LncDC: a machine learning-based tool for long non-coding RNA detection from RNA-Seq data

Long non-coding RNAs (lncRNAs) play an essential role in diverse biological processes and disease development. Accurate classification of lncRNAs and mRNAs is important for the identification of tissue- or disease-specific lncRNAs. Here, we present our tool LncDC (Long non-coding RNA detection) that...

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Bibliographic Details
Published in:Scientific reports Vol. 12; no. 1; pp. 19083 - 18
Main Authors: Li, Minghua, Liang, Chun
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 09.11.2022
Nature Portfolio
Subjects:
631/114
631/114/1305
Humanities and Social Sciences
Machine Learning
multidisciplinary
Proteins - genetics
RNA, Long Noncoding - genetics
RNA, Long Noncoding - metabolism
RNA, Messenger - genetics
RNA-Seq
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Long non-coding RNAs (lncRNAs) play an essential role in diverse biological processes and disease development. Accurate classification of lncRNAs and mRNAs is important for the identification of tissue- or disease-specific lncRNAs. Here, we present our tool LncDC (Long non-coding RNA detection) that is able to accurately predict lncRNAs with an XGBoost model using features extracted from RNA sequences, secondary structures, and translated proteins. Benchmarking experiments showed that LncDC consistently outperformed six state-of-the-art tools in distinguishing lncRNAs from mRNAs. Notably, the use of sequence and secondary structure (SASS) k-mer score features and flexible ORF features improved the classification capability of LncDC. We anticipate that LncDC will definitely promote the discovery of more and novel disease-specific lncRNAs. LncDC is implemented in Python and freely available at https://github.com/lim74/LncDC .
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-22082-7