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 accepted.
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/rnaseq/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: 2008/05/12 by Ali Mortazavi