DeepGSR: An optimized deep-learning structure for the recognition of genomic signals and regions
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Applied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberBAS/1/1606-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/628696
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AbstractMotivation \nRecognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have been developed to recognize GSRs, their pure computational identification remains challenging. Moreover, various GSRs usually require a specialized set of features for developing robust recognition models. Recently, deep-learning (DL) methods have been shown to generate more accurate prediction models than ‘shallow’ methods without the need to develop specialized features for the problems in question. Here, we explore the potential use of DL for the recognition of GSRs. \nResults \nWe developed DeepGSR, an optimized DL architecture for the prediction of different types of GSRs. The performance of the DeepGSR structure is evaluated on the recognition of polyadenylation signals (PAS) and translation initiation sites (TIS) of different organisms: human, mouse, bovine, and fruit fly. The results show that DeepGSR outperformed the state-of-the-art methods, reducing the classification error rate of the PAS and TIS prediction in the human genome by up to 29% and 86%, respectively. Moreover, the cross-organisms and genome-wide analyses we performed, confirmed the robustness of DeepGSR and provided new insights into the conservation of examined GSRs across species.
CitationKalkatawi M, Magana-Mora A, Jankovic B, Bajic VB (2018) DeepGSR: An optimized deep-learning structure for the recognition of genomic signals and regions. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/bty752.
SponsorsWe are grateful to Mohammad Shoaib Amini for helping with the data extraction. This research made use of the resources of the compute and GPU clusters at King Abdullah University of Science & Technology (KAUST), Thuwal, Saudi Arabia. This work was supported by the King Abdullah University of Science and Technology (KAUST) through the baseline research fund BAS/1/1606-01-01 for Vladimir B. Bajic. The open access charges for this article are covered from the same fund.
PublisherOxford University Press (OUP)
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