Biology subjects

Krasheninina, O.

Publications and source records attributed to Krasheninina, O..

7 recordsLinked to original sources

bioRxiv2023

Structural variation across 138,134 samples in the TOPMed consortium

Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hemotologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.

genomics↗

bioRxiv2020

Open-source mapping and variant calling for large-scale NGS data from original base-quality scores

AO_SCPLOWBSTRACTC_SCPLOWStandardized genome informatics protocols minimize reprocessing costs and facilitate harmonization across studies if implemented in a transparent, accessible and reproducible manner. Here we define the OQFE protocol, a lossless read-mapping protocol that retains key features of existing NGS standard methods. We demonstrate that variants can be called directly from NovaSeq OQFE data without the need for base quality score recalibration and describe a large-scale variant calling protocol for OQFE data. The OQFE protocol is open-source and a containerized implementation is provided.

bioinformatics↗

bioRxiv2020

Multiethnic catalog of structural variants and their translational impact for disease phenotypes across 19,652 genomes

Genome sequencing at population scale provides unprecedented access to the genetic foundations of human phenotypic diversity, but genotype-phenotype association analyses limited to small variants have failed to comprehensively characterize the genetic architecture of human health and disease because they ignore structural variants (SVs) known to contribute to phenotypic variation and pathogenic conditions1-3. Here we demonstrate the significance of SVs when assessing genotype-phenotype associations and the importance of ethnic diversity in study design by analyzing SVs across 19,652 individuals and the translational impact on 4,156 aptamerbased proteomic measurements across 4,021 multi-ethnic samples. The majority of 304,533 SVs detected are rare, although we identified 2,336 protein-coding genes impacted by common SVs.\ We identified 64 significant SV-protein associations that comprise 36 cis- and 28 trans-acting relationships, and 21 distinct SV regions overlapped with genome-wide association study loci. These findings represent a more comprehensive mapping of regulatory and translational endophenotypes underlying health and disease.

genomics↗

bioRxiv2018

Parliament2: Fast Structural Variant Calling Using Optimized Combinations of Callers

Here we present Parliament2 - a structural variant caller which combines multiple best-in-class structural variant callers to create a highly accurate callset. This captures more events than the individual callers achieve independently. Parliament2 uses a call-overlap-genotype approach that is highly extensible to new methods and presents users the choice to run some or all of Breakdancer, Breakseq, CNVnator, Delly, Lumpy, and Manta to run. Parliament2 applies an additional parallelization framework to speed certain callers and executes these in parallel, taking advantage of the different resource requirements to complete structural variant calling much faster than running the programs individually. Parliament2 is available as a Docker container, which pre-installs all required dependencies. This allows users to run any caller with easy installation and execution. This Docker container can easily be deployed in cloud or local environments and is available as an app on DNAnexus.

bioinformatics↗

bioRxiv2018

GLnexus: joint variant calling for large cohort sequencing

As ever-larger cohorts of human genomes are collected in pursuit of genotype/phenotype associations, sequencing informatics must scale up to yield complete and accurate genotypes from vast raw datasets. Joint variant calling, a data processing step entailing simultaneous analysis of all participants sequenced, exhibits this scaling challenge acutely. We present GLnexus (GL, Genotype Likelihood), a system for joint variant calling designed to scale up to the largest foreseeable human cohorts. GLnexus combines scalable joint calling algorithms with a persistent database that grows efficiently as additional participants are sequenced. We validate GLnexus using 50,000 exomes to show it produces comparable or better results than existing methods, at a fraction of the computational cost with better scaling. We provide a standalone open-source version of GLnexus and a DNAnexus cloud-native deployment supporting very large projects, which has been employed for cohorts of >240,000 exomes and >22,000 whole-genomes.

bioinformatics↗

bioRxiv2018

xAtlas: Scalable small variant calling across heterogeneous next-generation sequencing experiments

MotivationThe rapid development of next-generation sequencing (NGS) technologies has lowered the barriers to genomic data generation, resulting in millions of samples sequenced across diverse experimental designs. The growing volume and heterogeneity of these sequencing data complicate the further optimization of methods for identifying DNA variation, especially considering that curated highconfidence variant call sets commonly used to evaluate these methods are generally developed by reference to results from the analysis of comparatively small and homogeneous sample sets.\n\nResultsWe have developed xAtlas, an application for the identification of single nucleotide variants (SNV) and small insertions and deletions (indels) in NGS data. xAtlas is easily scalable and enables execution and retraining with rapid development cycles. Generation of variant calls in VCF or gVCF format from BAM or CRAM alignments is accomplished in less than one CPU-hour per 30x short-read human whole-genome. The retraining capabilities of xAtlas allow its core variant evaluation models to be optimized on new sample data and user-defined truth sets. Obtaining SNV and indels calls from xAtlas can be achieved more than 40 times faster than established methods while retaining the same accuracy.\n\nAvailabilityFreely available under a BSD 3-clause license at https://github.com/jfarek/xatlas.\n\nContactfarek@bcm.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years to interrogate a broad range of traits, across diverse populations. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power for trait mapping, and will enable studies of genome biology, population genetics and genome function at unprecedented scale. A central challenge for joint analysis is that different WGS data processing and analysis pipelines cause substantial batch effects in combined datasets, necessitating computationally expensive reprocessing and harmonization prior to variant calling. This approach is no longer tenable given the scale of current studies and data volumes. Here, in a collaboration across multiple genome centers and NIH programs, we define WGS data processing standards that allow different groups to produce \"functionally equivalent\" (FE) results suitable for joint variant calling with minimal batch effects. Our approach promotes broad harmonization of upstream data processing steps, while allowing for diverse variant callers. Importantly, it allows each group to continue innovating on data processing pipelines, as long as results remain compatible. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results - including single nucleotide (SNV), insertion/deletion (indel) and structural variation (SV) - and produce significantly less variability than sequencing replicates. Residual inter-pipeline variability is concentrated at low quality sites and repetitive genomic regions prone to stochastic effects. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for broad data sharing and community-wide \"big-data\" human genetics studies.

bioinformatics↗