Evidence map›Paper›PMID 39588827›Full record

ArticleIET systems biology2024

Identification of co-localised transcription factors based on paired motifs analysis.

Li Liu, Lu Han, Kaiyuan Han, Zheng Zhang, Haojiang Zhang, Lirong Zhang

Abstract read
In one paragraph

Article in IET systems biology, 2024. 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
–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

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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.

  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

6 authors.

Li LiuYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.ORCID 0000-0003-0535-4361
Lu HanYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Kaiyuan HanSchool of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
Zheng ZhangComputer Science and Information Systems, Murray State University, Murray, USA.
Haojiang ZhangSchool of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
Lirong ZhangSchool of Physical Science and Technology, Inner Mongolia University, Hohhot, China.

Funding

National Natural Science Foundation of China 62272085Quzhou Municipal Science and Technology Bureau 2023D037
6 · The paper itself

Abstract

The interaction of transcription factors (TFs) with DNA precisely regulates gene transcription. In mammalian cells, thousands of TFs often interact with DNA cis-regulatory elements in a combinatorial manner rather than act alone. The identification of cooperativity between TFs can help to explore the mechanism of transcriptional regulation. However, little is known about the cooperative patterns of TFs in the genome. To identify which TFs prefer co-localisation, the authors conducted a paired motif analysis in the accessible regions of the human genome based on the Poisson background model. Especially, the authors distinguish the cooperative binding TFs and the competitive binding TFs according to the distance between TF motifs. In the K562 cell line, the authors find that TFs from a same family are always competing the same binding sites, such as FOS_JUN family, whereas KLF family TFs show significant cooperative binding in the adjacency region. Furthermore, the comparative analysis across 16 human cell lines indicates that most TF combination patterns are conserved, but there are still some cell-line-specific patterns. Finally, in human prostate cancer cells (PC-3) and human prostate normal cells (RWPE-2), the authors investigate the specific TF combination patterns in the disease cell and normal cell. The results show that the cooperative binding TF pairs shared by PC-3 and RWPE-2 account for over 90%. Simultaneously, the authors also identify 26 specific TF combination pairs in PC-3 cancer cells.

Indexed as

Transcription FactorsBinding SitesCell Line, TumorComputational BiologyGenome, HumanHumansK562 CellsNucleotide MotifsTranscription Factorsbioinformaticsdata miningDNAgenomicsstatistical analysis

Identifiers

PMID39588827
PMCPMC11665839

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