MAmotif is used to compare two ChIP-seq samples of the same protein from different cell types (or conditions, e.g. wild-type vs mutant) and identify transcriptional factors (TFs) associated with the cell type-biased binding of this protein as its co-factors, by using TF binding information obtained from motif analysis (or from other ChIP-seq data). MAmotif automatically combines MAnorm model to perform quantitative comparison on input ChIP-seq samples together with Motif-Scan toolkit to scan ChIP-seq peaks for TF binding motifs, and uses a systematic integrative analysis to search for TFs whose binding sites are significantly associated with the cell type-biased peaks between two ChIP-seq samples. When applying to ChIP-seq data of histone marks of regulatory elements (such as H3K4me3 for active promoters and H3K9/27ac for active promoters and enhancers), or DNase/ATAC-seq data, MAmotif can be used to detect cell type-specific regulators .
To see the full documentation of MAmoitf, please refer to: http://bioinfo.sibs.ac.cn/shaolab/mamotif/index.php
The latest version release of MAmotif is available at PyPI:
$ pip install mamotif
MAmoitf uses setuptools for installation from source code. The source code of MAmoitf is hosted on GitHub: https://github.com/shao-lab/MAmotif
You can clone the repo and execute the following command under source directory:
$ python setup.py install
Before you use MAmotif, you need to build the prerequisites for corresponding genome assembly.
$ genomecompile [-h] [-v] -G sequences.fa -o output_dir
A directory contaning compiled genome sequence and information would be generated by this command.
Note: You only need run it once for each genome.
Note: MAmoitf provides some preprocessed motif PWM files under data/motif of the MotifScan package.
IF you have some motifs that have not be included in our pre-complied motif collection, you need to compile on your own by using the following command.
$ motifcompile –M motif_pwm_demo.txt –g hg19_for_motifscan
-M motif raw matrix file
-g a pre-compiled genome directory generated by genomecompile
Motif raw matrix file should follow the format as below:
motif id and motif name are followed by a positive weighted matrix, and columns are seperated by tabs.
>MA0599.1 KLF5 1429 0 0 3477 0 5051 0 0 0 3915 2023 11900 12008 9569 13611 0 13611 13611 13135 5595 7572 0 0 0 0 5182 0 0 0 0 2587 1711 1603 565 0 3378 0 0 476 4101
$ mamoitf --p1 sample1_peaks.bed --p2 sample2_peaks.bed --r1 sample1_reads.bed --r2 sample2_reads.bed -g hg19_for_motifscan –m motif_pwm_demo.txt -o sample1_vs_sample2
Note: Using -h/--help for the details of all arguments.
After finished running MAmotif, all output files will be written to the directory you specified with "-o" argument.
The main output file will include the following fields:
1.Motif Name 2.Target Number: Number of peaks with motif targets 3.Average of Target M-value 4.Deviation of Target M-value 5.Non-target Number: Number of peaks without motif targets 6.Average of Non-target M-value 7.Deviation of Non-target M-value 8.T-test Statistics: T-Statistics for M-values of (peaks with motif targets) against (peaks without motif targets) 9.T-test P-value(right-tail) 10.T-test P-value By Benjamin correction 11.RanSum-test Statistics 12.RankSum-test P-value(right-tail) 13.RankSum-test P-value By Benjamin correction 14.Maximal P-value: Maximal corrected P-value of T-test and RankSum test
MAmotif will also output tables to summarize the motif targets number and motif score of each peak region.
If you specified "-s" with MAmotif, it will also output the genome coordinates of every motif targets.