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Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA
Arturo Magana-Mora1, Manal Kalkatawi1, Vladimir B Bajic2
1Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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.
Conclusions:
The 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/ .
Insights
This study introduces Omni-PolyA, a new tool for accurately identifying poly(A) signals (PAS) in human DNA. Omni-PolyA significantly improves prediction accuracy compared to existing methods, aiding RNA processing research.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Polyadenylation is crucial for mature mRNA and non-coding RNA formation in eukaryotes.
- Accurate identification of poly(A) signals (PAS) is essential for understanding gene regulation and transcript diversity.
- In-silico prediction of PAS in genomic sequences remains a significant challenge.
Purpose of the Study:
- To develop a novel, highly accurate method for predicting poly(A) signals (PAS) in human genomic DNA.
- To identify key features involved in the regulation of the polyadenylation process.
- To provide a publicly accessible tool for PAS recognition.
Main Methods:
- Analysis of human genomic DNA sequences for 12 common PAS variants.
- Development of Omni-PolyA, a novel recognition tool combining machine learning classifiers in a decision tree structure and genetic algorithms.
- Comparison of Omni-PolyA's performance against state-of-the-art methods, including deep neural networks.
Main Results:
- Identification of a set of features crucial for true PAS recognition.
- Omni-PolyA demonstrated a significant 35.37% reduction in average classification error rate for 12 PAS variants compared to state-of-the-art methods.
- The developed tool, Omni-PolyA, achieved superior prediction accuracy.
Conclusions:
- Omni-PolyA represents the most accurate model currently available for PAS prediction in human DNA.
- The tool can effectively complement existing PAS recognition methods.
- Omni-PolyA is accessible as a public online tool.
