TMEM154 variants in OvLV susceptibility. The objective here was to identify additional loci associated with odds and/or control of OvLV infection.Like human immunodeficiency virus (HIV), ovine lentivirus (OvLV) is macrophage-tropic and causes lifelong infection. OvLV infects one quarter of U.S. sheep and induces pneumonia and body condition wasting. There is no vaccine to prevent OvLV infection and no cost-effective treatment for infected animals. However, breed differences in prevalence and proviral concentration have indicated a genetic basis for susceptibility to OvLV. A recent study identified TMEM154 , provided 12 additional genomic regions associated with odds of infection, and provided 13 regions associated with control of infection . Rapid decline of linkage disequilibrium with distance suggested many regions included few genes each. Genes in regions associated with odds of infection included DPPA2/DPPA4 , and SYTL3 (P = 0.051). Genes in regions associated with control of infection included a zinc finger cluster , C19orf42/TMEM38A (P = 0.047), and DLGAP1 (P = 0.092).This genome-wide association study (GWAS) included 964 sheep from Rambouillet, Polypay, and Columbia breeds with serological status and proviral concentration phenotypes. Analytic models accounted for breed and age, as well as genotype. This approach identified TMEM154, these genes have not been associated previously with lentiviral infection in any species, to our knowledge. Further, data from other species suggest functional hypotheses for future testing of these genes in OvLV and other lentiviral infections. Specifically, SYTL3 binds and may regulate RAB27A, which is required for enveloped virus assembly of human cytomegalovirus. Zinc finger transcription factors have been associated with positive selection for repression of retroviral replication. DLGAP1 binds and may regulate DLG1, a known regulator of HIV infectivity.These associations provide targets for mutation discovery in sheep susceptibility to OvLV. Aside from  Ovine lentivirus (OvLV), like the human immunodeficiency virus (HIV), is a macrophage-tropic lentivirus that leads to persistent, lifelong infection of the host TMEM154: Transmembrane Protein 154) were identified that contributed to relative susceptibility to OvLV in multiple breed backgrounds and widely differing environmental conditions TMEM154 genotype However, breed differences in susceptibility to OvLV suggest that it may be possible to breed animals with reduced susceptibility to the virus. It has been shown that reproducible breed differences exist in seroprevalence of OvLV across several studies done in different locations over many decades Recent developments in genotyping technology should hasten identification of additional genes that could expand marker-assisted selection in sheep. Sufficiently dense marker systems ) have recently been released to allow examination of genome-wide variants related to phenotypes of interest From the original set of 997 animals, sample quality control criteria eliminated 18 samples from additional analysis. Multi-dimensional scaling (MDS) identified 3 clusters corresponding to breed, and 15 animals were eliminated for outlier status . The remOf the 964 animals that passed initial tests for genotype call quality, 95% had serological phenotypes and 96% had proviral concentration phenotypic data. The remaining blood samples were not of sufficient quality to report results. Among those individuals with serological results, 35% were positive for OvLV.2<0.40 at 10 Kb and r2<0.25 at 35 Kb  will be discussed further.Genes involved in odds of infection could be used to select sheep with lower probabilities of natural infection, and the top GWAS markers provide important evidence for the involvement of several interesting genes. Only one gene had been identified previously as consistently associated with susceptibility to OvLV infection DPPA2  gene which was associated in both the all-breeds analysis  gene are expressed in embryo and germ line cells DPPA2 or DPPA4 may be involved in OvLV infection. Both DPPA2 and the nearby DPPA4 play essential roles in lung development and formation DPPA2 may play a role in some aspect of transmission. A third possibility is differential development of the immune system. Since DPPA2 is expressed during embryonic development, it is possible that DPPA2 variants may influence the development or maintenance of the immune system. The SheepQTLdb Haemonchus contortus fecal egg count DPPA2 influencing both OvLV and parasitic infection through alterations in immune system development, since alterations in embryonic and/or white blood cell development could influence formation and/or maintenance of immune responses.The locus most highly associated with odds of infection was a SNP in the DPPA2  and was located 837 Kb distal on ovine chromosome 1 from the SNP in DPPA2 . This diDPPA2 , but theDPPA2 . In Poly2 = 0.23 . It is pSYTL3 (Synaptotagmin-Like 3) on ovine chromosome 8, and this region had the second largest odds ratio after TMEM154  and is thought to play a role in vesicular trafficking SYTL3 associated with any lentivirus infection in any mammal, but RAB27A is required for enveloped virus assembly of human cytomegalovirus SYTL3 may regulate RAB27A and thereby interfere with viral assembly. Further, RAB27A is a known negative regulator of phagocytosis SYTL3 could also influence either of those processes. While the current version of the sheep genome also shows SNP OAR8_88021348 within transcriptional regulator GTF2H5 , the gene order does not agree with other mammal genomes including cow, human, dog, and mouse, and it may well be a mis-assembly in the current version of the sheep genome.A separate region associated with odds of infection included  TMEM154 . SYTL3 iTMEM154 diplotypes DPPA2/DPPA4 dropped from genome-wide significant to genome-wide suggestive, but the SNP in SYTL3 retained its level of significance  and HIV-1 As well as adding more genomic regions associated with odds of infection, this was the first genome-wide study to examine association with the control of viral replication as measured by proviral concentration, to our knowledge. The most significant SNP  was located in a cluster of zinc finger genes on ovine chromosome 20 . Zinc fiC19orf42 ([human] Chromosome 19 Open Reading Frame 19) and TMEM38A (Transmembrane Protein 38A) (C19orf42 or TMEM38A at this time. C19orf42 is a short (75 amino acid) open reading frame with similarity to Yos1, a yeast protein required for transport between endoplasmic reticulum and Golgi complex C19orf42 could be involved in efficiency of viral packaging, but data to demonstrate such involvement are currently lacking. TMEM38A encodes a protein associated with vascular smooth muscle control of blood pressure TMEM38A could be involved by an as-yet undetermined mechanism. Much work remains to be done to elucidate the potential roles of these genes in limiting OvLV replication.Another empirically significant SNP  was located within both ein 38A) . Not mucDLGAP1 (Discs Large (Drosophila) Homolog-Associated Protein 1) gene  with proviral concentration. The first included two SNP on ovine chromosome 9 in Polypays , but no  1) gene . The proTMEM154 associated with odds of infection, and this was the first genome-wide study to provide regions associated with OvLV control as measured by proviral concentration. These results confirmed that TMEM154 is an important component of host susceptibility to OvLV, but there were many additional factors that contributed to genetic differences in susceptibility to and control of OvLV infection. These included many genes never previously associated with lentiviral infection, which may extend structural and/or regulatory networks implicated in lentiviral transmission, infection, and/or replication. These genes provide targets for additional investigation into lentiviral infection that may generalize beyond OvLV to other members of the lentiviral family. Further, the associated genes provide the basis for additional work to identify genetic markers associated with odds of OvLV infection and proviral concentration. As has already been done with TMEM154, such markers may be used to select sheep with lower odds and/or improved control of OvLV infection. Finally, they may also be of great interest for further study in goats, which are host to the closely related caprine encephalitis arthritis virus.In conclusion, this GWAS found many additional genomic regions besides All animal care and handling procedures were reviewed and approved by the Washington State University Institutional Animal Care and Use Committee (Permit Number: 3171) and/or by the U.S. Sheep Experiment Station Animal Care and Use Committee . All efforts were made to minimize any discomfort during blood sampling.TMEM154Flocks were chosen for study with high historical OvLV seroprevalence among mature ewes Blood was collected by jugular venipuncture into EDTA-coated vacutainer tubes. DNA was isolated using the Invitrogen GeneCatcher™ gDNA 3–10 ml Blood Kit as per manufacturers' instructions . The DNAs were checked for quality and quantity using an ND-1000 spectrophotometer  and equilibrated to 50 ng/µl for genotyping. Genotyping services were provided by Geneseek Inc.  using the OvineSNP50 Infinium BeadChip  with a set of 54,977 SNP designed by the International Sheep Genome Consortium http://pngu.mgh.harvard.edu/purcell/plink/) written by Shaun Purcell −7, which is P = 0.05 after Bonferroni-correction for the total number of pairwise comparisons between animals. Where the resulting clusters contained additional information compared to recorded breed data, the clusters were included in addition to breed as stratification categories for later association analyses.Initial quality control criteria included low genotype call rates and high genotype identity to any other sample. Samples with low genotype call rates (<97%) were removed from additional analysis. PLINK identity-by-state analysis identified samples that could have been involved in label errors (as determined by IBS distances >0.95) for removal from additional analysis. Multidimensional scaling (MDS) was performed in the PLINK software package . Distance bins were calculated for each 5 Kbp interval beginning with 0–5 Kbp. Mean r2 was only included in analysis if a bin contained at least 1000 values to assure each reported mean was well-estimated.Analysis of high density SNP data was performed using PLINK v1.06 10-transformed proviral concentrations were analyzed using similar separate general linear models for the minor allele of each SNP in PLINK to account for breed and pairwise population concordance clusters, for animal age as a covariate, and for the SNP minor allele. For both logistic and linear analyses, PLINK screening criteria were employed including missingness by individual (0.1), missingness by marker (0.03), minor allele frequency (0.01), and Hardy-Weinberg equilibrium . Genome-wide significance was defined by empirical P≤0.05. Genome-wide suggestive results were defined by nominal P-values < 1×10−5http://gettinggeneticsdone.blogspot.com/2011/04/annotated-manhattan-plots-and-qq-plots.html, viewed on 11-15-11) using the R environment 10−transformed proviral concentration as a measure of effect size for control of OvLV.The OvLV positive or negative status as measured by cELISA was analyzed using separate logistic models for the minor allele of each SNP in PLINK to account for breed and pairwise population concordance clusters, for animal age as a covariate, and for the SNP minor allele. LogFigure S1Multidimensional scaling of genotypes showing 3 breed clusters of animals included in analysis. Columbias are included in the top cluster, Polypays in the bottom right cluster, and Rambouillets in the bottom left cluster. The clustering of individuals by breed is clear even from these related breeds. The Columbia breed was developed pre-1920 with ½ Rambouillet composition (PDF)Click here for additional data file.Figure S2Decline of linkage disequilibrium with distance by breed.(PDF)Click here for additional data file.Figure S3Quantile-Quantile plot for odds of infection. Quantile-quantile plots from association with serological status, where the red line shows the expected distribution. Representative data from the all-breeds, additive mode of inheritance analysis are shown. The results show deviation from the expected distribution indicating population stratification by factors unaccounted in the analytic model, which could include frequencies of underlying mutations for susceptibility loci that differ between seropositive and seronegative individuals.(PDF)Click here for additional data file.Figure S4Quantile-Quantile plot for odds of infection conditioned on TMEM154 risk status. A second analysis conditioned on TMEM154 risk status shows an observed distribution closer to expected than the primary analysis, but still does not account for the majority of apparent population stratification.(PDF)Click here for additional data file.Figure S5Quantile-Quantile plot for odds of infection conditioned on  SNP. A third analysis conditioned on all the SNP in (PDF)Click here for additional data file.Figure S6Quantile-Quantile plot for control of viral replication. Quantile-quantile plot from association with proviral concentration, where the red line shows the expected distribution. Representative data from the Rambouillet, dominant mode of inheritance analysis are shown. The results show deviation from the expected distribution indicating population stratification by factors unaccounted in the analytic model, which could include frequencies of underlying mutations for susceptibility loci that differ by proviral concentration.(PDF)Click here for additional data file.Figure S7Quantile-Quantile plot for control of viral replication conditioned on  SNP. A second analysis was performed by conditioning on all the SNP in 2>0.8; removed to prevent inestimable multicollinearity) for which only the best P-value SNP was retained from each pair. This analysis shows a distribution much closer to expected, demonstrating that host genetic factors tracked by these SNP account for the majority of apparent population stratification.(PDF)Click here for additional data file.Table S1Adjusted genotypic mean proviral concentrations for  SNP. Mean proviral concentrations by genotype, adjusted for age and breed.(DOC)Click here for additional data file.Table S2Genomic regions from Columbia breed associated with proviral concentration of ovine lentivirus. Genomic regions from Columbia breed associated with proviral concentration of ovine lentivirus.(DOC)Click here for additional data file.Table S3Genotype frequencies by animal set for SNP from , , and (XLSX)Click here for additional data file.Table S4Linkage disequilibrium by animal set between SNP from , , and (XLSX)Click here for additional data file.


Tomato species are of significant agricultural and ecological interest, with cultivated tomato being among the most common vegetable crops grown. Wild tomato species are native to diverse habitats in South America and show great morphological and ecological diversity that has proven useful in breeding programs. However, relatively little is known about nucleotide diversity between tomato species. Until recently limited sequence information was available for tomato, preventing genome-wide evolutionary analyses. Now, an extensive collection of tomato expressed sequence tags (ESTs) is available at the SOL Genomics Network (SGN). This database holds sequences from several species, annotated with quality values, assembled into unigenes, and tested for homology against other genomes. Despite the importance of polymorphism detection for breeding and natural variation studies, such analyses in tomato have mostly been restricted to cultivated accessions. Importantly, previous polymorphisms surveys mostly ignored the linked meta-information, limiting functional and evolutionary analyses. The current data in SGN is thus an under-exploited resource. Here we describe a cross-species analysis taking full-advantage of available information.Solanum lycopersicum and S. habrochaites or S. pennellii and 28,800 intraspecific polymorphisms within S. lycopersicum. Using the available meta-information we classified genes into functional categories and obtained estimations of single nucleotide polymorphisms (SNP) quality, position in the gene, and effect on the encoded proteins, allowing us to perform evolutionary analyses. Finally, we developed a set of more than 10,000 between-species molecular markers optimized by sequence quality and predicted intron position. Experimental validation of 491 of these molecular markers resulted in confirmation of 413 polymorphisms.We mined 20,000 interspecific polymorphisms between Solanum species to date. These SNPs, plus thousands of molecular makers designed to detect the polymorphisms are available to the community via a website. Evolutionary analyses on these polymorphism uncovered sets of genes potentially important for the evolution and domestication of tomato; interestingly these sets were enriched for genes involved in response to the environment.We present a new analysis of the extensive tomato EST sequences available that represents the most comprehensive survey of sequence diversity across  Solanum lycopersicum) is the second most popular vegetable crop in the world [Tomato  molecular markers to build on the original, time consuming and cost ineffective restriction fragment length polymorphisms (RFLPs) [A large amount of genetic and molecular information is available for tomato, most of which has been deposited at the SGN  (RFLPs) -12Recently, bioinformatic surveys of genome sequences have revealed the importance of SNPs in shaping evolution and also in serving as molecular markers . In tomaArabidopsis thaliana databases a set of markers was developed that targeted loci conserved throughout evolution both in sequence and copy number [Arabidopsis thaliana to detect intraspecific SNPs in noncoding regions, demonstrating the possibilities of bioinformatics predictions [There is now an increasing wealth of resources in the tomato databases and similar repositories such as sequence qualities, unigene assemblies, open reading frame (ORF) predictions, sequence similarity with other plant species and localization to genetic maps . The usey number ,19. Thisy number -22. Simidictions .Despite the availability of sequence-associated metadata and the need for functional genomic studies in tomato, there is surprisingly little information about the relevance and levels of variation found in coding regions. Previous SNP mining efforts are either based on noncoding regions or have no information about the effect of the polymorphisms on protein sequence. Moreover, most SNPs mined from EST sequences have little probability of being functionally important, since the majority are expected to fall in the un-translated regions (UTR), where the polymorphism rate increases more than five fold in comparison with coding regions, . InformaTo perform reliable evolutionary and phylogenetic analyses sufficient polymorphism data can be obtained from the sequences of the wild species already available. However, most studies have focused on finding intraspecific molecular markers between cultivated tomato cultivars. The few attempts to infer rates of polymorphism and selection on tomato genes among its wild relatives are reduced to small sets of genes likely providing biased estimations .S. lycopersicum, S. habrochaites and S. pennellii. We used additional metadata from the database to effectively predict SNPs and infer levels of polymorphism in coding versus noncoding regions and in gene families. We surveyed this dataset for signatures of sequence evolution, selection and/or adaptation using the McDonald-Kreitman test [http://www.plb.ucdavis.edu/labs/maloof/TomatoSNP/index.html.To fill the gap in knowledge about sequence similarities and differences among cultivated and wild tomato species, we mined the EST and unigene tomato database from SGN in search for substitutions and insertions/deletions between and within the three species with highest representation: man test  and codoman test ,28. BaseThe unigene database version 200607 build 1 from SGN consists of 239,172 ESTs grouped into 34,829 unigenes, each one containing between 1 and 1087 ESTs, with an average of 6.9 ESTs per unigene (data not shown). Unigenes can be formed by EST sequences from any tomato race or species, although most unigenes (87%) contain sequences from a single species  ESTs for intraspecific polymorphisms and assemblies containing sequences from both S. lycopersicum and S. habrochaites (LxH) or S. lycopersicum and S. pennellii (LxP) for interspecific SNPs. To reduce the complexity of each analysis we reassembled the unigenes using only ESTs from the species relevant to the analysis. The consensus sequence for each reassembled contig was then aligned to the original unigene sequence and, when available , to the predicted coding sequence (CDS). In the cases where a predicted CDS was available, each nucleotide in the assembly was annotated as 5' UTR, CDS or 3' UTR. We assigned quality scores to each position in the assemblies by calculating separately for each species the sum of the qualities of all nucleotides at that particular position.We mined sequence assemblies of S. lycopersicum data  were detected (summarized in Table S. pennellii and S. lycopersicum (1.02 to 1.61 SNPs/100 bp) making no distinction between coding and non coding regions [S. lycopersicum intraspecific analyses, where reported SNP rates range between 0.0117 and 0.585 SNPs/100 bp on all EST sequences analyzed regardless of their location in the gene [Regarding the differences between intra and interspecific analyses, we found only 26.5% of the analyzed loci to be polymorphic when mining a single species versus the 69.5% when analyzing pairs Table . For int regions ,33. The the gene -16,25.Arabidopsis thaliana homolog, and looked for categories with over- or under-representation of non-synonymous SNPs. To achieve higher power we grouped together the data from the interspecific analyses. The larger number of sequences available in the intraspecific analysis allowed greater statistical power, although the most over- and underrepresented gene classes are in agreement in both analyses  categories , which gs Figure . As expes Figure . On the cessions . Surpriscessions .S. lycopersicum and S. habrochaites analysis and 924 unigenes from the S. lycopersicum and S. pennellii analysis. Before correction for multiple testing we found significant excess of non-synonymous mutations (p < 0.05) in 16 unigenes in the LxH analysis and 3 unigenes in the LxP analysis (data not shown). However, none of those unigenes survived the Benjamini and Hochberg correction for multiple testing [The neutral evolution theory holds that most within species polymorphisms and between species differences have little fitness consequence . As a re testing .S. lycopersicum lineage. To test this idea, we fit two different models for each unigene using alignments of the coding regions from cultivated tomato, Arabidopsis thaliana and S. habrochaites or S. pennellii. One of the models used assumed similar non-synonymous/synonymous rates (dn/ds) in every branch of the evolutionary tree (one-branch rate model). The second model allowed for different dn/ds estimates in the branch leading to S. lycopersicum relative to the rest of the tree (two-branch rates model). For each gene we used a likelihood ratio test to ask if the two-branch model fit significantly better than the one-branch model. Support for two-branch rates is suggestive of directional selection on the branch leading to domesticated tomato, therefore raising the possibility of identifying genes important for domestication [S. lycopersicum-specific dn/ds ratios could occur due to natural selection acting after the S. lycopersicum lineage split from S. pennellii or S. habrochaites but before domestication. We were able to test 1682 unigenes from the S. lycopersicum and S. habrochaites analysis and 1384 unigenes from the S. lycopersicum and S. pennellii analysis. From those, 9 and 1 unigenes respectively presented evidence of elevated non-synonymous substitution rates on the branch leading to S. lycopersicum after correction for multiple testing  of the molecular markers to work as predicted. It is worth noting that 10% of the successful markers showed bands corresponding to both alleles in at least one of the species tested, suggesting heterozygosity in the lines used or the amplification of fragments from a family of genes sharing that sequence. Markers that failed could be due to undetected SNPs in the sequence that modify the predicted restriction sites, or errors in SNP prediction. All tested markers are available in the Additional file We validated a subset of markers by using 491 primer pairs designed to amplify fragments from Arabidopsis thaliana, has been used on tomato sequences to perform a pre-genomic analysis of gene variability and evolution.We report in this work more than forty-nine thousand inter and intraspecific polymorphisms mined from the EST databases of the cultivated and two wild species of tomato. By taking advantage of the additional information linked to each sequence we were able to more accurately estimate the quality and the position of each SNP with respect to the coding region, which allowed us to distinguish those polymorphisms more likely to have phenotypic effects. Comparison of the sequences to homologs from better characterized species also allowed us to functionally classify the predicted unigenes and perform gene evolution analysis and tests for positive selection. We were able to suggest candidate genes and gene families that may be related to domestication of the cultivated tomato and/or environmental adaptation of wild species, providing hypotheses for more involved evolutionary studies. The information obtained was also used to design a set of markers that we make available to the community via a website. To our knowledge this is the first time that substantial meta-information including quality values, open reading frame predictions and homology to genes in S. lycopersicum cDNA libraries belonging to at least two different accessions, 2 S. pennellii and 2 S. habrochaites cDNA libraries plus some individual cDNAs. Unigenes are the consensus sequences of these ESTs assembled with cap3 software as described in [Arabidopsis thaliana genome was bulk queried and downloaded from SGN. Arabidopsis thaliana sequences were downloaded from TAIR Fasta files containing version 200607 build 1 of the unigene sequences, EST sequences, EST qualities and estimated coding region for each unigene were downloaded from SGN. ESTs in this database originated from the sequencing of at least 43 ribed in . CDSs foribed in ,30. ThisS. lycopersicum (L) ESTs and interspecific SNPs between S. lycopersicum and S. habrochaites (LxH) and between S. lycopersicum and S. pennellii (LxP) ESTs. Intraspecific analysis of S. pennellii and S. habrochaites ESTs were not performed as the EST libraries for those species were developed from a single accession.We mined intraspecific SNPs in For each unigene we used cap3 with relaxed parameters (-p 66 -b 99 -e 200) to assemble the EST sequences from the species participating in each of the analyses into analysis-specific contigs. Using these assemblies we calculated the sum and average of the qualities for each nucleotide call at every position and obtained analysis-specific consensus sequences. These consensus sequences were aligned to the original unigene sequence and the predicted CDS to estimate the beginning and end of the coding region. Every step in this process was carried out and verified using custom Perl scripts.S. lycopersicum analysis and of 40 for both interspecific analyses  were downloaded from TAIR [Each nucleotide in the EST assemblies was assigned one or more GO categories based on the terms from the homologous rom TAIR . For theS. lycopersicum together with S. pennellii or S. habrochaites sequences. For the McDonald-Kreitman test we built fasta files for each unigene with the estimated CDS for the wild species alleles and two S. lycopersicum alleles differing in the SNPs identified in the L analysis. To maintain the fidelity of the analysis, those positions that did not pass the quality threshold using the methods described above were substituted with the 'unknown' character and not considered in the subsequent analysis. The number of synonymous and non-synonymous substitutions and p-values were calculated using the previously described MK.pl Perl script [We performed McDonald-Kreitman tests and estimated Maximum likelihood from codon-substitution models on those unigenes that contained l script .n/ds ratio with equal ratios in every branch. The second model allowed for two dn/ds ratios: one for the S. lycopersicum lineage and one for the rest of the tree. Then, a likelihood ratio test of the hypothesis of two branch rates was calculated by comparing the likelihood values from both models as in [Arabidopsis thaliana coding sequences. Alignments whose sum of qualities were not over the imposed quality threshold were substituted with the 'unknown' character. We also removed the parts of the alignments that lacked sequences from any of the three species. We constructed a phylogenetic tree of the three species and used PAML  to calcThe resulting p-values from these experiments were corrected for multiple testing using the Benjamini and Hochberg algorithm in the Bioconductor multtest package ,45.For each polymorphic unigene in the interspecific analysis we aligned its predicted protein sequence with the protein sequence of its Arabidopsis best BLAST hit using standalone BLAST and custom Perl scripts. We calculated intron positions in the unigene based on intron positions in the Arabidopsis CDS. For each SNP we designed primers using Primer3  with theS. lycopersicum VF36, S. pennellii LA716 and S. habrochaites LA1347. Touchdown PCR was performed in a MJ Research PTC-200 Thermocycler with a starting annealing temperature of 58°C, which decreased 0.5°C per cycle for 15 cycles and stayed constant at 55°C for 30 cycles. Extension time was 40 seconds and denaturizing steps were performed for 30 seconds at 96°C. PCR products were digested with the appropriate restriction enzymes to detect the polymorphisms. We developed a database and a website holding the molecular marker information for each interspecific SNP.We used Bioperl to generate virtual PCR fragments for each SNP allele based on the primers designed, and to find restriction endonucleases that would differentially cut the fragments, thus creating molecular markers. A set of these molecular markers was tested on genomic DNA from All R and Perl scripts are available by request.S. lycopersicum intraspecific analysis; LxH: S. lycopersicum versus S. habrochaites interspecific analysis; LxP: S. lycopersicum versus S. pennellii interspecific analysis; ORF: Open reading frame; PCR: Polymerase chain reaction; RFLP: Restriction fragment length polymorphism; SGN: Solanaceae genomics network; SNP: Single nucleotide polymorphism; UTR: Untranslated regions.CDS: Coding sequence; COS: Conserved orthologous set; EST: Expressed sequence tag; GO: Gene ontology; L: JMJG wrote the manuscript, conceived and designed the study and the website hosting the results. JMJG also performed the assemblies, SNP mining, evolutionary analysis and molecular marker design and essays. JNM conceived and participated in the design of the study, and helped to draft the manuscript. All authors read and approved the final manuscript.Non-synonymous SNPs. Consecutive and non-consecutive non-synonymous polymorphisms. "Analysis" Column, LxP: S. lycopersicum and S. pennellii interspecific analysis, LxH: S. lycopersicum and S. habrochaites interspecific analysis. L: S. lycopersicum intraspecific analysis. "Position" indicates the nucleotide position in the unigene sequence from the SGN database. "Nt1" and "Nt2": Nucleotide call (reverse nucleotide if the sequence is in reverse orientation). "# of Seqs 1" and "# of Seqs 2": number of ESTs in the assembly with that nucleotide call. "Quality Sum 1" and "Quality Sum 2": sum of qualities of all nucleotides with that call. "CDS Orientation": U if plus strand, C if minus strand. "Codon 1" and "Codon 2": estimated codon including the SNP, "Amino Acid 1" and "Amino Acid 2": translation for Codon 1 and Codon 2 respectively. For all headings "1" refers to cultivated tomato in every analysis whereas "2" indicates the wild species in the interspecific analyses and a second allele of cultivated tomato in the intraspecific analysis.Click here for fileSynonymous SNPs. Consecutive and non-consecutive synonymous polymorphisms. Legend as in Additional file 1Click here for fileSNPs affecting stop codons. Consecutive and non-consecutive polymorphisms causing premature stop codons or disrupting stop codons. Legend as in Additional file 1Click here for fileSNPs in UTRs. Consecutive and non-consecutive polymorphisms located in the predicted UTR regions. "Region" indicates if the SNP was found in the 3' or 5' UTR region. Other headings as in Additional file Click here for fileMolecular markers experimentally tested. Molecular markers that returned amplification products both polymorphic and non-polymorphic. "MNS", minimum number of sequences of any species at the SNP position; "MQS", minimum average sequence quality at the SNP position (See Methods). In the column names, L stands for S. lycopersicum and W for the wild species S. pennellii or S. habrochaites. "PREDICTED_RESTRICTION_SIZES" gives the sizes of the predicted fragments after digestion, separated by underscores if multiple bands are predicted. "COMMENTS": 'ok' if the sizes were as expected, 'extra bands' if additional bands amplified in addition to the ones expected, 'good pattern' if the sizes of the amplifications obtained were different than expected but the polymorphism was recognizable after restriction, 'het/gene family' if at least one of the parents presented both alleles at the same time, or empty is the polymorphism was not found.Click here for file


Pigeonpea is an important grain legume of the semi-arid tropics and sub-tropical regions where it plays a crucial role in the food and nutritional security of the people. The average productivity of pigeonpea has remained very low and stagnant for over five decades due to lack of genomic information and intensive breeding efforts. Previous SSR-based linkage maps of pigeonpea used inter-specific crosses due to low inter-varietal polymorphism. Here our aim was to construct a high density intra-specific linkage map using genic-SNP markers for mapping of major quantitative trait loci (QTLs) for key agronomic traits, including plant height, number of primary and secondary branches, number of pods, days to flowering and days to maturity in pigeonpea.2:3 lines derived from an intra-specific cross between inbred lines ‘Pusa Dwarf’ and ‘HDM04-1’ was used to construct a dense molecular linkage map of 296 genic SNP and SSR markers covering a total adjusted map length of 1520.22 cM for the 11 chromosomes of the pigeonpea genome. This is the first dense intra-specific linkage map of pigeonpea with the highest genome length coverage. Phenotypic data from the F2:3 families were used to identify thirteen QTLs for the six agronomic traits. The proportion of phenotypic variance explained by the individual QTLs ranged from 3.18% to 51.4%. Ten of these QTLs were clustered in just two genomic regions, indicating pleiotropic effects or close genetic linkage. In addition to the main effects, significant epistatic interaction effects were detected between the QTLs for number of pods per plant.A population of 186 FA large amount of information on transcript sequences, SSR markers and draft genome sequence is now available for pigeonpea. However, there is need to develop high density linkage maps and identify genes/QTLs for important agronomic traits for practical breeding applications. This is the first report on identification of QTLs for plant type and maturity traits in pigeonpea. The QTLs identified in this study provide a strong foundation for further validation and fine mapping for utilization in the pigeonpea improvement. Cajanus cajan L. Millsp.) is an economically important grain legume of the tropical and subtropical regions of the world. It is grown extensively in India, South-East Asia, East Africa, Latin America and the Caribbean, where its plays important role in the food and nutritional security of the people. It is a major source of food protein to about 20% of the world population and is an abundant source of minerals and vitamins and flanking sequences (about 50 bases each side of the SNPs) in the 1536-plex Illumina Goldengate assay, with the SNP Id, TSA (transcript shotgun assembly) contig Id of the sequence from variety 'Asha' (NCBI GenBank Ac. no. EZ647865-EZ683068) and SNP quality scores based on Illumina SNP assay design tool.Click here for fileTable showing SNP positions [in bracket] and flanking sequences (about 50 bases each side of the SNPs) in the 768-plex Illumina Goldengate assay, with the SNP Id, TSA (transcript shotgun assembly) contig Id of the sequence from variety 'Asha' (NCBI GenBank Ac. no. EZ647865-EZ683068) and SNP quality scores based on Illumina SNP assay design tool.Click here for fileTable showing observed and adjusted map lengths of the 11 linkage groups of pigeonpea.Click here for file


Our study included 395 healthy blood donors for whom global gene expression profiles in circulating monocytes were determined by Illumina BeadArrays. ASE was assessed in a subset of these monocytes from 188 donors by quantitative genotyping of mRNA using a genome-wide panel of SNP markers. The performance of the two methods for detecting cis-rSNPs was evaluated by comparing associations between SNP genotypes and gene expression levels in sample sets of varying size. We found that up to 8-fold more samples are required for eQTL mapping to reach the same statistical power as that obtained by ASE analysis for the same rSNPs. The performance of ASE is insensitive to SNPs with low minor allele frequencies and detects a larger number of significantly associated rSNPs using the same sample size as eQTL mapping. An unequivocal conclusion from our comparison is that ASE analysis is more sensitive for detecting cis-rSNPs than standard eQTL mapping. Our study shows the potential of ASE mapping in tissue samples and primary cells which are difficult to obtain in large numbers.A large number of genome-wide association studies have been performed during the past five years to identify associations between SNPs and human complex diseases and traits. The assignment of a functional role for the identified disease-associated SNP is not straight-forward. Genome-wide expression quantitative trait locus (eQTL) analysis is frequently used as the initial step to define a function while allele-specific gene expression (ASE) analysis has not yet gained a wide-spread use in disease mapping studies. We compared the power to identify  The cis-regulatory SNPs normally have stronger effects and are therefore easier to identify by eQTL mapping than trans-acting SNPs. Consequently, cis-regulatory SNPs identified in a relevant tissue are functional candidates for further investigation of disease-causing genetic variants.Owing to the rapid advances in genotyping technology a large number of genome-wide association studies have been performed during the past five years to identify associations between SNPs and human complex diseases and traits cis-regulatory SNPs associated with human diseases and traits cis-regulatory SNPs by ASE analysis with that of traditional eQTL mapping. As our study focuses exclusively on the analysis of cis-rSNPs, we refer here to eQTL mapping of cis-rSNPs as genotypic expression (GTE) mapping. We performed an experimental comparison of ASE and GTE by analyzing gene expression in primary monocytes purified from human peripheral blood samples. Monocytes were analyzed because they are a relevant cellular model for many complex diseases.Although quantitative, genome-wide allele-specific gene expression (ASE) analysis has previously been introduced as an alternative to eQTL mapping of cis-regulatory SNPs.By computing Bonferroni-corrected p-values, false discovery rate p-values and by determining the overlap between the SNPs with the strongest association signals identified by both methods in addition to an analysis of the effect of minor allele frequencies upon the power of the methods, we show unequivocally that ASE analysis is more powerful than GTE mapping for identifying In this study we compared two approaches for genome-wide association analysis of SNPs against gene expression levels. Using GTE mapping, the SNP genotypes are tested for an association with the total gene expression levels, and using ASE analysis, the relative expression levels between the two alleles of a transcript are used as the quantitative phenotype against which the SNP genotypes are analyzed. The results presented here are based on GTE mapping of RNA extracted from 395 monocyte samples and ASE analysis of a subset of 188 monocyte samples from the same donors. The donors are healthy adult blood donors of European origin recruited from the United Kingdom National Blood Service Centre in Cambridge, UK as part of the Cardiogenics Transcriptomic Study. The power of GTE mapping and ASE analysis to detect association signals from SNPs that affect gene expression in the monocytes was explored by comparing the number of SNPs with significant association signals obtained using different p-value thresholds in sample sets of different sizes. To enable comparison of the statistical power between the two approaches the ASE dataset was down-sampled to 95 and 50 samples, and the GTE dataset was down-sampled to 188, 95 and 50 samples. The principles of the two methods are illustrated in Supplementary corr = 0.05 these numbers imply that an observed p-value of 7.8e-8 is required for the complete ASE analysis and for pcorr = 0.01 a p-value of 1.6e-8 is required. Similarly, for the complete GTE analysis, an observed p-value of 1.2e-7 for pcorr = 0.05 and 2.3e-8 for pcorr = 0.01 are required. To avoid inflated false positive rates, both Bonferroni and FDR adjustments require that random sampling or permutation of the data should yield uniformly distributed p-values under the null hypothesis. To verify that the p-values were uniformly distributed, the GTE and ASE data was permuted 500 times by shuffling the sample identifiers once for each iteration. In this way, the connection between genotypes and ASE values are broken while the structure of the data is retained so that phasing and the calculation of the median ASE levels is not affected. According to the permutation tests, the number of p-values in the very low range was inflated in both the GTE and the ASE data. However, filtering out association tests that were based on linear regression analysis of less than four ASE values per genotype group in the ASE analysis and less than three expression values per genotype group in the GTE analysis, abolished the inflation, see QQ-plots in The comparative set of 517K SNP markers for which genotype data was available for all samples were used for association analysis against the total gene expression levels and the ASE levels. After filtering, 12145 probes representing 10059 Refseq transcripts remained for the GTE analysis and 13146 Refseq transcripts remained for the ASE analysis. Thus 544531 and 638845 association tests were performed for GTE mapping and ASE mapping, respectively. By filtering out association tests that were based on too few data values, for ASE we retain 620 k to 438 k tests depending on the sample size and for GTE 428 k to 343 k tests depending on the sample size, see further below. For significance according to Bonferroni at pcis-rSNPs detected with significant association signals between the ASE and GTE methods is most pronounced in the smaller sample sets, where up to 31 times more cis-rSNPs and up to nine times more transcripts were detected by ASE analysis using the same sample size and a 5% FDR threshold. As can be seen in corr = 0.05 cut-off. Most of the 13146 Refseq transcripts that were analyzed for ASE association show some degree of allele-specific expression, but it was unexpected that the ASE method would detect significant associations with transcripts showing only marginal effect sizes. However, formal statistical significance does not necessary imply that the effect is large enough to be biologically relevant. In a biological application one needs to consider having a cutoff for the minimal ASE-level as well as the p-value.The number of significantly associated rSNPs based on the FDR p-values indicates that up to eight times fewer samples are required for ASE analysis to achieve the same statistical power as GTE mapping, at least for the sample sizes analyzed in this study. From the bottom panel of The fraction of significantly associated rSNPs compared to all SNPs is plotted in When including SNPs within 500 kb regions flanking the transcripts instead of 100 kb for GTE mapping, to facilitate identification of SNPs located more distantly from the transcripts, the number of significant association signals decreased by one third using 5% FDR and all the samples. However, the number of significantly associated transcripts is similar with 805 associated transcripts lost in the 100 kb region and 913 new associated transcripts found in the extended region. When the same expanded 500 kb flanking regions were included in the ASE analysis a similar amount of significant association signals as for 100 kb flanking regions were observed, although the p-values were corrected for almost four times as many tests. Here, 127 transcripts associations were lost in the 100 kb region and 169 new transcripts were found in the extended region. For both methods it is worth investigating different sizes of flanking regions to define the optimal balance between the number of association tests and the number of false negative rSNPs. Additionally, the ASE levels were determined using the less stringent requirement of three informative SNPs instead of five for calculating the ASE level which yielded 1343 additional transcripts for analysis. In this case a small decrease in number of significant association signals was observed due to slightly larger number of tests and a lower signal to noise ratio.cis-rSNPs detected by the two methods at different sample sizes. The overlap in transcripts targeted by the GTE arrays and the requirement of heterozygous SNPs in ASE transcript windows sets an upper limit to the number of overlapping rSNPs that are detectable by both methods. In our data between 150 k and 200 k SNPs were eligible for assessing overlapping results between the two methods, depending on which sample sizes were compared. The SNPs were sorted according to p-value and the top 9536 SNPs from the GTE analysis were compared with the top 38203 SNPs from the ASE analysis, corresponding to a Bonferroni pcorr = 0.05 threshold for GTE sample size 395 and ASE sample size 188. The overlap was calculated for both methods and all sample sizes, see To further compare the power of GTE and ASE mapping, we determined the overlap in Using this approach it can be assessed if an increase in sample size would yield more overlapping rSNPs or if most of the rSNPs have already been identified. In cis-rSNPs, the genotype data was divided into bins, with all SNPs within a MAF window of 1% unit between 0% and 50% in each bin. The fraction of significantly associated rSNPs within each 1% MAF window was determined based on Bonferroni-corrected p-values at a pcorr = 0.05 threshold. Finally, to examine how the minor allele frequency (MAF) of the SNPs affects the power of the two methods to detect putative i.e. using adjusted p-value thresholds obtained by the Bonferroni and FDR approaches, by analysis of top-list overlaps and MAF analysis to assess the relative power of ASE analysis and eQTL mapping of cis-rSNPs (GTE mapping), that ASE analysis is more powerful than GTE analysis for mapping cis-regulatory SNPs. The high power of ASE originates from the fact that using ASE, the relative expression levels of the two alleles of a transcript are measured in the same sample, and thus trans-acting regulatory factors and other differences in gene expression between samples due to exposure of the samples to different environmental conditions are controlled for. A limitation of mapping rSNPs using ASE, compared to traditional GTE mapping is that ASE would not be applicable to the rare cases of transcripts lacking heterozygous SNPs and also that transcripts without enough heterozygous SNP coverage on the genotyping chip would be excluded. This limitation can be partly circumvented by using the sum of the fluorescence signals from the two alleles of each SNP as a quantitative measure for total gene expression levels. Also, our ASE approach is only sensitive for variants altering transcription and not those affecting splicing, while it is possible to find these using eQTL approaches if the microarray used have large enough resolution.In our study we show by three approaches, To perform a comparison that was as fair as possible, we analyzed exactly the same SNPs using both methods. For these SNPs linkage disequilibrium (LD) was not considered as it is not unequivocal to distinguish between SNPs that are in LD and SNPs that are not. For the purpose of comparing the relative power of ASE and GTE, a comparison without consideration of LD is adequate. The number of transcripts analyzed in the comparison was also similar, although not identical. The 15% higher number of transcripts that were eligible for ASE analysis has no impact on the conclusion from our study, but instead reflects another advantage of ASE, which is that the ASE approach does not require annotated transcripts and probe design prior to performing an experiment. The flexibility of avoiding predesigned probes and the high power of ASE using SNP genotyping is shared with second generation transcriptome sequencing (RNA-seq), where both total gene expression and allele-specific expression levels can be determined in the same data set. Thus ASE will be an informative approach for RNA-seq data, particularly for samples with available genotype data. An additional advantage of RNA-seq compared to ASE analysis using SNP genotyping is that alternatively spliced transcripts and strand specific gene expression can be detected, provided that the sequencing coverage is sufficient.cis-regulatory genetic variants. The high power of ASE is reassuring for functional refinement of SNP identified by genome-wide association studies of disease and traits in relevant tissue or cell samples, which in many cases are difficult to obtain in large numbers.In a previous study we have shown the low level of noise in ASE data for 8000 genes using a closely related genotyping assay The study is approved by the Cambridgeshire 1 Research Ethics Committee.Circulating monocytes were collected from healthy adult blood donors of European origin recruited from the United Kingdom National Blood Service Centre in Cambridge, UK as part of the Cardiogenics Transcriptomic Study. Samples from patients with a recent or acute illness were excluded. DNA was extracted from peripheral blood leukocytes using the guanidine hydrochloride - chloroform method followed by quantification by PicoGreen . CD14+ magnetic microbeads  were used to isolate monocytes from whole blood. RNA was extracted from cell pellets of freshly isolated monocytes by homogenization with Trizol-reagent , chloroform-ethanol extraction and purification using Qiagen RNAeasy columns and reagents, followed by on-column DNase treatment. cDNA was synthesized using reagents from the Illumina TotalPrep RNA Amplification Kit. Synthesis of cDNA was performed according to the protocol provided by the supplier, except that the poly-dT primers were substituted by random decamers .Human-Ref 8 v3.0 arrays (Illumina Inc) containing 24526 probes were used to generate the gene expression data for GTE mapping essentially as described previously in Of the monocyte samples, 247 were subjected to genome-wide genotyping for GTE mapping with Human 660 Quad BeadChips  and 188 samples were genotyped for the ASE analysis by the Infinium II assay with Human 1.2 M Duo custom BeadChips v1 (Illumina Inc). To apply a comparative set of markers for the comparison of GTE and ASE, only those 517 K markers that were present on both chips were used for GTE and ASE mapping. Genotypes from the 660 Quad BeadChips were called in Illuminus cDNA/(Allele1cDNA+Allele2cDNA) – Allele1gDNA/(Allele1gDNA+Allele2gDNA). The same 31535 transcripts as in GTE analysis were extracted from Refseq genes Genotyping of the 188 RNA samples for ASE analysis was performed using 1.2 M Duo custom BeadChips v1 (Illumina Inc). The cDNA and genomic DNA (gDNA) from each sample were genotyped on the same BeadChip to minimize experimental variation. Genotypes were called in gDNA using Genome Studio version 2009.2 (Illumina Inc.). A call rate of 0.99 was set as the threshold for genotype calls. A quadratic function was employed to normalize the raw two-colour fluorescence signals from the assay with respect to the dependency between signal intensity and the relative signal intensities of the two fluorophores corresponding to the two SNP-alleles. Phasing of the SNP data was performed by Impute2 Because the genotype calling from the 660 Quad and the 1.2 M Duo BeadChips differed slightly, the result from the genotype calls were compared for the overlapping 517 K SNPs in the 188 samples that where genotyped on both chip types. The genotypes in these data sets were found to differ at as little as 0.0056% of the bases and only 0.19% of the SNPs were in any way affected by this minor difference in genotype calls.https://www.ebi.ac.uk/ega).The allelic genotyping data and expression in this paper are in the process of being deposited in the EGA with accession number EGAS00000000119 *p, where m is the number of tests and i is the ranking of p-values among all observed p-values when they are sorted in increasing order. FDR p-values that do not monotonically increase were finally replaced with the smallest observed FDR value for any higher ranked probe.The set of 517 K genotyped SNPs that were shared between the GTE and ASE analysis was used to identify rSNPs. GTE mapping and ASE analysis was performed using the genotypes of all SNPs located in a region of 100 kb upstream and downstream of the transcript as well as within the region of the transcript itself. For ASE these SNPs were tested by linear regression for associations between SNPs and ASE levels essentially as described by Pastinen et al Figure S1Schematic picture of the principles of GTE and ASE analysis. In GTE the total expression level is measured by a specifically designed expression probe usually located in a transcript. The effect of different genotypes (shown in yellow) can then be tested against the total expression level of each sample to find regulatory SNPs using a linear regression association test. In ASE the allele-specific expression level is measured by the difference in fluorophore signal intensity between the two alleles in the same sample. The average level is calculated for all heterozygous SNPs in the region. This value is then used when testing whether there is a significant effect of the tested genotype (shown in yellow) in a similar association test as in GTE.(TIF)Click here for additional data file.Figure S2QQ-plots for the permuted p-value distribution compared with a uniform distribution. In Panel A–C, QQ-plots are shown for ASE and in panel D–G QQ-plots are shown for GTE using filtering that removes SNPs with few data points in each genotype group. The permuted data are expected to yield uniformly distributed p-values under the null hypothesis and should therefore follow the red line representing the uniform distribution, which is also the case. However, without filtering, the distribution is starting to skew towards inflated p-values prior to the Bonferroni correction threshold is reached, data not shown.(TIF)Click here for additional data file.Figure S3Fraction of rSNPs in relation to the position of TSS and TTS. The fraction of significant association signals compared to the total number of SNPs in 1 kb bins at different distances to the transcription start site  and transcription termination site  is shown for ASE using 188 samples and for GTE using 395 samples. In A–D the FDR 5% threshold is used and in E–H the Bonferroni 0.05 threshold. All figures show peaks around the start and termination sites.(TIF)Click here for additional data file.


Clupea pallasii) support commercially and culturally important fisheries but have experienced significant additional pressure from a variety of anthropogenic and environmental sources. In order to provide genomic resources to facilitate organismal and population level research, high-throughput pyrosequencing (Roche 454) was carried out on transcriptome libraries from liver and testes samples taken in Prince William Sound, the Bering Sea, and the Gulf of Alaska. Over 40,000 contigs were identified with an average length of 728 bp. We describe an annotated transcriptome as well as a workflow for single nucleotide polymorphism (SNP) discovery and validation. A subset of 96 candidate SNPs chosen from 10,933 potential SNPs, were tested using a combination of Sanger sequencing and high-resolution melt-curve analysis. Five SNPs supported between-ocean-basin differentiation, while one SNP associated with immune function provided high differentiation between Prince William Sound and Kodiak Island within the Gulf of Alaska. These genomic resources provide a basis for environmental physiology studies and opportunities for marker development and subsequent population structure analysis.Pacific herring ( Clupea pallasii) in Prince William Sound (PWS) have collapsed after the Exxon Valdez oil spill in 1989, resulting in a closure of commercial and traditional fisheries. Despite two decades of research and extensive restoration efforts, Pacific herring is one of only two resources still classified as ‘not recovered’ Many commercially exploited species face ecological and anthropogenic pressures in addition to fisheries, such as pollution, emerging diseases and climate change. Although demographic effects of such pressures are difficult to quantify, they are likely to affect both ecosystem structure and economic returns of dependent fisheries. Studies on the genetic and organismal effects of these pressures may provide insights into the phenotypic flexibility and the scope for adaptation that may allow resilience and resurgence of exploited populations. For example, Pacific herring , respectively De novo assembly of all data resulted in 42,953 sequences with an average length of 728 bp. A majority, 81% of the contigs, were between 100 and 1000 bp in length  with 13,379 features expressed at a higher level in the liver library . A largein silico by approximately 50%  20,966 and 25,416 contigs were generated from the liver and testes libraries, respectively  were transitions. A/T and C/G transversions were each present in 9% of the candidate SNPs while the G/T and A/C substitutions constituted 11% of the polymorphisms. Average coverage of putative SNPs was 16.6 (SD = 46.8), with 95% of the SNPs having coverage less than 25×.Distinguishing synonymous and non-synonymous SNPs from high-throughput sequence data in species without sequenced genomes can be a challenge. Over both libraries, 161,059 potential open reading frames were identified. SNP detection using these open reading frames to map all quality trimmed reads revealed 4448 putative SNPs. Of those SNPs, 1610 resulted in a predicted amino acid substitution. After removing sequences with e-values greater than 1.0E-10 (Swiss-Prot database) and less than 10× coverage, 257 non-synonymous SNPs and 722 synonymous candidates remained (dn/ds = 0.356).Fifty candidate SNPs did not pass the initial primer testing; many of these were likely true SNPS adjacent to intron/exon boundaries and would not amplify. Sanger sequencing confirmed the presence of one polymorphism in 14 templates and two or more polymorphisms in 16 templates. The 14 templates with a single polymorphism were used for HRMA. Additional Sanger sequencing demonstrated the presence of more than one polymorphism in four of these sequences in other populations . The 14 eH = 0.221) than the Gulf of Alaska (eH = 0.339) (i.e. Togiak) and the Gulf of Alaska  . A hieraf Alaska . Eight of Alaska , but becf Alaska .Danio rerio) was the most taxonomically similar species with significant genomic resources. Here, we provide over 2 million nucleotide reads from the Pacific herring transcriptome. From these data, we were able to generate over 40,000 contigs with a 10× average coverage depth, identify thousands of putative SNPs, and demonstrate realistic levels of population diversity and differentiation in a small subset of these SNPs.The large-scale sequencing effort characterized here was carried out to provide a foundation of such genomic information to assist in the research on the biology, ecology, and population genetics of herring. Prior to the completion of our sequencing project, there were less than 1000 publically available nucleotide sequences for the Pacific herring. Furthermore, sequence information from any species of the teleostean order Clupeiformes was limited, and zebrafish (Melitaea cinxiaAcipenser fulvescens), 47,060 reads were produced and assembled into 1831 contigs Salvelinus namaycush), 425,821 quality-trimmed reads from liver tissue were assembled into 2276 contigs that were then used for comparative transcriptomic analysis of two lake trout ecotypes Pyrosequencing using the 454 platform on non-model organisms is increasingly proven to be an effective and efficient means to provide large scale transcriptomic information. Given the dynamic nature of gene expression, advances in technology and the variety of analytical techniques available make it difficult to directly compare studies, however generally our results are similar to other sequencing efforts. One of the initial applications of 454 pyrosequencing on non-model organisms was carried out on the butterfly, The number of reads generated per given sequencing effort is relevant, particularly given any financial consideration. In order to characterize the benefit of additional reads to information yield, we examined the number of contigs generated for a given number of quality-trimmed sequence reads . This tyWhile RNA-seq analysis was not used in the study to address ecological issues, the analytical techniques were utilized to evaluate expression differences between the two tissues examined, liver and testes. This approach can provide information on the benefit of the multiple tissues as well as information of functional importance. There was a large difference in the expression levels between tissue types, with over 67% of the features differentially expressed in one tissue. Furthermore, 45% of the features were only expressed in a single tissue. This was expected, given the functional difference in gonad and hepatic tissue, and illustrates tissue diversity is useful for discovery of markers at the genomic DNA level from transcriptome sequences.RNA-seq-based approaches also allow for the identification of genes associated with specific biological function. One group of genes of particular interest in immune physiology is components of the complement system see . A complSeveral members of the Cytochrome p450 enzyme superfamily were identified in this pyrosequencing effort. In the consensus sequences generated from liver library reads, putative members of families CYP1, CYP2, CYP3, and CYP4 were all identified . One of One primary motivation for the high-throughput sequencing effort on Pacific herring was the discovery of SNPs in coding regions that could be used for population studies on selection and local adaptation. Neutral molecular markers, such as allozymes and microsatellites, have provided powerful insights into population structure and demographic history of wild populations, but they cannot detect adaptive genetic variation. Large-scale genome sequencing efforts combined with outlier tests now have the potential to find genes that are important for adaptation in wild populations. Multiple strategies to increase coverage and target potentially selected genes are available, including exome capture Another challenge in developing SNPs in non-model organisms is the determination whether a particular SNP will result in an alteration in amino acid. This information is particularly important when there is interest in selective environmental pressure that could be related to physiological responses and distinguish populations. Here, we developed and generalized a workflow that could be used for any species where there is an absence of an annotated genome. Essentially, a combination of open reading frame identification (based on absence of stop codons) and sequence similarity scores were used to identify SNPs that likely result in a predicted amino acid substitution. Based on the workflow described, we estimated that there were 26% non-synonymous SNPs. It should be pointed out we did not validate this prediction and only a fraction of SNPs (979 SNPs) were available for this form of characterization.Most loci conformed to HWE, even before Bonferroni correction, and significant deviations from HWE showed no clear concentration towards specific loci. However, the Bering Sea population (Togiak) had more loci that were out of HWE than populations from the Gulf of Alaska, which may be due to ascertainment bias or selection, but this needs further investigation. Ascertainment bias seems possible because even though sequences were obtained from a group of fish that included Bering Sea fish, three quarters of the fish originated from the Gulf of Alaska. It may be that in Bering Sea fish, these SNPs included a non-amplifying null allele more commonly known from microsatellite, though this would need to be confirmed by additional sequencing of homozygotes. Four templates had more than a single SNP, but three of the four showed no significant deviation from HWE in any of the populations, and the fourth only in the Togiak population. HRMA patterns were scored as biallelic loci, so even with multiple SNPs only a single polymorphism was considered, resulting in effective binning of rare alleles and conformance to HWE.The distribution of genetic diversity within and between populations was remarkably similar for SNPs as for previously analyzed markers  or very high (microsatellites: eH = 0.91) variability. Although the difference in diversity between Bering Sea and the Gulf of Alaska may have been due to ascertainment bias (17 of the 23 sequenced fish came from the Gulf of Alaska), the corresponding results between allozymes, microsatellites and SNPs suggest that differences in demographic history may have caused these genetic patterns, rather than a methodological issue in one of the three markers.Genetic diversity was higher in the Gulf of Alaska than the Bering Sea  and 5, aSTF>0.1) between the two ocean basins . Two of these four loci had three and two SNPs, respectively , and thus the differentiation may be due to any one of those SNPs. The fifth locus (Cpa_CYP2J5) demonstrated significant deficiencies of heterozygotes in both Gulf of Alaska samples. Despite this high differentiation, the variance component between ocean basins was not significant at any of the loci, likely because of the small number of populations  and the consequent low power of permutation approaches randomizing entire samples between groups , and deficiency of heterozygotes in PWS, which may suggest selection within PWS. That locus is a virus response gene, which corresponds to the notion that infection by viral hemorrhagic septicemia virus may have at least contributed to the delayed recovery of Prince William Sound herring Another locus showed high and significant differentiation between Kodiak Island and PWS  and one in the Bering Sea (Togiak Bay). Six fish each were sampled at Kodiak Island, Prince William Sound, and Togiak Bay, and five fish were sampled from Sitka. Tissue samples were immediately preserved in RNAlater (Ambion).Individual tissue samples were transferred to TriReagent (Molecular Research Center), and total RNA was isolated as per manufacturer's instruction. Individual liver and testes RNA samples  were pooled in equal quantity for the construction of two Pacific herring transcriptome libraries. Messenger RNA was isolated from each total RNA pool with the MicroPoly(A) Purist Kit (Ambion) according to the manufacturer's protocol. The isolated mRNA was purified again with the MicroPoly(A) Purist Kit (Ambion) to further minimize residual rRNA carryover. Libraries were constructed by MOgene, LC , following standard protocols from Roche Life Sciences.De novo assembly was carried out using CLC Genomics Workbench v3.7 (CLC Bio) with the following parameters: similarity = 0.98, length fraction = 0.9, insertion cost = 3, deletion cost = 3, mismatch cost = 2 and minimum size = 300. Where reference assemblies were performed, the same parameters were applied. Consensus sequences were compared to the Swiss-Prot database (http://uniprot.org) in order to determine putative annotation. Associated GO terms (Gene Ontology database: http://www.geneontology.org) were used to classify sequences based on biological process as well as categorize genes into parent categories (GO slim).Both libraries were sequenced using the Genome Sequencer FLX System (Roche) at MOgene, LC . Initially, all sequences were trimmed based on quality scores of 0.05 5 sequence read increments and de novo assembled as described.RNA-seq analysis was used to characterize the transcriptome tissue specificity (CLC Genomics Workbench v3.7 (CLC Bio)). Expression values were measured in RPKM . All quality trimmed reads were then mapped back to these possible open reading frames, and SNP detection was carried out as described (CLC Genomics Workbench v3.7 (CLC Bio)). Consensus sequences generated with getorf (EMBOSS) which contained putative SNPs were compared to the Swiss-Prot database.Candidate SNPs were identified from assembled reads using CLC Genomics Workbench v3.7 (CLC Bio). Parameters were as follows: maximum gap and mismatch count = 2, minimum average quality = 15, minimum central quality = 20, minimum coverage = 4, minimum variant frequency (%) = 35.0, window length = 11. In order to determine whether a SNP resulted in a potential amino acid substitution, putative open reading frames were identified and SNP detection carried out as described. Specifically, open reading frames were identified in the consensus sequences from the Ninety-six putative SNPs were selected for validation. Selection stringency was increased from parameters described above to include a window length of 151. In addition, functional annotation was taken into consideration. PCR primers were designed using Primer3 Those SNPs validated using Sanger sequencing were genotyped using high resolution melt analysis (HRMA) Deviations from Hardy Weinberg Equilibrium (HWE) and significance of population differentiation were tested using Fisher's exact test in Genepop 4 Table S1Description of select contig sequences. Description is based on BlastX analysis using the Swiss-Prot database. E-values and organism associated with BlastX hits are provided as are the number of unique reads that map to each contig.(XLS)Click here for additional data file.


Bemisia tabaci (Gennadius) is a phloem-feeding insect poised to become one of the major insect pests in open field and greenhouse production systems throughout the world. The high level of resistance to insecticides is a main factor that hinders continued use of insecticides for suppression of B. tabaci. Despite its prevalence, little is known about B. tabaci at the genome level. To fill this gap, an invasive B. tabaci B biotype was subjected to pyrosequencing-based transcriptome analysis to identify genes and gene networks putatively involved in various physiological and toxicological processes.B. tabaci transcriptome. De novo assembly generated 178,669 unigenes including 30,980 from insects, 17,881 from bacteria, and 129,808 from the nohit. A total of 50,835 (28.45%) unigenes showed similarity to the non-redundant database in GenBank with a cut-off E-value of 10–5. Among them, 40,611 unigenes were assigned to one or more GO terms and 6,917 unigenes were assigned to 288 known pathways. De novo metatranscriptome analysis revealed highly diverse bacterial symbionts in B. tabaci, and demonstrated the host-symbiont cooperation in amino acid production. In-depth transcriptome analysis indentified putative molecular markers, and genes potentially involved in insecticide resistance and nutrient digestion. The utility of this transcriptome was validated by a thiamethoxam resistance study, in which annotated cytochrome P450 genes were significantly overexpressed in the resistant B. tabaci in comparison to its susceptible counterparts.Using Roche 454 pyrosequencing, 857,205 reads containing approximately 340 megabases were obtained from the B. tabaci complex. Moreover, current pyrosequencing effort greatly enriched the existing whitefly EST database, and makes RNAseq a viable option for future genomic analysis.This transcriptome/metatranscriptome analysis sheds light on the molecular understanding of symbiosis and insecticide resistance in an agriculturally important phloem-feeding insect pest, and lays the foundation for future functional genomics research of the  Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae), is a phloem-feeding insect pest that causes severe damage in both agricultural and horticultural systems worldwide. More than 24 morphologically indistinguishable B. tabaci biotypes have been identified B. tabaci can cause considerable yield losses dierctly through phloem-feeding and indirectly through the transmission of plant pathogenic begomoviruses Bemisia tabaci has been controlled predominantly by synthetic insecticides. Due to consistent exposure to insecticides, B. tabaci has developed high levels of resistance to a wide range of commonly used synthetic insecticides B. tabaci was first recorded in the late 1940s, and the indigenous whiteflies have never elevated to the major pest status. However, this has changed when B. tabaci B biotype, originated in the Middle East-Asia Minor region including Iran, Israel, Jordan, Kuwait, Pakistan, Saudi Arabia, Syria, United Arab Republic, and Yemen, was introduced into the mainland China in the mid-1990s. Since then, B biotype has gradually displaced the indigenous whiteflies in most parts of the China The whitefly, B. tabaci not only harbors Portiera aleyrodidarum, an obligatory symbiotic bacterium which supplements the amino acid deficient diets Rickettsia, Hamiltonella, Wolbachia, Arsenophonus, Cardinium and Fritschea. Although these facultative symbionts are considered “secondary” because they are not essential for the survival of their hosts, they can manipulate hosts in many other ways, and bear evolutionary significance et al surveyed the composition of facultative symbionts among laboratory and field populations of B. tabaci from various host plants in Israel Hamiltonella was B -specific, whereas Wolbachia and Arsenophonus were detected only in the Q biotype. Such association suggests a possible contribution of these facultative bacterial symbionts to the biological differences observed among whitefly biotypes, including insecticide resistance, host range, competitive displacement, virus transmission, and speciation et al showed that a 63-kDa GroEL homologue produced by the endosymbiotic Hamiltonella is essential for the circulative transmission of the Tomato yellow leaf curl virus (TYLCV) in B. tabaciHamiltonella has only been detected in B biotype Hamiltonella, and it can barely transmit TYLCV. Based on these observations, a causal link between the transmission efficiency of TYLCV and the presence of Hamiltonella has been established B. tabaci B biotype is apparently facilitated by the rapid spread of Rickettsia sp. nr. Bellii, a maternally inherited facultative bacterial symbiont Rickettsia had a significantly higher fitness level and Rickettsia can manipulate the sex ratio of whitefly hosts by producing female-biased offspring. This dynamic interactions between Rickettsia and their whitefly hosts represent a rapid coevolution of both insects and their symbionts to optimize the newly established symbiosis Same as phloem-feeding aphids, B. tabaci has yet to be materialized as a result of its innate complexity in biology (symbiosis) and genome . Next-generation sequencing (NGS) Bt receptors Trialeurodes vaporariorum, uncovered a diverse array of transcripts potentially involved in the xenobiotics detoxification and the targets for major classes of synthetic insecticides B. tabaci transcriptomes gave a better understanding of molecular mechanisms underlying the conspecific divergence of the B and Q biotypes Despite its ever-increasing pest status and enormous economic impacts, the whole genome sequencing of B.tabaci B biotype. We generated over 300 million bases of high-quality DNA sequences and carried out transcriptome and metatranscriptome analyses to shed light on the molecular bases of symbiosis and insecticide resistance. Moreover, this transcriptome sequencing effort has significantly enriched the existing gene pool for this agriculturally important key pest and provides an invaluable resource for the subsequent RNAseq analysis as well as for the future B. tabaci genome annotation.In this study, we used the Roche 454 pyrosequencing platform to provide a comprehensive view of the genes expressed in an invasive Bemisia tabaci, B biotype in China, a polyphenic cDNA library including egg, nymph, and adult developmental stages was constructed and sequenced using the Roche 454 GS FLX Titanium platform. One picotiter plate of sequencing generated 1,109,732 raw reads with an average sequence length of 304 bases and an average GC content of 39%. After trimming the low quality sequences and removing the rRNAs and short reads of less than 100 bps, 907,985 reads were passed through to the next process. In addition, 50,780 reads mapped to B. tabaci mitochondrion were discarded. Eventually, a total of 857,205 reads were generated and used for the subsequent assembly.To obtain a comprehensive view of the transcriptional profile of the invasive sweet potato whitefly, B. tabaci transcriptome to accurately distinguish the sequences from different origins including insect, gut symbionts, parasites, and pathogens. After the assembly, 178,669 unigenes were obtained including 23,694 isotigs and 154,975 singletons  (9.70%) B. tabaci and A. pisum may be due to their similarity in the taxonomic status (Hemiptera), diet/feeding behavior (phloem-feeding), and symbiotic relationships with their gut microbiota (obligatory and facultative symbiosis).The unigenes were subjected to BLASTX similarity search against the NCBI non-redundant (nr) protein database to determine their putative functions. Based on the sequence similarity with known insect and bacteria genes, unigenes were subdivided into three groups: insect , bacteria , and nohit (129808). A total of 50,835 unigenes (28.5%) in these three groups exhibited significant similarity at the cutoff value of 1e-5 . The taxCimex lectulariusAgrilus planipennisIn total, 40,611 unigenes were assigned to one or more Gene Ontology (GO) terms . As showB. tabaciIn total, 9,075 simple sequence repeats (SSRs or microsatellites) including 1470 (16%) trinucleotide repeats, 671 (7.4%) dinucleotide, and 196 (2.2%) tetranucleotide repeats were identified . In addiB. tabaci. The most abundant phylum, class, order, family, and genus was Proteobacteria (92%)  , Betaproia (92%) , Burkholia (92%) , Comamonia (92%) , and Delia (92%) , respect. tabaci .B. tabaci, including Portiera (96 unigenes), Wolbachia (34 unigenes), Rickettsia (137 unigenes), Hamiltonella (112 unigenes) and Arsenophonus (9 unigenes). To date, one primary symbiont (Portiera) and six secondary symbionts  have been documented in B. tabaciB. tabaci as well. For example, single or repetitive infections with secondary symbionts can affect the susceptibility of B. tabaci to synthetic insecticides Rickettsia may be involved in B. tabaci’s ability to defend against natural enemies Hamiltonella was reported to provide the protection for A. pisum against parasitoids tomato yellow leaf virus by B. tabaci B biotype Arsenophonus sequences were found in this study, while a previous study showed that Arsenophonus was Q biotype-specific in Israel Hamiltonella was B-specific in Israeli populations For functional classification, bacterial unigenes were subjected to the Cluster of Orthologous Groups (COG) database. A total of 9,647 unigenes have a COG classification in four main categories and 20 subcategories . The mosWe found 533 KEGG Orthology (KO) numbers involved in the amino-acid biosynthesis, among which 276 from the insect group and 257 from the bacterial group. Not surprisingly, genes for biosyntheses of the amino acids essential for the whitefly host are predominantly supplied by the bacterial group  and thosB. tabaci have developed substantial resistance to a wide range of synthetic insecticides, especially to the neonicotinoids, over the past decade. The rapid competitive displacement of the invasive B. tabaci over the indigenous counterparts in China is explained, at least partially, by the onset development of insecticide resistance. Sequences encoding enzymes potentially involved in the xenobiotics detoxification and the targets of the major classes of synthetic insecticides were extracted and compared with sequences from the NCBI protein database. Genes potentially involved in the insecticide metabolic resistance are summarized in B. tabaci transcriptome, representing a gene subfamily that plays a key role in xenobiotic resistance Invasive biotypes of B. tabaci transcriptome. Specifically, among 223 P450 unigenes annotated in the NCBI nr database, 135 contained CYP family information, of which 4 unigenes were excluded from the subsequent analysis due to their sequence similarity with non-insect organisms including Arabidopsis thaliana and Uncinocarpus reesii 1704. The remaining 131 unigenes were subdivided into 4 clades and 13 families, including 4 families of CYP18, CYP304, CYP305 and CYP306 in CYP2 clade, 3 families of CYP6, CYP347 and CYP354 in CYP3 clade, 1 CYP4 family in CYP4 clade, and 5 families of CYP49, CYP301, CYP302, CYP315 and CYP353 in mitochondrial CYP clade , the number of putative P450s annotated in the B. tabaci transcriptome (131) is well within the range T. vaporariorum identified 57 P450s, although authors suspected the number of P450s should be greater P450s are a large superfamily of heme-containing monooxygenases that play a critical role in catalyzing the metabolisms of endogenous and exogenous compounds YP clade . The majB. tabaci and Trialeurodes vaporariorum, have developed substantial resistance to neonicotinoids B. tabaci, however, only linked the enhanced oxidative detoxification by P450 rather than the target site mutation to the resistance Neonicotinoids, targeting the postsynaptic nicotinic acetylcholine receptors (nAChRs), are one of the most effective insecticides against a broad spectrum of phloem-feeding insects, including hemipterans like aphids and whiteflies 50 (95% CL) = 1326 (1259–1615) mg L–1] and susceptible strains , 44 P450 genes from this study, several others from the traditional Sanger sequencing (4) CYP6a8, CYP4v2 and CYP6v5, showed significantly higher mRNA expression levels (>10-fold) in the resistant TH-2000 in comparison to the susceptible TH-S strain , and considerably longer than the Illumina-based sequences . However, the ultra high-throughput Illumina-based transcriptomes provide exponentially more sequencing information, i.e., billion in comparison to million base pair of total reads. Despite the relatively smaller sequencing outputs, the 454 pyrosequencing performs equally well or better at addressing specific biological questions. For instance, a survey of genes putatively involved in insecticide resistance deduced from current available whitefly transcriptomes showed the similar or greater number of transcripts predicted by the 454 sequencing platform in comparison to others  and/or a physiological  state of the tested organisms. The dynamic nature of transcriptome sequencing offers an unparallel opportunity to investigate fundamental biological questions at the global gene expression level. The transcriptome analyses of whiteflies shed light on the molecular understanding of insecticide resistance  every 2–3 generations To maximize the representation of The RNA sample was extracted using the Trizol reagent (Invitrogen) according to the manufacturer’s instructions. The purity and degradation of total RNAs were checked on 1% agarose gels, respectively. About 15 µg of total RNA from each sample were pooled equally, producing about 270 µg of total RNA. The concentration of the pooled sample was adjusted to 1 µg/µl. Poly(A)-containing RNA was separated from the total RNA (1 µg/µl) using Dynabeads® mRNA purification kit (Invitrogen). About 1 µg of mRNA was converted to the first-strand cDNA using SuperScripe® II Reverse Transcriptase (Invitrogen) and random primers (Promega). The. cDNA synthesis of the second strand was performed using DNA polymerase I (Promega). with aRNA as the template. The amplified double cDNA product was purified and extracted using the Min Elute® Gel Extraction Kit (QIAGEN). The cDNAs were sheared to 500–1000 bp and directly used to construct a sequencing library. Approximately 1.5 µg of the resultant cDNA was end polished followed by ligation with adapters and finally immobilized on beads.Single-strand DNA isolated from the beads was characterized for correct size using a. LabChip 7500. The concentration and proper ligation of the adapters were examined by. qRT-PCR. A full PicoTiter plate was sequenced following the manufacturer’s protocol using the Roche 454 GS FLX Titanium chemistry.Thiamethoxam susceptible (TH-S) and resistant (TH-R) strains were determined as described previously qRT-PCR. Total RNA was extracted from TH-2000 and TH-S adults, respectively, using Trizol (Invitrogen) following the manufacturer’s protocols. The total RNA obtained was resuspended in nuclease-free water and the concentration was measured using Nanodrop (Thermo Scientific Nanodrop 2000). About 0.5 µg of total RNA was used as template to synthesize the first-strand cDNA using a PrimerScript RT reagent Kit (TaKaRa) following the manufacturer’s protocols. The resultant cDNA was diluted to 0.1 µg/µl for further analysis in the qRT-PCR (ABI 7500) using an SYBR Green Realtime PCR Master Mix (TaKaRa).–ΔΔCt method 18s rRNA and β-actin as the references to eliminate sample-to-sample variations in the initial cDNA samples.qRT-PCR primers  for the B. tabaci mitochondrion B. tabaci transcriptome have been deposited into the NCBI Sequence Read Archive under the accession number SRA036954.Raw data were cleaned and ribosomal RNA was trimmed with SeqClean B. tabaci nucleotide sequences  in NCBI using the BLASTN program with a default cutoff of 1e-10. For functional classification, unigenes were searched against the InterPro protein signature databases through InterProScan http://www.genome.jp/kegg/) for pathway analysis Unigenes including isotigs and singletons were searched against the NCBI-nr protein database using the BLASTX program with a cutoff value of 1e-5, and the best hits were regarded as the annotations of the unigenes. The unigenes that had no hits in the nr database were further compared with all existing MIcroSAtellite MISA) The identification and localization of microsatellites were accomplished using a PERL5 script  The unigenes in the bacteria group were used for the Figure S1Summary of sequencing assembly. (A) Distribution of isotig lengths in the insect group; (B) distribution of the number of reads per isotig in the insect group;(C) distribution of isotig lengths in the bacterial group; (D) distribution of the number of reads per isotig in the bacterial group; (E) distribution of isotig lengths in the nohit group; (F) distribution of the number of reads per isotig in the nohit group.(TIF)Click here for additional data file.Figure S2Diversity and phylogeny of bacterial symbionts in B.tabaci. Phylogenetic relationships of symbiotic bacteria in B.tabaci were resolved at the Phylum levels.(TIF)Click here for additional data file.Figure S3Diversity and phylogeny of bacterial symbionts in B.tabaci. Phylogenetic relationships of symbiotic bacteria in B.tabaci were resolved at the Order levels.(TIF)Click here for additional data file.Figure S4Diversity and phylogeny of bacterial symbionts in B.tabaci. Phylogenetic relationships of symbiotic bacteria in B.tabaci were resolved at the Class levels.(TIF)Click here for additional data file.Figure S5Diversity and phylogeny of bacterial symbionts in B.tabaci. Phylogenetic relationships of symbiotic bacteria in B.tabaci were resolved at the Family levels.(TIF)Click here for additional data file.Table S1Transcriptome analyses in non-model insects using NGS platforms.(DOCX)Click here for additional data file.Table S2B. tabaci B biotype.NCBI-nr best blast hits in the transcriptome of (XLSX)Click here for additional data file.Table S3B. tabaci transcriptome.Gene ontology of (XLSX)Click here for additional data file.Table S4B. tabaci transcriptome.KEGG summary of (XLSX)Click here for additional data file.Table S5B. tabaci transcriptome.Putative microsatellite loci in (XLSX)Click here for additional data file.Table S6B. tabaci bacterial sequences.Genus-level distribution of (XLSX)Click here for additional data file.Table S7B. tabaci P450s.qRT-PCR analyses of selected (XLSX)Click here for additional data file.Table S8B. tabaci transcriptome.Putative trehalase receptor genes in (DOCX)Click here for additional data file.Table S9Genes putatively involved in insecticide resistance in whitefly transcriptomes.(DOCX)Click here for additional data file.Table S10Primers used for the qRT-PCR analyses.(XLSX)Click here for additional data file.