Evidence map›Paper›PMID 22261837›Full record

ArticleHuman heredity2012

A combined functional annotation score for non-synonymous variants.

Margarida C Lopes, Chris Joyce, Graham R S Ritchie, Sally L John, Fiona Cunningham, Jennifer Asimit, Eleftheria Zeggini

Abstract read
In one paragraph

Article in Human heredity, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

59 citing papers in PubMed.

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  19. Neurology. Genetics · 2018
    Article
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Margarida C LopesWellcome Trust Sanger Institute, Hinxton, Hinxton, UK. ml10@sanger.ac.uk
Chris Joyce
Graham R S Ritchie
Sally L John
Fiona Cunningham
Jennifer Asimit
Eleftheria Zeggini

Funding

Wellcome Trust 095908Wellcome Trust 098051Wellcome Trust WT088885/Z/09/Z
6 · The paper itself

Abstract

aimsNext-generation sequencing has opened the possibility of large-scale sequence-based disease association studies. A major challenge in interpreting whole-exome data is predicting which of the discovered variants are deleterious or neutral. To address this question in silico, we have developed a score called Combined Annotation scoRing toOL (CAROL), which combines information from 2 bioinformatics tools: PolyPhen-2 and SIFT, in order to improve the prediction of the effect of non-synonymous coding variants.

methodsWe used a weighted Z method that combines the probabilistic scores of PolyPhen-2 and SIFT. We defined 2 dataset pairs to train and test CAROL using information from the dbSNP: 'HGMD-PUBLIC' and 1000 Genomes Project databases. The training pair comprises a total of 980 positive control (disease-causing) and 4,845 negative control (non-disease-causing) variants. The test pair consists of 1,959 positive and 9,691 negative controls.

resultsCAROL has higher predictive power and accuracy for the effect of non-synonymous variants than each individual annotation tool (PolyPhen-2 and SIFT) and benefits from higher coverage.

conclusionThe combination of annotation tools can help improve automated prediction of whole-genome/exome non-synonymous variant functional consequences.

Indexed as

SoftwareAlgorithmsGenomicsHumansMolecular Sequence AnnotationPolymorphism, Single NucleotideROC Curve

Identifiers

PMID22261837
PMCPMC3390741

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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.