VarSCAT: A computational tool for sequence context annotations of genomic variants

Abstract

The sequence contexts of genomic variants play important roles in understanding biological significances of variants and potential sequencing related variant calling issues. However, methods for assessing the diverse sequence contexts of genomic variants such as tandem repeats and unambiguous annotations have been limited. Herein, we describe the Variant Sequence Context Annotation Tool (VarSCAT) for annotating the sequence contexts of genomic variants, including breakpoint ambiguities, flanking sequences, variant nomenclatures, adjacent variants, and tandem repeats with user customizable options. Our analysis demonstrate that VarSCAT is more versatile and customizable than current methods or strategies for annotating variants in short tandem repeat (STR) regions. Variant sequence context annotations of high-confidence human variant sets with VarSCAT revealed that more than 75% of all human individual germline and clinically relevant insertions and deletions (indels) have breakpoint ambiguities. Moreover, we illustrate that more than 80% of human individual germline small variants in STR regions are indels and that the sizes of these indels correlated with STR motif sizes. VarSCAT is available athttps://github.com/elolab/VarSCAT.Author SummaryThe sequence contexts have significant impacts on the biological and technical aspects of genomic variants. The sequence contexts, such as tandem repeats or nearby indels, may increase the mutation rate of a region compared to other genome regions. Besides, variants in specific sequence contexts like STRs may also have distinguished biological consequences, which can lead to certain human diseases and thus they may be used as biomarkers for disease diagnosis and treatments. Moreover, potential ambiguous variant representations such as equivalent or redundant indels are also related with their sequence contexts, which may complicate variant harmonization from different sources. Our previous study demonstrated that more than half of false positive indel calls detected through next generation sequencing data are related with STRs. Thus, the sequence contexts of genomic variants are important and cannot be ignored. However, the current methods or strategies for assessing the sequence contexts of genomic variants are limited and not feasible to use. Here, we developed a computational tool VarSCAT for sequence contexts annotation of genomic variants. Our tool provides diverse sequence contexts annotations providing users information to further explore the variants of their interests. By applying VarSCAT to high confidence human variant sets, we demonstrate the influence of sequence context of genomic variants and emphasize the importance of sequence context assessment.