Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA

Abstract
BackgroundPolyadenylation is a critical stage of RNA processing during the formation of mature mRNA, and is present in most of the known eukaryote protein-coding transcripts and many long non-coding RNAs. The correct identification of poly(A) signals (PAS) not only helps to elucidate the 3′-end genomic boundaries of a transcribed DNA region and gene regulatory mechanisms but also gives insight into the multiple transcript isoforms resulting from alternative PAS. Although progress has been made in the in-silico prediction of genomic signals, the recognition of PAS in DNA genomic sequences remains a challenge.ResultsIn this study, we analyzed human genomic DNA sequences for the 12 most common PAS variants. Our analysis has identified a set of features that helps in the recognition of true PAS, which may be involved in the regulation of the polyadenylation process. The proposed features, in combination with a recognition model, resulted in a novel method and tool, Omni-PolyA. Omni-PolyA combines several machine learning techniques such as different classifiers in a tree-like decision structure and genetic algorithms for deriving a robust classification model. We performed a comparison between results obtained by state-of-the-art methods, deep neural networks, and Omni-PolyA. Results show that Omni-PolyA significantly reduced the average classification error rate by 35.37% in the prediction of the 12 considered PAS variants relative to the state-of-the-art results.ConclusionsThe results of our study demonstrate that Omni-PolyA is currently the most accurate model for the prediction of PAS in human and can serve as a useful complement to other PAS recognition methods. Omni-PolyA is publicly available as an online tool accessible at www.cbrc.kaust.edu.sa/omnipolya/.

Citation
Magana-Mora A, Kalkatawi M, Bajic VB (2017) Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA. BMC Genomics 18. Available: http://dx.doi.org/10.1186/s12864-017-4033-7.

Acknowledgements
This research made use of the resources of CBRC and IT Research Computing at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. This work was supported by King Abdullah University of Science and Technology (KAUST) through the baseline fund BAS/1/1606–01-01 of VBB.

Publisher
Springer Nature

Journal
BMC Genomics

DOI
10.1186/s12864-017-4033-7

PubMed ID
28810905

Additional Links
http://link.springer.com/article/10.1186/s12864-017-4033-7

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