Bioconductor: open software development for computational biology and bioinformatics

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

Fibroblasts often constitute the majority of the stromal cells within a breast carcinoma, yet the functional contributions of these cells to tumorigenesis are poorly understood. Using a coimplantation tumor xenograft model, we demonstrate that carcinoma-associated fibroblasts (CAFs) extracted from human breast carcinomas promote the growth of admixed breast carcinoma cells significantly more than do normal mammary fibroblasts derived from the same patients. The CAFs, which exhibit the traits of myofibroblasts, play a central role in promoting the growth of tumor cells through their ability to secrete stromal cell-derived factor 1 (SDF-1); CAFs promote angiogenesis by recruiting endothelial progenitor cells (EPCs) into carcinomas, an effect mediated in part by SDF-1. CAF-secreted SDF-1 also stimulates tumor growth directly, acting through the cognate receptor, CXCR4, which is expressed by carcinoma cells. Our findings indicate that fibroblasts within invasive breast carcinomas contribute to tumor promotion in large part through the secretion of SDF-1.

We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization.

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.