Bioconductor Workflows

Bioconductor provides software to help analyze diverse high-throughput genomic data. Common workflows include:

• Sequence Analysis
  Import fasta, fastq, 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.

• RNAseq Differential Expression
  Use the parathyroidSE ExperimentData package and vignette to learn how to count reads and perform other common operations required for differential expression analysis.

• Oligonucleotide Arrays
  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.

• Accessing Annotation Data
  Use microarray probe, gene, pathway, gene ontology, homology and other annotations. Access GO, KEGG, NCBI, Biomart, UCSC, vendor, and other sources.

• Annotating Ranges
  Represent common sequence data types (e.g., from BAM, gff, bed, and wig files) as genomic ranges for simple and advanced range-based queries.

• High Throughput Assays
  Import, transform, edit, analyze and visualize flow cytometric, mass spec, HTqPCR, cell-based, and other assays.

• Transcription Factor Binding
  Finding 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.