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Review
. 2025:2932:1-46.
doi: 10.1007/978-1-0716-4566-6_1.

Bioconductor's Computational Ecosystem for Genomic Data Science in Cancer

Marcel Ramos  1 , Lori Shepherd  2 , Nathan C Sheffield  3 , Alexandru Mahmoud  4 , Hervé Pagès  5 , Andres Wokaty  1 , Dario Righelli  6 , Davide Risso  7 , Sean Davis  8 , Sehyun Oh  1 , Levi Waldron  1 , Martin Morgan  2 , Vincent Carey  9
Affiliations
Review

Bioconductor's Computational Ecosystem for Genomic Data Science in Cancer

Marcel Ramos et al. Methods Mol Biol. 2025.

Abstract

The Bioconductor project enters its third decade with over two thousand packages for genomic data science, over 100,000 annotation and experiment resources, and a global system for convenient distribution to researchers. Over 60,000 PubMed Central citations and terabytes of content shipped per month attest to the impact of the project on cancer genomic data science. This report provides an overview of cancer genomics resources in Bioconductor. After an overview of Bioconductor project principles, we address exploration of institutionally curated cancer genomics data such as TCGA. We then review genomic annotation and ontology resources relevant to cancer and then briefly survey analytical workflows addressing specific topics in cancer genomics. Concluding sections cover how new software and data resources are brought into the ecosystem and how the project is tackling needs for training of the research workforce. Bioconductor's strategies for supporting methods developers and researchers in cancer genomics are evolving along with experimental and computational technologies. All the tools described in this report are backed by regularly maintained learning resources that can be used locally or in cloud computing environments.

Keywords: Cancer genomics; data structures; epigenomics; mutations; ontology; open source software; spatial transcriptomics; transcriptomics.

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References

    1. R-Core. Writing R Extensions, 2024.
    1. Aevermann, B. D., Novotny, M., Bakken, T., Miller, J. A., Diehl, A. D., Osumi-Sutherland, D., Lasken, R. S., Lein, E. S., and Scheuermann, R. H. Cell type discovery using single-cell transcriptomics: Implications for ontological representation. Human Molecular Genetics, 27:R40–R47, 2018. - DOI - PubMed - PMC
    1. Tian, L., Su, S., Dong, X., Amann-Zalcenstein, D., Biben, C., Seidi, A., Hilton, D. J., Naik, S. H., and Ritchie, M. E. scpipe: A flexible r/bioconductor preprocessing pipeline for single-cell rna-sequencing data. PLOS Computational Biology, 14(8):e1006361, 2018.
    1. Wei, Z., Zhang, W., Fang, H., Li, Y., and Wang, X. esatac: An easy-to-use systematic pipeline for atac-seq data analysis. Bioinformatics (Oxford, England), March 2018.
    1. Ou, J., Liu, H., Yu, J., Kelliher, M. A., Castilla, L. H., Lawson, N. D., and Zhu, L. J. Atacseqqc: a bioconductor package for post-alignment quality assessment of atac-seq data. BMC Genomics, 19(1), 2018.

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