Evidence map›Paper›PMID 26424082›Full record

ArticleDatabase : the journal of biological databases and curation2015

RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse.

Zhi-Ping Liu, Canglin Wu, Hongyu Miao, Hulin Wu

Abstract readDataset
In one paragraph

Article in Database : the journal of biological databases and curation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 301 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
301citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

301 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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241 more citing papers are in PubMed but not listed here.

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

4 authors.

Zhi-Ping LiuDepartment of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China and.
Canglin WuDepartment of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Hongyu MiaoDepartment of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Hulin WuDepartment of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA Hulin.Wu@uth.tmc.edu.

Funding

Virology/Immunology CoreP30AI078498 · NIAID · UNIVERSITY OF ROCHESTER · PI DEWHURST, STEPHEN · 2008 to 2018
$14.6M
RNA Targeted Small Molecules: Connecting Binding Kinetics to Sequence SelectivityR01GM100788 · NIGMS · UNIVERSITY OF ROCHESTER · PI MILLER, BENJAMIN L · 2012 to 2015
$1.5M
NIAID NIH HHS P30AI078498NIGMS NIH HHS R01GM100788PHS HHS HHSN272201000055C
6 · The paper itself

Abstract

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named 'RegNetwork', of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org.

Indexed as

Databases, GeneticAnimalsGene Expression RegulationGene Regulatory NetworksHumansKnowledge BasesMiceMicroRNAsNucleotide MotifsResponse ElementsTranscription FactorsTranscription, GeneticMicroRNAsTranscription Factors

Identifiers

PMID26424082
PMCPMC4589691

What OpenQuestion holds

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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.