GenomeDISCO (DIfferences between Smoothed COntact maps) is a package for comparing contact maps of 3D genome structures, obtained from experiments such as Hi-C, Capture-C, ChIA-PET, HiChip, etc. It uses random walks on the contact map graph for smoothing before comparing the contact maps, resulting in a concordance score that can be used for quality control of biological replicates.
Read the full paper here: GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs. Oana Ursu, Nathan Boley, Maryna Taranova, Y. X. Rachel Wang, Galip Gurkan Yardimci, William Stafford Noble, Anshul Kundaje. Bioinformatics, 2018. https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty164/4938489?redirectedFrom=fulltext
- Install Anaconda. GenomeDISCO is compatible with Python 2.
- Obtain and install GenomeDISCO with the following commands:
git clone http://github.com/kundajelab/genomedisco pip install --editable genomedisco/
Say you want to compare 2 contact maps. For this example, we will use a subset of datasets from Rao et al., 2014.
First, configure the files used in the example: (this will create all input files necessary for the example on which we will run GenomeDISCO)
Then run the concordance analysis:
cd genomedisco genomedisco run_all --metadata_samples examples/metadata.samples --metadata_pairs examples/metadata.pairs --bins examples/Bins.w50000.bed.gz --outdir examples/output
For detailed explanations of all inputs to GenomeDISCO, see the "Inputs" section below
To run reproducibility analysis in batches (more than one comparison), all you need to do is modify the
--metadata_pairs to add the additional samples and sample pairs respectively that you wish to compare. For details, see "Analyzing multiple dataset pairs"
Running other methods for measuring concordance and QC of Hi-C data
To run other available methods for computing the reproducibility of Hi-C data, refer to the repository http://github.com/kundajelab/3DChromatin_ReplicateQC and follow the instructions there.
The reproducibility methods supported in 3DChromatin_ReplicateQC are:
- GenomeDISCO (http://github.com/kundajelab/genomedisco)
- HiCRep (http://github.com/qunhualilab/hicrep)
- HiC-Spector (http://github.com/gersteinlab/HiC-spector)
- QuASAR-Rep (part of the hifive suite at http://github.com/bxlab/hifive)
Note: given that both GenomeDISCO and 3DChromatin_ReplicateQC use the same underlying base code, they share the parameter options below, resulting in shared README sections for these.
Before running GenomeDISCO, make sure to have the following files:
contact map For each of your samples, you need a file containing the counts assigned to each pair of bins in your contact map, and should have the format
chr1 bin1 chr2 bin2 value. Note: GenomeDISCO assumes that this file contains the contacts for all chromosomes, and will split it into individual files for each chromosome.
bins This file contains the full set of genomic regions associated with your contact maps, in the format
chr start end namewhere name is the name of the bin as used in the contact map files above. GenomeDISCO supports both fixed-size bins and variable-sized bins (e.g. obtained by partitioning the genome into restriction fragments).
GenomeDISCO takes the following inputs:
--metadata_samplesInformation about the samples being compared. Tab-delimited file, with columns "samplename", "samplefile". Note: each samplename should be unique. Each samplefile listed here should follow the format "chr1 bin1 chr2 bin2 value
--metadata_pairsEach row is a pair of sample names to be compared, in the format "samplename1 samplename2". Important: sample names used here need to correspond to the first column of the --metadata_samples file.
--binsA (gzipped) bed file of the all bins used in the analysis. It should have 4 columns: "chr start end name", where the name of the bin corresponds to the bins used in the contact maps.
--re_fragmentsAdd this flag if the bins are not uniform bins in the genome (e.g. if they are restriction-fragment-based).By default, the code assumes the bins are of uniform length.
--parameters_fileFile with parameters for reproducibility and QC analysis. For details see "Parameters file"
--outdirName of output directory. DEFAULT: replicateQC
--running_modeThe mode in which to run the analysis. This allows you to choose whether the analysis will be run as is, or submitted as a job through sge or slurm. Available options are: "NA" (default, no jobs are submitted). Coming soon: "sge", "slurm"
--concise_analysisSet this flag to obtain a concise analysis, which means replicateQC is measured but plots that might be more time/memory consuming are not created. This is useful for quick testing or running large-scale analyses on hundreds of comparisons.
--subset_chromosomesComma-delimited list of chromosomes for which you want to run the analysis. By default the analysis runs on all chromosomes for which there are data. This is useful for quick testing
Analyzing multiple dataset pairs
To analyze multiple pairs of contact maps, all you need to do is add any additional datasets you want to analyze to the
--metadata_samples file and any additional pairs of datasets you want to compare to the
The parameters file specifies the parameters to be used with GenomeDISCO (and any of the other methods GenomeDISCO supports). The format of the file is:
method_name parameter_name parameter_value. The default parameters file used by GenomeDISCO is:
GenomeDISCO|subsampling lowest GenomeDISCO|tmin 3 GenomeDISCO|tmax 3 GenomeDISCO|norm sqrtvc GenomeDISCO|scoresByStep no GenomeDISCO|removeDiag yes GenomeDISCO|transition yes SGE|text "-l h_vmem=3G" slurm|text "--mem 3G"
Note: all of the above parameters need to be specified in the parameters file.
Here are details about setting these parameters:
GenomeDISCO|subsamplingThis allows subsampling the datasets to a specific desired sequencing depth. Possible values are:
lowest(subsample to the depth of the sample with the lower sequencing depth from the pair being compared),
<samplename>where is the name of the sample that is used to determine the sequencing depth to subsample from.
GenomeDISCO|tminThe minimum number of steps of random walk to perform. Integer, > 0.
GenomeDISCO|tmaxThe max number of steps of random walk to perform. Integer, > tmin.
GenomeDISCO|normThe normalization to use on the data when running GenomeDISCO. Possible values include:
GenomeDISCO|scoresByStepWhether to report the score at each t. By default (GenomeDISCO|scoresByStep no), only the final reproducibility score is returned.
GenomeDISCO|removeDiagWhether to set the diagonal to entries in the contact map to 0. By default (GenomeDISCO|removeDiag yes), the diagonal entries are set to 0.
GenomeDISCO|transitionWhether to convert the normalized contact map to an appropriate transition matrix before running the random walks. By default (GenomeDISCO|transition yes) the normalized contact map is converted to a proper transition matrix, such that all rows sum to 1 exactly.
SGE|textText to append to the job submission for SGE. The default is "-l h_vmem=3G".
slurm|textText to append to the job submission for slurm. The default is "--mem 3G".
Running GenomeDISCO step by step
GenomeDISCO consists of multiple steps, which are run in sequence by default. However, the user may decide to run the steps individually, which can be useful for instance when running GenomeDISCO with job submission engines that runs the comparisons in parallel as separate jobs.
Preprocesses all datasets provided in
genomedisco preprocess --metadata_samples examples/metadata.samples --bins examples/Bins.w50000.bed.gz --outdir examples/output --parameters_file examples/example_parameters.txt
Runs GenomeDISCO on all samples pairs provided in
genomedisco concordance --metadata_pairs examples/metadata.pairs --outdir examples/output
Summarizes scores across all comparisons.
genomedisco summary --metadata_samples examples/metadata.samples --metadata_pairs examples/metadata.pairs --bins examples/Bins.w50000.bed.gz --outdir examples/output
Clean up superfluous files, leaving only the scores.
genomedisco cleanup --outdir examples/output
Running GenomeDISCO with job submission engines
It is possible to run GenomeDISCO with job submission engines, specifically either SGE or slurm.
To do so, modify the parameters
slurm|text respectively, to add any additional parameters to the job run.
Then, run the steps sequentially (that is, wait for all jobs of a given step to complete before launching the next step), while specifying
--running_mode to either
For instance, an example analysis workflow for SGE would be:
genomedisco preprocess --running_mode sge --metadata_samples examples/metadata.samples --bins examples/Bins.w50000.bed.gz --outdir examples/output --parameters_file examples/example_parameters.txt genomedisco concordance --running_mode sge --metadata_pairs examples/metadata.pairs --outdir examples/output genomedisco summary --running_mode sge --metadata_samples examples/metadata.samples --metadata_pairs examples/metadata.pairs --bins examples/Bins.w50000.bed.gz --outdir examples/output genomedisco cleanup --running_mode sge --outdir examples/output
Similarly, for slurm, change sge to slurm for the
Submit an issue for this repository.
This code was put together by Oana Ursu (email@example.com).