GimmeMotifs is a pipeline for transcription factor motif analysis written in Python. It incorporates an ensemble of computational tools to predict motifs de novo from ChIP-sequencing data. Similar redundant motifs are compared using the weighted information content similarity score and clustered using an iterative procedure. A comprehensive output report is generated with several different evaluation metrics to compare and evaluate the results.

References:

• Simon J. van Heeringen∗ and Gert Jan C. Veenstra,
  GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.
  Bioinformatics, 2011 27(2), 270-271.
• Niklas Bruse, Simon J. van Heeringen
  GimmeMotifs: an analysis framework for transcription factor motif analysis.
  bioRxiv, 2018 doi: https://doi.org/10.1101/474403
  Documentation
  • GimmeMotifs documentation
  • GimmeMotifs Github page
  Important Notes
  • Module Name: GimmeMotifs (see the modules page for more information)
  • Multithreaded
  • Unusual environment variables set
    • GMOTIFS_HOME  installation directory
    • GMOTIFS_BIN       executable directory
    • GMOTIFS_DATA  sample data directory
  Interactive job
  Interactive jobs should be used for debugging, graphics, or applications that cannot be run as batch jobs.

  Allocate an interactive session and run the program. Sample session:

  [user@biowulf]$ sinteractive --mem=16g --cpus-per-task=16
  [user@cn3335 ~]$module load gimmemotifs 
  [+] Loading GimmeMotifs  0.17.2
  

  First, specify genome reference to be used. To do this, create a default configuration file

  [user@cn3335 ~]$genomepy config generate
  Created config file /home/user/.config/genomepy/genomepy.yaml
  

  
  Download sample data to the current folder:

  [user@cn3335 ~]$cp -r /usr/local/apps/gimmemotifs/0.17.2/sample_data/ .
  

  We are now ready to predict new motifs.

  [user@cn3335 ~]$ gimme motifs -h
  usage: gimme [-h]  [options] motifs [-h] [-b BACKGROUND]
                                                  [-g GENOME] [--denovo]
                                                  [--known] [--noreport]
                                                  [--rawscore] [--nogc] [-N INT]
                                                  [-p PFMFILE] [-t N]
                                                  [-a ANALYSIS] [-k] [-S]
                                                  [-f FRACTION] [-s N]
                                                  INPUT OUTDIR
  
  positional arguments:
    INPUT                 FASTA, BED, narrowPeak or region file.
    OUTDIR                Output directory.
  
  options:
    -h, --help            show this help message and exit
    -b BACKGROUND, --background BACKGROUND
                          Background type (random,genomic,gc,promoter,custom) or
                          a file with background sequences (FASTA, BED or
                          regions)
    -g GENOME             Genome name or fasta file
    --denovo              Only use de novo motifs
    --known               Only use known motifs
    --noreport            Don't create a HTML report.
    --rawscore            Don't z-score normalize motif scores
    --nogc                Don't use GC% bins
    -N INT, --nthreads INT
                          Number of threads (default 12)
  
  optional arguments for known motifs:
    -p PFMFILE            PFM file with motifs.(default:
                          gimme.vertebrate.v5.0.pfm)
  
  optional arguments for de novo motifs:
    -t N, --tools N       Tools to use, any combination of AMD,BioProspector,ChI
                          PMunk,DiNAMO,GADEM,HMS,Homer,Improbizer,MDmodule,MEME,
                          MEMEW,MotifSampler,Posmo,ProSampler,Trawler,Weeder,XXm
                          otif (default BioProspector,Homer,MEME)
    -a ANALYSIS, --analysis ANALYSIS
                          Analysis type: small, medium, large, xl (xl)
    -k, --keepintermediate
                          Don't delete intermediate files
    -S, --singlestrand    Only predict motifs for single + strand (default is
                          both)
    -f FRACTION, --fraction FRACTION
                          Fraction of peaks to use for motif predicton (0.2)
    -s N, --size N        Region size to use for motif prediction (200). Set to
                          0 to use the size of the input regions.
  

  Run 'quick' analysis to predict de novo motifs using the gimme motifs command on the sample data with 16 threads:

  [user@cn3335 ~]$ gimme motifs TAp73alpha.bed -g /fdb/genome/hg19 -N 16 -a small -t Homer,MDmodule,BioProspector . 
  2023-01-10 12:41:29,647 - INFO - Creating new config.
  2023-01-10 12:41:29,651 - INFO - Using included version of AMD.
  2023-01-10 12:41:29,651 - INFO - Using included version of BioProspector.
  2023-01-10 12:41:29,651 - INFO - Using included version of ChIPMunk.
  2023-01-10 12:41:29,651 - INFO - Using system version of DiNAMO.
  ...
  2023-01-10 12:47:39,662 - INFO - de novo finished
  2023-01-10 12:47:39,662 - INFO - output dir: .
  2023-01-10 12:47:39,662 - INFO - de novo report: ./gimme.denovo.html
  2023-01-10 12:47:59,949 - INFO - creating motif scan tables
  2023-01-10 13:26:37,701 - INFO - selecting non-redundant motifs
  2023-01-10 13:27:59,200 - INFO - selected 14 non-redundant motifs: ROC AUC 0.952, PR AUC 0.788
  2023-01-10 13:27:59,251 - INFO - creating BED files with scan results
  2023-01-10 13:28:28,847 - INFO - creating statistics report
  ...
  2023-01-10 13:29:05,250 - INFO - gimme motifs final report: ./gimme.motifs.html