Evidence map›Paper›PMID 26861822›Full record

ArticleBioinformatics (Oxford, England)2016

Measuring the spatial correlations of protein binding sites.

Yingying Wei, Hao Wu

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Yingying WeiDepartment of Statistics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong and.
Hao WuDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.

Funding

The Goizueta Alzheimer's Disease Research CenterP30AG066511 · NIA · EMORY UNIVERSITY · PI Amy D Rodriguez · 2020 to 2026
$29.0M
NIA NIH HHS P30 AG066511
6 · The paper itself

Abstract

motivationUnderstanding the interactions of different DNA binding proteins is a crucial first step toward deciphering gene regulatory mechanism. With advances of high-throughput sequencing technology such as ChIP-seq, the genome-wide binding sites of many proteins have been profiled under different biological contexts. It is of great interest to quantify the spatial correlations of the binding sites, such as their overlaps, to provide information for the interactions of proteins. Analyses of the overlapping patterns of binding sites have been widely performed, mostly based on ad hoc methods. Due to the heterogeneity and the tremendous size of the genome, such methods often lead to biased even erroneous results.

resultsIn this work, we discover a Simpson's paradox phenomenon in assessing the genome-wide spatial correlation of protein binding sites. Leveraging information from publicly available data, we propose a testing procedure for evaluating the significance of overlapping from a pair of proteins, which accounts for background artifacts and genome heterogeneity. Real data analyses demonstrate that the proposed method provide more biologically meaningful results. AVAILABILITY AND IMPLEMENTATION: An R package is available at http://www.sta.cuhk.edu.hk/YWei/ChIPCor.html CONTACTS: ywei@sta.cuhk.edu.hk or hao.wu@emory.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Binding SitesDNA-Binding ProteinsGene Knockout TechniquesGenomeGenomicsHigh-Throughput Screening AssaysLinear ModelsProtein BindingDNA-Binding Proteins

Identifiers

PMID26861822
PMCPMC12157953

What OpenQuestion holds

Textmetadata
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.