Statistical Methods and Software for
mRNA-Seq and ChIP-Seq
Centro
de
Investigación Príncipe Felipe
Valencia, Spain
November
8-10,
2010
Instructors
Dr. Laurent Jacob,
Genentech Innovation Fellow, Center for Computational Biology and
Department of Statistics, UC Berkeley
Oleg Mayba,
Department of Statistics, UC Berkeley
Course developed in collaboration with Professor Sandrine Dudoit,
Division of Biostatistics and Department of Statistics, UC Berkeley
Description
The short course will provide an overview of statistical methods and
software for the analysis of high-throughput sequencing (HTS) data,
with emphasis on transcriptome analysis (mRNA-Seq) and the study of
DNA-protein interactions (ChIP-Seq). The morning lectures on
statistical methodology will be followed by afternoon lab sessions
demonstrating the application of the methodology to mRNA-Seq and
ChIP-Seq data using R software packages from the Bioconductor Project
(http://www.bioconductor.org,
http://www.bioconductor.org/help/workflows/high-throughput-sequencing).
For registration and other practical matters: http://bioinfo.cipf.es/RNA-seq2010
Requirements
We expect the participants to have some familiarity with the R
programming environment, but not necessarily with the Bioconductor suite of packages. For the lab portion of the course, we require that the latest version of R (currently R-2.12.0) and the latest version of Bioconductor (currently version 2.7) be installed. These can be obtained from http://www.r-project.org and http://www.bioconductor.org, respectively.
In addition to Bioconductor core packages, we will also be using packages
- aroma.light
- ChIPpeakAnno
- GenomeGraphs
- Genominator
- rGADEM
- rtracklayer
- ShortRead
- BSgenome (NEW)
- BSgenome.Hsapiens.UCSC.hg19
- BSgenome.Scerevisiae.UCSC.sacCer2 (NEW)
- yeastRNASeq
Please make sure that these are installed and can be loaded into R.
NEW: As part of the first lab, we would like you to do short read alignment using Bowtie (http://bowtie-bio.sourceforge.net/index.shtml. Please install this aligner and the pre-built S.cerevisiae genome available from Bowtie's main page. The reads to be aligned come from a yeast data set that we will discuss and can be obtained from GEO (http://www.ncbi.nlm.nih.gov/geo/) with accession number GSM298523 (Isogenic wild-type Rep 1). You will need to download the .fastq file containing reads and their quality scores from ftp://ftp.ncbi.nlm.nih.gov/sra/static/SRX003%2FSRX003157 and unzip it.
Note: this list of requirements and packages is subject to change as the course syllabus is finalized, so please check back frequently.
Program
Day 1 - November 8th
Lecture 1: Introduction to mRNA-Seq
and ChIP-Seq
- High-throughput gene expression assays
- Experimental design
- Pre-processing: Image analysis, base-calling, and read-mapping
Lab 1: Introduction to statistical software for HTS
- Bioconductor basics
- Overview of Bioconductor packages for HTS
- Exploratory data analysis (EDA)
Day
2 - November 9th
Lecture 2: Statistical methods for mRNA-Seq
- Exploratory data analysis
- Controls
- Normalization and expression quantitation
- Differential expression
Lab 2: Statistical software for mRNA-Seq
Day 3 - November 10th
Lecture 3: Statistical methods for ChIP-Seq
- Experimental setup
- Statistical aspects of peak-finding
- Use of control samples and normalization
- Peak validation
Lab 3: Statistical software
for ChIP-Seq
- Introduction to relevant Bioconductor tools
- Peak annotation
- Motif finding
- Data visualization