Sherlock-Genome: an R Shiny application for genomic analysis and visualization

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Next-generation sequencing (NGS), including whole genome sequencing (WGS), is crucial in cancer genomics.
• Analyzing and visualizing WGS data across different pipelines presents challenges, especially for non-bioinformaticians.
• Limited accessibility hinders the use of WGS data for biological discovery.

Purpose of the Study:

• To develop an accessible platform for managing, visualizing, and analyzing WGS data in cancer genomics.
• To improve the usability of WGS results for researchers, particularly those without extensive bioinformatics expertise.
• To facilitate data sharing and integrative analysis in WGS-based cancer genomics studies.

Main Methods:

• Development of Sherlock-Genome, an R Shiny application.
• Implementation of FAIR data principles for data management and sharing.
• Inclusion of modules for major cancer genomic analyses with interactive visualizations.
• Support for both local and cloud deployment options.

Main Results:

• Sherlock-Genome provides a user-friendly interface for data harmonization and visualization of WGS results.
• The app enables integrative analyses of WGS data with other data types.
• It facilitates local inspection and sharing of sample-level WGS analysis results.
• The tool supports FAIR data principles, enhancing data management and reproducibility.

Conclusions:

• Sherlock-Genome significantly enhances the accessibility and usability of WGS analysis results in cancer genomics.
• The platform empowers researchers to perform comprehensive biological discovery using WGS data.
• Widespread adoption of Sherlock-Genome can advance cancer genomics research and facilitate publication of findings.