TY  - JOUR
A1  - Ardakani, Fatemeh Behjati
A1  - Schmidt, Florian
A1  - Schulz, Marcel Holger
T1  - Predicting transcription factor binding using ensemble random forest models [version 2; peer review: 2 approved]
T2  - F1000Research
N2  - Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs).
Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups.
Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier built based upon data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal.
Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).
KW  - ENCODE-DREAM in vivo Transcription Factor binding site prediction challenge
KW  - Transcription Factors
KW  - Chromatin accessibility
KW  - Ensemble learning
KW  - Indirect-binding
KW  - TF-complexes
KW  - DNase1-seq
Y1  - 2019
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/53556
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-535567
SN  - 2046-1402
N1  - Copyright:  © 2019 Behjati Ardakani F et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
VL  - 7
IS  - Art. 1603
SP  - 1
EP  - 30
PB  - F1000 Research Ltd
CY  - London
ER  -