Biology subjects

Komar, P.

Publications and source records attributed to Komar, P..

3 recordsLinked to original sources

bioRxiv2018

Comparing complex variants in family trios

Motivation: Several tools exist to count Mendelian violations in family trios by comparing variants at the same genomic positions. This naive variant comparison however, fails to assess regions where multiple variants need to be examined together, resulting in reduced accuracy of existing Mendelian violation checking tools.\n\nResults: We introduce VBT, a trio concordance analysis tool, that identifies Mendelian violations by approximately solving the 3-way variant matching problem to resolve variant representation differences in family trios. We show that VBT outperforms previous trio comparison methods by accuracy.\n\nAvailability: VBT is implemented in C++ and source code is available under GNU GPLv3 license at the following URL: https://github.com/sbg/VBT-TrioAnalysis.git\n\nContact: cagkantoptas@hotmail.com\n\nSupplementary information: Supplementary materials are available at Biorxiv.

bioinformatics↗

bioRxiv2017

geck: trio-based comparative benchmarking of variant calls

MotivationClassical methods of comparing the accuracies of variant calling pipelines are based on truth sets of variants whose genotypes are previously determined with high confidence. An alternative way of performing benchmarking is based on Mendelian constraints between related individuals. Statistical analysis of Mendelian violations can provide truth set-independent benchmarking information, and enable benchmarking less-studied variants and diverse populations.\n\nResultsWe introduce a statistical mixture model forcomparing two variant calling pipelines from genotype data they produce after running on individual members of a trio. We determine the accuracy of our model by comparing the precision and recall of GATK Unified Genotyper and Haplotype Caller on the high-confidence SNPs of the NIST Ashkenazim trio and the two independent Platinum Genome trios. We show that our method is able to estimate differential precision and recall between the two pipelines with 10-3 uncertainty.\n\nAvailabilityThe Python library geck, and usage examples are available at the following URL: https://github.com/sbg/geck\n\nContactpeter.komar@sbgdinc.com\n\nSupplementary informationSupplementary materials are available at bioRxiv.

bioinformatics↗

bioRxiv2017

Fast and Accurate Genomic Analyses using Genome Graphs

The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, which impairs read alignment and downstream analysis accuracy. Reference genome structures incorporating known genetic variation have been shown to improve the accuracy of genomic analyses, but have so far remained computationally prohibitive for routine large-scale use. Here we present a graph genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million indels. Our Graph Genome Pipeline requires 6.5 hours to process a 30x coverage WGS sample on a system with 36 CPU cores compared with 11 hours required by the GATK Best Practices pipeline. Using complementary benchmarking experiments based on real and simulated data, we show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, or about 20,000 additional variants being detected per sample, while variant calling specificity is unaffected. Structural variations (SVs) incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is a significant advance towards fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

bioinformatics↗