Evidence map›Paper›PMID 24629096›Full record

ArticleBMC bioinformatics2014

mrSNP: software to detect SNP effects on microRNA binding.

Mehmet Deveci, Umit V Catalyürek, Amanda Ewart Toland

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
3.2field-weighted citation impact, top 8% 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

27 citing papers in PubMed, 50 citations in OpenAlex.

  1. MicroRNA Databases and Tools.Methods in molecular biology (Clifton, N.J.) · 2022
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  15. A variant at a potentially functional microRNA-binding site in BRIP1 was associated with risk of squamous cell carcinoma of the head and neck.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016
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  20. Principles of miRNA-mRNA interactions: beyond sequence complementarity.Cellular and molecular life sciences : CMLS · 2015
    Review
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

3 authors at 1 institution in 1 country.

Mehmet DeveciBiomedical Informatics, Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA. mdeveci@bmi.osu.edu.
Umit V Catalyürek
Amanda Ewart Toland
The Ohio State University · US

Funding

Translational Therapeutics Research Program (TT)P30CA016058 · NCI · OHIO STATE UNIVERSITY · PI Tasleem J Padamsee · 1985 to 2026
$132.3M
Genetic interactions in colorectal cancer susceptibilityR01CA134461 · NCI · OHIO STATE UNIVERSITY · PI TOLAND, AMANDA EWART · 2010 to 2013
$1.2M
NCI NIH HHS P30 CA016058NCI NIH HHS R01 CA134461
6 · The paper itself

Abstract

backgroundMicroRNAs (miRNAs) are short (19-23 nucleotides) non-coding RNAs that bind to sites in the 3'untranslated regions (3'UTR) of a targeted messenger RNA (mRNA). Binding leads to degradation of the transcript or blocked translation resulting in decreased expression of the targeted gene. Single nucleotide polymorphisms (SNPs) have been found in 3'UTRs that disrupt normal miRNA binding or introduce new binding sites and some of these have been associated with disease pathogenesis. This raises the importance of detecting miRNA targets and predicting the possible effects of SNPs on binding sites. In the last decade a number of studies have been conducted to predict the location of miRNA binding sites. However, there have been fewer algorithms published to analyze the effects of SNPs on miRNA binding. Moreover, the existing software has some shortcomings including the requirement for significant manual labor when working with huge lists of SNPs and that algorithms work only for SNPs present in databases such as dbSNP. These limitations become problematic as next-generation sequencing is leading to large numbers of novel variants in 3'UTRs.

resultIn order to overcome these issues, we developed a web-server named mrSNP which predicts the impact of a SNP in a 3'UTR on miRNA binding. The proposed tool reduces the manual labor requirements and allows users to input any SNP that has been identified by any SNP-calling program. In testing the performance of mrSNP on SNPs experimentally validated to affect miRNA binding, mrSNP correctly identified 69% (11/16) of the SNPs disrupting binding.

conclusionsmrSNP is a highly adaptable and performing tool for predicting the effect a 3'UTR SNP will have on miRNA binding. This tool has advantages over existing algorithms because it can assess the effect of novel SNPs on miRNA binding without requiring significant hands on time.

Indexed as

Software3' Untranslated RegionsAlgorithmsBinding SitesHumansMicroRNAsPolymorphism, Single NucleotideRNA, MessengerSequence Analysis, RNA3' Untranslated RegionsMicroRNAsRNA, Messenger

Identifiers

PMID24629096
PMCPMC4067983
OpenAlexW1984525477

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.