GATES: A Lightweight Tool Automating Pathogenic Variant Discovery From Raw Whole-Exome Sequencing Data.

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 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.