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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:


The latest release of MAmotif is available at PyPI:

$ pip install mamotif

Or you can install MAmotif via conda:

$ conda install -c bioconda mamotif

MAmotif uses setuptools for installation from source code. The source code of MAmotif is hosted on GitHub:

You can clone the repo and execute the following command under source directory:

$ python install

Galaxy Installation



You need to build some prerequisites before running MAmotif:

Build genomes

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

Build motifs (Optional)

Note: MAmotif provides some preprocessed motif PWM files under data/motif of the MotifScan package.

You can download it by:

$wget --no-check-certificate

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

run MAmotif

$ 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.

Output of MAmotif

After finished running MAmotif, all output files will be written to the directory you specified with “-o” argument.

Main output

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

MAnorm output

MAmotif will invoke MAnorm and output the normalization results and MA-plot for samples under comparison.

MotifScan output

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.

Example Usage

Here we provide a step-by-step instruction on how to use MAmotif to find candidate cell-type specific regulators associated with certain histone modifications.

We take the H3K4me3 analysis between adult and fetal ProES in MAmotif paper as an example:

  1. Install MAmotif:

    $pip install mamotif
    $conda install -c bioconda mamotif
  2. Download all data MAmotif needs:

    $mkdir MAmotif_demo
    $cd MAmotif_demo
    $gzip -d *gz
    Remove the header line and ribosomal reads (You do not need to do this for modern ChIP-seq mapping softwares)
    $sed -i '1d' GSM908038_H3K4me3-F.bed
    $sed -i '1d' GSM908039_H3K4me3-A.bed
    $sed -i '8986927,$d' GSM908039_H3K4me3-F.bed
    $sed -i '14916308,$d' GSM908039_H3K4me3-A.bed
    Substitute space into tab for bed files (You do not need to do this for your own bed files are tab-separated)
    $sed -i "s/ /\t/g" GSM908038_H3K4me3-F.bed
    $sed -i "s/ /\t/g" GSM908039_H3K4me3-A.bed
  3. Build for genome sequences:

    $mkdir genome
    $cd genome
    $cat *fa > hg18.fa
    $genomecompile -G hg18.fa -o hg18
    $cd ..
  4. Build for motif PWM (Optional)

The motif matrix file which containing the motif score cutoff is already packaged under /data directory under MotifScan package.

You can download it by:

$wget --no-check-certificate

If you want you compile for your custom motifs, please run the following commands:

$mkdir motif
$cd motif
$tar -xzvf nonredundant.tar.gz
$motifcompile -M nonredundant/pfm_vertebrates.txt -g ../genome/hg18 -o hg18_jaspar2016_nonredundant_vertebrates
$cd ..
  1. Run MAmotif:

    $mamotif --p1 GSM908039_H3K4me3-A_peaks.bed --p2 GSM908038_H3K4me3-F_peaks.bed --r1 GSM908039_H3K4me3-A.bed --r2 GSM908038_H3K4me3-F.bed -g genome/hg18 -m motif/hg18_jaspar2016_nonredundant_vertebrates_1e-4.txt -o AvsF_H3K4me3_MAmotif
  2. Check the output of MAmotif

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