numbers: 87.17.Ee, 87.17.Aa, 87.10.Mn, 87.81Tt

10.1103/PhysRevLett.112.208102

biorxiv

10.1101/004424

Soft selective sweeps in complex demographic scenarios

Benjamin A Wilson;Dmitri Petrov;Philipp W Messer;

Benjamin A Wilson

Stanford University

2014-04-23

New Results

cc_by_nc

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/23/004424.source.xml

Recent studies have shown that adaptation from de novo mutation often produces so-called soft selective sweeps, where adaptive mutations of independent mutational origin sweep through the population at the same time. Population genetic theory predicts that soft sweeps should be likely if the product of the population size and the mutation rate towards the adaptive allele is sufficiently large, such that multiple adaptive mutations can establish before one has reached fixation; however, it remains unclear how demographic processes affect the probability of observing soft sweeps. Here we extend the theory of soft selective sweeps to realistic demographic scenarios that allow for changes in population size over time. We first show that population bottlenecks can lead to the removal of all but one adaptive lineage from an initially soft selective sweep. The parameter regime under which such 'hardening' of soft selective sweeps is likely is determined by a simple heuristic condition. We further develop a generalized analytical framework, based on an extension of the coalescent process, for calculating the probability of soft sweeps under arbitrary demographic scenarios. Two important limits emerge within this analytical framework: In the limit where population size fluctuations are fast compared to the duration of the sweep, the likelihood of soft sweeps is determined by the harmonic mean of the variance effective population size estimated over the duration of the sweep; in the opposing slow fluctuation limit, the likelihood of soft sweeps is determined by the instantaneous variance effective population size at the onset of the sweep. We show that as a consequence of this finding the probability of observing soft sweeps becomes a function of the strength of selection. Specifically, in species with sharply fluctuating population size, strong selection is more likely to produce soft sweeps than weak selection. Our results highlight the importance of accurate demographic estimates over short evolutionary timescales for understanding the population genetics of adaptation from de novo mutation.

10.1534/genetics.114.165571

biorxiv

10.1101/004440

The evolution of genetic diversity in changing environments

Oana Carja;Uri Liberman;Marcus W. Feldman;

Oana Carja

Stanford University

2014-04-23

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/23/004440.source.xml

The production and maintenance of genetic and phenotypic diversity under temporally fluctuating selection and the signatures of environmental and selective volatility in the patterns of genetic and phenotypic variation have been important areas of focus in population genetics. On one hand, stretches of constant selection pull the genetic makeup of populations towards local fitness optima. On the other, in order to cope with changes in the selection regime, populations may evolve mechanisms that create a diversity of genotypes. By tuning the rates at which variability is produced, such as the rates of recombination, mutation or migration, populations may increase their long-term adaptability. Here we use theoretical models to gain insight into how the rates of these three evolutionary forces are shaped by fluctuating selection. We compare and contrast the evolution of recombination, mutation and migration under similar patterns of environmental change and show that these three sources of phenotypic variation are surprisingly similar in their response to changing selection. We show that knowing the shape, size, variance and asymmetry of environmental runs is essential for accurate prediction of genetic evolutionary dynamics.

10.1073/pnas.1417664111

biorxiv

10.1101/004465

A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data

Josef C Uyeda;Luke J Harmon;

Josef C Uyeda

University of Idaho

2014-04-23

New Results

cc_by_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/23/004465.source.xml

Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model-fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data--such as microevolutionary estimates of selection parameters and paleontological timeseries--allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.

10.1093/sysbio/syu057

biorxiv

10.1101/004432

An integrated RNA and CRISPR/Cas toolkit for multiplexed synthetic circuits and endogenous gene regulation in human cells

Lior Nissim;Samuel D Perli;Alexandra Fridkin;Pablo Perez-Pinera;Timothy Lu;

Timothy Lu

Massachusetts Institute of Technology

2014-04-23

New Results

cc_no

Synthetic Biology

https://www.biorxiv.org/content/early/2014/04/23/004432.source.xml

RNA-based regulation, such as RNA interference, and CRISPR/Cas transcription factors (CRISPR-TFs), can enable scalable synthetic gene circuits and the modulation of endogenous networks but have yet to be integrated together. Here, we combined multiple mammalian RNA regulatory strategies, including RNA triple helix structures, introns, microRNAs, and ribozymes, with Cas9-based CRISPR-TFs and Cas6/Csy4-based RNA processing in human cells. We describe three complementary strategies for expressing functional gRNAs from transcripts generated by RNA polymerase II (RNAP II) promoters while allowing the harboring gene to be translated. These architectures enable the multiplexed expression of proteins and multiple gRNAs from a single compact transcript for efficient modulation of synthetic constructs and endogenous human promoters. We used these regulatory tools to implement tunable synthetic gene circuits, including multi-stage transcriptional cascades. Finally, we show that Csy4 can rewire regulatory connections in RNA-dependent gene circuits with multiple outputs and feedback loops to achieve complex functional behaviors. This multiplexable toolkit will be valuable for the construction of scalable gene circuits and the perturbation of natural regulatory networks in human cells for basic biology, therapeutic, and synthetic-biology applications.

biorxiv

10.1101/004259

Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses

Young Hwan Chang;Jim Korkola;Dhara N. Amin;Mark M. Moasser;Jose M. Carmena;Joe W. Gray;Claire J. Tomlin;

Claire J. Tomlin

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley

2014-04-23

New Results

cc_no

Systems Biology

https://www.biorxiv.org/content/early/2014/04/23/004259.source.xml

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight across multiple dimensions. For this potential to be realized, we need a suitable representation to understand the data. Since a wide range of experiments and the unknown complexity of the underlying system contribute to the heterogeneity of biological data, we propose a method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components when there are corrupted observations. The proposed method provides us a new representation of these data sets in terms of a common and aberrant response. This representation might help users to acquire a new insight from data.\n\nAuthor SummaryOne of the most exciting trends and important themes in science and engineering involves the use of high-throughput measurement data. With different dimensions, for example, various perturbations, different doses of drug or cell lines characteristics, such multidimensional data sets enable us to understand commonalities and differences across multiple dimensions. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. With this notion, we propose an RPCA-based method which models common variations as approximately the low-rank component and anomalies as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses.

10.1371/journal.pone.0121607

biorxiv

10.1101/004259

Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses

Young Hwan Chang;Jim Korkola;Dhara N. Amin;Mark M. Moasser;Jose M. Carmena;Joe W. Gray;Claire J. Tomlin;

Claire J. Tomlin

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley

2014-04-28

New Results

cc_no

Systems Biology

https://www.biorxiv.org/content/early/2014/04/28/004259.source.xml

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight across multiple dimensions. For this potential to be realized, we need a suitable representation to understand the data. Since a wide range of experiments and the unknown complexity of the underlying system contribute to the heterogeneity of biological data, we propose a method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components when there are corrupted observations. The proposed method provides us a new representation of these data sets in terms of a common and aberrant response. This representation might help users to acquire a new insight from data.\n\nAuthor SummaryOne of the most exciting trends and important themes in science and engineering involves the use of high-throughput measurement data. With different dimensions, for example, various perturbations, different doses of drug or cell lines characteristics, such multidimensional data sets enable us to understand commonalities and differences across multiple dimensions. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. With this notion, we propose an RPCA-based method which models common variations as approximately the low-rank component and anomalies as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses.

10.1371/journal.pone.0121607

biorxiv

10.1101/004564

Crowdsourced analysis of ash and ash dieback through the Open Ash Dieback project: A year 1 report on datasets and analyses contributed by a self-organising community.

Diane Saunders;Kentaro Yoshida;Christine Sambles;Rachel Glover;Bernardo Clavijo;Manuel Corpas;Daniel Bunting;Suomeng Dong;Ghanasyam Rallapalli;Matthew Clark;David Swarbreck;Sarah Ayling;Matthew Bashton;Steve Collin;Tsuyoshi Hosoya;Anne Edwards;Lisa Crossman;Graham Etherington;Joe Win;Liliana Cano;David Studholme;J Allan Downie;Mario Caccamo;Sophien Kamoun;Dan MacLean;

Dan MacLean

The Sainsbury Laboratory

2014-04-25

New Results

cc_by

Genomics

https://www.biorxiv.org/content/early/2014/04/25/004564.source.xml

Ash dieback is a fungal disease of ash trees caused by Hymenoscyphus pseudoalbidus that has swept across Europe in the last two decades and is a significant threat to the ash population. This emergent pathogen has been relatively poorly studied and little is known about its genetic make-up. In response to the arrival of this dangerous pathogen in the UK we took the unusual step of providing an open access database and initial sequence datasets to the scientific community for analysis prior to performing an analysis of our own. Our goal was to crowdsource genomic and other analyses and create a community analysing this pathogen. In this report on the evolution of the community and data and analysis obtained in the first year of this activity, we describe the nature and the volume of the contributions and reveal some preliminary insights into the genome and biology of H. pseudoalbidus that emerged. In particular our nascent community generated a first-pass genome assembly containing abundant collapsed AT-rich repeats indicating a typically complex genome structure. Our open science and crowdsourcing effort has brought a wealth of new knowledge about this emergent pathogen within a short time-frame. Our community endeavour highlights the positive impact that open, collaborative approaches can have on fast, responsive modern science.

biorxiv

10.1101/004564

Crowdsourced analysis of ash and ash dieback through the Open Ash Dieback project: A year 1 report on datasets and analyses contributed by a self-organising community.

Diane Saunders;Kentaro Yoshida;Christine Sambles;Rachel Glover;Bernardo Clavijo;Manuel Corpas;Daniel Bunting;Suomeng Dong;Ghanasyam Rallapalli;Matthew Clark;David Swarbreck;Sarah Ayling;Matthew Bashton;Steve Collin;Tsuyoshi Hosoya;Anne Edwards;Lisa Crossman;Graham Etherington;Joe Win;Liliana Cano;David Studholme;J Allan Downie;Mario Caccamo;Sophien Kamoun;Dan MacLean;

Dan MacLean

The Sainsbury Laboratory

2014-08-04

New Results

cc_by

Genomics

https://www.biorxiv.org/content/early/2014/08/04/004564.source.xml

Ash dieback is a fungal disease of ash trees caused by Hymenoscyphus pseudoalbidus that has swept across Europe in the last two decades and is a significant threat to the ash population. This emergent pathogen has been relatively poorly studied and little is known about its genetic make-up. In response to the arrival of this dangerous pathogen in the UK we took the unusual step of providing an open access database and initial sequence datasets to the scientific community for analysis prior to performing an analysis of our own. Our goal was to crowdsource genomic and other analyses and create a community analysing this pathogen. In this report on the evolution of the community and data and analysis obtained in the first year of this activity, we describe the nature and the volume of the contributions and reveal some preliminary insights into the genome and biology of H. pseudoalbidus that emerged. In particular our nascent community generated a first-pass genome assembly containing abundant collapsed AT-rich repeats indicating a typically complex genome structure. Our open science and crowdsourcing effort has brought a wealth of new knowledge about this emergent pathogen within a short time-frame. Our community endeavour highlights the positive impact that open, collaborative approaches can have on fast, responsive modern science.

biorxiv

10.1101/004556

MOHCA-seq: RNA 3D models from single multiplexed proximity-mapping experiments

Clarence Cheng;Fang-Chieh Chou;Wipapat Kladwang;Siqi Tian;Pablo Cordero;Rhiju Das;

Rhiju Das

Stanford University

2014-04-25

New Results

cc_no

Biochemistry

https://www.biorxiv.org/content/early/2014/04/25/004556.source.xml

Large RNAs control myriad biological processes but challenge tertiary structure determination. We report that integrating multiplexed *OH cleavage analysis with tabletop deep sequencing (MOHCA-seq) gives nucleotide-resolution proximity maps of RNA structure from single straightforward experiments. After achieving 1-nm resolution models for RNAs of known structure, MOHCA-seq reveals previously unattainable 3D information for ligand-induced conformational changes in a double glycine riboswitch and the sixth community-wide RNA puzzle, an adenosylcobalamin riboswitch.

biorxiv

10.1101/004572

Detecting patterns of species diversification in the presence of both rate shifts and mass extinctions

Sacha Laurent;Marc Robinson-Rechavi;Nicolas Salamin;

Nicolas Salamin

University of Lausanne

2014-04-25

New Results

cc_by_nc

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/25/004572.source.xml

AO_SCPCAPBSTRACTC_SCPCAPRecent methodological advances are enabling better examination of speciation and extinction processes and patterns. A major open question is the origin of large discrepancies in species number between groups of the same age. Existing frameworks to model this diversity either focus on changes between lineages, neglecting global effects such as mass extinctions, or focus on changes over time which would affect all lineages. Yet it seems probable that both lineages differences and mass extinctions affect the same groups. Here we used simulations to test the performance of two widely used methods, under complex scenarios. We report good performances, although with a tendency to over-predict events when increasing the complexity of the scenario. Overall, we find that lineage shifts are better detected than mass extinctions. This work has significance for assessing the methods currently used for estimating changes in diversification using phylogenies and developing new tests.

10.1186/s12862-015-0432-z

biorxiv

10.1101/004572

Detecting patterns of species diversification in the presence of both rate shifts and mass extinctions

Sacha Laurent;Marc Robinson-Rechavi;Nicolas Salamin;

Nicolas Salamin

University of Lausanne

2014-11-24

New Results

cc_by_nc

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/11/24/004572.source.xml

AO_SCPCAPBSTRACTC_SCPCAPRecent methodological advances are enabling better examination of speciation and extinction processes and patterns. A major open question is the origin of large discrepancies in species number between groups of the same age. Existing frameworks to model this diversity either focus on changes between lineages, neglecting global effects such as mass extinctions, or focus on changes over time which would affect all lineages. Yet it seems probable that both lineages differences and mass extinctions affect the same groups. Here we used simulations to test the performance of two widely used methods, under complex scenarios. We report good performances, although with a tendency to over-predict events when increasing the complexity of the scenario. Overall, we find that lineage shifts are better detected than mass extinctions. This work has significance for assessing the methods currently used for estimating changes in diversification using phylogenies and developing new tests.

10.1186/s12862-015-0432-z

biorxiv

10.1101/004481

Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

Amir Shahmoradi;Dariya K. Sydykova;Stephanie J. Spielman;Eleisha L. Jackson;Eric T. Dawson;Austin G. Meyer;Claus O. Wilke;

Claus O. Wilke

The University of Texas at Austin

2014-04-24

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/24/004481.source.xml

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on 9 non-homologous viral protein structures and from variation in homologous variants of those proteins, where available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1 to 0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than was structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than was buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than are more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

10.1007/s00239-014-9644-x

biorxiv

10.1101/004481

Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

Amir Shahmoradi;Dariya K. Sydykova;Stephanie J. Spielman;Eleisha L. Jackson;Eric T. Dawson;Austin G. Meyer;Claus O. Wilke;

Claus O. Wilke

The University of Texas at Austin

2014-07-21

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/07/21/004481.source.xml

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on 9 non-homologous viral protein structures and from variation in homologous variants of those proteins, where available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1 to 0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than was structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than was buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than are more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

10.1007/s00239-014-9644-x

biorxiv

10.1101/004416

The Evolution of Power and the Divergence of Cooperative Norms

Michael D Makowsky;Paul E Smaldino;

Michael D Makowsky

Johns Hopkins University

2014-04-24

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/04/24/004416.source.xml

We consider a model of multilevel selection and the evolution of institutions that distribute power in the form of influence in a groups collective interactions with other groups. In the absence of direct group-level interactions, groups with the most cooperative members will outcompete less cooperative groups, while within any group the least cooperative members will be the most successful. Introducing group-level interactions, however, such as raiding or warfare, changes the selective landscape for groups. Our model suggests that as the global population becomes more integrated and the rate of intergroup conflict increases, selection increasingly favors unequally distributed power structures, where individual influence is weighted by acquired resources. The advantage to less democratic groups rests in their ability to facilitate selection for cooperative strategies - involving cooperation both among themselves and with outsiders - in order to produce the resources necessary to fuel their success in inter-group conflicts, while simultaneously selecting for leaders (and corresponding collective behavior) who are unburdened with those same prosocial norms. The coevolution of cooperative social norms and institutions of power facilitates the emergence of a leadership class of the selfish and has implications for theories of inequality, structures of governance, non-cooperative personality traits, and hierarchy. Our findings suggest an amendment to the well-known doctrine of multilevel selection that \"Selfishness beats altruism within groups. Altruistic groups beat selfish groups.\" In an interconnected world, altruistic groups led by selfish individuals can beat them both.

biorxiv

10.1101/004416

The Evolution of Power and the Divergence of Cooperative Norms

Michael D Makowsky;Paul E Smaldino;

Michael D Makowsky

Johns Hopkins University

2015-01-29

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2015/01/29/004416.source.xml

We consider a model of multilevel selection and the evolution of institutions that distribute power in the form of influence in a groups collective interactions with other groups. In the absence of direct group-level interactions, groups with the most cooperative members will outcompete less cooperative groups, while within any group the least cooperative members will be the most successful. Introducing group-level interactions, however, such as raiding or warfare, changes the selective landscape for groups. Our model suggests that as the global population becomes more integrated and the rate of intergroup conflict increases, selection increasingly favors unequally distributed power structures, where individual influence is weighted by acquired resources. The advantage to less democratic groups rests in their ability to facilitate selection for cooperative strategies - involving cooperation both among themselves and with outsiders - in order to produce the resources necessary to fuel their success in inter-group conflicts, while simultaneously selecting for leaders (and corresponding collective behavior) who are unburdened with those same prosocial norms. The coevolution of cooperative social norms and institutions of power facilitates the emergence of a leadership class of the selfish and has implications for theories of inequality, structures of governance, non-cooperative personality traits, and hierarchy. Our findings suggest an amendment to the well-known doctrine of multilevel selection that \"Selfishness beats altruism within groups. Altruistic groups beat selfish groups.\" In an interconnected world, altruistic groups led by selfish individuals can beat them both.

biorxiv

10.1101/004507

Using 2k + 2 bubble searches to find SNPs in k-mer graphs

Reda Younsi;Dan MacLean;

Dan MacLean

The Sainsbury Laboratory

2014-04-24

New Results

cc_by

Bioinformatics

https://www.biorxiv.org/content/early/2014/04/24/004507.source.xml

This preprint is now available in published form as: Using 2k + 2 bubble searches to find SNPs in k-mer graphs, Reda Younsi; Dan MacLean, Bioinformatics 2014; doi: 10.1093/bioinformatics/btu706\n\nSingle Nucleotide Polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of data from modern technologies. Recent work has shown that the topology of these graphs captures enough information to allow the detection and characterisation of genetic variants, offering an alternative to alignment-based methods. Such methods rely on depth-first walks of the graph to identify closing bifurcations. These methods are conservative or generate many false-positive results, particularly when traversing highly inter-connected (complex) regions of the graph or in regions of very high coverage.We devised an algorithm that calls SNPs in converted De Bruijn graphs by enumerating 2k + 2 cycles. We evaluated the accuracy of predicted SNPs by comparison with SNP lists from alignment based methods. We tested accuracy of the SNP calling using sequence data from sixteen ecotypes of Arabidopsis thaliana and found that accuracy was high. We found that SNP calling was even across the genome and genomic feature types. Using sequence based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further.Together these results indicate that our algorithm is capable of finding SNPs accurately in complex sub-graphs and potentially comprehensively from whole genome graphs.The source code for a C++ implementation of our algorithm is available under the GNU Public Licence v3 at:https://github.com/redayounsi/2kplus2

biorxiv

10.1101/004507

Using 2k + 2 bubble searches to find SNPs in k-mer graphs

Reda Younsi;Dan MacLean;

Dan MacLean

The Sainsbury Laboratory

2014-10-27

New Results

cc_by

Bioinformatics

https://www.biorxiv.org/content/early/2014/10/27/004507.source.xml

This preprint is now available in published form as: Using 2k + 2 bubble searches to find SNPs in k-mer graphs, Reda Younsi; Dan MacLean, Bioinformatics 2014; doi: 10.1093/bioinformatics/btu706\n\nSingle Nucleotide Polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of data from modern technologies. Recent work has shown that the topology of these graphs captures enough information to allow the detection and characterisation of genetic variants, offering an alternative to alignment-based methods. Such methods rely on depth-first walks of the graph to identify closing bifurcations. These methods are conservative or generate many false-positive results, particularly when traversing highly inter-connected (complex) regions of the graph or in regions of very high coverage.We devised an algorithm that calls SNPs in converted De Bruijn graphs by enumerating 2k + 2 cycles. We evaluated the accuracy of predicted SNPs by comparison with SNP lists from alignment based methods. We tested accuracy of the SNP calling using sequence data from sixteen ecotypes of Arabidopsis thaliana and found that accuracy was high. We found that SNP calling was even across the genome and genomic feature types. Using sequence based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further.Together these results indicate that our algorithm is capable of finding SNPs accurately in complex sub-graphs and potentially comprehensively from whole genome graphs.The source code for a C++ implementation of our algorithm is available under the GNU Public Licence v3 at:https://github.com/redayounsi/2kplus2

biorxiv

10.1101/004382

Lack of association between Toll Like Receptor-2 &amp;amp; Toll Like Receptor-4 Gene Polymorphisms and Iranian Asthmatics risk or features

hamid bahrami;saeed daneshmandi;Ali Akbar Pourfathollah;hasan haidarnazhad;

Ali Akbar Pourfathollah

tarbiat modares university

2014-04-28

New Results

cc_by_nc_nd

Immunology

https://www.biorxiv.org/content/early/2014/04/28/004382.source.xml

BackgroundAsthma as chronic inflammatory airway disease is considered to be the most common chronic disease that is involving genetic and environmental factors. Toll like receptors (TLRs) and other inflammatory mediators are important in modulation of inflammation. In this study we evaluated the role of TLR2 Arg753Gln and TLR4 Asp299Gly polymorphisms in the asthma susceptibility, progress, control levels and lung functions in Iranian subjects.\n\nMethodsOn 99 asthmatic patients and 120 normal subjects, TLR2 Arg753Gln and TLR4 Asp299Gly polymorphism were evaluated by PCR-RFLP method recruiting Msp1 and Nco1 restriction enzymes, respectively. IgE serum levels by ELISA technique were determined and asthma diagnosis, treatment and control levels were considered using standard schemes and criteria.\n\nResultsOur results indicated that the genotype and allele frequencies of the TLR2 Arg753Gln and TLR4 Asp299Gly polymorphisms were not significantly different between control subjects and asthmatics (p > 0.05) or even in asthma features such as IgE levels, asthma history and pulmonary factors (p > 0.05).\n\nConclusionsMeanwhile some previous studies indicated TLRs and their polymorphisms role in asthma incidence and features, our data demonstrated that TLR2 Arg753Gln and TLR4 Asp299Gly gene variants were not risk factor of asthma or its features in Iranian patients. Genetic complexity, ethnicity, influence of other genes or polymorphisms may overcome these polymorphisms in our asthmatics.

10.1016/j.meegid.2012.11.009

biorxiv

10.1101/004473

The invariance hypothesis implies domain-specific regions in visual cortex

Joel Z. Leibo;Qianli Liao;Fabio Anselmi;Tomaso Poggio;

Joel Z. Leibo

MIT

2014-04-24

New Results

cc_no

Neuroscience

https://www.biorxiv.org/content/early/2014/04/24/004473.source.xml

Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition systems optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions in agreement with the available data. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions.

10.1371/journal.pcbi.1004390

biorxiv

10.1101/004473

The invariance hypothesis implies domain-specific regions in visual cortex

Joel Z. Leibo;Qianli Liao;Fabio Anselmi;Tomaso Poggio;

Joel Z. Leibo

MIT

2015-04-26

New Results

cc_no

Neuroscience

https://www.biorxiv.org/content/early/2015/04/26/004473.source.xml

Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition systems optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions in agreement with the available data. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions.

10.1371/journal.pcbi.1004390

biorxiv

10.1101/004580

Regulatory RNA design through evolutionary computation and strand displacement

William Rostain;Thomas E Landrain;Guillermo Rodrigo;Alfonso Jaramillo;

Alfonso Jaramillo

University of Warwick

2014-04-28

New Results

cc_no

Synthetic Biology

https://www.biorxiv.org/content/early/2014/04/28/004580.source.xml

The discovery and study of a vast number of regulatory RNAs in all kingdoms of life over the past decades has allowed the design of new synthetic RNAs that can regulate gene expression in vivo. Riboregulators, in particular, have been used to activate or repress gene expression. However, to accelerate and scale up the design process, synthetic biologists require computer-assisted design tools, without which riboregulator engineering will remain a case-by-case design process requiring expert attention. Recently, the design of RNA circuits by evolutionary computation and adapting strand displacement techniques from nanotechnology has proven to be suited to the automated generation of DNA sequences implementing regulatory RNA systems in bacteria. Herein, we present our method to carry out such evolutionary design and how to use it to create various types of riboregulators, allowing the systematic de novo design of genetic control systems in synthetic biology.

10.1007/978-1-4939-1878-2_4

biorxiv

10.1101/004499

In vivo generation of DNA sequence diversity for cellular barcoding

Ian Peikon;Diana Gizatullina;Anthony Zador;

Anthony Zador

Cold Spring Harbor Laboratory

2014-04-24

New Results

cc_by_nc

Synthetic Biology

https://www.biorxiv.org/content/early/2014/04/24/004499.source.xml

Heterogeneity is a ubiquitous feature of biological systems. A complete understanding of such systems requires a method for uniquely identifying and tracking individual components and their interactions with each other. We have developed a novel method of uniquely tagging individual cells in vivo with a genetic \"barcode\" that can be recovered by DNA sequencing. We demonstrate the feasibility of this technique in bacterial cells. This method should prove useful in tracking interactions of cells within a network, and/or heterogeneity within complex biological samples.

10.1093/nar/gku604

biorxiv

10.1101/004622

Graph-based data integration predicts long-range regulatory interactions across the human genome

Sofie Demeyer;Tom Michoel;

Tom Michoel

The University of Edinburgh

2014-04-29

New Results

cc_no

Bioinformatics

https://www.biorxiv.org/content/early/2014/04/29/004622.source.xml

Transcriptional regulation of gene expression is one of the main processes that affect cell diversification from a single set of genes. Regulatory proteins often interact with DNA regions located distally from the transcription start sites (TSS) of the genes. We developed a computational method that combines open chromatin and gene expression information for a large number of cell types to identify these distal regulatory elements. Our method builds correlation graphs for publicly available DNase-seq and exon array datasets with matching samples and uses graph-based methods to filter findings supported by multiple datasets and remove indirect interactions. The resulting set of interactions was validated with both anecdotal information of known long-range interactions and unbiased experimental data deduced from Hi-C and CAGE experiments. Our results provide a novel set of high-confidence candidate open chromatin regions involved in gene regulation, often located several Mb away from the TSS of their target gene.

biorxiv

10.1101/004614

Characterizing a collective and dynamic component of chromatin immunoprecipitation enrichment profiles in yeast

Lucas D. Ward;Junbai Wang;Harmen J. Bussemaker;

Harmen J. Bussemaker

Columbia University

2014-04-29

New Results

cc_no

Genomics

https://www.biorxiv.org/content/early/2014/04/29/004614.source.xml

Recent chromatin immunoprecipitation (ChIP) experiments in fly, mouse, and human have revealed the existence of high-occupancy target (HOT) regions or hotspots that show enrichment across many assayed DNA-binding proteins. Similar co-enrichment observed in yeast so far has been treated as artifactual, and has not been fully characterized. Here we reanalyze ChIP data from both array-based and sequencing-based experiments to show that in the yeast S. cerevisiae, the collective enrichment phenomenon is strongly associated with proximity to noncoding RNA genes and with nucleosome depletion. DNA sequence motifs that confer binding affinity for the proteins are largely absent from these hotspots, suggesting that protein-protein interactions play a prominent role. The hotspots are condition-specific, suggesting that they reflect a chromatin state or protein state, and are not a static feature of underlying sequence. Additionally, only a subset of all assayed factors is associated with these loci, suggesting that the co-enrichment cannot be simply explained by a chromatin state that is universally more prone to immunoprecipitation. Together our results suggest that the co-enrichment patterns observed in yeast represent transcription factor co-occupancy. More generally, they make clear that great caution must be used when interpreting ChIP enrichment profiles for individual factors in isolation, as they will include factor-specific as well as collective contributions.

10.1186/1471-2164-15-494

biorxiv

10.1101/004648

Ontogenic, phenotypic, and functional characterization of XCR1+ dendritic cells leads to a consistent classification of intestinal dendritic cells based on the expression of XCR1 and SIRPα

Martina Becker;Steffen Güttler;Annabell Bachem;Evelyn Hartung;Ahmed Mora;Anika Jäkel;Andreas Hutloff;Volker Henn;Hans Werner Mages;Stephanie Gurka;Richard A. Kroczek;

Richard A. Kroczek

Robert Koch-Institute

2014-04-30

New Results

cc_no

Immunology

https://www.biorxiv.org/content/early/2014/04/30/004648.source.xml

In the past, lack of lineage markers confounded the classification of dendritic cells (DC) in the intestine and impeded a full understanding of their location and function. We have recently shown that the chemokine receptor XCR1 is a lineage marker for cross-presenting DC in the spleen. Now we provide evidence that intestinal XCR1+ DC largely, but not fully, overlap with CD103+ CD11b- DC, the hypothesized correlate of \"cross-presenting DC\" in the intestine, and are selectively dependent in their development on the transcription factor Batf3. XCR1+ DC are located in the villi and epithelial crypts of the lamina propria of the small intestine, the T cell zones of Peyers Patches, and in the T cell zones and sinuses of the draining mesenteric lymph node. Functionally, we could demonstrate for the first time that XCR1+ / CD103+ CD11b- DC excel in the cross-presentation of orally applied antigen. Together, our data show that XCR1 is a lineage marker for cross-presenting DC also in the intestinal immune system. Further, extensive phenotypic analyses reveal that expression of the integrin SIRP consistently demarcates the XCR1- DC population. We propose a simplified and consistent classification system for intestinal DC based on the expression of XCR1 and SIRP.

10.3389/fimmu.2014.00326

biorxiv

10.1101/004663

Functional knockout of forebrain protein 14-3-3 disrupts conditioned taste aversion learning

Adam Kimbrough;Yuying Wu;Yi Zhou;Thomas A. Houpt;

Thomas A. Houpt

Dept. Biological Science, Program in Neuroscience, Florida State University

2014-04-30

New Results

cc_no

Neuroscience

https://www.biorxiv.org/content/early/2014/04/30/004663.source.xml

Protein 14-3-3 isoforms are key to many cellular processes and are ubiquitous throughout the brain. 14-3-3 is a regulator of ser/thr phospho-signaling by binding and sequestering phosphorylated substrates including kinases, histone deactylases, and transcription factors. The role of protein 14-3-3 in conditioned taste aversion learning (CTA) has not previously been examined. We parameterized CTA learning in difopein- YFP transgenic mice, which have widespread by expression of the artificial peptide difopein in the forebrain, including the basolateral amygdala and insular cortex, resulting in functional knock-out (FKO) of all 14-3-3 isoforms . We found that a single pairing of saccharin or NaCl (CS) and LiCl injection (US) was not sufficient to induce CTA in FKO mice. Multiples pairings of CS and US did lead to CTA acquisition in the FKO mice; however, the CTA rapidly extinguished within 30 minutes to 24 hours after acquisition. Additionally, we found that 14-3-3 FKO mice have an attenuated visceral neuraxis response to LiCl as measured by c-Fos induction. The deficit in FKO was not due to an inability to discriminate or avoid tastants, because they showed normal unconditioned taste preferences for both palatable (saccharin, maltodextrin , low concentration NaCl) and unpalatable tastants (quinine, HCl, and high concentration NaCl) and they were able to reduce intake of a maltodextrin solution adulterated with quinine. The FKO did not have a global deficit in ingestive learning, because they were able to form a conditioned flavor-nutrient preference. Thus, FKO of forebrain 14-3-3 appears to disrupt CTA learning leading to forgetting, rapid extinction, or failure to reconsolidate. This further implicates ser/thr phospho-signaling pathways in the regulation of long-term CTA learning.

biorxiv

10.1101/004655

Accounting for sources of bias and uncertainty in copy number-based statistical deconvolution of heterogeneous tumour samples

Christopher Yau;

Christopher Yau

University of Oxford

2014-04-30

New Results

cc_no

Bioinformatics

https://www.biorxiv.org/content/early/2014/04/30/004655.source.xml

Deconvolving heterogeneous tumour samples to identify constituent cell populations with differing copy number profiles using whole genome sequencing data is a challenging problem. Copy number calling algorithms have differential detection rates for different sizes and classes of copy number alterations. This paper describes how uncertainty in classification and differential detection rates can introduce biases in measures of clonal diversity. A simulation strategy is introduced that allows differential detection rates to be adjusted for and this process is shown to minimise bias.

biorxiv

10.1101/004671

The Landscape of Human STR Variation

Thomas F. Willems;Melissa Gymrek;Gareth Highnam;- The 1000 Genomes Project;David Mittelman;Yaniv Erlich;

Yaniv Erlich

Whitehead Institute for Biomedical Research

2014-05-01

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/05/01/004671.source.xml

Short Tandem Repeats are among the most polymorphic loci in the human genome. These loci play a role in the etiology of a range of genetic diseases and have been frequently utilized in forensics, population genetics, and genetic genealogy. Despite this plethora of applications, little is known about the variation of most STRs in the human population. Here, we report the largest-scale analysis of human STR variation to date. We collected information for nearly 700,000 STR loci across over 1,000 individuals in phase 1 of the 1000 Genomes Project. This process nearly saturated common STR variations. After employing a series of quality controls, we utilize this call set to analyze determinants of STR variation, assess the human reference genomes representation of STR alleles, find STR loci with common loss-of-function alleles, and obtain initial estimates of the linkage disequilibrium between STRs and common SNPs. Overall, these analyses further elucidate the scale of genetic variation beyond classical point mutations. The resource is publicly available at http://strcat.teamerlich.org/ both in raw format and via a graphical interface.

10.1101/gr.177774.114

biorxiv

10.1101/004671

The Landscape of Human STR Variation

Thomas F. Willems;Melissa Gymrek;Gareth Highnam;- The 1000 Genomes Project;David Mittelman;Yaniv Erlich;

Yaniv Erlich

Whitehead Institute for Biomedical Research

2014-05-01

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/05/01/004671.source.xml

Short Tandem Repeats are among the most polymorphic loci in the human genome. These loci play a role in the etiology of a range of genetic diseases and have been frequently utilized in forensics, population genetics, and genetic genealogy. Despite this plethora of applications, little is known about the variation of most STRs in the human population. Here, we report the largest-scale analysis of human STR variation to date. We collected information for nearly 700,000 STR loci across over 1,000 individuals in phase 1 of the 1000 Genomes Project. This process nearly saturated common STR variations. After employing a series of quality controls, we utilize this call set to analyze determinants of STR variation, assess the human reference genomes representation of STR alleles, find STR loci with common loss-of-function alleles, and obtain initial estimates of the linkage disequilibrium between STRs and common SNPs. Overall, these analyses further elucidate the scale of genetic variation beyond classical point mutations. The resource is publicly available at http://strcat.teamerlich.org/ both in raw format and via a graphical interface.

10.1101/gr.177774.114

biorxiv

10.1101/004671

The Landscape of Human STR Variation

Thomas F. Willems;Melissa Gymrek;Gareth Highnam;- The 1000 Genomes Project;David Mittelman;Yaniv Erlich;

Yaniv Erlich

Whitehead Institute for Biomedical Research

2014-07-10

New Results

cc_by_nc_nd

Genomics

https://www.biorxiv.org/content/early/2014/07/10/004671.source.xml

Short Tandem Repeats are among the most polymorphic loci in the human genome. These loci play a role in the etiology of a range of genetic diseases and have been frequently utilized in forensics, population genetics, and genetic genealogy. Despite this plethora of applications, little is known about the variation of most STRs in the human population. Here, we report the largest-scale analysis of human STR variation to date. We collected information for nearly 700,000 STR loci across over 1,000 individuals in phase 1 of the 1000 Genomes Project. This process nearly saturated common STR variations. After employing a series of quality controls, we utilize this call set to analyze determinants of STR variation, assess the human reference genomes representation of STR alleles, find STR loci with common loss-of-function alleles, and obtain initial estimates of the linkage disequilibrium between STRs and common SNPs. Overall, these analyses further elucidate the scale of genetic variation beyond classical point mutations. The resource is publicly available at http://strcat.teamerlich.org/ both in raw format and via a graphical interface.

10.1101/gr.177774.114

biorxiv

10.1101/004689

Detection and Polarization of Introgression in a Five-taxon Phylogeny

James B Pease;Matthew W. Hahn;

James B Pease

Indiana University

2014-05-01

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/05/01/004689.source.xml

In clades of closely related taxa, discordant genealogies due to incomplete lineage sorting (ILS) can complicate the detection of introgression. The D-statistic (a.k.a. the ABBA/BABA test) was proposed to infer introgression in the presence of ILS for a four-taxon clade. However, the original D-statistic cannot be directly applied to a symmetric five-taxon phylogeny, and the direction of introgression cannot be inferred for any tree topology. Here we explore the issues associated with previous methods for adapting the D-statistic to a larger tree topology, and propose new \"DFOIL\" tests to infer both the taxa involved in and the direction of introgressions for a symmetric five-taxon phylogeny. Using theory and simulations, we find that previous modifications of the D-statistic to five-taxon phylogenies incorrectly identify both the pairs of taxa exchanging migrants as well as the direction of introgression. The DFOIL statistics are shown to overcome this deficiency and to correctly determine the direction of introgressions. The DFOIL tests are relatively simple and computationally inexpensive to calculate, and can be easily applied to various phylogenomic datasets. In addition, our general approach to the problem of introgression detection could be adapted to larger tree topologies and other models of sequence evolution.

10.1093/sysbio/syv023

biorxiv

10.1101/004713

Spatial localization of recent ancestors for admixed individuals

Wen-Yun Yang;Alexander Platt;Charleston Wen-Kai Chiang;Eleazar Eskin;John Novembre;Bogdan Pasaniuc;

Bogdan Pasaniuc

University of California Los Angeles

2014-05-04

New Results

cc_no

Genetics

https://www.biorxiv.org/content/early/2014/05/04/004713.source.xml

Ancestry analysis from genetic data plays a critical role in studies of human disease and evolution. Recent work has introduced explicit models for the geographic distribution of genetic variation and has shown that such explicit models yield superior accuracy in ancestry inference over non-model-based methods. Here we extend such work to introduce a method that models admixture between ancestors from multiple sources across a geographic continuum. We devise efficient algorithms based on hidden Markov models to localize on a map the recent ancestors (e.g. grandparents) of admixed individuals, joint with assigning ancestry at each locus in the genome. We validate our methods using empirical data from individuals with mixed European ancestry from the POPRES study and show that our approach is able to localize their recent ancestors within an average of 470Km of the reported locations of their grandparents. Furthermore, simulations from real POPRES genotype data show that our method attains high accuracy in localizing recent ancestors of admixed individuals in Europe (an average of 550Km from their true location for localization of 2 ancestries in Europe, 4 generations ago). We explore the limits of ancestry localization under our approach and find that performance decreases as the number of distinct ancestries and generations since admixture increases. Finally, we build a map of expected localization accuracy across admixed individuals according to the location of origin within Europe of their ancestors.\n\nAuthor SummaryInferring ancestry from genetic data forms a fundamental problem with applications ranging from localizing disease genes to inference of human history. Recent approaches have introduced models of genetic variation as a function of geography and have shown that such models yield high accuracies in ancestry inference from genetic data. In this work we propose methods for modeling the mixing of genetic data from different sources (i.e. admixture process) in a genetic-geographic continuum and show that using these methods we can accurately infer the ancestry of the recent ancestors (e.g. grandparents) from genetic data.

10.1534/g3.114.014274

biorxiv

10.1101/004705

Comparison of Y-chromosomal lineage dating using either evolutionary or genealogical Y-STR mutation rates

Chuan-Chao Wang;Li Hui;

Li Hui

Fudan University

2014-05-03

New Results

cc_by_nc

Genetics

https://www.biorxiv.org/content/early/2014/05/03/004705.source.xml

We have compared the Y chromosomal lineage dating between sequence data and commonly used Y-SNP plus Y-STR data. The coalescent times estimated using evolutionary Y-STR mutation rates correspond best with sequence-based dating when the lineages include the most ancient haplogroup A individuals. However, the times using slow mutated STR markers with genealogical rates fit well with sequence-based estimates in main lineages, such as haplogroup CT, DE, K, NO, IJ, P, E, C, I, J, N, O, and R. In addition, genealogical rates lead to more plausible time estimates for Neolithic coalescent sublineages compared with sequence-based dating.

biorxiv

10.1101/004739

Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

Arnold Wiliem;Conrad Sanderson;Yongkang Wong;Peter Hobson;Rodney Minchin;Brian Lovell;

Conrad Sanderson

NICTA

2014-05-05

New Results

cc_no

Bioinformatics

https://www.biorxiv.org/content/early/2014/05/05/004739.source.xml

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

10.1016/j.patcog.2013.10.014

biorxiv

10.1101/004747

Pre-trial exogenous visual flicker does not affect behavioral or EEG signatures of conflict processing

Michael X Cohen;Fraser William Steel;

Michael X Cohen

University of Amsterdam

2014-05-05

New Results

cc_by

Neuroscience

https://www.biorxiv.org/content/early/2014/05/05/004747.source.xml

Activity in the theta frequency band (4-8 Hz) over medial prefrontal regions has been consistently implicated in top-down cognitive control processes, including recognizing and resolving response conflict. It remains an unanswered question whether these theta-band dynamics are a neural mechanism of cognitive control, or instead are epiphenomenal to the neural computational machinery but are useful indices of brain function. Here we addressed this question by attempting to boost conflict processing (or its EEG theta-band signatures) via pre-trial exogenous theta-band visual flicker. Although the flicker successfully entrained posterior brain networks, there were no effects of flicker on behavior or on EEG signatures of conflict processing. In this paper, we detail our attempts and discuss possible future directions for using exogenous flicker in the study of the role of endogenous brain oscillations in conflict processing.

biorxiv

10.1101/004762

Sequence co-evolution gives 3D contacts and structures of protein complexes

Thomas A. Hopf;Charlotta P.I. Schärfe;João P.G.L.M. Rodrigues;Anna G. Green;Chris Sander;Alexandre M.J.J. Bonvin;Debora S. Marks;

Debora S. Marks

Harvard Medical School

2014-05-06

New Results

cc_no

Bioinformatics

https://www.biorxiv.org/content/early/2014/05/06/004762.source.xml

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

10.7554/eLife.03430

biorxiv

10.1101/004762

Sequence co-evolution gives 3D contacts and structures of protein complexes

Thomas A. Hopf;Charlotta P.I. Schärfe;João P.G.L.M. Rodrigues;Anna G. Green;Chris Sander;Alexandre M.J.J. Bonvin;Debora S. Marks;

Debora S. Marks

Harvard Medical School

2014-05-23

New Results

cc_no

Bioinformatics

https://www.biorxiv.org/content/early/2014/05/23/004762.source.xml

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

10.7554/eLife.03430

biorxiv

10.1101/004762

Sequence co-evolution gives 3D contacts and structures of protein complexes

Thomas A. Hopf;Charlotta P.I. Schärfe;João P.G.L.M. Rodrigues;Anna G. Green;Chris Sander;Alexandre M.J.J. Bonvin;Debora S. Marks;

Debora S. Marks

Harvard Medical School

2014-09-15

New Results

cc_no

Bioinformatics

https://www.biorxiv.org/content/early/2014/09/15/004762.source.xml

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

10.7554/eLife.03430

biorxiv

10.1101/004796

N-BLR, a primate-specific non-coding transcript, modulates the epithelial-to-mesenchymal transition and leads to colorectal cancer invasion and migration

Isidore Rigoutsos;Sang Kil Lee;Su Youn Nam;Tina Catela Ivkovic;Martin Pichler;Simona Rossi;Peter Clark;Jing Yi;Hui Ling;Masayoshi Shimizu;Roxana Simona Redis;Maitri Yogen Shah;Xinna Zhang;Eun Jung Jung;Aristotelis Tsirigos;Li Huang;Jana Ferdin;Roberta Gafa;Riccardo Spizzo;Milena Nicoloso;Maryam Shariati;Aida Tiron;Jen Jen Yeh;Raul Teruel;Sonia Melo;Lianchun Xiao;Elsa Renee Flores;Massimo Negrini;Menashe Bar Eli;Sendurai Mani;Chang-Gong Liu;Ioana Berindan-Neagoe;Manel Esteller;Michael Keating;Giovanni Lanza;George Calin;

George Calin

Department of Experimental Therapeutics, The University of Texas M. D. Anderson Cancer Center, Houst

2014-05-06

New Results

cc_no

Genomics

https://www.biorxiv.org/content/early/2014/05/06/004796.source.xml

Non-coding RNAs have been commanding increasingly greater attention in recent years as the few that have been functionalized to date play important roles in key cellular processes. Here we show that N-BLR, a ~900 bp non-coding RNA, modulates the epithelial-to-mesenchymal transition, increases colorectal cancer invasion, and functions as a migration enabler by affecting the expression of ZEB1 and E-cadherin. In patients with colorectal cancer, N-BLR expression associates with tumor stage and invasion potential. As N-BLR contains several instances of a category of DNA motifs known as pyknons, we also designed a custom-made array to investigate the possibility that other pyknon loci may be transcribed. For several of the loci probed by the array we found that the corresponding pyknons are differentially expressed between cancer and normal tissue samples. Taken together the data suggest that a systematic study of other pyknon-containing non-coding RNAs like N-BLR may be warranted in the context of colorectal cancer.

biorxiv

10.1101/004846

Cooperation between Noncanonical Ras Network Mutations in Cancer

Edward C Stites;Paul C Trampont;Lisa B Haney;Scott F Walk;Kodi S Ravichandran;

Edward C Stites

Washington University School of Medicine

2014-05-06

New Results

cc_no

Cancer Biology

https://www.biorxiv.org/content/early/2014/05/06/004846.source.xml

Cancer develops after the acquisition of a collection of mutations that together create the cancer phenotype. How collections of mutations work together within a cell, and whether there is selection for certain combinations of mutations, are not well understood. Using a Ras signaling network mathematical model we tested potential synergistic combinations within the Ras network. Intriguingly, our modeling, including a \"computational random mutagenesis\" approach, and subsequent experiments revealed that mutations of the tumor suppressor gene NF1 can amplify the effects of mutations in multiple other components of the Ras pathway, including weakly activating, noncanonical, Ras mutants. Since conventional wisdom holds that mutations within the same pathway do not co-occur, it was surprising that modeling and experiments both suggested a functional benefit for co-occurring Ras pathway mutations. Furthermore, we analyzed >3900 sequenced cancer specimens from the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) and we uncovered an increased rate of co-occurrence between mutations the model predicted could display synergy. Overall, these data suggest that selective combinations of Ras pathway mutations could serve the role of cancer \"driver\". More generally, this work presents a mechanism by which the context created by one mutation influences the evolutionary trajectories of cancer development, and this work suggests that mutations that result in \"network instability\" may promote cancer in a manner analogous to genomic instability.

10.1016/j.celrep.2014.12.035

biorxiv

10.1101/004820

Some plants don&amp;#146;t play games: An ideal free distribution explains the root production of plants that do not engage in a tragedy of the commons game.

Gordon McNickle;Joel S Brown;

Gordon McNickle

Wilfrid Laurier University

2014-05-06

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2014/05/06/004820.source.xml

This is the pre-peer-reviewed version of the following article: G.G. McNickle and J.S. Brown. (2014) An ideal free distribution explains the root production of plants that do not engage in a tragedy of the commons game. Journal of Ecology. DOI: 10.1111/1365-2745.12259, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/1365-2745.12259/abstract\n\nO_LIGame theoretic models that seek to predict the most competitive strategy plants use for competition in soil are clear; they generally predict that over-proliferation of roots is the only evolutionarily stable strategy. However, empirical studies are equally clear that not all plants employ this strategy of over-proliferation of roots. Here, our goal was to develop and test an alternative non-game theoretic model that can be used to develop alternative hypotheses for plants that do not appear to play games.\nC_LIO_LIThe model is similar to previous models, but does not use a game theoretic optimization criterion. Instead, plants use only nutrient availability to select a root allocation strategy, ignoring neighbours. To test the model we compare root allocation and seed yield of plants grown either alone or with neighbours.\nC_LIO_LIThe model predicted plants that do not sense neighbours (or ignore neighbours) should allocate roots relative to resource availability following an ideal free distribution. This means that if a soil volume of quality R contains x roots, then a soil volume of quality R/n will contain x/n roots. The experimental data were consistent with this prediction. That is, plants grown with 1.2g of slow release fertilizer resources produced 0.043 g of roots, while plants grown with neighbours, or plants grown with half as much fertilizer produced half as much root mass (0.026g, and 0.24g respectively). Seed yield followed a similar pattern.\nC_LIO_LIThis model presents an alternative predictive framework for those plant species that do not seem to play a tragedy of the commons game for belowground competition.\nC_LIO_LISynthesis: It remains unclear why some plants do not engage in belowground games for competition. Models suggest over-proliferation is an unbeatable evolutionary stable strategy, yet plants that do not play the game apparently coexist with plants that do. We suggest that a greater understanding of trade-offs among traits that are important for other biotic interactions (above-ground competition, enemy defence, mutualisms) will lead to a greater understanding of why some species over-proliferate roots when in competition but other species do not.\nC_LI

10.1111/1365-2745.12259

biorxiv

10.1101/004820

Some plants don&amp;#146;t play games: An ideal free distribution explains the root production of plants that do not engage in a tragedy of the commons game.

Gordon McNickle;Joel S Brown;

Gordon McNickle

Wilfrid Laurier University

2014-05-07

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2014/05/07/004820.source.xml

This is the pre-peer-reviewed version of the following article: G.G. McNickle and J.S. Brown. (2014) An ideal free distribution explains the root production of plants that do not engage in a tragedy of the commons game. Journal of Ecology. DOI: 10.1111/1365-2745.12259, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/1365-2745.12259/abstract\n\nO_LIGame theoretic models that seek to predict the most competitive strategy plants use for competition in soil are clear; they generally predict that over-proliferation of roots is the only evolutionarily stable strategy. However, empirical studies are equally clear that not all plants employ this strategy of over-proliferation of roots. Here, our goal was to develop and test an alternative non-game theoretic model that can be used to develop alternative hypotheses for plants that do not appear to play games.\nC_LIO_LIThe model is similar to previous models, but does not use a game theoretic optimization criterion. Instead, plants use only nutrient availability to select a root allocation strategy, ignoring neighbours. To test the model we compare root allocation and seed yield of plants grown either alone or with neighbours.\nC_LIO_LIThe model predicted plants that do not sense neighbours (or ignore neighbours) should allocate roots relative to resource availability following an ideal free distribution. This means that if a soil volume of quality R contains x roots, then a soil volume of quality R/n will contain x/n roots. The experimental data were consistent with this prediction. That is, plants grown with 1.2g of slow release fertilizer resources produced 0.043 g of roots, while plants grown with neighbours, or plants grown with half as much fertilizer produced half as much root mass (0.026g, and 0.24g respectively). Seed yield followed a similar pattern.\nC_LIO_LIThis model presents an alternative predictive framework for those plant species that do not seem to play a tragedy of the commons game for belowground competition.\nC_LIO_LISynthesis: It remains unclear why some plants do not engage in belowground games for competition. Models suggest over-proliferation is an unbeatable evolutionary stable strategy, yet plants that do not play the game apparently coexist with plants that do. We suggest that a greater understanding of trade-offs among traits that are important for other biotic interactions (above-ground competition, enemy defence, mutualisms) will lead to a greater understanding of why some species over-proliferate roots when in competition but other species do not.\nC_LI

10.1111/1365-2745.12259

biorxiv

10.1101/004853

MOSAIC EPIGENETIC DYSREGULATION OF ECTODERMAL CELLS IN AUTISM SPECTRUM DISORDER

Esther R. Berko;Masako Suzuki;Faygel Beren;Christophe Lemetre;Christine M. Alaimo;R. Brent Calder;Karen Ballaban-Gil;Batya Gounder;Kaylee Kampf;Jill Kirschen;Shahina B. Maqbool;Zeineen Momin;David M. Reynolds;Natalie Russo;Lisa Shulman;Edyta Stasiek;Jessica Tozour;Maria Valicenti-McDermott;Shenglong Wang;Brett S. Abrahams;Joseph Hargitai;Dov Inbar;Zhengdong Zhang;Joseph D. Buxbaum;Sophie Molholm;John J. Foxe;Robert W. Marion;Adam Auton;John Greally;

John Greally

Albert Einstein College of Medicine

2014-05-06

New Results

cc_by_nc

Genomics

https://www.biorxiv.org/content/early/2014/05/06/004853.source.xml

DNA mutational events are increasingly being identified in autism spectrum disorder (ASD), but the potential additional role of dysregulation of the epigenome in the pathogenesis of the condition remains unclear. The epigenome is of interest as a possible mediator of environmental effects during development, encoding a cellular memory reflected by altered function of progeny cells. Advanced maternal age (AMA) is associated with an increased risk of having a child with ASD for reasons that are not understood. To explore whether AMA involves covert aneuploidy or epigenetic dysregulation leading to ASD in the offspring, we tested an homogeneous ectodermal cell type from 47 individuals with ASD compared with 48 typically developing (TD) controls born to mothers of [&ge;]35 years, using a quantitative genome-wide DNA methylation assay. We show that DNA methylation patterns are dysregulated in ectodermal cells in these individuals, having accounted for confounding effects due to subject age, sex and ancestral haplotype. We did not find mosaic aneuploidy or copy number variability to occur at differentially-methylated regions in these subjects. Of note, the loci with distinctive DNA methylation were found at genes expressed in the brain and encoding protein products significantly enriched for interactions with those produced by known ASD-causing genes, representing a perturbation by epigenomic dysregulation of the same networks compromised by DNA mutational mechanisms. The results indicate the presence of a mosaic subpopulation of epigenetically-dysregulated, ectodermally-derived cells in subjects with ASD. The epigenetic dysregulation observed in these ASD subjects born to older mothers may be associated with aging parental gametes, environmental influences during embryogenesis or could be the consequence of mutations of the chromatin regulatory genes increasingly implicated in ASD. The results indicate that epigenetic dysregulatory mechanisms may complement and interact with DNA mutations in the pathogenesis of the disorder.\n\nAUTHOR SUMMARYOlder mothers have a higher than expected risk of having a child with an autism spectrum disorder (ASD). The reason for this increased risk is unknown. The eggs of older mothers are more prone to abnormalities of chromosome numbers, suggesting this as one possible mechanism of the increased ASD risk. Age is also associated with a loss of control of epigenetic regulatory patterns that govern gene expression, indicating a second potential mechanism. To test both possibilities, we sampled cells from the same developmental origin as the brain, and performed genome-wide tests looking for unusual chromosome numbers and DNA methylation patterns. The studies were performed on individuals with ASD and typically developing controls, all born to mothers at least 35 years of age at the time of birth. We found the cells from individuals with ASD to have changes in DNA methylation at a number of loci, especially near genes encoding proteins known to interact with those already implicated in ASD. We conclude that epigenetic dysregulation occurring in gametes or early embryonic life may be one of the contributors to the development of ASD.

10.1371/journal.pgen.1004402

biorxiv

10.1101/004861

Missing kinaesthesia challenges precise naturalistic cortical prosthetic control

Ferran Galán;Mark R Baker;Kai Alter;Stuart N Baker;

Ferran Gal?n

Institute of Neuroscience, Newcastle University

2014-05-06

New Results

cc_by_nc_nd

Neuroscience

https://www.biorxiv.org/content/early/2014/05/06/004861.source.xml

A major assumption of brain-machine interface (BMI) research is that patients with disconnected neural pathways can still volitionally recall precise motor commands that could be decoded for naturalistic prosthetic control. However, the disconnected condition of these patients also blocks kinaesthetic feedback from the periphery, which has been shown to regulate centrally generated output responsible for accurate motor control. Here we tested how well motor commands are generated in the absence of kinaesthetic feedback by decoding hand movements from human scalp electroencephalography (EEG) in three conditions: unimpaired movement, imagined movement, and movement attempted during temporary disconnection of peripheral afferent and efferent nerves by ischemic nerve block. Our results suggest that the recall of cortical motor commands is impoverished in absence of kinaesthetic feedback, challenging the possibility of precise naturalistic cortical prosthetic control.

10.1002/hbm.22653

biorxiv

10.1101/004804

Visual areas exert feedforward and feedback influences through distinct frequency channels

Andre M Bastos;Julien Vezoli;Conrado A Bosman;Jan-Mathijs Schoffelen;Robert Oostenveld;Jarrod R Dowdall;Peter De Weerd;Henry Kennedy;Pascal Fries;

Pascal Fries

Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society

2014-05-06

New Results

cc_by_nc_nd

Neuroscience

https://www.biorxiv.org/content/early/2014/05/06/004804.source.xml

Visual cortical areas are thought to form a hierarchy and to subserve cognitive functions by interacting in both feedforward and feedback directions. While feedforward influences convey sensory signals, feedback influences modulate brain responses to a given sensory stimulus according to the current behavioural context. Many studies have demonstrated effects of feedback influences on feedforward driven responses and on behaviour. Also, anatomical projections in both directions have been identified. However, although these studies have revealed the anatomical paths and the neurophysiological consequences of influences in both directions, the neurophysiological mechanisms through which these influences are exerted remain largely elusive. Here we show that in the primate visual system, feedforward influences are carried by theta-band (~4 Hz) and gamma-band (~60-80 Hz) synchronization, and feedback influences by beta-band (~14-18 Hz) synchronization. These frequency-specific asymmetries in directed influences were revealed by simultaneous local field potential recordings from eight visual areas and an analysis of Granger-causal influences across all 28 pairs of areas. The asymmetries in directed influences correlated directly with asymmetries in anatomy and enabled us to build a visual cortical hierarchy from the influence asymmetries alone. Across different task periods, most areas stayed at their hierarchical position, whereas particularly frontal areas moved dynamically. Our results demonstrate that feedforward and feedback signalling use different frequency channels, which might subserve their differential communication requirements and lead to differential local consequences. The possibility to infer hierarchical relationships through functional data alone might make it possible to derive a cortical hierarchy in the living human brain.

10.1016/j.neuron.2014.12.018

biorxiv

10.1101/004788

A gene expression atlas of a bicoid-depleted Drosophila embryo reveals early canalization of cell fate

Max V Staller;Charless C Fowlkes;Meghan D.J. Bragdon;Zeba B. Wunderlich;Angela DePace;

Angela DePace

Harvard Medical School

2014-05-06

New Results

cc_by_nc_nd

Systems Biology

https://www.biorxiv.org/content/early/2014/05/06/004788.source.xml

In developing embryos, gene regulatory networks canalize cells towards discrete terminal fates. We studied the behavior of the anterior-posterior segmentation network in Drosophila melanogaster embryos depleted of a key maternal input, bicoid (bcd), by building a cellular- resolution gene expression atlas containing measurements of 12 core patterning genes over 6 time points in early development. With this atlas, we determine the precise perturbation each cell experiences, relative to wild type, and observe how these cells assume cell fates in the perturbed embryo. The first zygotic layer of the network, consisting of the gap and terminal genes, is highly robust to perturbation: all combinations of transcription factor expression found in bcd depleted embryos were also found in wild type embryos, suggesting that no new cell fates were created even at this very early stage. All of the gap gene expression patterns in the trunk expand by different amounts, a feature that we were unable to explain using two simple models of the effect of bcd depletion. In the second layer of the network, depletion of bcd led to an excess of cells expressing both even skipped and fushi tarazu early in the blastoderm stage, but by gastrulation this overlap resolved into mutually exclusive stripes. Thus, following depletion of bcd, individual cells rapidly canalize towards normal cell fates in both layers of this gene regulatory network. Our gene expression atlas provides a high resolution picture of a classic perturbation and will enable further modeling of canalization in this transcriptional network.

10.1242/dev.117796

biorxiv

10.1101/004887

Pushed beyond the brink: Allee effects, environmental stochasticity, and extinction

Gregory Roth;Sebastian Schreiber;

Sebastian Schreiber

University of California, Davis

2014-05-07

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2014/05/07/004887.source.xml

To understand the interplay between environmental stochasticity and Allee effects, we analyze persistence, asymptotic extinction, and conditional persistence for stochastic difference equations. Our analysis reveals that persistence requires that the geometric mean of fitness at low densities is greater than one. When this geometric mean is less than one, asymptotic extinction occurs with high probability for low initial population densities. Additionally, if the population only experiences positive density-dependent feedbacks, conditional persistence occurs provided the geometric mean of fitness at high population densities is greater than one. However, if the population experiences both positive and negative density-dependent feedbacks, conditional persistence only occurs if environmental fluctuations are sufficiently small. We illustrate counter-intuitively that environmental fluctuations can increase the probability of persistence when populations are initially at low densities, and can cause asymptotic extinction of populations experiencing intermediate predation rates despite conditional persistence occurring at higher predation rates.

10.1080/17513758.2014.962631

biorxiv

10.1101/004887

Pushed beyond the brink: Allee effects, environmental stochasticity, and extinction

Gregory Roth;Sebastian Schreiber;

Sebastian Schreiber

University of California, Davis

2014-09-02

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2014/09/02/004887.source.xml

To understand the interplay between environmental stochasticity and Allee effects, we analyze persistence, asymptotic extinction, and conditional persistence for stochastic difference equations. Our analysis reveals that persistence requires that the geometric mean of fitness at low densities is greater than one. When this geometric mean is less than one, asymptotic extinction occurs with high probability for low initial population densities. Additionally, if the population only experiences positive density-dependent feedbacks, conditional persistence occurs provided the geometric mean of fitness at high population densities is greater than one. However, if the population experiences both positive and negative density-dependent feedbacks, conditional persistence only occurs if environmental fluctuations are sufficiently small. We illustrate counter-intuitively that environmental fluctuations can increase the probability of persistence when populations are initially at low densities, and can cause asymptotic extinction of populations experiencing intermediate predation rates despite conditional persistence occurring at higher predation rates.

10.1080/17513758.2014.962631

biorxiv

10.1101/004895

Genetic dissection of MAPK-mediated complex traits across S. cerevisiae

Sebastian Treusch;Frank W Albert;Joshua S Bloom;Iulia E Kotenko;Leonid Kruglyak;

Leonid Kruglyak

Howard Hughes Medical Institute, Department of Human Genetics, Department of Biological Chemistry, U

2014-05-07

New Results

cc_no

Genetics

https://www.biorxiv.org/content/early/2014/05/07/004895.source.xml

Signaling pathways enable cells to sense and respond to their environment. Many cellular signaling strategies are conserved from fungi to humans, yet their activity and phenotypic consequences can vary extensively among individuals within a species. A systematic assessment of the impact of naturally occurring genetic variation on signaling pathways remains to be conducted. In S. cerevisiae, both response and resistance to stressors that activate signaling pathways differ between diverse isolates. Here, we present a quantitative trait locus (QTL) mapping approach that enables us to identify genetic variants underlying such phenotypic differences across the genetic and phenotypic diversity of S. cerevisiae. Using a Round-robin cross between twelve diverse strains, we determined the genetic architectures of phenotypes critically dependent on MAPK signaling cascades. Genetic variants identified fell within MAPK signaling networks themselves as well as other interconnected signaling pathways, illustrating how genetic variation can shape the phenotypic output of highly conserved signaling cascades.

10.1371/journal.pgen.1004913

biorxiv

10.1101/004911

Application of Global Transcriptome Data in Gene Ontology Classification and Construction of a Gene Ontology Interaction Network

Mario Fruzangohar;Esmaeil Ebrahimie;David L Adelson;

David L Adelson

University of Adelaide

2014-05-08

New Results

cc_by_nd

Bioinformatics

https://www.biorxiv.org/content/early/2014/05/08/004911.source.xml

Gene Ontology (GO) classification of statistically significant over/under expressed genes is a commonly used to interpret transcriptomics data in functional genomic analysis. In this approach, all significant genes contribute equally to the final GO classification regardless of their actual expression levels. However, the original level of gene expression can significantly affect protein production and consequently GO term enrichment, and genes with low expression levels can participate in the final GO enrichment through cumulative effects. In addition, GO terms have regulatory relationships that allow the construction of a regulatory network that incorporates gene expression levels to better study biological mechanisms. In this report, we have used gene expression levels in bacteria to determine GO term enrichments. This approach provided the opportunity to enrich GO terms across the entire transcriptome (instead of a subset of differentially expressed genes). In the second part, we show a dynamically developed enriched interaction network between Biological Process GO terms for any gene samples. This type of network presents regulatory relationships between GO terms and their genes. We then demonstrate the efficiency of these methods using public data from two important bacterial pathogens as models. We also explain how these methods help us understand potential pathogenesis mechanisms employed by these bacteria.

biorxiv

10.1101/004911

Application of Global Transcriptome Data in Gene Ontology Classification and Construction of a Gene Ontology Interaction Network

Mario Fruzangohar;Esmaeil Ebrahimie;David L Adelson;

David L Adelson

University of Adelaide

2014-05-14

New Results

cc_by_nd

Bioinformatics

https://www.biorxiv.org/content/early/2014/05/14/004911.source.xml

Gene Ontology (GO) classification of statistically significant over/under expressed genes is a commonly used to interpret transcriptomics data in functional genomic analysis. In this approach, all significant genes contribute equally to the final GO classification regardless of their actual expression levels. However, the original level of gene expression can significantly affect protein production and consequently GO term enrichment, and genes with low expression levels can participate in the final GO enrichment through cumulative effects. In addition, GO terms have regulatory relationships that allow the construction of a regulatory network that incorporates gene expression levels to better study biological mechanisms. In this report, we have used gene expression levels in bacteria to determine GO term enrichments. This approach provided the opportunity to enrich GO terms across the entire transcriptome (instead of a subset of differentially expressed genes). In the second part, we show a dynamically developed enriched interaction network between Biological Process GO terms for any gene samples. This type of network presents regulatory relationships between GO terms and their genes. We then demonstrate the efficiency of these methods using public data from two important bacterial pathogens as models. We also explain how these methods help us understand potential pathogenesis mechanisms employed by these bacteria.

biorxiv

10.1101/004903

Differential relationships between habitat fragmentation and within-population genetic diversity of three forest-dwelling birds

Benjamin Zuckerberg;Matt Carling;Roi Dor;Elise Ferree;Garth Spellman;Andrea Townsend;

Benjamin Zuckerberg

University of Wisconsin

2014-05-08

New Results

cc_by_nc_nd

Genetics

https://www.biorxiv.org/content/early/2014/05/08/004903.source.xml

Habitat fragmentation is a major driver of environmental change affecting wildlife populations across multiple levels of biological diversity. Much of the recent research in landscape genetics has focused on quantifying the influence of fragmentation on genetic variation among populations, but questions remain as to how habitat loss and configuration influences within-population genetic diversity. Habitat loss and fragmentation might lead to decreases in genetic diversity within populations, which might have implications for population persistence over multiple generations. We used genetic data collected from populations of three species occupying forested landscapes across a broad geographic region: Mountain Chickadee (Poecile gambeli; 22 populations), White-breasted Nuthatch (Sitta carolinensis; 13 populations) and Pygmy Nuthatch (Sitta pygmaea; 19 populations) to quantify patterns of haplotype and nucleotide diversity across a range of forest fragmentation. We predicted that fragmentation effects on genetic diversity would vary depending on dispersal capabilities and habitat specificity of the species. Forest aggregation and the variability in forest patch area were the two strongest landscape predictors of genetic diversity. We found higher haplotype diversity in populations of P. gambeli and S. carolinensis inhabiting landscapes characterized by lower levels of forest fragmentation. Conversely, S. pygmaea demonstrated the opposite pattern of higher genetic diversity in fragmented landscapes. For two of the three species, we found support for the prediction that highly fragmented landscapes sustain genetically less diverse populations. We suggest, however, that future studies should focus on species of varying life-history traits inhabiting independent landscapes to better understand how habitat fragmentation influences within-population genetic diversity.

biorxiv

10.1101/004986

Spatial variation in water loss predicts terrestrial salamander distribution and population dynamics

William Peterman;Raymond D Semlitsch;

William Peterman

University of Illinois

2014-05-09

New Results

cc_by_nc_nd

Ecology

https://www.biorxiv.org/content/early/2014/05/09/004986.source.xml

Many patterns observed in ecology, such as species richness, life history variation, habitat use, and distribution have physiological underpinnings. For many ectothermic organisms temperature relations shape these patterns, but for terrestrial amphibians, water balance may supersede temperature as the most critical physiologically-limiting factor. Many amphibian species have little resistance to water loss, which restricts them to moist microhabitats and may significantly affect foraging, dispersal, and courtship. Using plaster models as surrogates for terrestrial plethodontid salamanders, we measured water loss under ecologically-relevant field conditions to estimate the duration of surface activity time across the landscape. Surface activity time was significantly affected by topography, solar exposure, canopy cover, maximum air temperature, and time since rain. Spatially, surface activity times were highest in ravine habitats and lowest on ridges. Surface activity time was a significant predictor of salamander abundance, as well as a predictor of successful recruitment; the probability of a juvenile salamander occupying an area with high surface activity time was two times greater than an area with limited predicted surface activity. Our results suggest that survival, recruitment, or both are demographic processes that are affected by water loss and the ability of salamanders to be surface active. Results from our study extend our understanding of plethodontid salamander ecology, emphasize the limitations imposed by their unique physiology, and highlight the importance of water loss to spatial population dynamics. These findings are timely to understanding the effects that fluctuating temperature and moisture conditions predicted for future climates will have on plethodontid salamanders.

10.1007/s00442-014-3041-4

biorxiv

10.1101/005009

Data-intensive modeling of forest dynamics

Jean F Lienard;Dominique Gravel;Nikolay Strigul;

Nikolay Strigul

Washington State University

2014-05-10

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2014/05/10/005009.source.xml

Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

10.1016/j.envsoft.2015.01.010

biorxiv

10.1101/005009

Data-intensive modeling of forest dynamics

Jean F Lienard;Dominique Gravel;Nikolay Strigul;

Nikolay Strigul

Washington State University

2014-08-06

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2014/08/06/005009.source.xml

Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

10.1016/j.envsoft.2015.01.010

biorxiv

10.1101/005009

Data-intensive modeling of forest dynamics

Jean F Lienard;Dominique Gravel;Nikolay Strigul;

Nikolay Strigul

Washington State University

2014-12-08

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2014/12/08/005009.source.xml

Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

10.1016/j.envsoft.2015.01.010

biorxiv

10.1101/005009

Data-intensive modeling of forest dynamics

Jean F Lienard;Dominique Gravel;Nikolay Strigul;

Nikolay Strigul

Washington State University

2015-05-02

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2015/05/02/005009.source.xml

Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

10.1016/j.envsoft.2015.01.010

biorxiv

10.1101/004994

An appraisal of the classic forest succession paradigm with the shade tolerance index

Jean F Lienard;Ionut Florescu;Nikolay Strigul;

Nikolay Strigul

Washington State University, Vancouver WA

2014-05-10

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2014/05/10/004994.source.xml

In this paper we revisit the classic theory of forest succession that relates shade tolerance and species replacement and assess its validity to understand patch-mosaic patterns of forested ecosystems of the USA. We introduce a macroscopic parameter called the \"shade tolerance index\" and compare it to the classic continuum index in southern Wisconsin forests. We exemplify shade tolerance driven succession in White Pine-Eastern Hemlock forests using computer simulations and analyzing approximated chronosequence data from the USDA FIA forest inventory. We describe this parameter across the last 50 years in the ecoregions of mainland USA, and demonstrate that it does not correlate with the usual macroscopic characteristics of stand age, biomass, basal area, and biodiversity measures. We characterize the dynamics of shade tolerance index using transition matrices and delimit geographical areas based on the relevance of shade tolerance to explain forest succession. We conclude that shade tolerance driven succession is linked to climatic -variables and can be considered as a primary driving factor of forest dynamics mostly in central-north and northeastern areas in the USA. Overall, the shade tolerance index constitutes a new quantitative approach that can be used to understand and predict succession of forested ecosystems and biogeographic patterns.

10.1371/journal.pone.0117138

biorxiv

10.1101/004994

An appraisal of the classic forest succession paradigm with the shade tolerance index

Jean F Lienard;Ionut Florescu;Nikolay Strigul;

Nikolay Strigul

Washington State University, Vancouver WA

2014-06-03

New Results

cc_no

Ecology

https://www.biorxiv.org/content/early/2014/06/03/004994.source.xml

In this paper we revisit the classic theory of forest succession that relates shade tolerance and species replacement and assess its validity to understand patch-mosaic patterns of forested ecosystems of the USA. We introduce a macroscopic parameter called the \"shade tolerance index\" and compare it to the classic continuum index in southern Wisconsin forests. We exemplify shade tolerance driven succession in White Pine-Eastern Hemlock forests using computer simulations and analyzing approximated chronosequence data from the USDA FIA forest inventory. We describe this parameter across the last 50 years in the ecoregions of mainland USA, and demonstrate that it does not correlate with the usual macroscopic characteristics of stand age, biomass, basal area, and biodiversity measures. We characterize the dynamics of shade tolerance index using transition matrices and delimit geographical areas based on the relevance of shade tolerance to explain forest succession. We conclude that shade tolerance driven succession is linked to climatic -variables and can be considered as a primary driving factor of forest dynamics mostly in central-north and northeastern areas in the USA. Overall, the shade tolerance index constitutes a new quantitative approach that can be used to understand and predict succession of forested ecosystems and biogeographic patterns.

10.1371/journal.pone.0117138

biorxiv

10.1101/005017

Background selection as baseline for nucleotide variation across the Drosophila genome

Josep M Comeron;

Josep M Comeron

University of Iowa

2014-05-09

New Results

cc_by_nc_nd

Evolutionary Biology

https://www.biorxiv.org/content/early/2014/05/09/005017.source.xml

The constant removal of deleterious mutations by natural selection causes a reduction in neutral diversity and efficacy of selection at genetically linked sites (a process called Background Selection, BGS). Population genetic studies, however, often ignore BGS effects when investigating demographic events or the presence of other types of selection. To obtain a more realistic evolutionary expectation that incorporates the unavoidable consequences of deleterious mutations, we generated high-resolution landscapes of variation across the Drosophila melanogaster genome under a BGS scenario independent of polymorphism data. We find that BGS plays a significant role in shaping levels of variation across the entire genome, including long introns and intergenic regions distant from annotated genes. We also find that a very large percentage of the observed variation in diversity across autosomes can be explained by BGS alone, up to 70% across individual chromosome arms, thus indicating that BGS predictions can be used as baseline to infer additional types of selection and demographic events. This approach allows detecting several outlier regions with signal of recent adaptive events and selective sweeps. The use of a BGS baseline, however, is particularly appropriate to investigate the presence of balancing selection and our study exposes numerous genomic regions with the predicted signature of higher polymorphism than expected when a BGS context is taken into account. Importantly, we show that these conclusions are robust to the mutation and selection parameters of the BGS model. Finally, analyses of protein evolution together with previous comparisons of genetic maps between Drosophila species, suggest temporally variable recombination landscapes and thus, local BGS effects that may differ between extant and past phases. Because genome-wide BGS and temporal changes in linkage effects can skew approaches to estimate demographic and selective events, future analyses should incorporate BGS predictions and capture local recombination variation across genomes and along lineages.

10.1371/journal.pgen.1004434

biorxiv

10.1101/005025

Module organizational principles and dynamics in biological networks

Chun-Yu Lin;Tsai-ling Lee;Yi-Wei Lin;Yu-Shu Lo;Chih-Ta Lin;Jinn-Moon Yang;

Jinn-Moon Yang

Institute of Bioinformatics, National Chiao Tung University

2014-05-11

New Results

cc_no

Systems Biology

https://www.biorxiv.org/content/early/2014/05/11/005025.source.xml

A module is a group of closely related proteins that act in concert to perform specific biological functions through protein-protein interactions (PPIs) that occur in time and space. However, the underlying organizational principles of a module remain unclear. In this study, we collected CORUM module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores (PPIES) and interface evolution scores (IES) to infer module elements, including core and ring components. Functions of core components were highly correlated (Pearsons r = 0.98) with those of 11,384 essential genes. In comparison with ring components, core proteins and PPIs were conserved in multiple species. Subsequently, protein dynamics and module dynamics of biological networks and functional diversities confirmed that core components form dynamic biological network hubs and play key roles in various biological functions. PPIES and IES can reflect module organization principles and protein/module dynamics in biological networks. On the basis of the analyses of gene essentiality, module dynamics, network topology, and gene co-expression, the module organizational principles can be described as follows: 1) a module consists of core and ring components; 2) the core components play major roles in biological functions and collaborate with ring components to perform certain functions in some cases; 3) the core components are conserved and essential in module dynamics in time and space.

biorxiv

10.1101/004960

Mycoplasma stress response: adaptive regulation or broken brakes?

Pavel V Mazin;Gleb Y Fisunov;Alexey Y Gorbachev;Ilya A Altukhov;Tatiana A Semashko;Dmitry G Alexeev;Vadim M Govorun;

Gleb Y Fisunov

Research Institute of Physical-Chemical Medicine

2014-05-09

New Results

cc_no

Systems Biology

https://www.biorxiv.org/content/early/2014/05/09/004960.source.xml

The avian bacterial pathogen Mycoplasma gallisepticum is a good model for transcriptional regulation studies due to its small genome and relative simplicity. In this study, we used RNA-Seq experiments combined with MS-based proteomics to accurately map coding sequences (CDSs), transcription start sites (TSSs) and transcription terminators (TTs) and to decipher their roles in stress-induced transcriptional responses. We identified 1061 TSSs at an FDR (false discovery rate) of 10% and showed that almost all transcription in M. gallisepticum is initiated from classic TATAAT promoters, which are surrounded by A/T-rich sequences and rarely accompanied by a -35 element. Our analysis revealed the pronounced complexity of the operon structure: on average, each coding operon has one internal TSS and TT in addition to the primary ones. Our new transcriptomic approach based on the intervals between the two closest transcription initiators and/or terminators allowed us to identify two classes of TTs: strong, unregulated and hairpin-containing TTs and weak, heat shock-regulated and hairpinless TTs. Comparing the gene expression levels under different conditions (such as heat, osmotic and peroxide stresses) revealed widespread and divergent transcription regulation in M. gallisepticum. Modeling suggested that the structure of the core promoter plays a major role in gene expression regulation. We have shown that the heat stress activation of cryptic promoters combined with the suppression of hairpinless TTs leads to widespread, seemingly non-functional transcription.

biorxiv

10.1101/005082

diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals

Paula Tataru;Jasmine A. Nirody;Yun S. Song;

Paula Tataru

Bioinformatics Research Centre, Aarhus University, Denmark

2014-05-12

New Results

cc_by_nc_nd

Bioinformatics

https://www.biorxiv.org/content/early/2014/05/12/005082.source.xml

Summary: We present a tool, diCal-IBD, for detecting identity-by-descent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM.\n\nAvailability: http://sourceforge.net/p/dical-ibd\n\nContact: [email protected]

10.1093/bioinformatics/btu563

biorxiv

10.1101/005082

diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals

Paula Tataru;Jasmine A. Nirody;Yun S. Song;

Paula Tataru

Bioinformatics Research Centre, Aarhus University, Denmark

2014-05-13

New Results

cc_by_nc_nd

Bioinformatics

https://www.biorxiv.org/content/early/2014/05/13/005082.source.xml

Summary: We present a tool, diCal-IBD, for detecting identity-by-descent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM.\n\nAvailability: http://sourceforge.net/p/dical-ibd\n\nContact: [email protected]

10.1093/bioinformatics/btu563

biorxiv