Dataset Information

Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk.

ABSTRACT: BACKGROUND:With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell differentiation regulation and other aspects. However, the majority of the lncRNAs have not been functionally characterized. Explore the function of lncRNAs and the regulatory network has become a hot research topic currently. METHODS:In the work, a network-based model named BiRWLGO is developed. The ultimate goal is to predict the probable functions for lncRNAs at large scale. The new model starts with building a global network composed of three networks: lncRNA similarity network, lncRNA-protein association network and protein-protein interaction (PPI) network. After that, it utilizes bi-random walk algorithm to explore the similarities between lncRNAs and proteins. Finally, we can annotate an lncRNA with the Gene Ontology (GO) terms according to its neighboring proteins. RESULTS:We compare the performance of BiRWLGO with the state-of-the-art models on a manually annotated lncRNA benchmark with known GO terms. The experimental results assert that BiRWLGO outperforms other methods in terms of both maximum F-measure (Fmax) and coverage. CONCLUSIONS:BiRWLGO is a relatively efficient method to predict the functions of lncRNA. When protein interaction data is integrated, the predictive performance of BiRWLGO gains a great improvement.

SUBMITTER: Zhang J

PROVIDER: S-EPMC6245587 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk.

Zhang Jingpu J Zou Shuai S Deng Lei L

BMC medical genomics 20181120 Suppl 5

<h4>Background</h4>With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell differentiation regulation and other aspects. However, the majority of the lncRNAs have not been functionally characterized. Explore the function of lncRNAs and the regulatory network has become a hot re ...[more]

PMID: 30453964

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Project description:Computational analysis and bioinformatics have significantly advanced the ability of researchers to process and analyze biological data. Molecular data from human and model organisms may facilitate drug target validation and identification of biomarkers with increased predictive accuracy. The aim of the present study was to investigate the function of long non-coding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) using online databases, and to predict their regulatory mechanism. HCC-associated lncRNAs, their downstream transcription factors and microRNAs (miRNAs/miRs), as well as the HCC-associated target genes, were identified using online databases. HCC-associated lncRNAs, including HOX antisense intergenic RNA (HOTAIR) and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) were selected based on established databases of lncRNAs. The interaction between the HCC-associated lncRNAs and miRNAs (hsa-miR-1, hsa-miR-20a-5p) was predicted using starBase2.0. Signal transducer and activator of transcription 1, hepatocyte nuclear factor 4? (HNF4A), octamer-binding transcription factor 4, Nanog homeobox (NANOG), caudal type homeobox 2 (CDX2), DEAD-box helicase 5, brahma-related gene 1, MYC-associated factor X and MYC proto-oncogene, bHLH transcription factor have been identified as the transcription factors for HOTAIR and MALAT1 using ChIPBase. Additionally, CDX2, HNF4A, NANOG, ETS transcription factor, Jun proto-oncogene and forkhead box protein A1 were identified as the transcription factors for hsa-miR-1 and hsa-miR-20a-5p. CDX2, HNF4A and NANOG were the transcriptional factors in common between the lncRNAs and miRNAs. Cyclin D1, E2F transcription factor 1, epithelial growth factor receptor, MYC, MET proto-oncogene, receptor tyrosine kinase and vascular endothelial growth factor A were identified as target genes for the HCC progression, two of which were also the target genes of hsa-miR-1 and hsa-miR-20a-5p using the miRwalk and OncoDN. HCC databases. Additionally, these target genes may be involved in biological functions, including the regulation of cell growth, cell cycle progression and mitosis, and in disease progression, as demonstrated using DAVID clustering analysis. The present study aimed to predict a regulatory network of lncRNAs in HCC progression using bioinformatics analysis.

Dataset Information

Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk.

Publications

Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk.

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