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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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...

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

Lei Sun¹, Hui Liu², Lin Zhang²

¹School of Information Engineering, Yangzhou University, Yangzhou, Jiangsu Province, China; Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, Jiangsu Province, China.

|October 6, 2015

Summary

This summary is machine-generated.

Distinguishing long non-coding RNA transcripts (lncRNAs) from protein-coding transcripts (PCTs) is challenging. The novel lncRScan-SVM method accurately classifies these transcripts using support vector machines and integrated features.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Functional long non-coding RNAs (lncRNAs) offer new biological insights, but differentiating them from protein-coding transcripts (PCTs) remains difficult.
Existing data and methods necessitate improved accuracy in lncRNA identification.

Purpose of the Study:

To develop and evaluate lncRScan-SVM, a novel method for accurate classification of lncRNA transcripts (LNCTs) and protein-coding transcripts (PCTs).

Main Methods:

Utilized support vector machine (SVM) for classification.
Constructed gold-standard datasets from GENCODE gene annotations for human and mouse.
Integrated features including gene structure, transcript sequence, potential codon sequence, and evolutionary conservation.

Main Results:

lncRScan-SVM demonstrated superior performance compared to other methods.
Performance was evaluated using sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the curve (AUC).
The method was successfully applied to assess known human lncRNA datasets.

Conclusions:

lncRScan-SVM is an efficient and valuable tool for predicting lncRNAs.
This method significantly aids current research in long non-coding RNA studies.