A novel method for improved accuracy of transcription factor binding site prediction
AuthorsKhamis, Abdullah M.
Motwalli, Olaa Amin
Jankovic, Boris R.
Bajic, Vladimir B.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
Applied Mathematics and Computational Science Program
MetadataShow full item record
AbstractIdentifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.
CitationKhamis AM, Motwalli O, Oliva R, Jankovic BR, Medvedeva YA, et al. (2018) A novel method for improved accuracy of transcription factor binding site prediction. Nucleic Acids Research. Available: http://dx.doi.org/10.1093/nar/gky237.
SponsorsThe computational analysis for this study was performed on Dragon and Snapdragon compute clusters of the Computational Bioscience Research Center at KAUST. King Abdullah University of Science and Technology (KAUST) [BAS/1/1606-01-01 to V.B.B.]. Funding for open access charge: KAUST [BAS/1/1606-01-01].
PublisherOxford University Press (OUP)
JournalNucleic Acids Research
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact firstname.lastname@example.org
- EMQIT: a machine learning approach for energy based PWM matrix quality improvement.
- Authors: Smolinska K, Pacholczyk M
- Issue date: 2017 Aug 1
- HOCOMOCO: a comprehensive collection of human transcription factor binding sites models.
- Authors: Kulakovskiy IV, Medvedeva YA, Schaefer U, Kasianov AS, Vorontsov IE, Bajic VB, Makeev VJ
- Issue date: 2013 Jan
- Tree-based position weight matrix approach to model transcription factor binding site profiles.
- Authors: Bi Y, Kim H, Gupta R, Davuluri RV
- Issue date: 2011
- Transcription Factor Information System (TFIS): A Tool for Detection of Transcription Factor Binding Sites.
- Authors: Narad P, Kumar A, Chakraborty A, Patni P, Sengupta A, Wadhwa G, Upadhyaya KC
- Issue date: 2017 Sep
- Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data.
- Authors: Levitsky VG, Kulakovskiy IV, Ershov NI, Oshchepkov DY, Makeev VJ, Hodgman TC, Merkulova TI
- Issue date: 2014 Jan 29