Evidence map›Paper›PMID 29745842›Full record

ArticleBMC systems biology2018

Functional enrichment analysis based on long noncoding RNA associations.

Kuo-Sheng Hung, Chung-Chi Hsiao, Tun-Wen Pai, Chin-Hwa Hu, Wen-Shyong Tzou, Wen-Der Wang, Yet-Ran Chen

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
0.6field-weighted citation impact, top 40% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Article
  5. lncRNAlyzr: Enrichment Analysis for lncRNA Sets.Journal of molecular biology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Kuo-Sheng HungDepartment of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.
Chung-Chi HsiaoDepartment of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.
Tun-Wen PaiDepartment of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. twp@mail.ntou.edu.tw.
Chin-Hwa HuDepartment of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan.
Wen-Shyong TzouDepartment of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan.
Wen-Der WangDepartment of Bioagricultural Science, National Chiayi University, Chiayi, Taiwan.
Yet-Ran ChenAgricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan.
National Taiwan Ocean University · TWAgricultural Biotechnology Research Center, Academia Sinica · TWNational Chiayi University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDifferential 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.

methodsTranscriptomes 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.

resultsIn 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.

conclusionsWe 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.

Indexed as

Computational BiologyAnimalsApoptosisGene Expression ProfilingGene Knockdown TechniquesGene OntologyRNA, Long NoncodingSignal TransductionSurvivinZebrafishRNA, Long NoncodingSurvivinDifferential expressionGene ontology (GO)KEGGlncRNANeuron development

Identifiers

PMID29745842
PMCPMC5998891
OpenAlexW2802217527

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.