About
Bioconductor

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, 824 software packages, and an active user community. Bioconductor is also available as an Amazon Machine Image (AMI).

Use Bioconductor for...

  • Microarrays

    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 resources.

  • Variants

    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 changes.

  • Sequence Data

    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.

  • Annotation

    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 assays.

  • Transcription Factors

    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.

  • Recent Courses

    Explore material from courses in 2013 and 2014.

  • Counting Reads for Differential Expression

    The parathyroidSE ExperimentData package and vignette illustrates how to count reads and perform other common operations required for differential expression analysis.

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EdgeR: dispersion estimation
2014-04-24T19:39:50-07:00
googleVis installation error
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DESeq2 16S copy number correction
2014-04-24T16:55:25-07:00
countOverlaps Warnings
2014-04-24T14:07:15-07:00

Events

Data Analysis for Genomics (PH525x) online course
07 April - 02 June 2014 — Online

Microarray Analysis using R and Bioconductor
03 - 05 June 2014 — London, UK

CSAMA 2014: Computational Statistics for Genome Biology (11th edition)
23 - 27 June 2014 — Bressanone-Brixen, Italy

BioC2014
30 July - 01 August 2014 — Boston, USA

Brazilian Cancer Epigenomics Workshop
24 - 26 August 2014 — Ribeirão Preto, Brazil

The Third Summer School in Computational Biology 2014
08 - 10 September 2014 — Belfast, Northern Ireland

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Fred Hutchinson Cancer Research Center