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11 09, 2022;12 (1): 19083.

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

Minghua Li, Chun Liang
Author Information
  1. Minghua Li: Department of Biology, Miami University, Oxford, OH, 45056, USA. lim74@miamioh.edu.
  2. Chun Liang: Department of Biology, Miami University, Oxford, OH, 45056, USA. liangc@miamioh.edu.
PMID: 36351980 DOI: 10.1038/s41598-022-22082-7

Abstract

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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MeSH Term

RNA, Long Noncoding
RNA-Seq
Machine Learning
RNA, Messenger
Proteins

Chemicals

RNA, Long Noncoding
RNA, Messenger
Proteins
Journal Article Research Support, Non-U.S. Gov't
Article has an altmetric score of 5

Word Cloud

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