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

A. Magana-Mora, M. Kalkatawi, V.B. Bajic
BMC Genomics, 18(1):620, (2017)

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

Keywords

Polyadenylation, Prediction, Genomic DNA, Machine learning, Bioinformatics

Abstract

Background

Polyadenylation 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.

Results

In 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.

Code

DOI: org/10.1186/s12864-017-4033-7

Sources

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