1. Genomic data mining for functional annotation of human long noncoding RNAs
- Author
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Liangjiang Wang, Brian Gudenas, An-qi Wei, Jun Wang, Steven Cogill, and Shuzhen Kuang
- Subjects
0301 basic medicine ,Support Vector Machine ,Autism Spectrum Disorder ,Genomics ,Computational biology ,Review ,Biology ,ENCODE ,General Biochemistry, Genetics and Molecular Biology ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Gene expression ,Data Mining ,Humans ,General Pharmacology, Toxicology and Pharmaceutics ,Gene ,Genomic organization ,General Veterinary ,General Medicine ,Non-coding RNA ,Long non-coding RNA ,030104 developmental biology ,030220 oncology & carcinogenesis ,Human genome ,RNA, Long Noncoding - 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.
- Published
- 2019