Predicting transcription factor binding using ensemble random forest models.

Autor:	Behjati Ardakani F; High throughput Genomics and Systems Biology, Cluster of Excellence on Multimodel Computing and Interaction, Saarland University, Saarbruecken,, Saarland, 66123, Germany.; Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbruecken, Saarland, 66123, Germany.; Graduate School of computer science, Saarland University, Saarbruecken, Saarland, 66123, Germany., Schmidt F; High throughput Genomics and Systems Biology, Cluster of Excellence on Multimodel Computing and Interaction, Saarland University, Saarbruecken,, Saarland, 66123, Germany.; Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbruecken, Saarland, 66123, Germany.; Graduate School of computer science, Saarland University, Saarbruecken, Saarland, 66123, Germany.; Computational Systems Biology, Genome Institute of Singapore, Singapore, Singapore., Schulz MH; High throughput Genomics and Systems Biology, Cluster of Excellence on Multimodel Computing and Interaction, Saarland University, Saarbruecken,, Saarland, 66123, Germany.; Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbruecken, Saarland, 66123, Germany.; Institute for Cardiovasular Regeneration, Goethe University Frankfurt Am Main, Frankfurt Am Main, Hessen, 60590, Germany.
Jazyk:	angličtina
Zdroj:	F1000Research [F1000Res] 2018 Oct 04; Vol. 7, pp. 1603. Date of Electronic Publication: 2018 Oct 04 (Print Publication: 2018).
DOI:	10.12688/f1000research.16200.2
Abstrakt:	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). Competing Interests: No competing interests were disclosed. (Copyright: © 2019 Behjati Ardakani F et al.)
Databáze:	MEDLINE
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