Introduction to Bioconductor for High-Throughput Sequence Analysis

Seattle, USA

2014-02-27 ~ 2014-02-28



Introduction to Bioconductor for Sequence Analysis introduces users with some R experience to Bioconductor, especially working with high-throughput sequence data. Day 1 develops core R and Bioconductor concepts for working with large and complicated data. Participants will become familiar with data classes, packages, and scripting and programming concepts that are important for common and integrated work flows in Bioconductor. Day 2 will put these skills to use for the analysis of RNAseq differential expression data, including initial quality assessment, pre-processing, differential representation, annotation, and visualization. The course involves a combination of presentations and hands-on exercises; participants should come prepared with a modern laptop with wireless internet access.


Download and install the package (containing all material) for use with R-3.1.0 / Bioconductor 2.14.

Install the course package with

dependencies <- c("Biostrings", "ShortRead", "ggplot2")
install.packages("BiocIntro_0.0.3.tar.gz", repos=NULL)

Optionally, install suggested packages (used in exercises, etc) with

suggested <- c("BiocStyle", "knitr", "AnnotationHub",
    "BSgenome.Hsapiens.UCSC.hg19", "BiocParallel", "Biostrings",
    "GenomicAlignments", "GenomicFeatures", "GenomicRanges",
    "Gviz", "IRanges", "PSICQUIC", "RNAseqData.HNRNPC.bam.chr14",
    "TxDb.Hsapiens.UCSC.hg19.knownGene", "VariantAnnotation",
    "biomaRt", "knitr", "", "parallel", "rtracklayer")

Explore the material through the following documents:


Working with R

Sequencing work flows

Bioconductor for Sequence Analysis


Annotation and visualization

Packages »

Bioconductor's stable, semi-annual release:

Bioconductor is also available via Docker and Amazon Machine Images.

Documentation »


R / CRAN packages and documentation

Fred Hutchinson Cancer Research Center