RNAdetect: efficient computational detection of novel non-coding RNAs

Bioinformatics
Chun-Chi ChenByung-Jun Yoon

Abstract

Non-coding RNAs (ncRNAs) are known to play crucial roles in various biological processes, and there is a pressing need for accurate computational detection methods that could be used to efficiently scan genomes to detect novel ncRNAs. However, unlike coding genes, ncRNAs often lack distinctive sequence features that could be used for recognizing them. Although many ncRNAs are known to have a well conserved secondary structure, which provides useful cues for computational prediction, it has been also shown that a structure-based approach alone may not be sufficient for detecting ncRNAs in a single sequence. Currently, the most effective ncRNA detection methods combine structure-based techniques with a comparative genome analysis approach to improve the prediction performance. In this paper, we propose RNAdetect, a computational method incorporating novel features for accurate detection of ncRNAs in combination with comparative genome analysis. Given a sequence alignment, RNAdetect can accurately detect the presence of functional ncRNAs by incorporating novel predictive features based on the concept of generalized ensemble defect (GED), which assesses the degree of structure conservation across multiple related sequences and the ...Continue Reading

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Citations

Jul 8, 2020·BMC Bioinformatics·Lisardo FernándezJoan Marquez-Molins
Jul 6, 2021·Frontiers in Microbiology·Mariana Teixeira Dornelles PariseJan Baumbach

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