Genomic data mining for functional annotation of human long noncoding RNAs.
- Author:
Brian L GUDENAS
1
;
Jun WANG
1
;
Shu-Zhen KUANG
1
;
An-Qi WEI
1
;
Steven B COGILL
1
;
Liang-Jiang WANG
1
Author Information
- Publication Type:Journal Article
- Keywords: Long noncoding RNA; Functional annotation; Genomic data mining; Machine learning
- MeSH: Autism Spectrum Disorder; genetics; Data Mining; Genomics; Humans; Machine Learning; RNA, Long Noncoding; analysis; physiology; Support Vector Machine
- From: Journal of Zhejiang University. Science. B 2019;20(6):476-487
- CountryChina
- Language:English
- Abstract: Life may have begun in an RNA world, which is supported by increasing evidence of the vital role that RNAs perform in biological systems. In the human genome, most genes actually do not encode proteins; they are noncoding RNA genes. The largest class of noncoding genes is known as long noncoding RNAs (lncRNAs), which are transcripts greater in length than 200 nucleotides, but with no protein-coding capacity. While some lncRNAs have been demonstrated to be key regulators of gene expression and 3D genome organization, most lncRNAs are still uncharacterized. We thus propose several data mining and machine learning approaches for the functional annotation of human lncRNAs by leveraging the vast amount of data from genetic and genomic studies. Recent results from our studies and those of other groups indicate that genomic data mining can give insights into lncRNA functions and provide valuable information for experimental studies of candidate lncRNAs associated with human disease.
