Abstract
Background
Whole-exome sequencing is a widely used technology to identify pathogenic variants in cancer. Although sequencing itself has become increasingly accessible, downstream analysis remains computationally complex, presenting a challenge for many researchers. Existing pipelines lack integrated support for somatic and germline variant detection and still require significant computational resources.
Methods
We developed GATES (GATK Automated Tool for Exome Sequencing), a lightweight pipeline that automates data preprocessing, variant calling, and variant annotation directly from raw paired-end FASTQ files through a simplified command-line interface. GATES implements the GATK Best Practices for somatic and germline variant detection and leverages Ensembl’s Variant Effect Predictor for functional annotation, outputting the results in a human-readable tab-separated values (TSV) file. We evaluated the pipeline’s performance using the SEQC-II benchmarking dataset and demonstrated its application using a clinical sample harboring known pathogenic germline and somatic variants.
Results
GATES was run on a standard laptop and performed end-to-end variant analysis for each sample within a few hours. In benchmarking with SEQC-II samples, germline and tumor-normal somatic variant calling modes demonstrated high concordance with their respective truth sets. Tumor-only somatic mode showed decreased accuracy, consistent with expected germline contamination. GATES demonstrated high performance across various hardware configurations and compared to the established nf-core/sarek pipeline. GATES further successfully performed somatic and germline analysis of a >100X clinical sample in under 7 hours. Importantly, the pipeline accurately distinguished the known
KRAS
p.G12V and
KEAP1
p.S338L as somatic and germline, respectively.
Conclusion
By lowering the technical barriers to exome sequencing analysis, GATES provides a practical solution for pathogenic variant discovery for researchers both with and without computational expertise.