Evidence map›Paper›PMID 22888987›Full record

ArticleBMC genomics2012

Towards biological characters of interactions between transcription factors and their DNA targets in mammals.

Guangyong Zheng, Qi Liu, Guohui Ding, Chaochun Wei, Yixue Li

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 38% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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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

5 authors at 2 institutions in 1 country.

Guangyong ZhengKey Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. zhenggy@sibs.ac.cn
Qi Liu
Guohui Ding
Chaochun Wei
Yixue Li
Shanghai Center For Bioinformation Technology · CNShanghai Institutes for Biological Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn post-genomic era, the study of transcriptional regulation is pivotal to decode genetic information. Transcription factors (TFs) are central proteins for transcriptional regulation, and interactions between TFs and their DNA targets (TFBSs) are important for downstream genes' expression. However, the lack of knowledge about interactions between TFs and TFBSs is still baffling people to investigate the mechanism of transcription.

resultsTo expand the knowledge about interactions between TFs and TFBSs, three biological features (sequence feature, structure feature, and evolution feature) were utilized to build TFBS identification models for studying binding preference between TFs and their DNA targets in mammals. Results show that each feature does have fairly well performance to capture TFBSs, and the hybrid model combined all three features is more robust for TFBS identification. Subsequently, correspondence between TFs and their TFBSs was investigated to explore interactions among them in mammals. Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.

conclusionsOur work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions. In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

Indexed as

AnimalsDNAMammalsProtein BindingTranscription FactorsDNATranscription Factors

Identifiers

PMID22888987
PMCPMC3472306
OpenAlexW2020877556

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.