A tool for combining bed regions from multiple bed files in a probabilistically prinipled manner.
Project description
A tool for combining bed regions from multiple bed files in a probabilistically-prinipled manner.
Installation
It is recommended to install mumerge using a virtual environment or package manager—e.g. venv or conda. Specifically, because bedtools must be available at the command line we recommend you create a new environment with conda and install bedtools from bioconda, as follows:
(base) $ conda create -n mumerge_env (base) $ conda activate mumerge_env (mumerge_env) $ conda install -c bioconda bedtools
To confirm installation, check the bedtools version:
(mumerge_env) $ bedtools --version bedtools v2.30.0
Now, with bedtools available within your environment Via pip ———– The simplest way of installing mumerge within your virtual environment is using pip. Be sure to use the appropriate version of Python if you have multiple versions installed. mumerge can then be installed with one of the following commands.
From PyPI:
$ python -m pip install mumerge
From GitHub:
$ python -m pip install git+https://github.com/Dowell-Lab/mumerge
If successful, mumerge should now be callable from the command line.
In order to upgrade to the latest version of mumerge from a previous one, include --upgrade in other of the previous pip commands.
Via git clone
Alternatively, you can download mumerge and all supporting files by cloning the GitHub repository to your local machine using git:
$ git clone https://github.com/Dowell-Lab/mumerge.git
If you clone the repo, you may want to add directory mumerge/mumerge to your system PATH variable (this will depend on your platform/OS) so that you can run mumerge directly from the command-line.
Dependencies
Python>=3.6 https://www.python.org/downloads/
NumPy https://numpy.org/
bedtools https://bedtools.readthedocs.io/en/latest/content/installation.html
NumPy will be installed automatically when using pip to install mumerge. However, bedtools must be installed manually and made available in your system path prior to running mumerge.
Bedtools
muMerge relies on bedtools in order to group together those bed regions from the input bed files that will be combined by muMerge probabilistically. This grouping is done using the bedtools merge command. A bedtools binary is included as a part of the package, located at /mumerge/bin/bedtools.
Running demo
To demonstrate the functionality of muMerge a simple example including bedfiles and an input file are included in the package.
Usage
For general usage, used the help command:
$ mumerge -h
This will return the general commands needed to run muMerge:
usage: mumerge.py [-h] [-H] [-i INPUT] [-o OUTPUT] [-w WIDTH] [-m MERGED] [-r] [-v] Merges region calls (mu) generated by Tfit, or other peak calling functions across multiple samples and replicates. optional arguments: -h, --help show this help message and exit -H, --HELP Verbose help info about the input format. -i INPUT, --input INPUT Input file (full path) containing bedfiles, sample ID's and replicate grouping names (tab delimited). Each sample on separate line. First line header, equal to '#file<TAB>sampid<TAB>group', required. 'file' must be full path. 'sampid' can be any string. 'group' can be string or integer. See '-H' help flag for more information. -o OUTPUT, --output OUTPUT Output file basename (full path, sans extension). WARNING: will overwrite any existing file) -w WIDTH, --width WIDTH The ratio of a the sigma for the corresponding probabilty distribution to the bed region (half-width) --- sigma:half-bed (default: 1). The choice for this parameter will depend on the data type as well as how bed regions were inferred from the expression data. -m MERGED, --merged MERGED Sorted bedfile (full path) containing the regions over which to combine the sample bedfiles. If not specified, mumerge will generate one directly from the sample bedfiles. -r, --remove_singletons Remove calls not present in more than 1 sample -v, --verbose Verbose printing during processing.
Input file
The <INPUT> file is a tab delimited text file that contains paths to BED files to be merged along with sample names as condition/replicate information for each sample. In the example below, there are 4 samples with two treatment groups.
#file sampid group /path/to/sample1.bed sample1 control /path/to/sample2.bed sample2 control /path/to/sample3.bed sample3 treatment /path/to/sample4.bed sample4 treatment
You can find this information using the -H flag—i.e. running mumerge -H, which will return the following:
Input file containing bedfiles, sample ID's, and replicate groupings. Input file (indicated by the '-i' flag) should be of the following (tab delimited) format: #file sampid group /full/file/path/filename1.bed sampid1 A /full/file/path/filename2.bed sampid2 B ... Header line indicated by '#' character must be included and fields must follow the same order as non-header lines. The order of subsequent lines does matter. 'group' identifiers should group files that are technical/biological replicates. Different experimental conditions should recieve different 'group' identifiers. The 'group' identifier can be of type 'int' or 'str'. If 'sampid' is not specified, then default sample ID's will be used.
Output files
muMerge returns the merged regions in BED file format (project_id_MUMERGE.bed). Additionally, a log file (project_id.log) that details the summary of the run is also inlcuded along with intermediate files (project_id_MISCALLS.bed and project_id_BEDTOOLS_MERGE.bed).
Runtime
The overall run time depends on the the number for input BED files and regions being merged. A test case, where 8 samples (~30,000 regions) with 6 condition groups were merged, took about 12 minutes on a MacBook Pro iCore i9 2.3 GHz running macOS v 10.14.6.
Cite
Please cite the following article if you use muMerge: Transcription factor enrichment analysis (TFEA) quantifies the activity of multiple transcription factors from a single experiment <https://doi.org/10.1038/s42003-021-02153-7>
BibTeX citation:
@article{rubin2021transcription, title={Transcription factor enrichment analysis (TFEA) quantifies the activity of multiple transcription factors from a single experiment}, author={Rubin, Jonathan D and Stanley, Jacob T and Sigauke, Rutendo F and Levandowski, Cecilia B and Maas, Zachary L and Westfall, Jessica and Taatjes, Dylan J and Dowell, Robin D}, journal={Communications biology}, volume={4}, number={1}, pages={1--15}, year={2021}, publisher={Nature Publishing Group} }
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