Evidence map›Paper›PMID 25236458›Full record

ArticleBioinformatics (Oxford, England)2015

Using population data for assessing next-generation sequencing performance.

Darren T Houniet, Thahira J Rahman, Saeed Al Turki, Matthew E Hurles, Yaobo Xu, Judith Goodship, Bernard Keavney, Mauro Santibanez Koref

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. 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

8 authors.

Darren T HounietOxford Gene Technology, Begbroke Science Park, Oxford, Oxfordshire, OX5 1PF, Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway NE1 3BZ, Newcastle upon Tyne and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
Thahira J RahmanOxford Gene Technology, Begbroke Science Park, Oxford, Oxfordshire, OX5 1PF, Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway NE1 3BZ, Newcastle upon Tyne and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
Saeed Al TurkiOxford Gene Technology, Begbroke Science Park, Oxford, Oxfordshire, OX5 1PF, Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway NE1 3BZ, Newcastle upon Tyne and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
Matthew E HurlesOxford Gene Technology, Begbroke Science Park, Oxford, Oxfordshire, OX5 1PF, Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway NE1 3BZ, Newcastle upon Tyne and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
Yaobo XuOxford Gene Technology, Begbroke Science Park, Oxford, Oxfordshire, OX5 1PF, Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway NE1 3BZ, Newcastle upon Tyne and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
Judith GoodshipOxford Gene Technology, Begbroke Science Park, Oxford, Oxfordshire, OX5 1PF, Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway NE1 3BZ, Newcastle upon Tyne and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
Bernard KeavneyOxford Gene Technology, Begbroke Science Park, Oxford, Oxfordshire, OX5 1PF, Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway NE1 3BZ, Newcastle upon Tyne and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
Mauro Santibanez KorefOxford Gene Technology, Begbroke Science Park, Oxford, Oxfordshire, OX5 1PF, Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway NE1 3BZ, Newcastle upon Tyne and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.

Funding

British Heart Foundation FS/10/008/28146British Heart Foundation FS/11/63/28944Wellcome Trust WT098051
6 · The paper itself

Abstract

motivationDuring the past 4 years, whole-exome sequencing has become a standard tool for finding rare variants causing Mendelian disorders. In that time, there has also been a proliferation of both sequencing platforms and approaches to analyse their output. This requires approaches to assess the performance of different methods. Traditionally, criteria such as comparison with microarray data or a number of known polymorphic sites have been used. Here we expand such approaches, developing a maximum likelihood framework and using it to estimate the sensitivity and specificity of whole-exome sequencing data.

resultsUsing whole-exome sequencing data for a panel of 19 individuals, we show that estimated sensitivity and specificity are similar to those calculated using microarray data as a reference. We explore the effect of frequency misspecification arising from using an inappropriately selected population and find that, although the estimates are affected, the rankings across procedures remain the same. AVAILABILITY AND IMPLEMENTATION: An implementation using Perl and R can be found at busso.ncl.ac.uk (Username: igm101; Password: Z1z1nts).

Indexed as

Genetics, PopulationGenome, HumanAlgorithmsComputational BiologyExomeGenetic VariationGenome-Wide Association StudyHigh-Throughput Nucleotide SequencingHumansSample Size

Identifiers

PMID25236458
PMCPMC4271148

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LicenceCC BY
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Registered trials

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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.