The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants
Open accessAbstract
Significance: Whole-exome sequencing (WES) is gradually being optimized to identify mutations in increasing proportions of the protein-coding exome, but whole-genome sequencing (WGS) is becoming an attractive alternative. WGS is currently more expensive than WES, but its cost should decrease more rapidly than that of WES. We compared WES and WGS on six unrelated individuals. The distribution of quality parameters for single-nucleotide variants (SNVs) and insertions/deletions (indels) was more uniform for WGS than for WES. The vast majority of SNVs and indels were identified by both techniques, but an estimated 650 high-quality coding SNVs (∼3% of coding variants) were detected by WGS and missed by WES. WGS is therefore slightly more efficient than WES for detecting mutations in the targeted exome.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,621
Fast and accurate long-read alignment with Burrows-Wheeler transform.
Li, H., & Durbin, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp698 · Cited by 12,063
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R., Garimella, K. V., Maguire, J. R., Hartl, C., Philippakis, A. A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,456
Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration
Thorvaldsdottir, H., Robinson, J. T., & Mesirov, J. P. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs017 · Cited by 9,049
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183
Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt
Durinck, S., Spellman, P. T., Birney, E., & Huber, W. (2009) · Nature Protocols · doi:10.1038/nprot.2009.97 · Cited by 4,197
Cited by
Whole-genome sequencing of patients with rare diseases in a national health system
Turro, E., Astle, W. J., Megy, K., Gräf, S., Greene, D., Shamardina, O., Allen, H. L., Sanchis-Juan, A., Frontini, M., Thys, C., et al. (2020) · Nature · doi:10.1038/s41586-020-2434-2 · Cited by 554
Clinical sequencing: is WGS the better WES?
Meienberg, J., Bruggmann, R., Oexle, K., & Matyas, G. (2016) · Human Genetics · doi:10.1007/s00439-015-1631-9 · Cited by 361
The impact of rare and low-frequency genetic variants in common disease
Bomba, L., Walter, K., & Soranzo, N. (2017) · Genome Biology · doi:10.1186/s13059-017-1212-4 · Cited by 344
Deep sequencing of 10,000 human genomes
Telenti, A., Pierce, L. C. T., Biggs, W. H., di Iulio, J., Wong, E. H. M., Fabani, M. M., Kirkness, E. F., Moustafa, A., Shah, N., Xie, C., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1613365113 · Cited by 336
Next-Generation Sequencing and Emerging Technologies
Kumar, K. R., Cowley, M. J., & Davis, R. L. (2019) · Seminars in Thrombosis and Hemostasis · doi:10.1055/s-0039-1688446 · Cited by 292
Metadata
- Authors
- Belkadi, A., Bolze, A., Itan, Y., Cobat, A., Vincent, Q. B., Antipenko, A., Shang, L., Boisson, B., Casanova, J.-L., & Abel, L.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2015-03-31
- Volume
- 112
- Issue
- 17
- Pages
- 5473-5478
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2022714591
- Cited by
- 638
- References
- 44
- FWCI
- 33.10
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. https://app.cochat.ai/papers/10.1073/pnas.1418631112
- The paper
- Belkadi, A., Bolze, A., Itan, Y., Cobat, A., Vincent, Q. B., Antipenko, A., Shang, L., Boisson, B., Casanova, J.-L., & Abel, L. (2015). Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1418631112
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
