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Functional enrichment analysis based on long noncoding RNA associations

BACKGROUND: Differential gene expression analysis using RNA-seq data is a popular approach for discovering specific regulation mechanisms under certain environmental settings. Both gene ontology (GO) and KEGG pathway enrichment analysis are major processes for investigating gene groups that particip...

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Autores principales: Hung, Kuo-Sheng, Hsiao, Chung-Chi, Pai, Tun-Wen, Hu, Chin-Hwa, Tzou, Wen-Shyong, Wang, Wen-Der, Chen, Yet-Ran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998891/
https://www.ncbi.nlm.nih.gov/pubmed/29745842
http://dx.doi.org/10.1186/s12918-018-0571-0
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author Hung, Kuo-Sheng
Hsiao, Chung-Chi
Pai, Tun-Wen
Hu, Chin-Hwa
Tzou, Wen-Shyong
Wang, Wen-Der
Chen, Yet-Ran
author_facet Hung, Kuo-Sheng
Hsiao, Chung-Chi
Pai, Tun-Wen
Hu, Chin-Hwa
Tzou, Wen-Shyong
Wang, Wen-Der
Chen, Yet-Ran
author_sort Hung, Kuo-Sheng
collection PubMed
description BACKGROUND: Differential gene expression analysis using RNA-seq data is a popular approach for discovering specific regulation mechanisms under certain environmental settings. Both gene ontology (GO) and KEGG pathway enrichment analysis are major processes for investigating gene groups that participate in common biological responses or possess related functions. However, traditional approaches based on differentially expressed genes only detect a few significant GO terms and pathways, which are frequently insufficient to explain all-inclusive gene regulation mechanisms. METHODS: Transcriptomes of survivin (birc5) gene knock-down experimental and wild-type control zebrafish embryos were sequenced and assembled, and a differential expression (DE) gene list was obtained for traditional functional enrichment analysis. In addition to including DE genes with significant fold-change levels, we considered additional associated genes near or overlapped with differentially expressed long noncoding RNAs (DE lncRNAs), which may directly or indirectly activate or inhibit target genes and play important roles in regulation networks. Both the original DE gene list and the additional DE lncRNA-associated genes were combined to perform a comprehensive overrepresentation analysis. RESULTS: In this study, a total of 638 DE genes and 616 DE lncRNA-associated genes (lncGenes) were leveraged simultaneously in searching for significant GO terms and KEGG pathways. Compared to the traditional approach of only using a differential expression gene list, the proposed method of employing DE lncRNA-associated genes identified several additional important GO terms and KEGG pathways. In GO enrichment analysis, 60% more GO terms were obtained, and several neuron development functional terms were retrieved as complete annotations. We also observed that additional important pathways such as the FoxO and MAPK signaling pathways were retrieved, which were shown in previous reports to play important roles in apoptosis and neuron development functions regulated by the survivin gene. CONCLUSIONS: We demonstrated that incorporating genes near or overlapped with DE lncRNAs into the DE gene list outperformed the traditional enrichment analysis method for effective biological functional interpretations. These hidden interactions between lncRNAs and target genes could facilitate more comprehensive analyses.
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spelling pubmed-59988912018-06-25 Functional enrichment analysis based on long noncoding RNA associations Hung, Kuo-Sheng Hsiao, Chung-Chi Pai, Tun-Wen Hu, Chin-Hwa Tzou, Wen-Shyong Wang, Wen-Der Chen, Yet-Ran BMC Syst Biol Research BACKGROUND: Differential gene expression analysis using RNA-seq data is a popular approach for discovering specific regulation mechanisms under certain environmental settings. Both gene ontology (GO) and KEGG pathway enrichment analysis are major processes for investigating gene groups that participate in common biological responses or possess related functions. However, traditional approaches based on differentially expressed genes only detect a few significant GO terms and pathways, which are frequently insufficient to explain all-inclusive gene regulation mechanisms. METHODS: Transcriptomes of survivin (birc5) gene knock-down experimental and wild-type control zebrafish embryos were sequenced and assembled, and a differential expression (DE) gene list was obtained for traditional functional enrichment analysis. In addition to including DE genes with significant fold-change levels, we considered additional associated genes near or overlapped with differentially expressed long noncoding RNAs (DE lncRNAs), which may directly or indirectly activate or inhibit target genes and play important roles in regulation networks. Both the original DE gene list and the additional DE lncRNA-associated genes were combined to perform a comprehensive overrepresentation analysis. RESULTS: In this study, a total of 638 DE genes and 616 DE lncRNA-associated genes (lncGenes) were leveraged simultaneously in searching for significant GO terms and KEGG pathways. Compared to the traditional approach of only using a differential expression gene list, the proposed method of employing DE lncRNA-associated genes identified several additional important GO terms and KEGG pathways. In GO enrichment analysis, 60% more GO terms were obtained, and several neuron development functional terms were retrieved as complete annotations. We also observed that additional important pathways such as the FoxO and MAPK signaling pathways were retrieved, which were shown in previous reports to play important roles in apoptosis and neuron development functions regulated by the survivin gene. CONCLUSIONS: We demonstrated that incorporating genes near or overlapped with DE lncRNAs into the DE gene list outperformed the traditional enrichment analysis method for effective biological functional interpretations. These hidden interactions between lncRNAs and target genes could facilitate more comprehensive analyses. BioMed Central 2018-04-24 /pmc/articles/PMC5998891/ /pubmed/29745842 http://dx.doi.org/10.1186/s12918-018-0571-0 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Hung, Kuo-Sheng
Hsiao, Chung-Chi
Pai, Tun-Wen
Hu, Chin-Hwa
Tzou, Wen-Shyong
Wang, Wen-Der
Chen, Yet-Ran
Functional enrichment analysis based on long noncoding RNA associations
title Functional enrichment analysis based on long noncoding RNA associations
title_full Functional enrichment analysis based on long noncoding RNA associations
title_fullStr Functional enrichment analysis based on long noncoding RNA associations
title_full_unstemmed Functional enrichment analysis based on long noncoding RNA associations
title_short Functional enrichment analysis based on long noncoding RNA associations
title_sort functional enrichment analysis based on long noncoding rna associations
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998891/
https://www.ncbi.nlm.nih.gov/pubmed/29745842
http://dx.doi.org/10.1186/s12918-018-0571-0
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