This is the Github repository for the collection of scripts developed by members of The Barton Group at The University of Dundee to process and analyse the data from a 48-replicate RNA-seq experiment conducted specifically to test the underlying assumptions and performance of popular RNA-seq Differential Gene Expression (DGE) tools. A full description of the experiment is given in Schurch et. al. 2015.
The scripts in this collection are written in a variety of languages including perl, python and R. None of the scripts in the package require any specific installation however there are a considerable number of dependencies for the full suite of scripts including (in some cases) access to a DRMAA-enabled cluster and they have only been tested with specific versions of the languages and dependencies. For more information on the specific requirements see the main documentation in the codebase (Doc/html).
This is a very brief overview of how to get started using the code. For a detailed walk-through please refer to the Getting Started section of the codebase documentation (Doc/html/getting_started.html). Before running any of the scripts you will need to clone the repository, set up the appropriate environmental variables PERL5LIB, PYTHONPATH, and PATH, and you will need some data.
The codebase includes pre-processed intermediate level data or the original 48-replicate experiment (in the Preprocessed_data folder). Alternatively the raw fastq data for the experiment can be obtained from the European Nucleotide Archive or any sufficiently replicated fastq data can be used. If you are using fastq data the data will need to be aligned to the relevant genome and provided as indexed .bam files (the genome sequence used for the 48-replicate experiment is available in the Annotations folder of the codebase).
The performance of each DGE tool as a function of replicate number and expression fold-change is evaluated by comparing the DGE results from sub-selections of the replicate data against a 'gold standard' set of DGE results calculated for each tool using the full set of (clean) replicates. The codebase includes pre-computed gold standards for each of the tools tested in the 48 replicate experiment (in the Preprocessed_data/full_rep_gold_standards folder). Alternatively, gold standards can be computed using the Bootstrapping/generic_wrapper.py script with details of which tool to use. A simple example command-line for generating a gold standard for edgeR might be something line:
Bootstrapping/generic_wrapper.py -r DE_tool_scripts/edgeR.R -d data_dir -a annotation.gtt -o golds/edgeR_gold.txt -l edgeR_gold.log
The DGE from bootstrapped sub-selections of replicates is computed in a similar fashion to the gold standards, however we now additionally specify a sqlite output format, a number of replicates to select and a number of bootstraps to perform. For the analysis in Schurch et. al. 2015 use 100 bootstraps and replicate sub-selection from 2..40. Again, an example for edgeR with 10 bootstraps os sub-selections with 3 replicates in each condition might be something like:
Bootstrapping/generic_wrapper.py -r DE_tool_scripts/edgeR.R -d data_dir -a annotation.gtf -b 10 -k 3 -o edgeR/edgeR_k03.db -l edgeR_k03.log
The output sqlite database contains the results from and log information for all the individual bootstrap calls. These can then be compared directly with the gold standard results. A variety of statistics and plotting scripts are available the codebase for this. Please see the Getting Started section of the codebase documentation (Doc/html/getting_started.html) for a simple example of how to plot a tools performance or see the individual documentation for the individual scripts (Docs/html/package_inventory.html) for more details.
For further information or assistance with this repository please contact one of:
- Nick Schurch: nschurch@dundee.ac.uk
- Marek Gierlinski: mgierlinski@dundee.ac.uk
- Pieta Schofield: pschofield@dundee.ac.uk
- Chris Cole: ccole@dundee.ac.uk
- Geoff Barton: gjbarton@dundee.ac.uk