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lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

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RNAdetect: efficient computational detection of novel non-coding RNAs.

Chun-Chi Chen^1,2, Xiaoning Qian^1,2, Byung-Jun Yoon^1,2

¹Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.

Bioinformatics (Oxford, England)

|September 1, 2018

Summary

RNAdetect accurately detects non-coding RNAs (ncRNAs) using novel features and comparative genome analysis. This method outperforms existing tools in identifying functional ncRNAs, aiding genomic research.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Non-coding RNAs (ncRNAs) are vital for biological processes but challenging to detect computationally due to lack of distinctive sequence features.
Structure-based approaches alone are insufficient for single-sequence ncRNA detection.
Current effective methods combine structure and comparative genome analysis.

Purpose of the Study:

To introduce RNAdetect, a novel computational method for accurate ncRNA detection.
To improve ncRNA identification by integrating new predictive features with comparative genome analysis.

Main Methods:

RNAdetect utilizes generalized ensemble defect (GED) to assess structure conservation across sequences.
N-gram models (NGMs) capture sequence homology for ncRNA family recognition.
The method combines GED and NGMs with comparative genome analysis.

Main Results:

RNAdetect accurately detects functional ncRNAs in sequence alignments.
The method demonstrates superior performance in identifying novel ncRNAs compared to state-of-the-art approaches.
Evaluations on the Rfam database and bacterial genomes confirm RNAdetect's reliability.

Conclusions:

RNAdetect offers a powerful and reliable tool for computational ncRNA detection.
The method enhances the ability to discover novel ncRNAs, advancing genomic research.
The source code and data are publicly available for further research and application.