The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Accurate and exact CNV identification from targeted high-throughput sequence data
Open accessAbstract
BACKGROUND: Massively parallel sequencing of barcoded DNA samples significantly increases screening efficiency for clinically important genes. Short read aligners are well suited to single nucleotide and indel detection. However, methods for CNV detection from targeted enrichment are lacking. We present a method combining coverage with map information for the identification of deletions and duplications in targeted sequence data. RESULTS: Sequencing data is first scanned for gains and losses using a comparison of normalized coverage data between samples. CNV calls are confirmed by testing for a signature of sequences that span the CNV breakpoint. With our method, CNVs can be identified regardless of whether breakpoints are within regions targeted for sequencing. For CNVs where at least one breakpoint is within targeted sequence, exact CNV breakpoints can be identified. In a test data set of 96 subjects sequenced across ~1 Mb genomic sequence using multiplexing technology, our method detected mutations as small as 31 bp, predicted quantitative copy count, and had a low false-positive rate. CONCLUSIONS: Application of this method allows for identification of gains and losses in targeted sequence data, providing comprehensive mutation screening when combined with a short read aligner.
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
Accurate whole human genome sequencing using reversible terminator chemistry
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684
Origins and functional impact of copy number variation in the human genome
The Wellcome Trust Case Control Consortium, Conrad, D. F., Pinto, D., Redon, R., Feuk, L., Gokcumen, O., Zhang, Y., Aerts, J., Andrews, T. D., Barnes, C., et al. (2010) · Nature · doi:10.1038/nature08516 · Cited by 1,970
Gnirke, A., Melnikov, A., Maguire, J., Rogov, P., LeProust, E. M., Brockman, W., Fennell, T., Giannoukos, G., Fisher, S., Russ, C., et al. (2009) · Nature Biotechnology · doi:10.1038/nbt.1523 · Cited by 1,433
Cited by
Pennington, K. P., Walsh, T., Harrell, M. I., Lee, M. K., Pennil, C. C., Rendi, M. H., Thornton, A., Norquist, B. M., Casadei, S., Nord, A. S., et al. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-2287 · Cited by 1,051
Self-citationWalsh, T., Casadei, S., Lee, M. K., Pennil, C. C., Nord, A. S., Thornton, A. M., Roeb, W., Agnew, K. J., Stray, S. M., Wickramanayake, A., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1115052108 · Cited by 933
Self-citationInherited Mutations in Women With Ovarian Carcinoma
Norquist, B. M., Harrell, M. I., Brady, M. F., Walsh, T., Lee, M. K., Gulsuner, S., Bernards, S. S., Casadei, S., Yi, Q., Burger, R. A., et al. (2016) · JAMA Oncology · doi:10.1001/jamaoncol.2015.5495 · Cited by 769
Self-citationPearlman, R., Frankel, W. L., Swanson, B., Zhao, W., Yilmaz, A., Miller, K., Bacher, J., Bigley, C., Nelsen, L., Goodfellow, P. J., et al. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2016.5194 · Cited by 707
Comprehensive genetic testing in the clinical evaluation of 1119 patients with hearing loss
Sloan-Heggen, C. M., Bierer, A. O., Shearer, A. E., Kolbe, D. L., Nishimura, C. J., Frees, K. L., Ephraim, S. S., Shibata, S. B., Booth, K. T., Campbell, C. A., et al. (2016) · Human Genetics · doi:10.1007/s00439-016-1648-8 · Cited by 559
Metadata
- Authors
- Nord, A. S., Lee, M., King, M.-C., & Walsh, T.
- Venue
- BMC Genomics
- Published
- 2011-04-12
- Volume
- 12
- Issue
- 1
- Pages
- 184-184
- ISSN
- 1471-2164 (ISSN-L)
- OpenAlex
- W2116138166
- Cited by
- 208
- References
- 3
- FWCI
- 9.68
- Type
- journal article
- Language
- en
- Open access
- gold
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: Accurate and exact CNV identification from targeted high-throughput sequence data. https://app.cochat.ai/papers/10.1186/1471-2164-12-184
- The paper
- Nord, A. S., Lee, M., King, M.-C., & Walsh, T. (2011). Accurate and exact CNV identification from targeted high-throughput sequence data. BMC Genomics. https://doi.org/10.1186/1471-2164-12-184
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.
