Evidence map›Paper›PMID 25820422›Full record

ArticleNucleic acids research2015

New insights into the performance of human whole-exome capture platforms.

Janine Meienberg, Katja Zerjavic, Irene Keller, Michal Okoniewski, Andrea Patrignani, Katja Ludin, Zhenyu Xu, Beat Steinmann, Thierry Carrel, Benno Röthlisberger and 3 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.

0numbers the graph read from it
0cells of the map it votes in
68citing 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

68 citing papers in PubMed.

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8 more citing papers are in PubMed but not listed here.

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

13 authors.

Janine MeienbergCenter for Cardiovascular Genetics and Gene Diagnostics, Foundation for People with Rare Diseases, Schlieren-Zurich CH-8952, Switzerland.
Katja ZerjavicCenter for Cardiovascular Genetics and Gene Diagnostics, Foundation for People with Rare Diseases, Schlieren-Zurich CH-8952, Switzerland.
Irene KellerDepartment of Clinical Research, University of Berne, Berne CH-3010, Switzerland.
Michal OkoniewskiFunctional Genomics Center Zurich, Zurich CH-8057, Switzerland Division of Scientific IT Services, ETH Zurich, Zurich CH-8092, Switzerland.
Andrea PatrignaniFunctional Genomics Center Zurich, Zurich CH-8057, Switzerland.
Katja LudinDivision of Medical Genetics, Center for Laboratory Medicine, Aarau CH-5001, Switzerland.
Zhenyu XuSophia Genetics SA, Lausanne CH-1015, Switzerland.
Beat SteinmannDivision of Metabolism, University Children's Hospital, Zurich CH-8032, Switzerland.
Thierry CarrelDepartment of Cardiovascular Surgery, University Hospital, Berne CH-3010, Switzerland.
Benno RöthlisbergerDivision of Medical Genetics, Center for Laboratory Medicine, Aarau CH-5001, Switzerland.
Ralph SchlapbachFunctional Genomics Center Zurich, Zurich CH-8057, Switzerland.
Rémy BruggmannInterfaculty Bioinformatics Unit and Swiss Institute of Bioinformatics, University of Berne, Berne CH-3012, Switzerland.
Gabor MatyasCenter for Cardiovascular Genetics and Gene Diagnostics, Foundation for People with Rare Diseases, Schlieren-Zurich CH-8952, Switzerland Department of Cardiovascular Surgery, University Hospital, Berne CH-3010, Switzerland Zurich Center for Integrative Human Physiology, University of Zurich, Zurich CH-8057, Switzerland matyas@genetikzentrum.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whole exome sequencing (WES) is increasingly used in research and diagnostics. WES users expect coverage of the entire coding region of known genes as well as sufficient read depth for the covered regions. It is, however, unknown which recent WES platform is most suitable to meet these expectations. We present insights into the performance of the most recent standard exome enrichment platforms from Agilent, NimbleGen and Illumina applied to six different DNA samples by two sequencing vendors per platform. Our results suggest that both Agilent and NimbleGen overall perform better than Illumina and that the high enrichment performance of Agilent is stable among samples and between vendors, whereas NimbleGen is only able to achieve vendor- and sample-specific best exome coverage. Moreover, the recent Agilent platform overall captures more coding exons with sufficient read depth than NimbleGen and Illumina. Due to considerable gaps in effective exome coverage, however, the three platforms cannot capture all known coding exons alone or in combination, requiring improvement. Our data emphasize the importance of evaluation of updated platform versions and suggest that enrichment-free whole genome sequencing can overcome the limitations of WES in sufficiently covering coding exons, especially GC-rich regions, and in characterizing structural variants.

Indexed as

ExomeAllelesBase CompositionDNAHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNADNA

Identifiers

PMID25820422
PMCPMC4477645

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.