Evidence map›Paper›PMID 20238089›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2010

Bioinformatic tools for identifying disease gene and SNP candidates.

Sean D Mooney, Vidhya G Krishnan, Uday S Evani

Abstract readReview
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
11.3field-weighted citation impact, top 2% 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

31 citing papers in PubMed, 62 citations in OpenAlex.

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  11. A novel homozygous frameshift variant in the ABCC2-gene in Dubin-Johnson syndrome may predispose to chronic liver disease.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2021
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  17. atSNPInfrastructure, a case study for searching billions of records while providing significant cost savings over cloud providers.IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum : [proceedings]. IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · 2018
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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

3 authors at 1 institution in 1 country.

Sean D MooneyDepartment of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
Vidhya G Krishnan
Uday S Evani
Indiana University – Purdue University Indianapolis · US

Funding

GENETICS OF BONE LOSS AND BONE STRENGTH IN WOMEN &MENP01AG018397 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI ECONS, MICHAEL J · 2000 to 2010
$19.1M
TAMAXIFEN PHARMACOGENETICSU01GM061373 · NIGMS · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI FLOCKHART, DAVID ALASTAIR · 2000 to 2009
$17.0M
Informatic Profiling of Clinically Relevant MutationR01LM009722 · NLM · UNIVERSITY OF WASHINGTON · PI MOONEY, SEAN DAVID · 2007 to 2014
$3.5M
Bioinformatics approaches to characterizing amino acid function.K22LM009135 · NLM · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI MOONEY, SEAN DAVID · 2006 to 2008
$465k
NIA NIH HHS P01 AG018397NIA NIH HHS P01AG018397NIGMS NIH HHS U01 GM061373NIGMS NIH HHS U01GM061373NLM NIH HHS K22 LM009135NLM NIH HHS K22LM009135NLM NIH HHS R01 LM009722NLM NIH HHS R01LM009722
6 · The paper itself

Abstract

As databases of genome data continue to grow, our understanding of the functional elements of the genome grows as well. Many genetic changes in the genome have now been discovered and characterized, including both disease-causing mutations and neutral polymorphisms. In addition to experimental approaches to characterize specific variants, over the past decade, there has been intense bioinformatic research to understand the molecular effects of these genetic changes. In addition to genomic experimental assays, the bioinformatic efforts have focused on two general areas. First, researchers have annotated genetic variation data with molecular features that are likely to affect function. Second, statistical methods have been developed to predict mutations that are likely to have a molecular effect. In this protocol manuscript, methods for understanding the molecular functions of single nucleotide polymorphisms (SNPs) and mutations are reviewed and described. The intent of this chapter is to provide an introduction to the online tools that are both easy to use and useful.

Indexed as

Polymorphism, Single NucleotideAmino Acid SubstitutionComputational BiologyGenetic Predisposition to DiseaseHumansMutationRNA Splicing

Identifiers

PMID20238089
PMCPMC3957484
OpenAlexW1899479364

What OpenQuestion holds

Textmetadata
LicenceTDM
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.