Evidence map›Paper›PMID 30534948›Full record

ArticleBioinformatics (Oxford, England)2019

atSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding.

Sunyoung Shin, Rebecca Hudson, Christopher Harrison, Mark Craven, Sündüz Keleş

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 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

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Biomedicines · 2024
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  15. Association betweenGenes · 2023
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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.

Sunyoung ShinDepartment of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA.
Rebecca HudsonDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
Christopher HarrisonDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
Mark CravenDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
Sündüz KeleşDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.

Funding

TrainingU54AI117924 · NIAID · UNIVERSITY OF WISCONSIN-MADISON · PI CRAVEN, MARK W. · 2014 to 2018
$11.3M
Statistical Methods for the Analysis of ChlP-chip DataR01HG003747 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI KELES, SUNDUZ · 2007 to 2024
$4.8M
Statistical Methods For Annotating Repetitive Genomic Regions Through ENCODE-deriU01HG007019 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI BRESNICK, EMERY H, DEWEY, COLIN NOEL · 2012 to 2015
$1.5M
NHGRI NIH HHS R01 HG003747NHGRI NIH HHS U01 HG007019NIAID NIH HHS U54 AI117924
6 · The paper itself

Abstract

summaryUnderstanding the regulatory roles of non-coding genetic variants has become a central goal for interpreting results of genome-wide association studies. The regulatory significance of the variants may be interrogated by assessing their influence on transcription factor binding. We have developed atSNP Search, a comprehensive web database for evaluating motif matches to the human genome with both reference and variant alleles and assessing the overall significance of the variant alterations on the motif matches. Convenient search features, comprehensive search outputs and a useful help menu are key components of atSNP Search. atSNP Search enables convenient interpretation of regulatory variants by statistical significance testing and composite logo plots, which are graphical representations of motif matches with the reference and variant alleles. Existing motif-based regulatory variant discovery tools only consider a limited pool of variants due to storage or other limitations. In contrast, atSNP Search users can test more than 37 billion variant-motif pairs with marginal significance in motif matches or match alteration. Computational evidence from atSNP Search, when combined with experimental validation, may help with the discovery of underlying disease mechanisms. AVAILABILITY AND IMPLEMENTATION: atSNP Search is freely available at http://atsnp.biostat.wisc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Genome-Wide Association StudySoftwareGenetic VariationHumansProtein BindingTranscription FactorsTranscription Factors

Identifiers

PMID30534948
PMCPMC6662080

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.