Sherlock-Genome: an R Shiny application for genomic analysis and visualization
Abstract Motivation Next-generation sequencing technologies, such as whole genome sequencing (WGS), have become prominent in cancer genomics. However, managing, visualizing, and integratively analyzing WGS results across various bioinformatic pipelines remains challenging, particularly for non-bioin...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-024-11147-8 |
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| author | Alyssa Klein Jun Zhong Maria Teresa Landi Tongwu Zhang |
| author_facet | Alyssa Klein Jun Zhong Maria Teresa Landi Tongwu Zhang |
| author_sort | Alyssa Klein |
| collection | DOAJ |
| description | Abstract Motivation Next-generation sequencing technologies, such as whole genome sequencing (WGS), have become prominent in cancer genomics. However, managing, visualizing, and integratively analyzing WGS results across various bioinformatic pipelines remains challenging, particularly for non-bioinformaticians, hindering the usability of WGS data for biological discovery. Results We developed Sherlock-Genome, an R Shiny app for data harmonization, visualization, and integrative analysis of WGS-based cancer genomics studies. Following FAIR principles, Sherlock-Genome provides a platform and guidelines for managing and sharing finalized sample-level WGS analysis results, enabling users to upload results, inspect analyses locally, and perform integrative analyses. It includes modules for major cancer genomic analyses, allowing interactive data visualizations and integrative analyses with other data types. Sherlock-Genome supports both local and cloud deployment, facilitating the sharing of results for related publications. This tool has the potential to be widely adopted in cancer genomics, significantly enhancing the accessibility and usability of sample-level WGS analysis results for comprehensive biological discovery and research advancements. Availability and implementation The source code and installation instructions for Sherlock-Genome can be accessed via Github https://github.com/xtmgah/Sherlock-Genome . Documentation and data requirements for user project data can also be found on the same GitHub page. |
| format | Article |
| id | doaj-art-adb0dcff7b214cf08ef5975187cfb9c1 |
| institution | Kabale University |
| issn | 1471-2164 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj-art-adb0dcff7b214cf08ef5975187cfb9c12025-01-19T12:11:27ZengBMCBMC Genomics1471-21642025-01-012611410.1186/s12864-024-11147-8Sherlock-Genome: an R Shiny application for genomic analysis and visualizationAlyssa Klein0Jun Zhong1Maria Teresa Landi2Tongwu Zhang3Division of Cancer Epidemiology and Genetics, National Cancer InstituteDivision of Cancer Epidemiology and Genetics, National Cancer InstituteDivision of Cancer Epidemiology and Genetics, National Cancer InstituteDivision of Cancer Epidemiology and Genetics, National Cancer InstituteAbstract Motivation Next-generation sequencing technologies, such as whole genome sequencing (WGS), have become prominent in cancer genomics. However, managing, visualizing, and integratively analyzing WGS results across various bioinformatic pipelines remains challenging, particularly for non-bioinformaticians, hindering the usability of WGS data for biological discovery. Results We developed Sherlock-Genome, an R Shiny app for data harmonization, visualization, and integrative analysis of WGS-based cancer genomics studies. Following FAIR principles, Sherlock-Genome provides a platform and guidelines for managing and sharing finalized sample-level WGS analysis results, enabling users to upload results, inspect analyses locally, and perform integrative analyses. It includes modules for major cancer genomic analyses, allowing interactive data visualizations and integrative analyses with other data types. Sherlock-Genome supports both local and cloud deployment, facilitating the sharing of results for related publications. This tool has the potential to be widely adopted in cancer genomics, significantly enhancing the accessibility and usability of sample-level WGS analysis results for comprehensive biological discovery and research advancements. Availability and implementation The source code and installation instructions for Sherlock-Genome can be accessed via Github https://github.com/xtmgah/Sherlock-Genome . Documentation and data requirements for user project data can also be found on the same GitHub page.https://doi.org/10.1186/s12864-024-11147-8Cancer GenomicsWhole Genome SequencingR ShinyData VisualizationBioinformatic Pipeline |
| spellingShingle | Alyssa Klein Jun Zhong Maria Teresa Landi Tongwu Zhang Sherlock-Genome: an R Shiny application for genomic analysis and visualization BMC Genomics Cancer Genomics Whole Genome Sequencing R Shiny Data Visualization Bioinformatic Pipeline |
| title | Sherlock-Genome: an R Shiny application for genomic analysis and visualization |
| title_full | Sherlock-Genome: an R Shiny application for genomic analysis and visualization |
| title_fullStr | Sherlock-Genome: an R Shiny application for genomic analysis and visualization |
| title_full_unstemmed | Sherlock-Genome: an R Shiny application for genomic analysis and visualization |
| title_short | Sherlock-Genome: an R Shiny application for genomic analysis and visualization |
| title_sort | sherlock genome an r shiny application for genomic analysis and visualization |
| topic | Cancer Genomics Whole Genome Sequencing R Shiny Data Visualization Bioinformatic Pipeline |
| url | https://doi.org/10.1186/s12864-024-11147-8 |
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