Accuracy and Efficiency of Germline Variant Calling Pipelines for Human Genome Data

Overview

Journal Sci Rep

Specialty Science

Date 2020 Nov 20

PMID 33214604

Citations 41

Authors

Sen Zhao

Oleg Agafonov

Abdulrahman Azab

Tomasz Stokowy

Eivind Hovig

Affiliations

Soon will be listed here.

Abstract

Advances in next-generation sequencing technology have enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data. However, there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using the Genome in a Bottle Consortium, "synthetic-diploid" and simulated WGS datasets. DRAGEN and DeepVariant show better accuracy in SNP and indel calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient execution speed, and therefore superior performance in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also suggests a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical applications.

Citing Articles

Neoadjuvant triplet immune checkpoint blockade in newly diagnosed glioblastoma.

Long G, Shklovskaya E, Satgunaseelan L, Mao Y, Pires da Silva I, Perry K Nat Med. 2025; .

PMID: 40016450 DOI: 10.1038/s41591-025-03512-1.

Whole exome sequencing reveals ABCD1 variant as a potential contributor to male infertility.

Redouane S, Harmak H, El Hamouchi A, Charoute H, Louanjli N, Malki A Mol Biol Rep. 2025; 52(1):148.

PMID: 39841288 DOI: 10.1007/s11033-025-10234-7.

Case report: A case study of variant calling pipeline selection effect on the molecular diagnostics outcome.

Skitchenko R, Smirnov S, Krapivin M, Smirnova A, Artomov M, Loboda A Front Oncol. 2024; 14:1422811.

PMID: 39544296 PMC: 11560904. DOI: 10.3389/fonc.2024.1422811.

Dissecting the Reduced Penetrance of Putative Loss-of-Function Variants in Population-Scale Biobanks.

Blair D, Risch N medRxiv. 2024; .

PMID: 39399029 PMC: 11469360. DOI: 10.1101/2024.09.23.24314008.

NCBench: providing an open, reproducible, transparent, adaptable, and continuous benchmark approach for DNA-sequencing-based variant calling.

Hanssen F, Gabernet G, Bauerle F, Stocker B, Wiegand F, Smith N F1000Res. 2024; 12:1125.

PMID: 39345270 PMC: 11428021. DOI: 10.12688/f1000research.140344.1.

References

Hwang S, Kim E, Lee I, Marcotte E . Systematic comparison of variant calling pipelines using gold standard personal exome variants. Sci Rep. 2015; 5:17875. PMC: 4671096. DOI: 10.1038/srep17875. View

Radder J, Zhang Y, Gregory A, Yu S, Kelly N, Leader J . Extreme Trait Whole-Genome Sequencing Identifies PTPRO as a Novel Candidate Gene in Emphysema with Severe Airflow Obstruction. Am J Respir Crit Care Med. 2017; 196(2):159-171. PMC: 5519967. DOI: 10.1164/rccm.201606-1147OC. View

Li H, Durbin R . Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010; 26(5):589-95. PMC: 2828108. DOI: 10.1093/bioinformatics/btp698. View

McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A . The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010; 20(9):1297-303. PMC: 2928508. DOI: 10.1101/gr.107524.110. View

Yu X, Sun S . Comparing a few SNP calling algorithms using low-coverage sequencing data. BMC Bioinformatics. 2013; 14:274. PMC: 3848615. DOI: 10.1186/1471-2105-14-274. View

Saunders C, Miller N, Soden S, Dinwiddie D, Noll A, Alnadi N . Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci Transl Med. 2012; 4(154):154ra135. PMC: 4283791. DOI: 10.1126/scitranslmed.3004041. View

DePristo M, Banks E, Poplin R, Garimella K, Maguire J, Hartl C . A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011; 43(5):491-8. PMC: 3083463. DOI: 10.1038/ng.806. View

Mangul S, Martin L, Hill B, Lam A, Distler M, Zelikovsky A . Systematic benchmarking of omics computational tools. Nat Commun. 2019; 10(1):1393. PMC: 6437167. DOI: 10.1038/s41467-019-09406-4. View

Khan F, Melton P, McCarthy N, Morar B, Blangero J, Moses E . Whole genome sequencing of 91 multiplex schizophrenia families reveals increased burden of rare, exonic copy number variation in schizophrenia probands and genetic heterogeneity. Schizophr Res. 2018; 197:337-345. DOI: 10.1016/j.schres.2018.02.034. View

10.

Zook J, McDaniel J, Olson N, Wagner J, Parikh H, Heaton H . An open resource for accurately benchmarking small variant and reference calls. Nat Biotechnol. 2019; 37(5):561-566. PMC: 6500473. DOI: 10.1038/s41587-019-0074-6. View

11.

Roach J, Glusman G, Smit A, Huff C, Hubley R, Shannon P . Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science. 2010; 328(5978):636-9. PMC: 3037280. DOI: 10.1126/science.1186802. View

12.

Saunders C, Wong W, Swamy S, Becq J, Murray L, Cheetham R . Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics. 2012; 28(14):1811-7. DOI: 10.1093/bioinformatics/bts271. View

13.

Hardwick S, Deveson I, Mercer T . Reference standards for next-generation sequencing. Nat Rev Genet. 2017; 18(8):473-484. DOI: 10.1038/nrg.2017.44. View

14.

Zook J, Chapman B, Wang J, Mittelman D, Hofmann O, Hide W . Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat Biotechnol. 2014; 32(3):246-51. DOI: 10.1038/nbt.2835. View

15.

Koboldt D, Steinberg K, Larson D, Wilson R, Mardis E . The next-generation sequencing revolution and its impact on genomics. Cell. 2013; 155(1):27-38. PMC: 3969849. DOI: 10.1016/j.cell.2013.09.006. View

16.

Nielsen R, Paul J, Albrechtsen A, Song Y . Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet. 2011; 12(6):443-51. PMC: 3593722. DOI: 10.1038/nrg2986. View

17.

Poplin R, Chang P, Alexander D, Schwartz S, Colthurst T, Ku A . A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018; 36(10):983-987. DOI: 10.1038/nbt.4235. View

18.

Bamshad M, Ng S, Bigham A, Tabor H, Emond M, Nickerson D . Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011; 12(11):745-55. DOI: 10.1038/nrg3031. View

19.

Hwang K, Lee I, Li H, Won D, Hernandez-Ferrer C, Negron J . Comparative analysis of whole-genome sequencing pipelines to minimize false negative findings. Sci Rep. 2019; 9(1):3219. PMC: 6397176. DOI: 10.1038/s41598-019-39108-2. View

20.

Roy S, Coldren C, Karunamurthy A, Kip N, Klee E, Lincoln S . Standards and Guidelines for Validating Next-Generation Sequencing Bioinformatics Pipelines: A Joint Recommendation of the Association for Molecular Pathology and the College of American Pathologists. J Mol Diagn. 2017; 20(1):4-27. DOI: 10.1016/j.jmoldx.2017.11.003. View