Dataset Information

Sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data.

ABSTRACT: Structural variants (SVs) are an important class of genetic variation implicated in a wide array of genetic diseases including cancer. Despite the advances in whole genome sequencing, comprehensive and accurate detection of SVs in short-read data still poses some practical and computational challenges. We present sv-callers, a highly portable workflow that enables parallel execution of multiple SV detection tools, as well as provide users with example analyses of detected SV callsets in a Jupyter Notebook. This workflow supports easy deployment of software dependencies, configuration and addition of new analysis tools. Moreover, porting it to different computing systems requires minimal effort. Finally, we demonstrate the utility of the workflow by performing both somatic and germline SV analyses on different high-performance computing systems.

SUBMITTER: Kuzniar A

PROVIDER: S-EPMC6951283 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data.

Kuzniar Arnold A Maassen Jason J Verhoeven Stefan S Santuari Luca L Shneider Carl C Kloosterman Wigard P WP de Ridder Jeroen J

PeerJ 20200106

Structural variants (SVs) are an important class of genetic variation implicated in a wide array of genetic diseases including cancer. Despite the advances in whole genome sequencing, comprehensive and accurate detection of SVs in short-read data still poses some practical and computational challenges. We present <i>sv-callers</i>, a highly portable workflow that enables parallel execution of multiple SV detection tools, as well as provide users with example analyses of detected SV callsets in a ...[more]

PMID: 31934500

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Project description:Next generation sequencing (NGS) has been leading the genetic study of human disease into an era of unprecedented productivity. Many bioinformatics pipelines have been developed to call variants from NGS data. The performance of these pipelines depends crucially on the variant caller used and on the calling strategies implemented. We studied the performance of four prevailing callers, SAMtools, GATK, glftools and Atlas2, using single-sample and multiple-sample variant-calling strategies. Using the same aligner, BWA, we built four single-sample and three multiple-sample calling pipelines and applied the pipelines to whole exome sequencing data taken from 20 individuals. We obtained genotypes generated by Illumina Infinium HumanExome v1.1 Beadchip for validation analysis and then used Sanger sequencing as a "gold-standard" method to resolve discrepancies for selected regions of high discordance. Finally, we compared the sensitivity of three of the single-sample calling pipelines using known simulated whole genome sequence data as a gold standard. Overall, for single-sample calling, the called variants were highly consistent across callers and the pairwise overlapping rate was about 0.9. Compared with other callers, GATK had the highest rediscovery rate (0.9969) and specificity (0.99996), and the Ti/Tv ratio out of GATK was closest to the expected value of 3.02. Multiple-sample calling increased the sensitivity. Results from the simulated data suggested that GATK outperformed SAMtools and glfSingle in sensitivity, especially for low coverage data. Further, for the selected discrepant regions evaluated by Sanger sequencing, variant genotypes called by exome sequencing versus the exome array were more accurate, although the average variant sensitivity and overall genotype consistency rate were as high as 95.87% and 99.82%, respectively. In conclusion, GATK showed several advantages over other variant callers for general purpose NGS analyses. The GATK pipelines we developed perform very well.

Dataset Information

Sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data.

Publications

sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data.

Similar Datasets

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