The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing
Open accessAbstract
BACKGROUND: Structural variations (SVs) or copy number variations (CNVs) greatly impact the functions of the genes encoded in the genome and are responsible for diverse human diseases. Although a number of existing SV detection algorithms can detect many types of SVs using whole genome sequencing (WGS) data, no single algorithm can call every type of SVs with high precision and high recall. RESULTS: We comprehensively evaluate the performance of 69 existing SV detection algorithms using multiple simulated and real WGS datasets. The results highlight a subset of algorithms that accurately call SVs depending on specific types and size ranges of the SVs and that accurately determine breakpoints, sizes, and genotypes of the SVs. We enumerate potential good algorithms for each SV category, among which GRIDSS, Lumpy, SVseq2, SoftSV, Manta, and Wham are better algorithms in deletion or duplication categories. To improve the accuracy of SV calling, we systematically evaluate the accuracy of overlapping calls between possible combinations of algorithms for every type and size range of SVs. The results demonstrate that both the precision and recall for overlapping calls vary depending on the combinations of specific algorithms rather than the combinations of methods used in the algorithms. CONCLUSION: These results suggest that careful selection of the algorithms for each type and size range of SVs is required for accurate calling of SVs. The selection of specific pairs of algorithms for overlapping calls promises to effectively improve the SV detection accuracy.
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
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
DELLY: structural variant discovery by integrated paired-end and split-read analysis
Rausch, T., Zichner, T., Schlattl, A., Stütz, A. M., Benes, V., & Korbel, J. O. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts378 · Cited by 2,459
An integrated map of structural variation in 2,504 human genomes
Sudmant, P. H., Rausch, T., Gardner, E. J., Handsaker, R. E., Abyzov, A., Huddleston, J., Zhang, Y., Ye, K., Jun, G., Hsi-Yang Fritz, M., et al. (2015) · Nature · doi:10.1038/nature15394 · Cited by 2,354
(2015) · doi:10.1093/bioinformatics/btv710 · Cited by 2,234
Landscape of somatic mutations in 560 breast cancer whole-genome sequences
Nik-Zainal, S., Davies, H., Staaf, J., Ramakrishna, M., Glodzik, D., Zou, X., Martincorena, I., Alexandrov, L. B., Martin, S., Wedge, D. C., et al. (2016) · Nature · doi:10.1038/nature17676 · Cited by 2,218
Ye, K., Schulz, M. H., Long, Q., Apweiler, R., & Ning, Z. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp394 · Cited by 2,022
Abyzov, A., Urban, A. E., Snyder, M., & Gerstein, M. (2011) · Genome Research · doi:10.1101/gr.114876.110 · Cited by 1,658
Accurate detection of complex structural variations using single-molecule sequencing
Sedlazeck, F. J., Rescheneder, P., Smolka, M., Fang, H., Nattestad, M., von Haeseler, A., & Schatz, M. C. (2018) · Nature Methods · doi:10.1038/s41592-018-0001-7 · Cited by 1,654
Genome structural variation discovery and genotyping
Alkan, C., Coe, B. P., & Eichler, E. E. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2958 · Cited by 1,630 · cited 4×
“Sequencing-based methods take several conceptual approaches to derive information about SVs from short read sequencing data [2, 9, 16 – 18].”
BackgroundMethodInfluentialART: a next-generation sequencing read simulator
Huang, W., Li, L., Myers, J. R., & Marth, G. T. (2012) · Bioinformatics · doi:10.1093/bioinformatics/btr708 · Cited by 1,596
Cited by
A structural variation reference for medical and population genetics
Collins, R. L., Brand, H., Karczewski, K. J., Zhao, X., Alföldi, J., Francioli, L. C., Khera, A. V., Lowther, C., Gauthier, L. D., Wang, H., et al. (2020) · Nature · doi:10.1038/s41586-020-2287-8 · Cited by 1,037
100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care — Preliminary Report
The 100, 0. G. P. P. I. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2035790 · Cited by 716
Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes
Shafin, K., Pesout, T., Lorig-Roach, R., Haukness, M., Olsen, H. E., Bosworth, C., Armstrong, J., Tigyi, K., Maurer, N., Koren, S., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0503-6 · Cited by 567
A guide for the diagnosis of rare and undiagnosed disease: beyond the exome
Marwaha, S., Knowles, J. W., & Ashley, E. A. (2022) · Genome Medicine · doi:10.1186/s13073-022-01026-w · Cited by 359
A roadmap for understanding the evolutionary significance of structural genomic variation
Mérot, C., Oomen, R. A., Tigano, A., & Wellenreuther, M. (2020) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2020.03.002 · Cited by 357
Metadata
- Authors
- Kosugi, S., Momozawa, Y., Liu, X., Terao, C., Kubo, M., & Kamatani, Y.
- Venue
- Genome Biology
- Published
- 2019-06-03
- Volume
- 20
- Issue
- 1
- Pages
- 117-117
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- OpenAlex
- W2948024819
- Cited by
- 508
- References
- 82
- FWCI
- 35.48
- 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: Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing. https://app.cochat.ai/papers/10.1186/s13059-019-1720-5
- The paper
- Kosugi, S., Momozawa, Y., Liu, X., Terao, C., Kubo, M., & Kamatani, Y. (2019). Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing. Genome Biology. https://doi.org/10.1186/s13059-019-1720-5
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.
