Introduction to Bioconductor for High-Throughput Sequence Analysis

Seattle, USA

2014-02-27 ~ 2014-02-28

Instructors

Description

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.

Materials

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

Install the course package with

source("http://bioconductor.org/biocLite.R")
dependencies <- c("Biostrings", "ShortRead", "ggplot2")
biocLite(dependencies)
install.packages("BiocIntro_0.0.3.tar.gz", repos=NULL)

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

source("http://bioconductor.org/biocLite.R")
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", "org.Hs.eg.db", "parallel", "rtracklayer")
biocLite(suggested)

Explore the material through the following documents:

Introduction

Working with R

Sequencing work flows

Bioconductor for Sequence Analysis

RNA-Seq

Annotation and visualization

Bioconductor Release »

Packages in the stable, semi-annual release:


Bioconductor is also available as an Amazon Machine Image.
Fred Hutchinson Cancer Research Center