MAmotif is used to compare two ChIP-seq samples of the same protein from different cell types or conditions (e.g. Mutant vs Wild-type) 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 given 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 promoter/enhancers), or DNase/ATAC-seq data, MAmotif can be used to detect cell-type specific regulators.
To see the full documentation of MAmotif, please refer to: http://mamotif.readthedocs.io/en/latest/
The latest release of MAmotif is available at PyPI:
$ pip install mamotif
Or you can install MAmotif via conda:
WIP!
$ conda install -c bioconda mamotif
MAmotif uses setuptools for installation from source code. The source code of MAmotif 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
WIP!
You need to build some prerequisites before running MAmotif:
Preprocess sequences and genome-wide nucleotide frequency for the corresponding genome assembly.
$ genomecompile [-h] [-v] -G hg19.fa -o hg19_genome
Note: You only need to run this command once for each genome
Note: MAmotif provides some preprocessed motif PWM files under data/motif of the MotifScan package.
Build motif PWM/motif-score cutoff for custom motifs that are not included in our pre-complied motif collection:
$ motifcompile [-h] [-v] –M motif_pwm_demo.txt –g hg19_genome -o hg19_motif
$ mamotif --p1 sample1_peaks.bed --p2 sample2_peaks.bed --r1 sample1_reads.bed --r2 sample2_reads.bed -g hg19_genome –m hg19_motif_p1e-4.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.
1.Motif Name 2.Target Number: Number of motif-present peaks 3.Average of Target M-value: Average M-value of motif-present peaks 4.Deviation of Target M-value: M-value Std of motif-present peaks 5.Non-target Number: Number of motif-absent peaks 6.Average of Non-target M-value: Average M-value of motif-absent peaks 7.Deviation of Non-target M-value: M-value Std of motif-absent peaks 8.T-test Statistics: T-Statistics for M-values of motif-present peaks against motif-absent peaks 9.T-test P-value: Right-tailed P-value of T-test 10.T-test P-value By Benjamin correction 11.RanSum-test Statistics 12.RankSum-test P-value 13.RankSum-test P-value By Benjamin correction 14.Maximal P-value: Maximal corrected P-value of T-test and RankSum-test
MAmotif will invoke MAnorm and output the normalization results and MA-plot for samples under comparison.
MAmotif will also output tables to summarize the enrichment of motifs and the motif target number and motif-score of each peak region.
If you specified "-s" with MAmotif, it will also output the genome coordinates of every motif target site.