Abstract 2087: Sherlock-Genome: a R shiny app for genomic analysis and visualization

Abstract

Abstract Over the last few decades, next generation sequencing technologies, including whole genome sequencing (WGS), have been increasingly used in cancer genomics studies. However, there is no efficient way to manage, visualize and integratively analyze sample-level WGS results across different bioinformatic pipelines, particularly for non-bioinformaticians. This greatly limits the usability of WGS data for biological discovery. Following the FAIR principles (findability, accessibility, interoperability, and reusability), we developed Sherlock-Genome, a R Shiny app designed for data harmonization, visualization and integrative analyses for WGS-based cancer genomics studies. Specifically, Sherlock-Genome provides pipelines for WGS analyses coupled with detailed guidelines on data preparation. The users can download the pipelines on their local computer, run the analyses and then upload the sample-level results into the Sherlock-Genome for data harmonization and integrative analyses across different genomic features. Sherlock-Genome also implements rigorous WGS-related data QC steps based on NGSpurity, a workflow that estimates the tumor purity, ploidy and clonal architecture by integrating somatic copy number alterations (SCNA), single nucleotide variants (SNV) and cancer cell fraction. Currently, Sherlock-Genome includes a few independent modules that allow users to generate a variety of interactive data inspection and visualization images, and perform many integrative analyses also using clinical and epidemiological data. These modules include study information and sample-level data QC (study overview, manifest information, NGSpurity), summaries of major genomics alterations (mutations, SCNA, structural variants), reports of advanced genomics analyses (mutational signatures, genomic landscape, clonal evolution) and functions related to different types of integrative analyses (survival analysis, integrative analysis). In addition, all plots in the Sherlock-Genome app can be downloaded as publication-ready figures from each module. Sherlock-Genome can be deployed in both local and cloud environments, allowing users to share sample-level analytical results together with the related publications. Sherlock-Genome has the potential to be widely used in cancer genomics allowing sample-level integrative analyses across multiple WGS and additional multi-omics features. A Sherlock-Genome demo with major module functions could be presented to allow users to know about this new interactive user-friendly tool for data analysis and visualization that can greatly advance biological discoveries. Citation Format: Alyssa Klein, Maria Teresa Landi, Tongwu Zhang. Sherlock-Genome: a R shiny app for genomic analysis and visualization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2087.