Population Sequencing Data Research Articles

Assembly of the poorly differentiated Verasper variegatus W chromosome by different sequencing technologies

The assembly of W and Y chromosomes poses significant challenges in vertebrate genome sequencing and assembly. Here, we successfully assembled the W chromosome of Verasper variegatus with a length of 20.48 Mb by combining population and PacBio HiFi sequencing data. It was identified as a young sex chromosome and showed signs of expansion in repetitive sequences. The major component of the expansion was Ty3/Gypsy. The ancestral Osteichthyes karyotype consists of 24 protochromosomes. The sex chromosomes in four Pleuronectiformes species derived from a pair of homologous protochromosomes resulting from a whole-genome duplication event in teleost fish, yet with different sex-determination systems. V. variegatus and Cynoglossus semilaevis adhere to the ZZ/ZW system, while Hippoglossus stenolepis and H. hippoglossus follow the XX/XY system. Interestingly, V. variegatus and H. hippoglossus derived from one protochromosome, while C. semilaevis and H. stenolepis derived from another protochromosome. Our study provides valuable insights into the evolution of sex chromosomes in flatfish and sheds light on the important role of whole-genome duplication in shaping the evolution of sex chromosomes.

52 The Power of Pangenome Assemblies in Ruminants

Abstract High quality reference genomes are the bases of all genomic research and they have facilitated a wealth of discoveries and research tools for the improvement of livestock species. These works have enabled major advances in the fields of animal science and genetics and impacted livestock production and associated industries for the benefit of society. However, because a single reference genome fails to represent all of the genetic material present across populations, it inherently limits the ability to comprehensively evaluate all genomic variants within a species. A complete understanding of how small and large genomic variants, from single nucleotide polymorphisms (SNP) to larger structural variations (SV) including insertions/deletions (InDels) and copy number variation (CNVs), have been selected for over time for specific environments and phenotypes, remains elusive. To address this, it is necessary to generate a catalog of genomic variants in populations subjected to natural or artificial selection pressures and evaluate their relationship among phenotypic classes. The pangenome approach is a means to comprehensively identify and catalog genomic variation and representation that will make more suitable reference for mapping population sequence data for variant detection and frequency evaluation. The goal of the most pangenome projects is to provide genomic resources to represent the immense genetic diversity of this species more completely. This will increase our understanding of genetic variation and its role in dictating different phenomes, empowering scientists to predict the physiological outcomes more effectively from genomic variation towards the improvement of breeding strategies. These pangenomes can then be used to discover the underlying mechanism of biological traits and inform future breeding strategies and support sustainable production of this global source of food and fiber.

Translating pharmacogenomic sequencing data into drug response predictions - how to interpret variants of unknown significance.

The rapid development of sequencing technologies during the past twenty years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well-known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants were functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technology advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.

Advanced variant classification framework reduces the false positive rate of predicted loss-of-function variants in population sequencing data

Predicted loss of function (pLoF) variants are often highly deleterious and play an important role in disease biology, but many pLoF variants may not result in loss of function (LoF). Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines' PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 genes associated with autosomal-recessive disease from the Genome Aggregation Database (gnomAD v.2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in a low proportion expressed across transcripts (pext) scored region, or the presence of cryptic in-frame splice rescues. Variants predicted to evade LoF or to be potential artifacts were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of pLoF variants predicted as likely not LoF/not LoF, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.

TeaPVs: a comprehensive genomic variation database for tea plant (Camellia sinensis)

Genome variation not only plays an important role in plant phenotypic modeling and adaptive evolution, but also enhances population genetic diversity and regulates gene expression. The tea tree (Camellia sinensis) has a large genome (~ 3.0 Gb), making the identification of genome-wide variants time-consuming and expensive. With the continuous publication of a large number of different types of population sequencing data, there is a lack of an open platform to integrate these data and identify variants in the tea plant genome.To integrate the genetic variation confidence in the tea plant population genome, 238 whole-genome resequencing, 213 transcriptome sequencing, and 96 hybrid F1 individuals with a total of more than 20 Tb were collected for mutation site identification. Based on these variations information, we constructed the first tea tree variation web service database TeaPVs (http://47.106.184.91:8025/ and http://liushang.top:8025/). It supports users to search all SNP, Indel, SV mutations and SSR/Polymorphic SSR sequences by location or gene ID. Furthermore, the website also provides the functions of gene expression search of different transcriptome, sequence blast, sequence extraction of CDS and mutation loci, etc.The features of the TeaPVs database make it a comprehensive tea plant genetic variation bioinformatics platform for researchers, and will also be helpful for revealing new functional mutations in the tea plant genome and molecular marker-assisted breeding.

Identifying rare variants inconsistent with identity‐by‐descent in population‐scale whole‐genome sequencing data

Abstract Analyses of genetic variation typically assume that rare variants within a population are inherited from a single common ancestral event identity‐by‐descent (IBD). However, there are genetic and technical processes through which rare variants in population genetic data may deviate from this simple evolutionary model, including recurrent mutations, gene conversions and genotyping error. All these processes can decrease the expected length of shared background haplotype surrounding a rare variant if that variant was inherited from a single event descending from a common ancestor. No method exists to computationally infer rare variants inconsistent with this simple model—denoted here as ‘IBD‐inconsistent’—using unphased population sequencing data. We hypothesized that the difference in shared haplotype background length can distinguish variants consistent and inconsistent with this simple IBD transmission population sequencing data without pedigree information. We implemented a Bayesian hierarchical model and used Gibbs sampling to estimate the posterior probability of IBD state for rare variants, using simulated recurrent mutations to demonstrate that our approach accurately distinguishes rare variants consistent and inconsistent with a simple IBD inheritance model. Applying our method to whole‐genome sequencing data from 3,621 human individuals in the UK10K consortium, we found that IBD‐inconsistent variants correlated with higher local mutation rates and genomic features like replication timing. Using a heuristic to categorize IBD‐inconsistent variants as gene conversions, we found that potential gene conversions had expected properties such as enriched local GC content. By identifying IBD‐inconsistent variants, we can better understand the spectrum of recent mutations in human populations, a source of genetic variation driving evolution and a key factor in understanding recent demographic history.

Assessing the digenic model in rare disorders using population sequencing data

An important fraction of patients with rare disorders remains with no clear genetic diagnostic, even after whole-exome or whole-genome sequencing, posing a difficulty in giving adequate treatment and genetic counseling. The analysis of genomic data in rare disorders mostly considers the presence of single gene variants in coding regions that follow a concrete monogenic mode of inheritance. A digenic inheritance, with variants in two functionally-related genes in the same individual, is a plausible alternative that might explain the genetic basis of the disease in some cases. In this case, digenic disease combinations should be absent or underrepresented in healthy individuals. We develop a framework to evaluate the significance of digenic combinations and test its statistical power in different scenarios. We suggest that this approach will be relevant with the advent of new sequencing efforts including hundreds of thousands of samples.

Population sequencing data reveal a compendium of mutational processes in the human germ line.

Mechanistic processes underlying human germline mutations remain largely unknown.Variation in mutation rate and spectra along the genome is informative about the biological mechanisms. We statistically decompose this variation into separate processes using a blind source separation technique. The analysis of a large-scale whole genome sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. Seven of these processes lend themselves to a biological interpretation. One process is driven by bulky DNA lesions that resolve asymmetrically with respect to transcription and replication. Two processes independently track direction of replication fork and replication timing. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions. We also demonstrate that a recently discovered mutagenic process specific to oocytes can be localized solely from population sequencing data. This process is spread across all chromosomes and is highly asymmetric with respect to the direction of transcription, suggesting a major role of DNA damage.

Accurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations.

Genetic association studies seek to uncover the link between genotype and phenotype, and often utilize inbred reference panels as a replicable source of genetic variation. However, inbred reference panels can differ substantially from wild populations in their genotypic distribution, patterns of linkage-disequilibrium, and nucleotide diversity. As a result, associations discovered using inbred reference panels may not reflect the genetic basis of phenotypic variation in natural populations. To address this problem, we evaluated a mapping population design where dozens to hundreds of inbred lines are outbred for few generations, which we call the Hybrid Swarm. The Hybrid Swarm approach has likely remained underutilized relative to pre-sequenced inbred lines due to the costs of genome-wide genotyping. To reduce sequencing costs and make the Hybrid Swarm approach feasible, we developed a computational pipeline that reconstructs accurate whole genomes from ultra-low-coverage (0.05X) sequence data in Hybrid Swarm populations derived from ancestors with phased haplotypes. We evaluate reconstructions using genetic variation from the Drosophila Genetic Reference Panel as well as variation from neutral simulations. We compared the power and precision of Genome-Wide Association Studies using the Hybrid Swarm, inbred lines, recombinant inbred lines (RILs), and highly outbred populations across a range of allele frequencies, effect sizes, and genetic architectures. Our simulations show that these different mapping panels vary in their power and precision, largely depending on the architecture of the trait. The Hybrid Swam and RILs outperform inbred lines for quantitative traits, but not for monogenic ones. Taken together, our results demonstrate the feasibility of the Hybrid Swarm as a cost-effective method of fine-scale genetic mapping.

Prevalence of HIV-1 Integrase Strand Transfer Inhibitor Resistance in Treatment-Naïve Voluntary Counselling and Testing Clients by Population Sequencing and Illumina Next-Generation Sequencing in Taiwan.

PurposeIntegrase strand transfer inhibitors (INSTIs) are used as first-line therapy for HIV-1-infected patients. Next-generation sequencing (NGS) can detect low-frequency mutants; however, the clinical value of NGS to detect resistance variants is unknown. This study aimed to evaluate the prevalence of INSTI resistance in southern Taiwan and determine the clinical implications of using NGS to detect integrase region low-level resistant variants.Patients and MethodsThis retrospective cohort study included antiretroviral therapy-naïve HIV-1-infected individuals at Kaohsiung Veterans General Hospital, Taiwan, from 2013 to 2017. Drug-resistance mutations were determined, and an in-house polymerase chain reaction was used for genotyping INSTI resistance. NGS was used to assess INSTI resistance (≧1%), and the results were compared with those from population sequencing. Drug resistance-associated mutations were defined according to the 2019 IAS-USA HIV drug resistance-associated mutations list, and accessory mutations by a Stanford HIVdb score ≥10 to at least one INSTI.ResultsA total of 224 patients were included. Subtype B HIV-1 strains were found in 96% of the individuals and subtype CRF01_AE in 4%. The prevalence rates for nucleoside reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors, protease inhibitors and INSTI resistance were 4%, 5.8%, 0.4% and 0.9%, respectively. The most common INSTI resistance-associated mutations were G163K (0.4%) and E138A (0.4%). Of the 38 patients diagnosed in 2017 who had both NGS and population sequencing data, none had INSTI resistance-associated mutations by population sequencing; however, NGS detected four more INSTI resistance-associated mutations with low frequencies (G163R 3.25%, S153F 3.21%, S153Y 1.36% and Y143H 2.06%). Two patients with S153F and S153Y low frequencies mutations started INSTI-based highly active antiretroviral therapy, and none had virological failure by week 48.ConclusionOur findings showed a low rate of HIV drug resistance to INSTIs (0.9%) in treatment-naïve patients. NGS detected more INSTI resistance-associated mutations at a low frequency. Low-level drug resistance-associated mutations to INSTIs identified by NGS did not have an impact on the treatment response to INSTI-based first-line therapy.

Establishment and application of a novel method based on single nucleotide polymorphism analysis for detecting \u03b2-globin gene cluster deletions

β-Globin gene mutations reduce or terminate the production of beta globin chains, of which approximately 10% are large deletions within the β-globin gene cluster. Because gene deletion leads to loss of heterozygosity at single nucleotide polymorphism (SNP), a novel method for detecting β-globin gene cluster deletions based on SNP heterozygosity analysis was established in this study. The location range of SNPs was selected according to the breakpoint of β-globin gene cluster deletions. SNPs were screened using bioinformatics analysis and population sequencing data. A novel method which enables genotyping of multiplex SNPs based on tetra-primer ARMS-PCR was designed and optimized. Forty clinical samples were tested in parallel by this method and MLPA to verify the performance of this method for detecting β-globin gene cluster deletion. Six informative SNPs were obtained, achieving heterozygote coverage of 93.3% in normal individuals. Genotyping of six SNPs were successfully integrated into two multiplex tetra-primer ARMS-PCR reactions. The sensitivity, specificity, positive predictive value and negative predictive value of the method for detecting β-globin gene cluster deletion were 100%, 96.30%, 92.86%, and 100%, respectively. This is a simple, cost-effective and novel method for detecting β-globin gene cluster deletions, which may be suitable for use in combination with MLPA for thalassemia molecular testing.

Bi-Phenotypic Trait May Be Conferred by Multiple Alleles in a Germplasm Population.

The Chinese soybean germplasm pool (CSGP) comprises annual wild (WA), farmers’ landrace (LR) and released cultivar (RC) populations, and ecoregion subpopulations in WA/LR/RC (ecoregion IV/III/II/I). A representative sample consisted of 1,024 accessions was studied for pubescence color (PC) and flower color (FC). In the evolution from WA (brown PC and mainly purple FC) to LR then to RC, with above wild characteristic changed, while gray PC, and white FC emerged and their frequency increased. Using 36,952 genomic SNPLDB markers with 100,092 haplotypes, the association between markers and bi-phenotypic traits was detected using χ2 association analysis under single locus model and RTM-GWAS procedure under multi-locus model, respectively. Multiple markers co-associated with individual bi-phenotypic trait with the most significant markers containing multiple rather than two haplotypes even for a bi-phenotypic trait. On a marker/locus, each haplotype corresponds to two colors, except one (FC-1-5) out of 11 haplotypes for single color. The major candidate gene was annotated with its alleles identified from the population sequencing data. Similarly, multiple alleles identified and each corresponds to two colors except three (a8/a9/b3) out of 12 alleles for single color. The major haplotypes/alleles in LR and RC were traced to WA ecoregion subpopulations, the WAIV and WAIII genotypes showed genetically more close to the cultivated subpopulations, therefore, WA from Ecoregion IV and III were inferred as the common ancestor for cultivated soybeans. The marker-haplotypes/gene-alleles not exactly coincided with the bi-phenotypic trait has challenged to the traditional Mendelian genetics, which was discussed and to be further studied.

Carrier frequency of CFTR variants in the non-Caucasian populations by genome aggregation database (gnomAD)-based analysis.

The complexity in the molecular diagnosis of Cystic Fibrosis (CF) also depends on the variable prevalence/incidence of the disease associated with the wide CFTR allelic heterogeneity among different populations. In fact, CF incidence in Asian and African countries is underestimated and the few patients reported so far have rare or unique CFTR pathogenic variants. To obtain insights into CF variants profile and frequency, we used the large population sequencing data in the Genome Aggregation Database (gnomAD). We selected 207 CF-causing/varying clinical consequence variants from CFTR2 database and additional 15 variants submitted to the ClinVar database. Only 14 of these variants were found in the East-Asian population, while for South-Asian and African populations we identified 43 and 52 variants, respectively, confirming the peculiarity of the CFTR allelic spectrum with only few population-specific variants. These data could be used to optimize CFTR carrier screening in non-Caucasian subjects, choosing between the full gene sequencing and cost and time-effective targeted panels.

Characterising the loss-of-function impact of 5\u2019 untranslated region variants in 15,708 individuals

Upstream open reading frames (uORFs) are tissue-specific cis-regulators of protein translation. Isolated reports have shown that variants that create or disrupt uORFs can cause disease. Here, in a systematic genome-wide study using 15,708 whole genome sequences, we show that variants that create new upstream start codons, and variants disrupting stop sites of existing uORFs, are under strong negative selection. This selection signal is significantly stronger for variants arising upstream of genes intolerant to loss-of-function variants. Furthermore, variants creating uORFs that overlap the coding sequence show signals of selection equivalent to coding missense variants. Finally, we identify specific genes where modification of uORFs likely represents an important disease mechanism, and report a novel uORF frameshift variant upstream of NF2 in neurofibromatosis. Our results highlight uORF-perturbing variants as an under-recognised functional class that contribute to penetrant human disease, and demonstrate the power of large-scale population sequencing data in studying non-coding variant classes.

An integrated software for virus community sequencing data analysis

BackgroundA virus community is the spectrum of viral strains populating an infected host, which plays a key role in pathogenesis and therapy response in viral infectious diseases. However automatic and dedicated pipeline for interpreting virus community sequencing data has not been developed yet.ResultsWe developed Quasispecies Analysis Package (QAP), an integrated software platform to address the problems associated with making biological interpretations from massive viral population sequencing data. QAP provides quantitative insight into virus ecology by first introducing the definition “virus OTU” and supports a wide range of viral community analyses and results visualizations. Various forms of QAP were developed in consideration of broader users, including a command line, a graphical user interface and a web server. Utilities of QAP were thoroughly evaluated with high-throughput sequencing data from hepatitis B virus, hepatitis C virus, influenza virus and human immunodeficiency virus, and the results showed highly accurate viral quasispecies characteristics related to biological phenotypes.ConclusionsQAP provides a complete solution for virus community high throughput sequencing data analysis, and it would facilitate the easy analysis of virus quasispecies in clinical applications.

On Variant Discovery in Genomes of Fungal Plant Pathogens.

Comparative genome analyses of eukaryotic pathogens including fungi and oomycetes have revealed extensive variability in genome composition and structure. The genomes of individuals from the same population can exhibit different numbers of chromosomes and different organization of chromosomal segments, defining so-called accessory compartments that have been shown to be crucial to pathogenicity in plant-infecting fungi. This high level of structural variation confers a methodological challenge for population genomic analyses. Variant discovery from population sequencing data is typically achieved using established pipelines based on the mapping of short reads to a reference genome. These pipelines have been developed, and extensively used, for eukaryote genomes of both plants and animals, to retrieve single nucleotide polymorphisms and short insertions and deletions. However, they do not permit the inference of large-scale genomic structural variation, as this task typically requires the alignment of complete genome sequences. Here, we compare traditional variant discovery approaches to a pipeline based on de novo genome assembly of short read data followed by whole genome alignment, using simulated data sets with properties mimicking that of fungal pathogen genomes. We show that the latter approach exhibits levels of performance comparable to that of read-mapping based methodologies, when used on sequence data with sufficient coverage. We argue that this approach further allows additional types of genomic diversity to be explored, in particular as long-read third-generation sequencing technologies are becoming increasingly available to generate population genomic data.

Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

BackgroundFor assembling large whole-genome sequence datasets for routine use in research and breeding, the sequencing strategy should be adapted to the methods that will be used later for variant discovery and imputation. In this study, we used simulation to explore the impact that the sequencing strategy and level of sequencing investment have on the overall accuracy of imputation using hybrid peeling, a pedigree-based imputation method that is well suited for large livestock populations.MethodsWe simulated marker array and whole-genome sequence data for 15 populations with simulated or real pedigrees that had different structures. In these populations, we evaluated the effect on imputation accuracy of seven methods for selecting which individuals to sequence, the generation of the pedigree to which the sequenced individuals belonged, the use of variable or uniform coverage, and the trade-off between the number of sequenced individuals and their sequencing coverage. For each population, we considered four levels of investment in sequencing that were proportional to the size of the population.ResultsImputation accuracy depended greatly on pedigree depth. The distribution of the sequenced individuals across the generations of the pedigree underlay the performance of the different methods used to select individuals to sequence and it was critical for achieving high imputation accuracy in both early and late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of 2× rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2× provided high imputation accuracy. The gain in imputation accuracy from additional investment decreased with larger populations and higher levels of investment. However, to achieve the same imputation accuracy, a proportionally greater investment must be used in the smaller populations compared to the larger ones.ConclusionsSuitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing ~2% of the population at a uniform coverage 2×, distributed preferably across all generations of the pedigree, except for the few earliest generations that lack genotyped ancestors. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.

Genomic prediction based on selected variants from imputed whole-genome sequence data in Australian sheep populations

BackgroundWhole-genome sequence (WGS) data could contain information on genetic variants at or in high linkage disequilibrium with causative mutations that underlie the genetic variation of polygenic traits. Thus far, genomic prediction accuracy has shown limited increase when using such information in dairy cattle studies, in which one or few breeds with limited diversity predominate. The objective of our study was to evaluate the accuracy of genomic prediction in a multi-breed Australian sheep population of relatively less related target individuals, when using information on imputed WGS genotypes.MethodsBetween 9626 and 26,657 animals with phenotypes were available for nine economically important sheep production traits and all had WGS imputed genotypes. About 30% of the data were used to discover predictive single nucleotide polymorphism (SNPs) based on a genome-wide association study (GWAS) and the remaining data were used for training and validation of genomic prediction. Prediction accuracy using selected variants from imputed sequence data was compared to that using a standard array of 50k SNP genotypes, thereby comparing genomic best linear prediction (GBLUP) and Bayesian methods (BayesR/BayesRC). Accuracy of genomic prediction was evaluated in two independent populations that were each lowly related to the training set, one being purebred Merino and the other crossbred Border Leicester x Merino sheep.ResultsA substantial improvement in prediction accuracy was observed when selected sequence variants were fitted alongside 50k genotypes as a separate variance component in GBLUP (2GBLUP) or in Bayesian analysis as a separate category of SNPs (BayesRC). From an average accuracy of 0.27 in both validation sets for the 50k array, the average absolute increase in accuracy across traits with 2GBLUP was 0.083 and 0.073 for purebred and crossbred animals, respectively, whereas with BayesRC it was 0.102 and 0.087. The average gain in accuracy was smaller when selected sequence variants were treated in the same category as 50k SNPs. Very little improvement over 50k prediction was observed when using all WGS variants.ConclusionsAccuracy of genomic prediction in diverse sheep populations increased substantially by using variants selected from whole-genome sequence data based on an independent multi-breed GWAS, when compared to genomic prediction using standard 50K genotypes.

A Bayesian Framework for Inferring the Influence of Sequence Context on Point Mutations.

The probability of point mutations is expected to be highly influenced by the flanking nucleotides that surround them, known as the sequence context. This phenomenon may be mainly attributed to the enzyme that modifies or mutates the genetic material, because most enzymes tend to have specific sequence contexts that dictate their activity. Here, we develop a statistical model that allows for the detection and evaluation of the effects of different sequence contexts on mutation rates from deep population sequencing data. This task is computationally challenging, as the complexity of the model increases exponentially as the context size increases. We established our novel Bayesian method based on sparse model selection methods, with the leading assumption that the number of actual sequence contexts that directly influence mutation rates is minuscule compared with the number of possible sequence contexts. We show that our method is highly accurate on simulated data using pentanucleotide contexts, even when accounting for noisy data. We next analyze empirical population sequencing data from polioviruses and HIV-1 and detect a significant enrichment in sequence contexts associated with deamination by the cellular deaminases ADAR 1/2 and APOBEC3G, respectively. In the current era, where next-generation sequencing data are highly abundant, our approach can be used on any population sequencing data to reveal context-dependent base alterations and may assist in the discovery of novel mutable sites or editing sites.

MBKbase for rice: an integrated omics knowledgebase for molecular breeding in rice.

To date, large amounts of genomic and phenotypic data have been accumulated in the fields of crop genetics and genomic research, and the data are increasing very quickly. However, the bottleneck to using big data in breeding is integrating the data and developing tools for revealing the relationship between genotypes and phenotypes. Here, we report a rice sub-database of an integrated omics knowledgebase (MBKbase-rice, www.mbkbase.org/rice), which integrates rice germplasm information, multiple reference genomes with a united set of gene loci, population sequencing data, phenotypic data, known alleles and gene expression data. In addition to basic data search functions, MBKbase provides advanced web tools for genotype searches at the population level and for visually displaying the relationship between genotypes and phenotypes. Furthermore, the database also provides online tools for comparing two samples by their genotypes and finding target germplasms by genotype or phenotype information, as well as for analyzing the user submitted SNP or sequence data to find important alleles in the germplasm. A soybean sub-database is planned for release in 3 months and wheat and maize will be added in 1–2 years. The data and tools integrated in MBKbase will facilitate research in crop functional genomics and molecular breeding.