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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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Related Experiment Video

Updated: Mar 9, 2026

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Published on: July 9, 2021

Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts

Siyu Han¹, Yanchun Liang², Ying Li¹

¹College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.

Biomed Research International

|January 3, 2017

Summary

This summary is machine-generated.

Long noncoding RNAs (lncRNAs) play crucial roles in cellular processes. This review summarizes popular computational methods for identifying lncRNAs, aiding researchers in selecting optimal tools for reliable results.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Long noncoding RNAs (lncRNAs) are RNA molecules longer than 200 nucleotides.
Thousands of lncRNAs encoded in the human genome significantly impact cellular regulation.
High-throughput sequencing generates vast amounts of RNA sequence data requiring analysis.

Purpose of the Study:

To review and compare popular computational methods for distinguishing long noncoding RNA (lncRNA) transcripts from coding transcripts.
To provide guidance on selecting appropriate lncRNA identification tools based on specific research needs.
To enhance the reliability of lncRNA identification and analysis.

Main Methods:

Review of existing literature on computational tools for lncRNA identification.
Comparative analysis of different methods based on their advantages, disadvantages, and application scopes.
Summary of popular algorithms and their performance metrics.

Main Results:

Various computational tools exist for lncRNA identification, each with specific strengths and weaknesses.
The choice of method depends on factors such as data type, research question, and desired accuracy.
A systematic comparison aids researchers in making informed decisions.

Conclusions:

Selecting the right computational method is crucial for accurate lncRNA identification.
Understanding the scope and limitations of different tools improves research outcomes.
This review serves as a guide for researchers utilizing lncRNA identification tools.