Learning R / Bioconductor for Sequence Analysis

Seattle, USA

2014-10-27 ~ 2014-10-29



This course is directed at beginning and intermediate users who would like an introduction to the analysis and comprehension of high-throughput sequence data using R and Bioconductor. Day 1 focuses on learning essential background: an introduction to the R programming language; central concepts for effective use of Bioconductor software; and an overview of high-throughput sequence analysis work flows. Day 2 emphasizes use of Bioconductor for specific tasks: an RNA-seq differential expression work flow; exploratory, machine learning, and other statistical tasks; gene set enrichment; and annotation. Day 3 transitions to understanding effective approaches for managing larger challenges: strategies for working with large data, writing re-usable functions, developing reproducible reports and work flows, and visualizing results. The course combines lectures with extensive hands-on practicals; students are required to bring a laptop with wireless internet access and a modern version of the Chrome or Safari web browser.


Download the package (containing all material) for use with R-3.1.1 / Bioconductor 3.0.

Install the course package with

install.packages("LearnBioconductor_0.1.6.tar.gz", repos=NULL)

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

biocLite(c("knitr", "BiocStyle", "BiocInstaller", "ALL",
    "BSgenome.Hsapiens.UCSC.hg19", "BiocParallel", "Biostrings",
    "GenomicAlignments", "GenomicFeatures", "Gviz", "MLSeq",
    "PoiClaClu", "RColorBrewer", "RNAseqData.HNRNPC.bam.chr14",
    "Rsamtools", "ShortRead", "TxDb.Hsapiens.UCSC.hg19.knownGene",
    "VariantAnnotation", "airway", "class", "cn.mops",
    "dendextend", "fission", "genefilter", "ggplot2", "gplots",
    "org.Hs.eg.db", "sva", "xtable", "PoiClaClu", "sva",
    "fission", "kernlab", "e1071"))

Explore the material through the following documents:

Packages »

Bioconductor's stable, semi-annual release:

Bioconductor is also available via Docker and Amazon Machine Images.

Documentation »


R / CRAN packages and documentation