BaMM!motif specifications

Information


Unique identifier OMICS_11505
Name Bayesian Markov Model motif discovery
Alternative names BaMMmotif2, BaMMmotif
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS
Programming languages C++, Python, R
License GNU General Public License version 3.0
Computer skills Advanced
Version 2
Stability Stable
Requirements GCC, CMake, Boost, zoo, argparse, fdrtool, LSD, grid, gdata
Maintained Yes

Subtools


  • De-novo motif discovery
  • Motif scan
  • Motif database
  • Motif-motif comparison

Download


Versioning


Add your version

Documentation


Maintainer


  • person_outline Johannes Soeding <>

Additional information


http://bammserver.readthedocs.io/en/latest/ The previous version can be found at https://github.com/soedinglab/BaMMmotif

Information


Unique identifier OMICS_11505
Name Bayesian Markov Model motif discovery
Alternative names BaMMmotif2, BaMMmotif
Interface Web user interface
Restrictions to use None
Programming languages Javascript, Python
Computer skills Basic
Version 1.1
Stability Stable
Maintained Yes

Subtools


  • De-novo motif discovery
  • Motif scan
  • Motif database
  • Motif-motif comparison

Download


Documentation


Maintainer


  • person_outline Johannes Soeding <>

Additional information


http://bammserver.readthedocs.io/en/latest/ The previous version can be found at https://github.com/soedinglab/BaMMmotif

BaMM!motif articles

BaMM!motif citations

 (2)
2013
PMCID: 3694976

[…] cutoff of 1.00e-05 for the p-value. we then mapped those peaks on genome-wide scale relative to refseq mouse genes (figure 6a). 7.1% of peaks were within 1kb of the transcription start site (tss). a de novo motif discovery algorithm, meme [54], was performed on the top 1000 ranked evi1 chip-seq peaks. meme identified an aggaag ets-like motif (e-value = 2.1e-193). we then refined this motif […]

2012
PMCID: 3430608

[…] set and annotation database using publicly available data from cgd, sgd and biogrid, together with transcription factor binding data from all currently published chip-chip experiments, our own tf motif database, lists of modulated genes from both transcriptional profiling experiments, and genetic-association data obtained from sga screens measuring cell growth., for de novo identification […]

BaMM!motif institution(s)
Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Gottingen, Germany
BaMM!motif funding source(s)
Supported by the German Federal Ministry of Education and Research (BMBF) within the frameworks of e:Bio [SysCore, project 0316176A]; SPP 1935 (project CR 227/6-1) of the German Research Foundation (DFG); and the International Max Planck Research School for Genome Science (IMPRS-GS).

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