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
Online Publication Date2018-04-02
Print Publication Date2018-07-06
Permanent link to this recordhttp://hdl.handle.net/10754/627486
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
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