We provide here the documentation of the TFFM-framework developed in Python. The Transcription Factor Flexible Models (TFFMs) represent TFBSs and are based on hidden Markov models (HMM). They are flexible and are able to model both position interdependence within TFBSs and variable length motifs within a single dedicated framework.
The framework also implements methods to generate a new graphical representation of the modeled motifs that convey properties of position interdependences.
TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that the new HMM-based framework performs, in most cases, better than both PWMs and the dinucleotide weight matrix (DWM) extension in discriminating motifs within ChIP-seq sequences from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we observe that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS predictions. All the details have been published in Mathelier and Wasserman, The next generation of transcription binding site prediction.
TFFMs can be saved and opened from files using the XML format already used by the GHMM library.
We recommend you to read the documentation to get more information at http://cisreg.cmmt.ubc.ca/TFFM/doc/index.html.
Anthony Mathelier and Wyeth W. Wasserman
Centre for Molecular Medicine and Therapeutics
950 West 28th Avenue
Vancouver, BC
V5Z 4H4 Canada
anthony.mathelier@ncmm.uio.no
wyeth@cmmt.ubc.ca
Refer to the AUTHORS file.
- The TFFM-framework 2.0 has been developed and tested under Ubuntu Linux operating system. It has also been tested on CentOS.
- Python should be installed (version 2.7 has been used successfully).
- Biopython (at least version 1.61) should be installed and accessible from your Python executable. See http://biopython.org for instructions on how to install it.
- The GHMM library should be installed and accessible from Python. See http://ghmm.org for instructions on how to install it.
For a brief tutorial on how to use the TFFMs, go to http://cisreg.cmmt.ubc.ca/TFFM/doc/index.html.
The TFFM-framework can be downloaded on github.
You can generate TFFMs and scan DNA sequences with TFFMs using the dedicated web application at http://cisreg.cmmt.ubc.ca/TFFM/webapp/.
The TFFMs and the TFFM-framework have been described in Mathelier and Wasserman. The next generation of transcription binding site prediction. Plos Computational Biology, 2013.
Please cite this publication when using the TFFMs.
- The TFFM-framework has been developed under the GNU Lesser General Public Licence.
- The TFFM-framework uses the GHMM library which is also licenced under the GNU LGPL.