Bioconductor provides tools for the analysis and comprehension of
high-throughput genomic data. Bioconductor uses the R statistical
programming language, and is open source and open development. It
has two releases each year,
749 software packages,
and an active user community. Bioconductor is also
available as an
Amazon Machine Image (AMI).
Use Bioconductor for...
Import Affymetrix, Illumina, Nimblegen, Agilent, and
other platforms. Perform quality assessment,
normalization, differential expression, clustering,
classification, gene set enrichment, genetical genomics
and other workflows for expression, exon, copy number,
SNP, methylation and other assays. Access GEO,
ArrayExpress, Biomart, UCSC, and other community
Read and write VCF files. Identify structural location of
variants and compute amino acid coding changes for
non-synonymous variants. Use SIFT and PolyPhen database
packages to predict consequence of amino acid coding
Import fasta, fastq, ELAND, MAQ, BWA, Bowtie, BAM, gff,
bed, wig, and other sequence formats. Trim, transform,
align, and manipulate sequences. Perform quality
assessment, ChIP-seq, differential expression, RNA-seq,
and other workflows. Access the Sequence Read Archive.
Use microarray probe, gene, pathway, gene ontology,
homology and other annotations. Access GO, KEGG, NCBI,
Biomart, UCSC, vendor, and other sources.
High Throughput Assays
Import, transform, edit, analyze and visualize flow
cytometric, mass spec, HTqPCR, cell-based, and other
Find candidate binding sites for known transcription factors via sequence matching.
Explore material from recent courses, including
R / Bioconductor for High Throughput Sequence
Counting Reads for Differential Expression
ExperimentData package and vignette illustrates how to
count reads and perform other common operations
required for differential expression analysis.