Artemisia tridentata) is one of the most widely distributed and ecologically important shrub species in western North America. This species serves as a critical habitat and food resource for many animals and invertebrates. Habitat loss due to a combination of disturbances followed by establishment of invasive plant species is a serious threat to big sagebrush ecosystem sustainability. Lack of genomic data has limited our understanding of the evolutionary history and ecological adaptation in this species. Here, we report on the sequencing of expressed sequence tags (ESTs) and detection of single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) markers in subspecies of big sagebrush.Big sagebrush  protein database using 29,541 consensus sequences obtained from a combined assembly resulted in 21,436 sequences with significant blast alignments (≤ 1e-15). A total of 20,952 SNPs and 119 polymorphic SSRs were detected between the two subspecies. SNPs were validated through various methods including sequence capture. Validation of SNPs in different individuals uncovered a high level of nucleotide variation in EST sequences. EST sequences of a third, tetraploid subspecies (ssp. wyomingensis) obtained by Illumina sequencing were mapped to the consensus sequences of the combined 454 EST assembly. Approximately one-third of the SNPs between sspp. tridentata and vaseyana identified in the combined assembly were also polymorphic within the two geographically distant ssp. wyomingensis samples.cDNA of Artemisia tridentata, which contains a large sample of the big sagebrush leaf transcriptome. SNP mapping among the three subspecies suggest the origin of ssp. wyomingensis via mixed ancestry. A large number of SNP and SSR markers provide the foundation for future research to address questions in big sagebrush evolution, ecological genetics, and conservation using genomic approaches.We have produced a large EST dataset for  Artemisia (Asteraceae) is a widely distributed plant genus that is predominantly found in temperate regions of the northern hemisphere. Some species within this genus are valued in medicine, cooking, and forage for livestock [Artemisia tridentata) is recognized for its importance to ecosystem function. Big sagebrush is one of the most abundant, widespread, and ecologically important woody shrub species in the intermountain regions of western North America. This species contains three widespread subspecies  that occupy distinct habitats and two less common subspecies (A. t. ssp. spiciformis and A. t. ssp. xericensis) [e.g., conversion to agriculture, overgrazing and wildfire frequencies) are major factors in the degradation of these ecosystems. Such disturbances can lead to invasions by cheat grass (Bromus tectorum) and other weeds that fundamentally change the wildfire frequency and severely reduce the frequency of sagebrush in ecosystems where it historically dominated [ivestock ,2. Big sicensis) ,4. Prioricensis) . Changesominated ,7. Restoominated , where ttridentata grows in alluvial flats at elevation typically lower than 1800 m, ssp. vaseyana is found in higher altitude uplands at elevations above 1660 m up to timberline, and ssp. wyomingensis occupies drier sites with shallow soils [wyomingensis is universally tetraploid, whereas sspp. tridentata and vaseyana are typically diploid; although both sspp. tridentata and vaseyana also include tetraploid populations [tridentata and ssp. vaseyana is common under the appropriate ecological conditions. Hybridization among big sagebrush subspecies has been studied using reciprocal transplants, showing that natural selection tends to limit the hybrids of sspp. tridentata and vaseyana to a zone between the parental subspecies habitat [Polyploidy and intra- and interspecific hybridization are likely the important factors in big sagebrush adaptation and landscape dominance. Big sagebrush subspecies occupy specific ecological niches: ssp. ow soils . Subspeculations . Hybridi habitat ,10. McAr habitat  suggest A. tridentata nucleotide sequences in the NCBI database yielded less than 50 nucleotide sequences. As a genus, Artemisia has approximately 3.8 million sequences (~823 Mbp) of which 3.7 million reads are archived in the Sequence Read Archive (SRA), from A. annua EST projects [A. annua genome project [A. annua is a medicinal herb native to temperate Asia and is not found in the western hemisphere. Sequences of A. tridentata are needed to conduct research studies involving phylogenetics, population genetics, and ecological genetics in North American big sagebrush populations. Transcriptome sequencing and annotation, and marker detection within big sagebrush EST sequences will provide a rapid means to satisfy these information needs and set the stage for future studies.Though widely acknowledged as an important shrub of the intermountain ecosystem in western North America, limited DNA sequence data has been collected on big sagebrush. A search for uences ~83 Mbp of projects , and an  project . A. annuA.t. ssp. tridentata and A.t. ssp. vaseyana and compared the resulting ESTs. We also sequenced A. t. ssp. wyomingensis ESTs to assess SNP distribution in this subspecies compared to sspp. tridentata and vaseyana. Our objectives were to 1) identify and characterize a large number of expressed genes in A. tridentata, 2) detect sequence differences within and between sspp. tridentata and vaseyana that could be used as markers to further our understanding of adaptive, phenotypic variation within the species, and 3) gain inference into the origins of the tetraploid A. t. ssp. wyomingensis.In this study, we characterized the leaf transcriptome of two big sagebrush subspecies, A. tridentata: sspp. tridentata and vaseyana. Independent sequencing of these cDNA libraries generated 823,392 sequence reads containing 332,578,737 bases of sequence from ssp. tridentata and 702,001 sequence reads containing 233,854,535 bases of sequence from ssp. vaseyana  and out of 275,866 singleton sequences, 70,836 sequences (25.7%) had significant alignments (≤1e-15). Of the contigs with BLASTx hits, 9,696 (45.2%) sequences had their best blast alignment to Vitis vinifera and 215 (7.3%) and 80 (0.27%) to Helianthus annus and Lactuca sativa respectively, both of which are in the Asteraceae. Only fifty-four (0.2%) contigs had their best matches to Artemisia annua, owing to the limited number of Artemisia proteins (167) in the NR protein database. The NR BLAST results were utilized by Blast2GO [Protein homologies between big sagebrush ESTs and the NR protein database were identified using BLASTx. Out of 29,541 assembled contig sequences, 21,436 sequences (72.6%) had significant alignments  matching at least one protein domain model. The distribution of the domains ranged from a maximum of 13 domains assigned to the same contig to a minimum of one domain per contig  which is then oxidized to p-coumarate by the enzyme cinnamate 4-hydroxylase. The enzyme 4-hydroxycinnamoyl CoA ligase then ligates an S-CoA group to p-coumarate, forming p-coumaroyl CoA. We were able to detect putative gene sequences for these three main enzymes involved in coumarin synthesis in our annotated contig dataset. Individual tests of enzymatic substrates and products are needed to confirm their roles in coumarin synthesis within big sagebrush.Big sagebrush leaves are known to synthesize and store large quantities of terpenoids on the epidermal surfaces of glandular leaf trichomes . Thereforoyl CoA ,18. At ttridentata (1 SNP/246 bp) SNPs with an average base coverage of 18 × and 61,028 ssp. vaseyana SNPs (1 SNP/207 bp) with an average base coverage of 15×. These putative SNPs may represent a high level of heterozygosity that exists in big sagebrush through its large effective population size and its wind-pollinated reproductive strategy. Future segregation analysis would be needed to ascertain if these putative SNPs are truly two alleles at a single locus or an artifact of the sequencing and/or assembly process. The individual assemblies contained many fewer SNPs than detected in the combined assembly  prior to filtering those SNPs that were not discriminatory between sspp. tridentata and vaseyana .A large number of resident SNPs were discovered within the accessions examined in this study. A search for SNPs yielded 59,093 ssp. tridentata and vaseyana when a threshold of 8 × coverage was applied  are likely artifacts of applying a single set of assembly parameters to a set of heterogeneous genes and splice forms - each with their own evolutionary history. Collectively, contigs with an abundance of SNPs (>13) are best avoided in future population genetic studies, though individually each contig with a multitude of SNPs might provide an interesting case study of gene or gene family evolution. Contigs with a high number of SNPs have been highlighted in Additional File ss252842630 to ss252863866; build B133). Full contig sequences are available upon request.When only SNPs that were near homogenous (>90%) within the two accessions were considered, a total of 20,952 SNPs were detected between the sspp. f SNPs >1 SNPs aretridentata and ssp. vaseyana, respectively. Homopolymer SSRs that are reported by default in MISA were not reported because of known limitations of 454 sequencing chemistry. The occurrence and frequency of different SSR motif repeats in EST sequences of the two subspecies were explored , followed by a dinucleotide motif  and a hexanucleotide motif  in ssp. tridentata, whereas the most dominant repeat motif in both subspecies is ACC/GGT with a frequency of 13.4% (134 of 1003 in ssp. tridentata) and 20.7% (105 of 507 in ssp. vaseyana). We were unable to detect any CG/GC motif in either subspecies' EST sequences. This could be due to limitations of emPCR used by the 454 sequencing protocol. Additional details about di- and trinucleotide repeat motifs in both subspecies are listed in Additional File The individual and combined assemblies of ESTs were used for the SSR analysis. The individual assembly analysis resulted in a total of 908 contigs containing 1,003 SSRs and 466 contigs containing 507 SSRs in ssp. tridentata and ssp. vaseyana in the combined assembly. Within an assembled contig, the polymorphic SSRs were identified by counting differences in the numbers of repeat motifs during informatic comparison of ssp. tridentata sequences to ssp. vaseyana sequences. This resulted in detection of 119 polymorphic SSRs in 117 contigs between the two subspecies. Comparing these 119 SSR motif structures to the SSR motif structures identified in individual assemblies, we observed that 111 (93%) SSRs in the combined assembly were found to be identical to those in the individual assemblies and 8 (7%) had a different number of repeats than detected in the individual assemblies.In addition to MISA-detected SSRs, a custom Perl script was used to identify putative polymorphic SSRs between ssp. A. tridentata subspecies were directly validated using two different experimental approaches: subsequent Sanger re-sequencing of cDNA amplicons  targeted with primers in both subspecies cDNA. Of these loci, 6 were chosen for Sanger re-sequencing. Three SNPs were transitions  and three were transversions . The SNP base were validated in cDNA from both subspecies for six of six SNPs (Type 1) and three of six (Type 2), confirming their respective identification within the combined assembly. In the EST assembly, coverage of the chosen SNPs ranged from 9 to 27X and from 20% to 46% in their minor allele frequency. There was no obvious relationship between the amount of EST coverage and SNP validation in this small subset.tridentata and two distinct individuals of ssp. vaseyana , only a single type of base was detected in these four individuals (monomorphic) and the base call always matched one of the expected bases at the SNP. For these monomorphic SNPs, additional sequence coverage of SNP likely would not identify the 'other' base and change our interpretation because these SNPs had an average of 12.6 sequence coverage. 8% of the SNP positions had an additional or third base at the SNP position of a single read in these four individuals.Re-sequencing targeted loci by sequence capture was also used to validate SNPs in two distinct individuals of ssp. tridentata and polymorphic between the two individuals of ssp. vaseyana, respectively. Individual genotypic differences could also explain the 67% level of two-base detection at SNP positions (intra- or inter-subspecies). Of the 403 SNP loci, 16-36% had both bases in individual plants (i.e. putative heterozygotes). Thus, it is not too surprising that the four individuals sampled for validation were also coincidently homozygous for many SNPs found in ESTs of a ssp. tridentata and in ESTs of a ssp. vaseyana individual, particularly if either of the two originally EST-sampled individuals contained a low frequency allele.The low validation rates of SNPs (Type 1: 67% and Type 2: 54%) derived from ESTs could be due to several factors including different genotypes of individual plants, allelic expression biases of sagebrush genes combined with a moderate amount 454 EST sequencing, and errors due to mapping reads to a non-sequenced genome. Different genotypes of individual plants could explain the low SNP validation rate between subspecies. For example, 38% and 10% of SNPs initially detected in our EST assembly were polymorphic between the two individuals of ssp. 6 and (GCA)7 were bioinformatically detected a priori as polymorphic, based on the Perl script parameters, whereas (ATA)5 was not, suggesting that the number of bioinformatically identified polymorphic SSRs was an underestimate of the number of truly polymorphic SSRs. We expect that more SSR loci likely exist but they were under the conservative thresholds used in our bioinformatic analysis. The sequence capture experiment also validated a number of SSRs in contig consensus sequences of the combined assembly. Capture targets included 17 putative SSRs, of which 14 had overlapping reads from sequence capture. In every instance, the presence of an SSR was confirmed. Of these 17 SSRs, five SSRs were polymorphic in repeat number, four SSRs contained SNP polymorphisms in one or more repeats, and five SSRs did not have any polymorphisms detected in the sequence capture reads.SSRs were validated by re-sequencing of Sanger amplicons, 15 loci were selected from the combined EST assembly. Ten of the 15 primer pairs amplified loci in leaf cDNA from both subspecies. Of these 10 loci, 5 loci were selected for Sanger re-sequencing. Re-sequencing of the selected PCR-amplified cDNA sequences confirmed the MISA-detected SSRs  SSR loci out of 15 SSR loci amplified in ssp. wyomingensis. These 11 primers pairs produced fragments of expected sizes in all three subspecies. Re-sequencing of genomic DNA amplicons for SSR validation was not performed, but we expect that the amplified genomic DNA fragments also contain the targeted SSRs.Because of our interest in marker utilization for population genetic studies in genomic DNA (as opposed to ESTs), 15 SSR and 15 SNP primer pairs were evaluated in big sagebrush genomic DNA. Genomic SSR loci were also amplified from the same individuals using the same primers used for SSR validation in cDNA. Fourteen (93%) SSR loci out of 15 SSR loci amplified in both sspp. tridentata individuals, three ssp. vaseyana individuals and two ssp. wyomingensis individuals. For two loci, we observed that both sspp. tridentata and vaseyana were homozygous at each SNP allele (as expected from the combined assembly) while ssp. wyomingensis was dimorphic (i.e. contained both bases). In two different loci, ssp. wyomingensis sequences contained a single variant matching either ssp. tridentata or ssp. vaseyana variant. The remaining SNP remained unconfirmed due to poor Sanger sequencing results. Additional Sanger validation of individual SNP loci would have been an overly laborious process since other sequencing methods exist for validating larger numbers of SNPs (sequence capture and Illumina re-sequencing). Instead of individually genotyping SNP additional loci, genotypic assessment of ssp. wyomingensis at putative SNPs loci was determined en masse using Illumina sequencing (see below).Of the 15 SNP primer pairs, 11 (73%) amplified targeted loci in all three subspecies including the five loci used for cDNA SNP validation. The genomic fragments of these five loci were sequenced in two ssp. wyomingensis samples, respectively. After trimming the 5' ends of the sequences to remove barcodes, the sequences were aligned to the combined EST assembly  as a sequence reference. In the Montana sample, the Illumina reads overlapped 695 SNP positions at a depth of ≥ 20 × with 10% of the reads containing at least one variant. At these SNP positions, both allelic variants (matching the two diploid subspecies) were verified at 251 SNPs. The ssp. tridentata base matched at 138 additional SNP positions and the ssp. vaseyana base matched at 306 other SNP positions. In the Utah sample, Illumina reads overlapped 1,039 SNP positions at a depth of ≥ 20 × with 10% of the reads containing at least one variant. At these SNP positions, both allelic variants (matching the two diploid subspecies) were verified at 458 SNPs. The ssp. tridentata base matched 157 additional SNP positions and the ssp. vaseyana based matched at 424 other SNPs positions. Verified SNPs from the Montana sample were distributed among 484 contigs of the combined assembly, and verified SNPs from the Utah sample were distributed among 767 contigs. This variability of SNP detection is not surprising as the leaf tissue samples for Illumina cDNA sequencing were collected in different environments under different conditions and the cDNA fragments were not normalized (like the diploid cDNAs) or modified in any way to alter the association between gene expression levels and number of reads per gene.Approximately 2.5 million and 10.5 million Illumina reads were obtained from the Montana and Utah ssp. tridentata and vaseyana. Comparison of synonymous base substitutions (Ks) and non-synonymous base substitutions (Ka) between the two subspecies would suggest whether these sequences were influenced by stabilizing or diversifying selection. The distribution of the calculated Ka/Ks ratios for the contigs was found to be ranging from 0.0132 to 6.4000; however any Ka/Ks value greater than 2.5000 was discarded during the analysis assuming that such high values likely resulted from alignment errors. The resultant distribution had a median of 0.2959 (standard deviation = 0.2627). A bootstrap analysis with 146,744 data points at 95% confidence level (α = 0.025) was also performed on the Ka/Ks distribution, which resulted in 0.0791 as the lower limit and 1.0880 as the upper limit of the confidence interval bracket. A histogram of the Ka/Ks distribution is shown  contigs in stabilizing category and 103 GO IDs distributed among 448 (48.5%) contigs in diversifying category.To detect functional sequence diversity and diversifying selection, putative exon sequences were compared between sspp. e et al. , we furtPrevious reports of ESTs from non-model, yet ecologically and economically important organisms have been successfully sequenced and annotated using next generation sequencing -21. HereArtemisia annua sequences is probably due to fewer number of A. annua sequences available in the NR database compared to species such as Vitis vinifera. We expect that the numbers of hits will substantially increase with the eventual publication and annotation of an A. annua and other Artemisia and Asteraceae genome sequences. A majority (69.8%) of the assembled sequences did not align with any peptide in the NR database, possibly indicating the presence of substantial number of novel genes in A. tridentata transcriptome and related taxa. Genes of unknown function are not unexpected, as the discovery of novel genes has been demonstrated in other EST sequencing projects within non-agricultural plant families [To generate a functional characterization of the big sagebrush transcriptome, we compared the contigs and singletons obtained from the combined assembly to peptides within the non-redundant protein database using BLASTx. The low number of matches (54 contigs) to families ,22.Many of the contigs and singleton ESTs identified in this study are expected to have ecological and adaptive relevance. Previous studies relating sagebrush biochemistry to mule deer feeding preference suggest strong correlation between the composition and concentration of secondary metabolites, especially terpenoids, and mule deer preference of sagebrush ,24. We wwyomingensis, and sequence capture. We verified (Type 1) six of six tested SNPs using amplicon Sanger sequencing of individually selected PCR fragments. Additional verification (Sanger sequencing of next-generation sequencing results) was deemed unnecessary due to past experience in Arabidopsis [A large number of SNP and SSR markers were discovered and different subsets of SNPs were validated using Sanger amplicon sequencing of cDNA and genomic DNA, Illumina cDNA sequencing of ssp. bidopsis , Amarantbidopsis ,28, and wyomingensis, and in the sequence capture assemblies  suggested significant amounts of nucleotide diversity between individual genomes of Artemisia. This evidence was supported by three findings. 1) When discriminating SNPs between ssp. tridentata and ssp. vaseyana were re-identified at a higher stringency than 90% (at 99%), 13% of the SNPs were not detected because of a single parameter requiring a degree of homogeneity among residues originating from a single DNA source. This suggests that both individuals used for EST sequencing contained a high number of heterozygous loci. 2) Using Illumina sequencing, only 36% and 44% of the SNP positions had both alleles detected in the ssp. wyomingesis samples respectively, where nearly all of the SNP positions were at least represented by one or the other allele. This indicated that both alleles of a significant number of the SNPs exist in a third A. tridentata subspecies, but a true polyploid hybrid of these the two diploid subspecies would contain both alleles of all SNPs. Thus, the ssp. wyomingensis samples used here were likely derived from different diploids and those individuals had significantly different genotypes than those used for EST sequencing. 3) Using sequence capture, only 54% of the 403 SNP positions were validated as discriminatory between ssp. tridentata and ssp. vaseyana, but 67% of the SNP positions had both bases detected. Thus, 13% of the sequence capture validated SNP positions also appeared to be heterogeneous (two nucleotides) within the collected individuals used for sequence capture. Indeed, a significant number of SNPs were found between individual plants within A. tridentata subspecies. Much of this nucleotide diversity at initially identified SNP loci could be at heterozygous loci, though we are careful not to describe it as such until allelism between nucleotide residues is fully established through segregation analysis. Recall that these EST sequences contain both coding and non-coding sequence (particularly the 3' UTR as the poly-A tail was used for priming the cDNA synthesis). A high level of nucleotide diversity in these coding and non-coding sequences is certainly plausible considering the very large effective population size of big sagebrush and wind-pollination strategy [In our efforts to describe SNPs in big sagebrush, we have also quantified the number of SNPs that were due to subspecies differences and those that were due to individual differences. The high numbers of SNPs between individuals, apparent in the individual assemblies , in the validation using ssp. strategy .A. tridentata individuals. Such an approach would avoid extrapolation of our putative SNPs specific to these individuals to a larger population of individuals  by generating accession-specific data for each newly sequenced accession. Implementation of such study among spatially distributed big sagebrush populations would 1) enlighten our understanding of natural selection on genes and gene complexes controlling adaptive traits, and the evolution of these trait-linked loci and 2) provide relatedness metrics between natural populations of these subspecies and their hybrid zones. Though we briefly touched on these questions by using independent genotypes for SNP validation, these questions are out of the scope of this particular study that aims to primarily characterize EST sequences of big sagebrush and provide insight regarding the origins of ssp. wyomingensis.Given the high level of heterozygosity due to the out-crossing nature of big sagebrush populations , we expetridentata compared to ssp. vaseyana. The reduced levels of SSR in ssp. vaseyana ESTs compared to ssp. tridentata could be due to differential gene expression since different loci were sampled with our non-replicated experimental design. While leaves from both plants were harvested at the same time in common garden, phenological differences between the subspecies might have caused differences in expression levels and thus, changes in the number and types of detected SSRs. While gene expression could explain some of the differences, many such EST-SSRs have been found to be reliable molecular markers in other species [i.e. testable) genetic divergences between the subspecies.Regarding the discovered SSRs, we were surprised to find that all SSR repeat motif types detected were much more abundant in ssp.  species ,30-33 antridentata and vaseyana suggest possible selection pressure resulting in the maintenance of subspecies divergence, as similar trends have been observed in various organisms [a/Ks ratio of less than 1 for a large number of contigs could be a result of either stabilizing or diversifying selection within both subspecies, depending upon the magnitude of the ratio. Or if divergence times are very recent, it could also be the hallmark of purifying selection on the adapted common ancestor of these two subspecies. For example, Contig_29840 (Ka/Ks = 0.106) was annotated for 'aquaporin' protein. Considering that big sagebrush grows in variety of soils and arid plains, valleys and foothills of mountains, the importance of aquaporin proteins in water balance is critical and the genes coding for aquaporin proteins could have been under stabilizing selection. A formal investigation of molecular evolution within these species (with a proper outgroup) would place selection pressure relative to species divergence.The ratio of synonymous and non-synonymous mutations between sspp. rganisms -37. SincA. t. ssp. tridentata and A. t. ssp. vaseyana to investigate hybridization events. Generally, these hybrid populations are formed in a narrow zone between the two ecotypes [tridentata and ssp. vaseyana populations. Instead, selected ssp. tridentata and ssp. vaseyana accessions were chosen for EST sequencing based on penetrance of specific, subspecies morphological markers (i.e. trueness to type). Thus, variation at SNP loci for the diploid-tetraploid comparison is a mixture of individual variation, variation within inter-mating populations, and variation between subspecies in this study. Based on the number of Illumina reads that actually did map to discriminating SNPs between sspp. tridentata and vaseyana, the tetraploid ssp. wyomingensis samples appeared to contain both alleles for a large number of loci . The presence of both alleles at approximately one-third of the loci suggests that ssp. wyomingensis either originated as an allotetraploid from a hybridization event of 2 n gametes between sspp. tridentata and vaseyena or formed as a autopolyploid from both diploid subspecies with subsequent hybridization. Since allopolyploids have been reported between diploids and tetraploids of ssp. tridentata and ssp. vaseyena [wyomingensis. A focused genetic study within and between putative hybrid zones of big sagebrush is needed to further elucidate the origins and reproducibility of hybridization processes involved in ssp. wyomingensis formation. If tetraploid recurrence is a common feature of ssp. wyomingensis, perhaps only populations of ssp. tridentata and ssp. vaseyana need active management during environmental conversation of wildlands because a tetraploid hybrid between the two locally adapted accessions could be expected to form and repopulate geographic zones between the diploid subspecies.Hybridization can be of great importance to the ecological adaptation and subsequent evolution of offspring because of the novel genetic recombination and spatial selection -40. Geneecotypes ,44-47. Ivaseyena ,46,48, aA. tridentata subspecies and the estimation of depth and divergence of mutations provide insights about the magnitude of neutral divergence and natural selection between these subspecies, and a foundation of sequence references for future population genomic and functional genetic studies. The cost-effective, rapid and reliable way of obtaining nuclear sequences through transcriptome sequencing also provided insights on gene divergence and marker development in big sagebrush. Future studies integrating common garden, provenance and reciprocal transplantation of defined genetic stocks with this genomic information will immeasurably add to our understanding patterns of genes and their roles in adaptive traits among big sagebrush populations.This study is the first of its kind to perform transcriptome sequencing of big sagebrush subspecies, generating large selections of genetic resources  for this ecologically important group of range and forest plants. The EST sequences were annotated to identify putative gene functions, and select genes involved in putative terpenoid and coumarin synthesis were bioinformatically identified. The distribution of SNPs among A. tridentata ssp. tridentata and A. tridenata ssp. vaseyana, were harvested from plants growing in USDA Shrub Lab greenhouse in Provo, UT for 454-pyrosequencing , but the cDNA synthesis primer for first strand synthesis was replaced by a modified oligo-dT primer (5'-AAGCAGTGGTATCAACGCAGAGTCGCAGTCGGTACTTTTTTCTTTTTTV-3') . The polA. t. ssp. wyomingensis plants growing in USDA Shrub Lab greenhouse in Provo, UT. The plants were grown from seeds collected in their natural habitat in two different states - Montana and Utah. Geographic information on sampled individuals is provided in Additional file A. tridentata ssp. tridentata diploid standard were finely chopped in a buffer and then nuclei were stained with DAPI solution . Total RNA was harvested and quantified in the same manner as mentioned above. The RNA was processed for sequencing following directions in the Illumina mRNA Sequencing Sample Prep Guide , with the addition of custom barcoded adapters designed for the paired-end sequencing process [wyomingensis individuals were multiplexed in approximately equal concentrations and sequenced in two separate runs  on the Illumina Genome Analyzer at the Oregon State University Center for Gene Research and Biocomputing, Corvallis, OR. Pooled libraries were loaded onto one lane of an Illumina Genome Analyzer II at 5 pM concentration. Cluster generation and sequencing used Illumina version 3.0 reagents, and image acquisition and base calling used the Illumina pipeline version 1.5. These Illumina sequences were used only to verify in ssp. wyomingensis the SNP loci detected on the combined assembly of sspp. tridentata and vaseyana obtained from 454 sequences.Leaves were harvested from two young  process . The quawyomingensis reads. For confident SNP calling, we required only the SNPs covered by coverage of 20 × or more be counted; and 10% of all the reads overlapping a SNP were required to be of a particular variant in order to avoid SNP detection that could have resulted from sequencing errors.Bowtie  was usedde novo assembly of the sequences from both subspecies was performed using CLC Genomics Workbench Version 3.7.1 . The sequence ends were trimmed to remove the barcodes added during library preparation, and any sequence shorter than 50 bp was not included in building the assembly. The mismatch cost for the nucleotides was set at 2 while both the insertion cost and deletion cost for nucleotides in the reads were set at 3. The length fraction and the similarity of the sequences were set at 0.5 and 0.9, respectively. Any conflicts among the individual bases in the reads were resolved by voting for the base with maximum number of repetitions. A minimum read length of 200 bp was set for an assembled sequence to be counted as a contig. Identical parameters were also used to create individual assemblies from both subspecies. Homologies of the contigs and singletons were identified by comparing against the NCBI NR protein database using BLASTx with cut-off e-value of 1e-15. The blast results were imported into Blast2GO Version 2.4.2 for mapping the consensus sequences into GO terms. To summarize the distribution of the sequences into GO terms of three main categories - biological processes, cellular components and molecular functions, GO annotations were formatted for input into the GOSlim program [Arabidopsis thaliana gene sequences as the reference matrix, were used for this purpose.A combined  program . The con program . Proteinet al. [i.e. 'heterozygosity' at 10%). For example, a G↔A SNP would be included in the list of SNPs at coverage of 100×, if, out of 100 aligned sequences, 80 sequences came from one subspecies with at least 72 sequences calling for a G, and 20 sequences came from another subspecies with at least 18 sequences calling for an A at the SNP position. Primers for SNP validation were designed using Primer3 [SNPs were identified between the subspecies using the Perl script used by Maughan et al. . For the Primer3 .Perl script MISA  was alstridentata individuals, two diploid and one tetraploid ssp. vaseyana individuals and two tetraploid ssp. wyomingensis individuals from geographically divergent sources were used for SNP and SSR marker evaluation in genomic DNA. Geographic information on these individuals is provided in Additional File 2, 1 × PCR buffer, 0.2 mM dNTPs, 0.6 μM of each primer, 1 U Taq polymerase and dH2O to a final reaction volume of 10 μl ran in the following thermal profile: 94°C 3 min, 35 ×  and final extension of 72°C 10 min. PCR reactions of both SNP loci and SSR loci were cleaned using Qiaquick PCR Purification kit , and then mixed with appropriate primers prior to sequencing. Fragments were sequenced with an ABI 3730xl DNA analyzer at the University of Wisconsin Biotechnology Center, Madison, WI. The primers used for SNP and SSR validation are listed in Additional File Sanger re-sequencing of both subspecies cDNA validated consensus sequences containing SNPs and the SSRs. Fifteen putative SNP loci and SSR loci were randomly selected for PCR amplification. We selected 11 SNPs with transition mutation and 4 with transversion mutations for PCR amplification followed by SNP validation by re-sequencing. For validation purpose, we selected equal number of transitions and transversions . The SSR primers were designed to amplify 4 di-, 5 tri-, and 4 tetra-, 1 penta- and 1 hexanucleotide SSRs of varying lengths. Two tetraploid ssp. tridentata and vaseyana using sequence capture. DNA extractions were made of NVT-2 and UTT-2  and UTV-1 and UTV-3  RNA baits (120-mer) of the target genes were synthesized after the contigs obtained from the combined assembly. The DNA libraries for bait hybridization were prepared using GS FLX Titanium Rapid Library Preparation Kit . The baits were then hybridized to the library and the capture sequences were prepared for 454-sequencing following the MYselect protocol . The captured DNA libraries were pooled and sequenced at the Brigham Young University sequencing center, Provo, UT. Using 0.95 sequence similarity, the capture reads were mapped to the reference contigs in order to place individual reads at previously called SNPs and SSRs using Genomics Workbench 4 . Custom Perl scripts were used to assess SSR and SNP coverage and validate the presence of SNP bases in distinct DNA samples of sspp. tridentata and vaseyana.SNPs and SSRs were validated by sequencing 369 genes containing 572 SNPs in sspp. tridentata and vaseyana contigs that had their coding frame predicted using ESTScan. The proportion of non-synonymous to synonymous mutations (Ka/Ks) was calculated for each contig, following the Jukes-Cantor corrected model of substitution using Bioperl modules [a/Ks estimation in instances where either type of substitution was absent. Without this modification, we would have obtained Ka/Ks value equal to zero for genes with no observed non-synonymous substitutions, regardless of their Ks values. Similarly, genes without any synonymous substitutions would have undefined Ka/Ks.To find the changes in amino acid sequences due to single nucleotide mutations, we aligned sspp.  modules . We howewyomingensis reads to 454 contigs to identify SNPs. RCC prepared the ssp. wyomingensis transcriptome for Illumina sequencing and supervised the sequencing. JU conceived this study, provided bioinformatic training, and drafted the final manuscript. All authors read and approved the final manuscript.PB prepared the cDNA libraries, assembled the EST sequences, carried out the GO annotation, developed the SNP and SSR markers, and drafted the manuscript. BAR helped with the collection of plant materials from the field for 454 and Illumina sequencing and edited the manuscript. JCP sorted the Illumina sequences, and mapped the ssp. Distribution of protein domain vs number of contigs. The number of contigs on Y-axis represents total number of contigs that had a match against a protein domain. Only the top 25 most common domains (of 3065 domains found) are illustrated in the figure.Click here for fileThe distribution and sequences of putative sagebrush homologs of enzymes involved in terpenoid and coumarin synthesis pathways. The data consists of contigs of each subspecies annotated as terpenoid and coumarin pathway enzymes, as well as the contigs that resulted from combined assembly. The nucleotide sequences of the putative genes (contigs from the combined assembly) have also been included in the file.Click here for fileA list of contigs containing discriminatory SNPs between ssp. tridentata and ssp. vaseyana including contig name, SNP position, base for each subspecie, read count per base, flagged contigs with >13 SNPs, and SNPs that were found to be heterogeneous when the parameter of homogeneity was raised to 99%.Click here for fileAdditional details of SSRs including frequencies of di- and tri-nucleotide repeats.Click here for fileDetails of SNP and SSR primers used for polymorphism validation and the list of big sagebrush individuals used during the project.Click here for fileResults for SNP validation during sequence capture.Click here for file


Macaca mulatta) is the most widely used nonhuman primate. Although a draft genome sequence was completed in 2007, there has been no systematic genome-wide comparison of genetic variation of this species to humans. Comparative analysis of functional and nonfunctional diversity in this highly abundant and adaptable non-human primate could inform its use as a model for human biology, and could reveal how variation in population history and size alters patterns and levels of sequence variation in primates.As a model organism in biomedicine, the rhesus macaque  in the macaque is more closely equivalent to that of the human.We sequenced the mRNA transcriptome and H3K4me3-marked DNA regions in hippocampus from 14 humans and 14 rhesus macaques. Using equivalent methodology and sampling spaces, we identified 462,802 macaque SNPs, most of which were novel and disproportionately located in the functionally important genomic regions we had targeted in the sequencing. At least one SNP was identified in each of 16,797 annotated macaque genes. Accuracy of macaque SNP identification was conservatively estimated to be >90%. Comparative analyses using SNPs equivalently identified in the two species revealed that rhesus macaque has approximately three times higher SNP density and average nucleotide diversity as compared to the human. Based on this level of diversity, the effective population size of the rhesus macaque is approximately 80,000 which contrasts with an effective population size of less than 10,000 for humans. Across five categories of genomic regions, intergenic regions had the highest SNP density and average nucleotide diversity and CDS (coding sequences) the lowest, in both humans and macaques. Although there are more coding SNPs (cSNPs) per individual in macaques than in humans, the ratio of dThis large panel of newly identified macaque SNPs enriched for functionally significant regions considerably expands our knowledge of genetic variation in the rhesus macaque. Comparative analysis reveals that this widespread, highly adaptable species is approximately three times as diverse as the human but more closely equivalent in damaging variation. Macaca mulatta) and human (Homo sapiens) are thought to have shared a common ancestor approximately 25 million years ago [Macaca genus originated in North Africa, and dispersed to various sites in Asia at least three million years ago [The rhesus macaque . In 2007, Malhi et al. reported approximately 23,000 candidate SNPs detected by pyrosequencing [A single rhesus macaque of Indian origin was the source for a macaque draft genome sequence, which was completed in 2007 . This drquencing . Fawcettquencing . Some 22. However, the number of loci on which this conclusion was based was relatively small, and the loci were not selected in unbiased fashion. In this study, we have used SNPs equivalently identified in 14 humans and 14 rhesus macaques (mostly of Indian origin) by massively parallel sequencing with both H3K4me3 (trimethylated histone H3-lysine 4) ChIPseq  and RNAseq  as sources of sequenced fragments. From more than 16,000 genic regions, some half million macaque SNPs, most newly identified, were further analyzed. By sequencing diversity in the tissue-specific transcriptomes and histone-marked regions of the two species, we were able, without the use of DNA capture technology (which did not exist for the macaque) or whole-genome sequencing, to compare diversity in equivalent, functionally relevant regions and detect effects of selection and drift on sequence substitutions.There have been some more limited efforts to comparatively estimate diversity in the rhesus macaque. As revealed in the original sequencing of a single animal and compatible with a larger effective population size of the macaque across evolutionary timeframes, the macaque appeared to have higher sequence diversity than the human ,14. SNP http://pubs.niaaa.nih.gov/publications/LNGBMCGenetics2012/LNG_2012.htm.Diversity was determined in short sequence reads (36 bases) equivalently detected and analyzed in 14 humans and 14 rhesus macaques  in humans of approximately 0.691 ± 0.017 and a dN/dS ratio of 0.567 ± 0.022 in macaque , introns, UTR (5’- and 3’-UTRs), and CDS as annotated in refGene (human) or ENSEMBL (macaque). In all five genomic regions, macaques had significant  higher SNP densities than humans  lower than human. In an equivalent genomic search space, twice as many putative SNPs were identified in macaque as compared to the human  were located in coding regions (cSNPs), whereas only 0.7% of the total SNPs identified in 1000 Genomes Project Pilot 2 data were cSNPs  is somewhat lower than values that can be calculated  and is also slightly lower than values of 5.4 × 10−4 to 8.3 × 10−4 reported previously for humans [SNP as compared to previously reported values was expected due to the higher genic percentage of sequenced regions. In line with this assertion, within intergenic regions that comprise most of the genome our θSNP estimate of ~6.78 x 10−4 is very close to the previous estimates based on high coverage whole genome sequencing. Therefore, although we do not have a direct reference sample for the macaque, the θSNP values we have computed for both macaque and human appear to be robust, reflect parallel methodology and sampling and are informative for both genome-wide and regional increases in genetic diversity in the macaque compared to human.The average nucleotide heterozygosity (diversity) for SNPs θal. 2007 ) was mea007  was r humans -26. Our Within coding regions it is possible to compare diversity that is more likely to be functionally significant with diversity that is more likely to be selectively neutral. In coding regions, both human and macaque had approximately 2 times more diversity for synonymous cSNPs as compared to nsSNPs Figure .SNP. By this standard, the macaque was approximately three times as diverse as the human across all types of sequence categories. This could point to the maintenance of nsSNPs by balancing selection. This is an important mechanism of evolutionary adaptation in all genetically diverse species but may be operative at a larger percentage of loci in the macaque than in the human. Speculatively, although the macaque does not have proportionately more nsSNPs, it may be that those that it does have are more likely to be maintained at higher frequency by balancing selection. Although this would explain the reason for which nsSNP density does not increase proportionately with overall SNP density and with diversity, other validating data would be required to establish this point. One indirect test would be linkage disequilibrium analysis that could detect signals of selection (selective sweeps) at genes containing nsSNPs. In fact, one use of the SNPs we and Fawcett et al. have discovered would be the creation of marker panels enabling genome wide evaluation of LD. When that is done, the results may again be surprising.In line with theory that most of the increased diversity of the rhesus macaque should be selectively neutral in nature, the increase in macaque SNP density was not proportionately maintained from non-coding sequence to coding sequence, to nsSNPs and to putatively damaging nsSNPs. Instead, the macaque more closely resembled the human in its SNP density within these more functionally significant categories. However, a different picture was observed using the diversity measure θAt equilibrium, LD depends on the recombination rate and effective population size. Therefore, it might be anticipated that LD blocks in the rhesus macaque will be substantially smaller than the human. Thus a macaque SNP panel effective for genome-wide use might have to be larger than human 1 M panels that are now the standard. However, it is also possible that cross-population admixture has already occurred in the rhesus macaque, at least in some samples of macaques, which could have led to the presence of much larger haplotype blocks than anticipated on the basis of population size. For example, LD blocks in some Indian macaques were observed to be even larger than humans . On averNeu with average nucleotide diversity (θSNP) in intergenic regions   estimatos Figure C as θ beor human ,30-32 an macaque , the effFrom our comparative analysis of large number of SNPs equivalently identified from 14 humans and 14 rhesus macaques, the rhesus macaque has three times the SNP density and nucleotide diversity of humans. However, macaque has a lower ratio of nonsynonymous to synonymous substitutions in cSNPs, and the total number of damaging coding variations in individual macaques is closely equivalent to the human, indicating that selection has dampened the increase in functional diversity in this species.Using an equivalent methodology and sampling space for the human and rhesus macaque, we identified 462,802 macaque SNPs, most novel and disproportionately located in functionally important genomic regions. Accuracy of macaque SNP identification was conservatively estimated to be >90% based on verification with Sanger sequencing and because 90% of SNPs identified in our human samples using parallel methods were previously listed in dbSNP. This large panel of newly identified macaque SNPs significantly expands our knowledge of genetic variation in the rhesus macaque. Comparative analysis with the human reveals that this widespread, highly adaptable species is approximately three times as diverse as the human but more closely equivalent in damaging variation.H. sapiens) males, age 30–50 yrs was obtained from the University of Miami Brain Endowment Bank . In ethnic background the sample consisted of six African Americans and eight Caucasians/Hispanics. Postmortem hippocampus of 14 rhesus macaque (M. mulatta) males, most unrelated, age 3.5-7 yrs, was obtained from the National Institutes of Health Animal Center in Poolesville, Maryland. Among the macaques, eleven were of Indian origin, one was of Chinese origin and two were approximately 50% Chinese/50% Indian as indicated by genotyping of a panel of 96 markers, which have been used to screen over 600 rhesus macaques for ancestry determination optimized for macaque population origin  of 14 unrelated human  and homogenized using a glass-Teflon homogenizer. Following mixing with chloroform and centrifugation, the aqueous phase was collected and isopropanol was added. The samples were then loaded onto RNeasy spin columns  for purification. To eliminate residual genomic DNA contamination, RNA samples were incubated with DNase I (Qiagen) on column at room temperature for 15 min and washed several times before collection in elution buffer. To isolate mRNA, 35 μg of total RNA was heated at 65°C for 2 min, and then mixed with 0.5 mg of Dynabeads oligo (dT)25  in binding buffer . After incubation at room temperature for 5 min and several washing steps, mRNA was eluted from the beads by heating at 80°C for 2 min. The purified mRNA was fragmented to the 150 – 500 bp range by mixing with 10x fragmentation buffer  and heating at 70°C for 3 min. The samples were purified with RNeasy spin column. Two hundred nanograms of fragmented mRNA was reverse-transcribed to first strand cDNA by random priming, using 3 μg of random hexamer oligos and 200 units of Superscript II reverse transcriptase (Invitrogen). The reaction was carried out at 45°C for 1 hr in First Strand Buffer (Invitrogen) with 10 mM DTT and 0.5 mM dNTP. For second-strand cDNA synthesis, 400 units of Escherichia coli DNA polymerase, 2 units of E. coli RNase H, and 10 units of E. coli DNA ligase was added, and the reaction was carried out at 16°C for 2 hr in Second Strand Buffer with 0.2 mM dNTP. Twenty units of T4 DNA polymerase was also added at the end of incubation for end repair. The synthesized double-stranded cDNA library was purified with QIAquick purification kit (Qiagen).Total RNA was extracted from 100 mg of hippocampus collected postmortem. Briefly, tissue samples were submerged in guanidinium thiocyanate and phenol-based RNA extraction solution STAT-60 (Tel-Test Inc.Postmortem brain tissue (100 mg) was cut into slices less than 1 mm in thickness, and fixed in 3 ml of 1% formaldehyde/PBS solution for 10 min at room temperature to cross-link chromatin DNA and proteins. The tissue samples were then homogenized using a glass-Teflon homogenizer. Following homogenization, chromatin was isolated using the Upstate Magna ChIP G kit . Briefly, cells were lysed in Cell Lysis Buffer in the presence of protein inhibitor cocktail. Nuclei were isolated from lysed cells by centrifugation, and re-suspended in Nuclear Lysis Buffer. The chromatin DNA was then fragmented into the 150 – 500 base-pair range by sonication using a Branson Sonifer . To immunoprecipitate specific genomic regions of chromatin DNA, isolated chromatin was incubated with antibodies  against H3K4me3 and magnetic protein G beads (Millipore) at 4°C for 2.5 hr. Following incubation, beads were washed with low salt, high salt, LiCl salt, and TE buffers, and reverse cross-linked by proteinase K digestion at 62°C for 2 hr. The enriched DNA was purified after reverse cross-linking by column purification.-). A universal adaptor was added to the both ends of the DNA fragments by A-T ligation. Following 18 cycles of PCR with Phusion DNA polymerase, the DNA library was purified on a 2% agarose gel, and fragments 170 – 350 bp in size were recovered. Approximately 10 ng of the prepared DNA was then used for cluster generation on a grafted Flow Cell, and sequenced on the Genome Analyzer for 36 cycles using the “sequencing-by-synthesis” method.Sample preparation and sequencing on an Illumina Genome Analyzer  were carried out according to Illumina protocols for single-end libraries with some modifications. Briefly, double-stranded cDNA and ChIP-enriched genomic DNA were treated with T4 DNA polymerase and Klenow fragment for end repair. The 5’ ends of DNA fragments were then phosphorylated by T4 polynucleotide kinase, and an adenosine base was added to the 3’ end of the fragments by Klenow  and aligned to the corresponding reference genome (UCSC rheMac2 for macaque and UCSC hg18 for human) using Extended Eland in the GApipeline. The uniquely mapped reads were parsed with in-house Perl scripts to generate base coverage and SNP calls as described previously  was usedDG, QY and CB conceived this study. ZZ performed all next-generation sequencing. QY analyzed sequencing data. QY and DG prepared the manuscript. BB performed SNP functional predictions. SL and MB performed SNP verification using Sanger sequencing. DM provided human postmortem samples and perspective on the project design. DH, BF, and SS provided leadership and insights in rhesus macaque biology. DH and SS provided essential input in the population history of the Poolesville colony and played leadership roles in the creation and maintenance of that colony. JL and RT collected and dissected the brain tissue. BF was the source of the macaque population SNP panel. All authors read and approved this manuscript.Table S1. Ancestry assay for rhesus macaque samples used in this study*. Table S2. Putative SNPs covered with sequence reads from ChIPseq and/or RNAseq. Table S3. SNPs from 1000 Genomes Project Pilot 2. Table S4. The average nucleotide heterozygosity from 1000 Genomes Project Pilot 2. Figure S1: A: RNAseq correlation between human vs macaque. Data points: mean of normalized gene expression level (log2). B: H3K4me3 ChIPseq correlation between human vs macaque. Data points mean of normalized area under curve (log10) of covered reads within 1Kb of TSS. C. Sequencing coverage (H3K4me3 ChIPseq and RNAseq) in NPY genic region. Figure S2: Human cSNPs identified in 1000 Genomes Project Pilot 2 samples and this study. C or CEU: CEU trio from 1000 Genomes Project Pilot 2; Y or YRI: YRI trio from 1000 Genomes Project Pilot 2; M or Miami: 14 samples from Miami dataset in this study. Figure S3: SNPs shared between individuals in 1000 Genomes Project Pilot 2. Click here for file


Background. This study was aimed at comparing angiogenesis, seen as microvessel density (MVD) in subtypes of non-Hodgkin's lymphoma (NHL). Methods. In this study, 64 cases of NHL diagnosed over a three-year period were included along with 15 lymph node biopsies of reactive hyperplasia. NHLs were classified using REAL classification, and immunohistochemistry was performed for CD34 in all cases. CD34-stained sections were evaluated for “hot spots,” where MVD was assessed and expressed as per mm2. Appropriate statistical methods were applied. Results. There were 6 cases of well-differentiated lymphocytic lymphoma (SLL), 21 diffuse large B-cell lymphoma (DLBCL), 15 follicular lymphoma, 10 lymphoblastic lymphoma, 7 MALToma, and 5 peripheral T-cell lymphoma (PTCL). Mean MVD was highest in reactive hyperplasia (191.92 ± 12.16 per mm2) compared to all NHLs. Among NHLs, PTCL demonstrated the highest MVD (183.42 ± 8.24) followed by DLBCL (149.91 ± 13.68). A significant difference was found in MVD between reactive and individual lymphoma groups. SLL had significantly lower MVD than other lymphoma subtypes. Conclusion. Angiogenesis, assessed by MVD, showed significant differences among subtypes of NHL, especially the indolent types like SLL. The higher MVD in aggressive lymphomas like PTCL and DLBCL can potentially be utilized in targeted therapy with antiangiogenic drugs.  Angiogenesis or the process of formation of new blood vessels exerts a crucial role in the progression and metastasis of various tumors, including solid organ tumors and hematologic malignancies. Of the various methods to assess angiogenesis, microvessel density (MVD) has been studied extensively in various tumors , 2. ReceThis recent and increasing interest in evaluation of angiogenesis in NHLs arises from the availability of antiangiogenic therapy as a directed therapeutic tool . This moThe present study was aimed at assessing the angiogenesis in the form of microvessel density in NHL along with comparison between various subtypes of NHLs, in view of the present conflicting data on this subject. This was a retrospective study including 64 consecutive cases of non-Hodgkin's lymphoma (NHL) diagnosed over a period of three years 2008–2010). The patients included 28 females and 36 males, with active disease at stage III or IV. The histological sections were reviewed and NHL subclassified according to the REAL classification system [–2010. Th2), and a mean value of both investigators was taken. MVD was calculated and expressed as number of microvessels per mm2. For comparison, 15 lymph node biopsies with features of reactive lymphoid hyperplasia were included and concurrently stained with anti-CD34 antibody. MVD was calculated in a similar way in these biopsies as well. Angiogenesis was assessed using immunohistochemistry for CD34  in all the cases. Immunohistochemistry was performed using standard protocols with streptavidin-biotin complex technique and microwave heat-induced antigen retrieval in citrate buffer (pH 6.0). The CD34-stained sections were examined at 100x magnification to delineate “hot spots,” that is, areas of maximal MVD. In three such hot spots in each case, all microvessels  were counted at 400x magnification by two investigators independently (each field representing an area of 0.375 mmt-test) was applied to assess the significance of difference between various groups. A P value of <0.05 was taken as statistically significant.Statistical test (Student's The subcategorization of the included cases according to the REAL classification is shown in 2). This was significantly higher compared to all the lymphomas included in this study. Among lymphomas, the highest MVD was recorded in peripheral T-cell lymphoma (183.42 ± 8.24 per mm2) followed by diffuse large B-cell lymphoma (149.91 ± 13.68 per mm2) and follicular lymphoma (141.21 ± 23.33 per mm2). Small lymphocytic lymphoma had the lowest MVD (76.78 ± 10.41 per mm2). The MVD of various groups of NHL and reactive lymphoid hyperplasia is tabulated in The mean MVD in lymph nodes with reactive lymphoid hyperplasia was 191.92 (±12.16 per mmt-test), reactive lymphoid hyperplasia showed a significantly higher MVD (P < 0.001) than individual NHL subgroups, except PTCL (P > 0.05). A significant difference in MVD was also noted between small lymphocytic lymphoma and other NHL categories. PTCL showed a significantly higher MVD (P < 0.05) than diffuse large cell lymphoma, follicular lymphoma, lymphoblastic lymphoma, and mucosa-associated lymphoid tissue lymphoma (MALToma). No significant difference in MVD was found between other categories of non-Hodgkin's lymphoma.On statistical analysis  has shown conflicting results in various studies , 16. A rThe increasing interest in evaluation of angiogenesis in lymphomas stems from the availability and success of anti-angiogenic therapy in certain solid tumors, like colorectal cancer . Anti-an


With the advance of new massively parallel genotyping technologies, quantitative trait loci (QTL) fine mapping and map-based cloning become more achievable in identifying genes for important and complex traits. Development of high-density genetic markers in the QTL regions of specific mapping populations is essential for fine-mapping and map-based cloning of economically important genes. Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation existing between any diverse genotypes that are usually used for QTL mapping studies. The massively parallel sequencing technologies , have been widely applied to identify genome-wide sequence variations. However, it is still remains unclear whether sequence data at a low sequencing depth are enough to detect the variations existing in any QTL regions of interest in a crop genome, and how to prepare sequencing samples for a complex genome such as soybean. Therefore, with the aims of identifying SNP markers in a cost effective way for fine-mapping several QTL regions, and testing the validation rate of the putative SNPs predicted with Solexa short sequence reads at a low sequencing depth, we evaluated a pooled DNA fragment reduced representation library and SNP detection methods applied to short read sequences generated by Solexa high-throughput sequencing technology.A total of 39,022 putative SNPs were identified by the Illumina/Solexa sequencing system using a reduced representation DNA library of two parental lines of a mapping population. The validation rates of these putative SNPs predicted with low and high stringency were 72% and 85%, respectively. One hundred sixty four SNP markers resulted from the validation of putative SNPs and have been selectively chosen to target a known QTL, thereby increasing the marker density of the targeted region to one marker per 42 K bp.We have demonstrated how to quickly identify large numbers of SNPs for fine mapping of QTL regions by applying massively parallel sequencing combined with genome complexity reduction techniques. This SNP discovery approach is more efficient for targeting multiple QTL regions in a same genetic population, which can be applied to other crops. Glycine max) group and that 10,000 - 75,000 SNPs would be necessary for whole genome associations in soybean [http://www.phytozome.net/soybean. It provides an invaluable resource for soybean genome mapping and marker development [Identifying polymorphic markers associated with complex, economically important traits in crops has been hindered for most crops by the lack of whole genomic sequence, high-resolution maps and cost-effective platforms for high-density genotyping. In soybean, several thousands of genetic markers (mostly SSR and SNP markers) have been identified and mapped in the past ten years -4. Howev soybean . The 8x elopment . AlthougWith the recent advances in massively parallel sequencing technologies and the availability of whole genome sequence, the identification of a large number of polymorphic markers by re-sequencing different genotypes in model species such as human  and ArabBrassica napus), SNP discovery was exploited using Solexa transcriptome sequencing by aligning ESTs to the reference sequences, a set of publicly available unigenes [For large and complex genomes, the identification of SNPs by massively parallel sequencing technology has been explored using reduced complexity sequencing approaches. There are several methods which aim to reduce the complexity of the sequencing template, in particular, by reducing the representation of low-information-content repetitive sequences. One such method is expressed sequence tag (EST) sequencing. For example, in the polyploid crop oilseed rape  system , which cThe feasibility of identifying SNP markers for QTL fine mapping by high-throughput sequencing at a low sequencing depth depends on the false call rate, genome coverage, and sequencing cost. To identify SNP markers in a cost effective way for fine-mapping of several QTL regions and determine the false call rate of putative SNPs at a low sequencing depth, we evaluated a pooled DNA fragment reduced representation library and the SNP detection method applied to short read sequences generated by Solexa massively parallel sequencing technology.In silico analysis results  as we expected [in silico digestion results we tested the real digestions of genomic DNA of Forrest, Williams 82, and PI 342622 (G. soja) with three enzymes  which produced the number of fragments more close to what we expected  corresponding to 9.46 million unique 33-mer sequences, resulting in an average coverage of 4.4x. However, the sequence clusters were seriously biased. It was shown that 72.7% of the 33-mer sequences were just single read, 24.4% of those had multiple reads, and 2.9% of unique sequences were low quality sequences containing at least one unknown nucleotide 'N'. After removing 295,788 low-quality sequences, over 41.7 million 33-mer reads passed the standard Illumina filtering and more than 40 million reads  could be aligned to the reference Williams 82 genome sequence, 6.5x scaffold assembly. Finally, 6.8 million sequences had a single read and 2.3 million sequences with multiple reads remained for SNP discovery. When plotting the depth-coverage of those sequences used for SNP discovery over the chromosome coordinates, the distribution exhibited relatively uniform across the whole genome (data not shown). However, we noticed that the distribution of read coverage was extremely biased  were initially used for SNP prediction and filtering. The less stringent parameters identified 39,022 putative SNPs between the Solexa short-reads and the 6.5x assembly with an average SNP density of 31.5 Kbp across the entire genome, which were not uniformly distributed across soybean chromosomes, especially more SNPs were clustered in gene-rich, high-recombination euchromatic regions than in repeat-rich, low-recombination heterochromatic regions Figure . The numhttp://maq.sourceforge.net/maqview.shtml for visualization  validation rate. The validation rates in different scenarios provided a useful guideline for SNP predictions and selection of SNPs for different applications.Fine-mapping multiple targeted QTL regions requires a large number of molecular markers that could be used to narrow down the regions and pinpoint candidate genes or target markers associated more closely with traits. Our objective was to design a solid experiment to test whether we could develop 50-200 markers within any targeted QTL region (5-20 cM) using high-throughput sequencing technology. Our results indicated that massively parallel sequencing technologies could offer an opportunity for simultaneously developing large numbers of high quality SNPs within target mapping populations. These SNPs could serve to fine-map the QTLs that were identified in QTL mapping studies or to conduct association mapping. Especially, with the improvements in sequencing output, paired-end reads and multiplexing techniques, the massively parallel sequencing technologies makes the whole genome SNP discovery more effective and efficient.in silico digests of a draft genome sequence and test digest. Our results indicated that CviR I was the best enzyme for soybean. Of course, selection of one or multiple restriction enzymes for reduced representation library also relies on genome features in a given organism and applications. For example, Hae III was used for construction of bovine reduced representation libraries for deep sequencing by the Solexa platform [Glycine soja) for SNP discovery to help soybean sequence assembling [In theory, any genome complexity reduction strategies could be applied to generate less complex genomic DNA libraries for high-throughput sequencing. Especially for complex genomes such as maize, wheat, barley and soybean, genome complexity reduction is an important step so that genome-wide polymorphism discovery would become successful in organisms with high levels of repetitive DNA in the genome and/or low levels of polymorphism in breeding germplasm. Compared to methylation complexity reduction approach, restriction enzyme-based methods may be more appropriate for SNP discovery, because most of the short fragments generated by restriction enzymes from different genotypes would have a perfect alignment. The complexity reduction approach can be optimized by identifying an appropriate restriction enzymes using platform . Recentlsembling .The pooling of DNA samples from the parental lines for the target population, combined with the high-throughput sequencing of a small fraction of the genome results in the identification of a large number of SNPs genome wide. Most importantly, this approach also allows us to develop a large number of SNP markers for less well characterized species in an efficient and cost-effective way. Especially, with the increase of the output of highly parallel sequences and the decrease of the cost per run, it is more feasible to get deep sequences for multiple individual genotypes or pooled samples. However, due to a few drawbacks of pooling the DNA samples, the pooling strategy may not be necessary for sample preparation for duplicated genomes. For duplicate genomes such as soybean and maize, a portion of putative SNPs are derived from the alignment of paralogous sequences, i.e. the sequences aligned may come from different loci that are duplicated through evolution in the genome. The 85% SNP validation rate in our pooled sample, even at a high stringency level, indicated that 'paralogous' SNP is a problem for pooled samples. But it can be overcome by using a barcoding technique. It is not a problem to sequence many samples in a Solexa instrument run if using a barcoding technique. It is becoming a standard approach to increase the number of samples run on high-throughput instruments .Comparing to the current Solexa sequencing system, our sequencing data were limited in terms of sequence length, accuracy (paired-end) and output because the super-high throughput Solexa sequencing system was not available when we conducted this experiment. Therefore, the depth-coverage of the short sequence reads was not enough to take account of allele frequency in SNP calling . Paired-In addition, the short reads cannot be assembled to give complete sequence of complex plant genomes  and repeWe noticed that the threshold settings were important for SNP prediction. The more stringent thresholds gave a higher validation rate. But we had to balance the number of predicted SNPs that could be used for fine mapping and the validation rates. We may also take account of the SNP distribution along the 33-mer Solexa sequence positions  total number of fragments predicted, 2) number of fragments predicted in the target size range (70-200 bp), 3) proportion of repetitive nucleotides in the terminal 33 base pairs of each fragment end, and 4) number of putative SNPs predicted. The identification of repetitive elements was based on the soybean repetitive sequence database and annotation of soybean genome browser http://www.phytozome.net/cgi-bin/gbrowse/soybean/. The number of putative SNPs was predicted based on the SNP frequency in the soybean genome [CviRI (New England Biolabs) with 60 units of enzyme in a total volume of 150 μl, as suggested by the manufacturer. The digestion proceeded overnight at 37°C to ensure complete digestion. The resultant fragments were size fractionated on a 6% non-denaturing polyacrylamide gel , stained with Syber Gold , and imaged under a Dark Reader. The digested products between 70 and 200 bp were excised and sliced into smaller pieces. The gel pieces were sheared by squeezed through a hole punctured by an 18-gauge needle at the bottom of a 0.5 ml microcentrifuge tube, with the help of centrifuging at 16,000 g for 2-3 min. The sheared gel was collected into a 2 ml microcentrifuge tube, in which the above 0.5 ml tube was nested. The DNA fragments in the sheared gel were recovered by elution into 2x of volume of DNA elution buffer  at room temperature overnight, followed by a 15 min incubation at 65°C. The eluted DNA fragments were transferred into a 15 ml falcon tube after centrifuging at 16000 g for 2 min and a couple of times washing with the elution buffer. The eluted DNA was then purified with a SNAP column , and precipitated by 2.5x volume of 70% ethanol at -20°C for 1 h, followed by centrifuging at 4000 g for 15 min. The pellet was twice washed with 500 μl cold 70% ethanol, dried, and resuspended in 50 μl of TE . DNA quality and quantity were checked by reading O.D. on a Nanodrop spectrophotometer.Genomic DNAs of cultivars "Forrest" and "Williams 82" were used for the reduced representation genomic DNA library construction. The genomic DNA concentration of each sample was measured by the Pico green assay with the Quant-iT ds DNA Assay Kit  per manufacturer's instructions. Equal amounts of the two genomic DNAs were pooled and 6 μg pooled DNA was digested by CviRI digestion were treated with Klenow fragment of DNA polymerase lacking exonuclease activity in the presence of dATP to add an adenine overhang to the 3' ends of each strand. This reaction was performed in a 50 μl reaction volume using the buffer and the enzyme in the kit provided by the supplier  at 37°C for 20 min, followed by inactivation of the enzyme at 70°C for 5 min. The modified fragments were precipitated with ethanol in the presence of 0.5 μg/μl final concentration of glycogen carrier, dried, and resuspended in 10 μl TE. Adapters required for sequencing on the Illumina CSMA/SBS platform were added to each DNA fragment by ligation in a 50 μl volume, using adapters and buffers supplied in the kit . Ligation proceeded for 15 min at 22°C to produce fragments with 65 bases added from the adapter primers.The blunt-ended DNA fragments produced by The ligation products were separated on a 6% non-denaturing polyacrylamide gel as described above. The ligation products in the size range from 130 to 190 bp were excised, eluted from the gel slice, precipitated and resuspended in 15 μl TE, using the same methods described above. The adapter-modified DNA fragments were amplified as per the instructions of the manufacturer's kit. The amplified products were purified using a Qiaquick PCR Purification Kit  and collected in 50 μl of the provided buffer. After quantification by O.D. reading, calculation of molar concentration and quality examination (as described below), this PCR product of the reduced representation genomic DNA library of pooled Forrest and Williams 82 was shipped to Illumina for sequencing.® Terminator v3.1 Cycle Sequencing Kit , followed by sequence analysis on the ABI 3730. Sequence traces were analyzed and aligned with software CromasPro v1.5 http://www.technelysium.com.au/ChromasPro.html.Before submitting to Illumina for sequencing, the quality of the library was determined by cloning a portion of DNA fragments into the pPCR-Script Amp SK(+) Cloning Vector provided in the PCR-Script Amp Cloning Kit , per manufacturer's instruction, with the molar ratio of insert to vector DNA ranges at 50:1. The insert-vector DNA was transformed into the Maximum Efficiency DH 10 B Competent Cells  according to the manufacturer's instruction. The plasmid DNAs were extracted by alkaline lysis method, and then sequencing reaction was prepared with BigDyeThe library constructed from Forrest and Williams 82 was loaded onto two independent flow cells. Quality scores were generated from Illumina's data analysis pipeline and are similar to SAGE Phred scores and have a maximum value of 40. Quality scores are based on the relative confidence of base calls using elements of cluster generation and image quality.http://www.phytozome.net/soybean. SNP prediction was initially performed using MAQ mapping and assembly software [http://soykb.org/.The Forrest and Williams 82 pooled Solexa sequencing data from two flow cells were combined. The Williams 82 6.5x scaffold assemblies were acquired from Phytozome software . To deteXW designed and coordinated the study, performed primer design and data analysis, and drafted the manuscript. CR created the reduced representation library, and assisted the manuscript. TJ and DX performed SNP prediction analysis, and assisted the manuscript. TV performed SNP validation resequencing. HTN designed and oversaw the study, and assisted in preparing the manuscript. All authors read and approved the final manuscript.Histogram of read coverage for the sequences with 3 reads and more. This figure revealed the distribution of read coverage in bins (bin width = 1). The x-axis displays different value of read coverage and the y-axis displays the possibility of a particular read coverage.Click here for fileDistribution of putative SNPs over the different positions of the 33-mer short-read. The x-axis represents the number of the predicted SNPs, and the y-axis represents the position of the 33 short-read.Click here for fileDistribution of 7947 SNPs. This figure showed the distribution of the 7,947 SNPs predicted by strict stringency on the reference genome. The x-axis represents short read coverage, and the y-axis represents the SNP position on the chromosomes of the reference genome.Click here for file


We base the model on single nucleotide polymorphisms (SNPs) and break the genomes into blocks to handle the rearrangement problem. Then we apply a dynamic programming algorithm to model whether changes within each block are likely a result of mutations, sequencing errors, or HREs. Results from simulation experiments show that we can detect 31%–61% of HREs and the precision of our detection is about 48%–90% depending on the rates of mutation and missing data. The HREfinder software for predicting HREs in a set of whole genomes is available as open source ( An HRE is caused by a homologous recombination, in which the incoming DNA molecules are highly similar to those in the recipient genome. HREs may cause the incongruence between gene trees drawn by different genes, and may lead to inaccurate construction of phylogenetic trees Phylogenetic trees are commonly used to represent the evolutionary history of a set of extant species in biology. If all organisms only inherit their genetic materials vertically, i.e., from their parents, then the tree representation would be sufficient. However, there is evidence that organisms may get genetic materials from organisms other than their parents To detect HREs, a standard approach is to compare the gene trees and the species tree, construct the reconciled tree and detect the HREs e.g. . These mIn this paper, we study the detection of mutations, HREs and sequencing errors given the SNPs and SNP positions of a set of closely related strains with an evolutionary species tree. The SNPs of all leaf nodes are mostly known with some missing, but the SNPs of all internal nodes are unknown. Some known SNPs might be incorrect because of sequencing errors. Some genomes might be in the form of contigs, i.e., the SNP positions are only in the correct order and orientation within a contig. We want to reconstruct the SNPs of internal nodes with regard to 3 possible events. (1) Mutations. A single SNP may change when an internal node passes its SNPs to its child node. (2) HREs. A node may get a segment of SNPs from any other node which is not one of its descendants. (3) Sequencing errors. The data we have may be wrong.blocks in which inversions do not take place. We then use the dynamic programming technique to assign mutations/HREs/errors in each block. We also consider possible HREs from an out-group, i.e., some species not in the given evolutionary species tree. If a genome has a many SNPs alleles that differ from other genomes in the tree within a small segment of adjacent SNP loci, then we consider assigning an HRE from an out-group to this segment (see Section 2.3). We cannot distinguish sequencing errors from mutations that occur on the leaf nodes. For simplicity, all SNP disagreements between a leaf node and its parent node are considered as “errors” . Therefore, mutations refer to SNP changes at internal nodes, and errors refer to SNP changes at leaf nodes. Each event has a weight. The weights of mutation/HRE/error are We have implemented our algorithms that partition genomes into blocks and assign mutations/HREs/errors. We have tested the program on both simulated data and real data. The experimental simulation results demonstrate that there are many HREs and mutation events that leave no evidence to be detected, and the detection accuracy mainly depends on the mutation rate, HRE rate, and the size of the evolutionary tree.locally collinear block is a homologous region of sequence shared by two or more of the genomes under study, and does not contain any rearrangements of homologous sequence blocks to refer locally collinear blocks. SNPs in a block should be in the same order across all genomes, with some exceptions explained in Section 2.1.The sequences of source and destination of a HRE should be similar, i.e., there should be the same set of SNPs in the same order and orientation in the HRE regions of both donor and recipient genomes. However, the SNP order/orientation may not be identical all genomes, because of genome rearrangement events, i.e., inversions and transpositions, and we have to focus on regions in which all genomes have the same SNP order and orientation. A We first partition the genomes into blocks by a greedy block extension algorithm, then we consider each block separately. Within each block, for each SNP locus, we use dynamic programming to reconstruct the SNPs of internal nodes in the evolutionary tree with the minimum number of mutations. Then within each block, we check if we can assign HREs to further reduce the total weight by dynamic programming. We also consider possible HREs from an out-group not in the input genomes. After assigning mutations/HREs/errors from within the tree, we trace the origin of each SNP allele and evaluate if there is any evidence indicating HRE from an out-group. Note that these steps represent only one reasonable approach to this problem, and optimal solution for each step does not guarantee optimal solution at the end.agrees with Considering a possible block Missing. A SNP may appear in Duplication. There might be duplicated SNPs inserted in Contigs. The genome may be in contig form, which makes the SNP order in We formally define the notion of agreement as follows.Let jump-over-contig step.When considering if a genome We try to explain all genomes with the minimum number of inversion endpoints, i.e., as few blocks as possible. We use a greedy block extension algorithm so that every block is maximal, and minimize the number of blocks. The block extension algorithm works as follows. A block starts from a single SNP. Each round we try to extend a block Bulkhorderia pseudomallei dataset with 122 thousand SNPs and 26 strains. This is because duplications and jump-over-contigs do not occur very frequently.The time complexity of the algorithm is determined by how fast we can determine if a genome agrees with a block. Assume overlap. Duplications may also create overlapping blocks. For example, increased number of SNPs, is usually much more than the number of given SNPs. Note that overlapping blocks may result in duplicated HREs at the end, and we may overcount the number of HREs. However, we simply accept overcounting since our objective is to find HREs, not to count HREs.In our algorithm, if a SNP We now consider a single block, and the corresponding SNPs of the block in all genomes. Our objective is to reconstruct the history of the block on each node of the evolutionary tree. The SNP order of the block should be the same in all genomes but there might be missing SNPs. For each SNP locus, we reconstruct the SNPs of internal nodes assuming there are only mutations and errors, and minimize the total weight of mutations , but do the HRE ). Note t the HRE  also allIf there are several consecutive mismatches of SNPs of a node and its parent node, it is likely that the segment is affected by some HRE. However, there might be no similar SNP segment in the given data, and we suspect it might be an HRE from an out-group. Suppose we try to assign an HRE from the out-group, since there are no known SNPs, we are free to create whatever SNPs we need to match the SNPs of the node we consider. If the weight of such HRE is a constant, it may lead to matching all the SNPs with an HRE from the out-groups. We borrow the idea of affine gap penalty in sequence alignment These recurrence relations can be solved by standard dynamic programming with backtracking technique, and help assign sparse mismatches as mutations/errors and dense mismatches as out-group HREs.Sometimes the algorithm may assign two HREs of the same segment to two nodes, and they are predicted to inherit the HRE segment from each other. We consider this scenario as evidence of an out-group HRE. After assigning mutations/HREs/errors by dynamic programming and backtracking, for each SNP of a node, we trace the ancestor of the SNP allele. A SNP within an HRE segment is inherited from the HRE source, and a SNP not in an HRE segment is inherited from the parent node. If there is no HRE from out-groups, we should be able to trace all SNPs all the way to the root. If the tracing falls into a cycle, then we output the SNPs and involved nodes as evidence of an out-group HRE. This algorithm also detects inheritance patterns that form a cycle by more than two nodes.We have implemented our algorithm in C/C++, denoted as HREfinder. We have also implemented a simulator to generate simulated data and estimate the accuracy of HREfinder. We also run HREfinder on real data obtained from SNP analysis according to http://sourceforge.net/projects/ksnp/) which we used here with k = 25. Likewise, the method for building a phylogeny is up to the user. Here we used maximum likelihood of the SNP allele sequences HREfinder takes as input the SNP alleles and positional information, the genome sequences, and a phylogenetic tree. SNP detection and building a phylogenetic tree occurs prior to running HREfinder, and may be accomplished with alignment-based approaches . Therefore, there are still some events that leave no evidence when generating simulated data, and these events will have an impact on calculating the accuracy of HREfinder. There are still many scenarios in which HREs cannot be detected: two or more HREs may overlap and can be explained by a few mutations/errors, an inversion may separate an HRE into different blocks and we cannot detect it because we consider each block separately, etc. Identifying and discarding all these events would be very difficult, and we choose not to identify all these events when generating simulated data. Therefore, many events cannot be identified as HREs, so we expect that HREfinder can detect only a subset of HREs in the simulations.recall as the number of correctly detected HREs divided by the total number of actual HREs, and precision as the number of correctly detected HREs divided by the total number of predicted HREs by HREfinder. The average branch length is always fixed. In each set, we try 4 different values for a parameter, and all other parameters are fixed. For each parameter setup, we run the simulation 200 times, and compute the recall and precision.The weights of the events are set as We have tried different weights of events to see how weights may affect recall and precision. The weight assignments we have tried for Bacillus anthracis, Burkholderia mallei, Burkholderia pseudomallei, Burkholderia genus, vaccinia virus and variola virus. Nine of the B.pseudomallei genomes are in more than 1000 contigs each, so we also ran HREfinder on the subset of genomes in assembled into fewer than 100 contigs. Of the Burkholderia genus genomes, 28 were draft contigs, 11 in more than 1000 contigs. For the calculations, separate contigs or chromosomes were concatenated with 250 N’s as separators into a single sequence for each genome. Burkholderia calculations were performed on an Intel Xeon 5660 CPU with 2.8 GHz. and 48 GB RAM, and timings are given in https://sourceforge.net/projects/hrefinder/files/HGT_paper_data.zip. We would expect Burkholderia mallei and Bacillus anthracis to show little recombination, i.e., few HREs, and Burkholderia pseudomallei to show large amounts of recombination based on extensive published work Vaccinia virus is also expected to show high rates of HRE resulting from a complex history due to broad host range, extensive passage in domesticated animals and chick embryos, culturing spiked with cowpox and variola, scarification practices of vaccination that reintroduced vaccinia virus to nature many times, and mixing of multiple vaccinia strains in vaccine preparations variola virus is much more homogeneous than vaccinia virus, and its evolution is thought to be a result of natural selection via human-to-human transmission. As a result much lower levels of recombination have been found We ran HREfinder on all publicly available draft and finished genomes of There are far too many predicted events to detail all of them, but we have looked at a subset and make the following observations, recognizing that there may be good alternative interpretations. HREfinder is intended to be used as a tool for hypothesis generation, so results are best interpreted and verified by more detailed analyses of the HRE predictions. We note that overlapping blocks result in repeatedly counting HREs, mutations, and errors, so the number of events is an overestimate that can be corrected by a user’s detailed examination of the positions and sequences of the predicted transfers. The overlapping blocks and event duplicate counting are an area for improvement of the algorithm and code. The program output includes the full sequences of the regions around the putative HRE for each of the leaf strains under the recipient node, so the region, not just the SNP allele, may be more easily compared across genomes by BLAST. For cases with many predicted HREs, increasing wx and re-running HREfinder may be required, particularly in taxa with high rates of mutation and convergent evolution.For variola, HREfinder predicts an HRE containing 16 SNPs in a sequence fragment of 1041 bases from Node 1 (the branch leading to the India 1964 Vellore strains) to the Somalia_1977_gi109726076 genome, highlighted in red in Another HRE of 82 SNPs over a 6761 bp region from 17233–23969 is predicted from node 2 to the Nepal_1973_gi109725669 strain  with highest similarity to Bacillus thuringiensis serovar andalousiensis BGSC 4AW1 and other regions with highest similarity to proprietary, unpublished draft isolates sequenced by collaborators.The only HREs predicted in For vaccinia, all of the HREs predicted by HREfinder from outside the tree are to Vaccinia_Horsepox_virusMNR-76_gi111184167. One of these putative HRE regions spans positions 211–1425 and has the top BLAST hit to monkeypox Zaire (gi|17529780). Another very large putative HRE from positions 88078–106214 in Tian Tan is predicted to come from within the tree from the WR strain  and more to do with ecological opportunity. Factors like co-infection or co-habitation in the environment with multiple strains or species could lead to more opportunities for HRE, as could the prevalence of genetic mechanisms for HRE like transposons or other mobile elements. There are more HREs to internal nodes than to leaf nodes. We believe it is because the weight of an error is smaller than that of a mutation, so HREfinder with the weight parameter settings used here tends to assign errors on leaf nodes but HREs on internal nodes.For homogeneous species like Burkholderia pseudomallei dataset in Burkholderia pseudomallei that are predicted to come from out-groups shows many with high homology to transposon, phage, and plasmid sequences, which are prime candidates for HREs. Others show strong homology to soil and water inhabiting microbes like Rhizobium, Pseudomonas, and other Burkholderia species, consistent with HREs occurring in soil and aquatic environments.We plot the number of mutation/HRE/error events of We designed and implemented an algorithm to do HRE detection among many whole genomes using dynamic programming, based on SNPs. Our experimental results on simulated data show that there are many HREs that cannot be detected, but the HREs detected by our program are mostly true events. The tradeoff between recall and precision depend on the weights used, so a user may modify depending on tolerance for false positives/negatives. HREfinder is intended for hypothesis generation, and should be followed up by more detailed analyses of sequences, not just SNPs, to verify predicted HREs. The experimental results on real sequence data show that the number of HREs we predict for several bacteria and viruses is consistent with expectations based on the literature, and BLAST similarity of some of the putatively transferred regions support the predictions of HREfinder.


ABO, as being significantly associated with hematological traits. To confirm the effects of ABO on hematological traits, we examined the link between the ABO locus and hematological traits in Korean population-based cohorts.Recently, genome-wide association studies identified a pleiotropic gene locus, ABO were analyzed with regard to their effects on hematological traits . Linear regression analyses were performed, controlling for recruitment center, sex, and age as covariates. Of the 6 tagging SNPs, 3  and 3  were significantly associated with RBC and MCV, respectively . rs2073823 and a reported SNP (rs8176746), as well as rs495828 and a reported SNP (rs651007), showed perfect linkage disequilibrium status (r2s = 0.99). Of the remaining 3 SNPs , rs8176717 generated an independent signal with moderate p-value (= 0.045) when it was adjusted for by rs2073823 (the most significant SNP). We also identified a copy number variation (CNV) that was tagged by the SNP rs8176717, the minor allele of which correlated with the deletion allele of CNV. Our haplotype analysis indicated that the haplotype that contained the CNV deletion was significantly associated with MCV .Six tagging SNPs for ABO is one of the genetic factors that are associated with hematological traits in the Korean population. This result is notable, because GWASs fail to evaluate the link between a CNV and phenotype traits.Our findings confirm that  ABO as a candidate marker of the risk for coronary artery disease (CAD) .Click here for fileTable S1. Genomic inflation factor for hematological trait genome-wideassociation studies.Click here for fileFigure S2. Linkage disequilibrium blocks of ABO gene region.Click here for fileTable S2. SNP list of ABO gene region, minor allele frequency comparison, and genetic distance calculation between KARE and other populations. Underlined SNPs indicate the tagging SNPs for the ABO gene region used in the main paper.Click here for fileFigure S3. CNV clustering results. We used CNV tools to summarize the signal intensity data and assign a [specific OR discrete] CNV genotype within the CNV region. (A) Histogram of the clustering procedure using data, transformed by the linear discriminant function (LDF). (B) Cluster plot of the CNV region predicted from the LDF signal.Click here for file