Evidence map›Paper›PMID 33076954›Full record

ArticleBiology direct2020

Predicted functional interactome of Caenorhabditis elegans and a web tool for the functional interpretation of differentially expressed genes.

Peng-Cheng Chen, Li Ruan, Jie Jin, Yu-Tian Tao, Xiao-Bao Ding, Hai-Bo Zhang, Wen-Ping Guo, Qiao-Lei Yang, Heng Yao, Xin Chen

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biology direct, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 41% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
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

10 authors at 2 institutions in 1 country.

Peng-Cheng ChenInstitute of Pharmaceutical Biotechnology of Zhejiang University School of Medicine and Department of Radiology of the First Affiliated Hospital, Hangzhou, 310058, China.
Li RuanInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China.
Jie JinInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China.
Yu-Tian TaoInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China.
Xiao-Bao DingInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China.
Hai-Bo ZhangInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China.
Wen-Ping GuoInstitute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China.
Qiao-Lei YangInstitute of Pharmaceutical Biotechnology of Zhejiang University School of Medicine and Department of Radiology of the First Affiliated Hospital, Hangzhou, 310058, China.
Heng YaoInstitute of Pharmaceutical Biotechnology of Zhejiang University School of Medicine and Department of Radiology of the First Affiliated Hospital, Hangzhou, 310058, China.
Xin ChenInstitute of Pharmaceutical Biotechnology of Zhejiang University School of Medicine and Department of Radiology of the First Affiliated Hospital, Hangzhou, 310058, China. xinchen@zju.edu.cn.
Taizhou University · CNZhejiang University · CN

Funding

Breeding program of Taizhou University 2018PY010National Natural Science Foundation of China 31571356 and 81830073
6 · The paper itself

Abstract

backgroundThe nematode worm, Caenorhabditis elegans, is a saprophytic species that has been emerging as a standard model organism since the early 1960s. This species is useful in numerous fields, including developmental biology, neurobiology, and ageing. A high-quality comprehensive molecular interaction network is needed to facilitate molecular mechanism studies in C. elegans.

resultsWe present the predicted functional interactome of Caenorhabditis elegans (FIC), which integrates functional association data from 10 public databases to infer functional gene interactions on diverse functional perspectives. In this work, FIC includes 108,550 putative functional associations with balanced sensitivity and specificity, which are expected to cover 21.42% of all C. elegans protein interactions, and 29.25% of these associations may represent protein interactions. Based on FIC, we developed a gene set linkage analysis (GSLA) web tool to interpret potential functional impacts from a set of differentially expressed genes observed in transcriptome analyses.

conclusionWe present the predicted C. elegans interactome database FIC, which is a high-quality database of predicted functional interactions among genes. The functional interactions in FIC serve as a good reference interactome for GSLA to annotate differentially expressed genes for their potential functional impacts. In a case study, the FIC/GSLA system shows more comprehensive and concise annotations compared to other widely used gene set annotation tools, including PANTHER and DAVID. FIC and its associated GSLA are available at the website http://worm.biomedtzc.cn .

Indexed as

TranscriptomeAnimalsCaenorhabditis elegansGene Expression ProfilingInternetCaenorhabditis elegansDatabaseFunctional interactionGene set linkage analysisTranscriptomic analysis tool

Identifiers

PMID33076954
PMCPMC7574172
OpenAlexW3093446149

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.