KATZLGO: Large-Scale Prediction of LncRNA Functions by Using the KATZ Measure Based on Multiple Networks

Aggregating evidences have shown that long non-coding RNAs (lncRNAs) generally play key roles in cellular biological processes such as epigenetic regulation, gene expression regulation at transcriptional and post-transcriptional levels, cell differentiation, and others. However, most lncRNAs have no...

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Vydané v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 16; číslo 2; s. 407 - 416
Hlavní autori: Zhang, Zuping, Zhang, Jingpu, Fan, Chao, Tang, Yongjun, Deng, Lei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.03.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Annotations
Benchmarks
Biological activity
Cell differentiation
Computation
Computational Biology - methods
Computer applications
Correlation
Differentiation (biology)
Encoding
Gene expression
Gene Expression Profiling
Gene Ontology
Gene regulation
Genes
Heterogeneous networks
Humans
Katz measure
lncRNA function
Models, Statistical
multiple networks
Neoplasms - genetics
Ontologies
Post-transcription
Protein interaction
Proteins
Proteins - genetics
RNA
RNA, Long Noncoding - genetics
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ISSN:1545-5963, 1557-9964, 1557-9964
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Popis
Shrnutí:Aggregating evidences have shown that long non-coding RNAs (lncRNAs) generally play key roles in cellular biological processes such as epigenetic regulation, gene expression regulation at transcriptional and post-transcriptional levels, cell differentiation, and others. However, most lncRNAs have not been functionally characterized. There is an urgent need to develop computational approaches for function annotation of increasing available lncRNAs. In this article, we propose a global network-based method, KATZLGO, to predict the functions of human lncRNAs at large scale. A global network is constructed by integrating three heterogeneous networks: lncRNA-lncRNA similarity network, lncRNA-protein association network, and protein-protein interaction network. The KATZ measure is then employed to calculate similarities between lncRNAs and proteins in the global network. We annotate lncRNAs with Gene Ontology (GO) terms of their neighboring protein-coding genes based on the KATZ similarity scores. The performance of KATZLGO is evaluated on a manually annotated lncRNA benchmark and a protein-coding gene benchmark with known function annotations. KATZLGO significantly outperforms state-of-the-art computational method both in maximum F-measure and coverage. Furthermore, we apply KATZLGO to predict functions of human lncRNAs and successfully map 12,318 human lncRNA genes to GO terms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2017.2704587