Wold Lab
Mapping and Quantifying Mammalian Transcriptomes by RNA-Seq
Ali Mortazavi, Brian Williams, Kenneth McCue, Lorian Schaeffer, Barbara Wold
This is the page of the underlying data and code for the analysis of the paper above, which has been published in Nature Methods in 2008. While the paper focuses on mouse tissues, we have since used the same code in C elegans and human cell lines with great success.
If using Bowtie 0.10.X, please make sure to use the new '--strata' flag in order to handle multireads correctly. Note that ERANGE is not compatible with bowtie 0.9.9.X.
New: ERANGE 3.2.1
ERANGE3.2.1 is now released. This is the version we are using in the Wold lab for both ChIP-seq and RNA-seq analyses. Some of the new supported features are:
- Support for SAM/BAM format
The current version is: ERANGE3.2.1.tgz, which was released on 2010/09/10. Note that this version of ERANGE requires an upgrade of Cistematic to version 3.0.
The following READMEs constitute the bulk of the documentation for ERANGE:
A guide to building RDS files: README.build-rds
A guide to using ERANGE for ChIP-seq: README.chip-seq
A guide to using ERANGE for (expressed) SNPs: README.rna-esnp
A guide to using ERANGE for RNA-seq: README.rna-seq
A list of steps describing the standard RNA-seq pipeline: RNA-seq.analysisSteps.txt
You are highly encouraged to use the following pipeline scripts rather than the individual commands for RNA-seq:
- unpaired reads: runStandardAnalysis.sh
- unpaired reads, stranded: runStrandedAnalysis.sh
- paired reads (assumes on network partition): runRNAPairedAnalysis.sh
- expressed SNP analysis: runSNPAnalysis.sh
Please note that ERANGE3.X is a major departure from the bed-based formats used in ERANGE2.0 and requires re-importing read mappings into sqlite based read datasets (RDS). However, we suggest that you run v3.X instead of v2.X for production purposes.
Dual-use E-RANGE
E-RANGE is our Python package for doing RNA-seq and ChIP-seq (hence the "dual-use"), and is a descendant of the ChIPSeq mini peak finder (Johnson, 2007).
To use it for RNA-seq, first go through the RNA-seq README, then read the file analysisSteps.txt and take a look at the pipeline shell script runStandardAnalysis.sh.
Note that E-RANGE assumes the following requirements: Python 2.5, Linux / Mac OS X (preferably with the Python Psyco compiler), and Cistematic 2.0 (all scripts with a command line genome specification rely on Cistematic!), which you can get here.
If you want to rerun our entire analysis starting with either the raw data (eland files) or the bed files, you will need the following files:
ERANGE2.tgz (the code)
mm9splices_spikes.tgz (the files for building the exapnded genomes and remapping splices)
http://woldlab.caltech.edu/erange/RNAFAR.tgz | RNAFAR.tgz ]] (the consolidated RNAFAR analysis, includes repeat library from UCSC - large!)
The Mouse Reference data
Briefly, each tissue has two replicates, the second of which was done with spike-ins, as described in the paper. For each replicate we provide:<br>
- Normalized wigglegrams of the unique reads to display them on UCSC (mm9)
- Bed files of all of the reads (uniques, splices, multireads, spikes) - note that only the splice bed files are small enough for loading onto UCSC
- RPKM counts for each of the major steps of E-RANGE
- ELAND results files run with the --multi option on the expanded genomes for those who want to look at the raw data (these files are *huge* - up to 1GB)
Brain 1 (no spike)
Brain 2 (spike)
Liver 1 (no spike)
Liver 2 (spike)
Muscle 1 (no spike)
Muscle 2 (spike)
Last Modified: 2010/08/09 by Sean Upchurch