Bioconductor is a project dedicated to the analysis and comprehension of high throughput genomic (sequence, microarray, flow cytometry, proteomics, imaging) data. Bioconductor is based on the R statistical programming language. It consists of software, annotation, and `experiment data' packages developed and contributed by individuals funded by this grant, and by the national and international scientific community. Bioconductor is highly respected, widely used in the global bioinformatics community, and highly cited.
The specific aims of this project reflect the Bioconductor model of (1) ground-breaking statistical software development (Resource Project Core) and (2) robust core and contributed software repository and support infrastructure to amplify relatively modest funding into highly-effective investment in broad areas of academic, government, and industry biomedical research.
The final aim of the project is (3) effective and long-term project administration, dissemination, and training to transform many students into scientific professionals, and naive software users, into sophisticated leaders in statistical genomics.
Technology increasingly enables researchers to generate very large amounts of genomic data. The Bioconductor project makes available statistical software that helps researchers analyze and comprehend the data they have generated. Bioconductor's unique approaches mean that a large number of research professionals contribute to and benefit from the project.
| Pasolli, Edoardo; Schiffer, Lucas; Manghi, Paolo et al. (2017) Accessible, curated metagenomic data through ExperimentHub. Nat Methods 14:1023-1024 |
| Ramos, Marcel; Schiffer, Lucas; Re, Angela et al. (2017) Software for the Integration of Multiomics Experiments in Bioconductor. Cancer Res 77:e39-e42 |
| Kannan, Lavanya; Ramos, Marcel; Re, Angela et al. (2016) Public data and open source tools for multi-assay genomic investigation of disease. Brief Bioinform 17:603-15 |
| Spratt, Daniel E; Chan, Tiffany; Waldron, Levi et al. (2016) Racial/Ethnic Disparities in Genomic Sequencing. JAMA Oncol 2:1070-4 |
| Waldron, Levi; Riester, Markus; Ramos, Marcel et al. (2016) The Doppelgänger Effect: Hidden Duplicates in Databases of Transcriptome Profiles. J Natl Cancer Inst 108: |
| Carlson, Marc R J; Pagès, Hervé; Arora, Sonali et al. (2016) Genomic Annotation Resources in R/Bioconductor. Methods Mol Biol 1418:67-90 |
| Huber, Wolfgang; Carey, Vincent J; Gentleman, Robert et al. (2015) Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12:115-21 |
| Lawrence, Michael; Morgan, Martin (2014) Scalable Genomics with R and Bioconductor. Stat Sci 29:214-226 |
| Obenchain, Valerie; Lawrence, Michael; Carey, Vincent et al. (2014) VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics 30:2076-8 |
| Shioda, Toshi; Rosenthal, Noel F; Coser, Kathryn R et al. (2013) Expressomal approach for comprehensive analysis and visualization of ligand sensitivities of xenoestrogen responsive genes. Proc Natl Acad Sci U S A 110:16508-13 |
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