Yury A. Barbitoff1*, Alexandra Panteleeva1, Alexander V. Predeus1
1Institute of Bioinformatics Research and Education, Belgrade, Serbia
barbitoff [at] bioinf.institute
Abstract
Accurate and comprehensive variant discovery is extremely important for rare disease diagnostics using next-generation sequencing (NGS) methods. Over the recent years, a plethora of methods have been developed for short variant calling from NGS data, and the most recent tools extensively use machine learning algorithms for both variant discovery and filtering. In our study, we took an effort to systematically evaluate the performance of different pipelines for short variant calling in the human genome.
To perform such a systematic comparison, we collected a large dataset of both “gold standard” (provided by the Genome In A Bottle (GIAB) consortium) and in-house whole-exome sequencing (WES) and whole-genome sequencing (WGS) datasets. (a total of 20 different datasets was used). We tested all combinations of 4 popular short read aligners (BWA, Bowtie2, Isaac, and Novoalign) and 9 novel and well-established variant calling and filtering methods (Freebayes, Clair3, DeepVariant, Genome Analysis ToolKit (GATK), Octopus, Strelka2). We also used several different tools for preprocessing of short reads.
Our analysis showed negligible effects of adapter trimming on the accuracy of short variant calling. Among read aligners, Bowtie2 performed significantly worse than other tools, suggesting it should not be used for medical variant calling. For pipelines based on BWA, Isaac, and Novoalign, the accuracy of variant discovery mostly depended on the variant caller and not the read aligner. DeepVariant consistently showed the best performance and the greatest robustness compared to all other tested variant callers. We have also compared the consistency of variant calls in GIAB and non-GIAB samples. With few important caveats, best-performing tools have shown little evidence of overfitting.
Taken together, our study showed that modern strategies for NGS data analysis allow for high accuracy of genetic variant discovery within coding regions of the human genome. However, there is still a need for development of new library preparation and variant calling methods to enhance variant discovery in the challenging regions of the human genome.
Keywords: pipeline, variant calling, human genetics, medical genetics
Acknowledgement: We thank JetBrains Ltd. for providing financial support and computing resources for the project.
