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.
Cloud-enabled cis-eQTL search and annotation
Bioconductor can be used to perform detailed analyses of relationships between
DNA variants and mRNA abundance. Genotype (potentially imputed) and expression data are organized in packages prior to analysis, using very concise representations. SNP and probe filters can be specified at run time. Transcriptome-wide testing can be carried out using multiple levels of concurrency (chromosomes to nodes, genes to cores is a common approach). Default outputs of the cloud-oriented interface ciseqByCluster include FDR for all SNP-gene pairs in cis, along with locus-specific annotations of genetic and genomic contexts.
Explore material from courses in
Counting Reads for Differential Expression
ExperimentData package and vignette illustrates how to
count reads and perform other common operations
required for differential expression analysis.