Dataset Information

VariantValidator: Accurate validation, mapping, and formatting of sequence variation descriptions.

ABSTRACT: The Human Genome Variation Society (HGVS) variant nomenclature is widely used to describe sequence variants in scientific publications, clinical reports, and databases. However, the HGVS recommendations are complex and this often results in inaccurate variant descriptions being reported. The open-source hgvs Python package (https://github.com/biocommons/hgvs) provides a programmatic interface for parsing, manipulating, formatting, and validating of variants according to the HGVS recommendations, but does not provide a user-friendly Web interface. We have developed a Web-based variant validation tool, VariantValidator (https://variantvalidator.org/), which utilizes the hgvs Python package and provides additional functionality to assist users who wish to accurately describe and report sequence-level variations that are compliant with the HGVS recommendations. VariantValidator was designed to ensure that users are guided through the intricacies of the HGVS nomenclature, for example, if the user makes a mistake, VariantValidator automatically corrects the mistake if it can, or provides helpful guidance if it cannot. In addition, VariantValidator has the facility to interconvert genomic variant descriptions in HGVS and Variant Call Format with a degree of accuracy that surpasses most competing solutions.

SUBMITTER: Freeman PJ

PROVIDER: S-EPMC5765404 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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VariantValidator: Accurate validation, mapping, and formatting of sequence variation descriptions.

Freeman Peter J PJ Hart Reece K RK Gretton Liam J LJ Brookes Anthony J AJ Dalgleish Raymond R

Human mutation 20171017 1

The Human Genome Variation Society (HGVS) variant nomenclature is widely used to describe sequence variants in scientific publications, clinical reports, and databases. However, the HGVS recommendations are complex and this often results in inaccurate variant descriptions being reported. The open-source hgvs Python package (https://github.com/biocommons/hgvs) provides a programmatic interface for parsing, manipulating, formatting, and validating of variants according to the HGVS recommendations, ...[more]

PMID: 28967166

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Project description:BACKGROUND: Rice feeds much of the world, and possesses the simplest genome analyzed to date within the grass family, making it an economically relevant model system for other cereal crops. Although the rice genome is sequenced, validation and gap closing efforts require purely independent means for accurate finishing of sequence build data. RESULTS: To facilitate ongoing sequencing finishing and validation efforts, we have constructed a whole-genome SwaI optical restriction map of the rice genome. The physical map consists of 14 contigs, covering 12 chromosomes, with a total genome size of 382.17 Mb; this value is about 11% smaller than original estimates. 9 of the 14 optical map contigs are without gaps, covering chromosomes 1, 2, 3, 4, 5, 7, 8 10, and 12 in their entirety - including centromeres and telomeres. Alignments between optical and in silico restriction maps constructed from IRGSP (International Rice Genome Sequencing Project) and TIGR (The Institute for Genomic Research) genome sequence sources are comprehensive and informative, evidenced by map coverage across virtually all published gaps, discovery of new ones, and characterization of sequence misassemblies; all totalling ~14 Mb. Furthermore, since optical maps are ordered restriction maps, identified discordances are pinpointed on a reliable physical scaffold providing an independent resource for closure of gaps and rectification of misassemblies. CONCLUSION: Analysis of sequence and optical mapping data effectively validates genome sequence assemblies constructed from large, repeat-rich genomes. Given this conclusion we envision new applications of such single molecule analysis that will merge advantages offered by high-resolution optical maps with inexpensive, but short sequence reads generated by emerging sequencing platforms. Lastly, map construction techniques presented here points the way to new types of comparative genome analysis that would focus on discernment of structural differences revealed by optical maps constructed from a broad range of rice subspecies and varieties.

Project description:BackgroundGenotyping of sequence variants typically involves, as a first step, the alignment of sequencing reads to a linear reference genome. Because a linear reference genome represents only a small fraction of all the DNA sequence variation within a species, reference allele bias may occur at highly polymorphic or divergent regions of the genome. Graph-based methods facilitate the comparison of sequencing reads to a variation-aware genome graph, which incorporates a collection of non-redundant DNA sequences that segregate within a species. We compared the accuracy and sensitivity of graph-based sequence variant genotyping using the Graphtyper software to two widely-used methods, i.e., GATK and SAMtools, which rely on linear reference genomes using whole-genome sequencing data from 49 Original Braunvieh cattle.ResultsWe discovered 21,140,196, 20,262,913, and 20,668,459 polymorphic sites using GATK, Graphtyper, and SAMtools, respectively. Comparisons between sequence variant genotypes and microarray-derived genotypes showed that Graphtyper outperformed both GATK and SAMtools in terms of genotype concordance, non-reference sensitivity, and non-reference discrepancy. The sequence variant genotypes that were obtained using Graphtyper had the smallest number of Mendelian inconsistencies between sequence-derived single nucleotide polymorphisms and indels in nine sire-son pairs. Genotype phasing and imputation using the Beagle software improved the quality of the sequence variant genotypes for all the tools evaluated, particularly for animals that were sequenced at low coverage. Following imputation, the concordance between sequence- and microarray-derived genotypes was almost identical for the three methods evaluated, i.e., 99.32, 99.46, and 99.24% for GATK, Graphtyper, and SAMtools, respectively. Variant filtration based on commonly used criteria improved genotype concordance slightly but it also decreased sensitivity. Graphtyper required considerably more computing resources than SAMtools but less than GATK.ConclusionsSequence variant genotyping using Graphtyper is accurate, sensitive and computationally feasible in cattle. Graph-based methods enable sequence variant genotyping from variation-aware reference genomes that may incorporate cohort-specific sequence variants, which is not possible with the current implementation of state-of-the-art methods that rely on linear reference genomes.

Dataset Information

VariantValidator: Accurate validation, mapping, and formatting of sequence variation descriptions.

Publications

VariantValidator: Accurate validation, mapping, and formatting of sequence variation descriptions.

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