The DOI matches its Crossref record; 215 of the 215 references it cites were found in a registry or an index.
Structural variation in the sequencing era
Open accessAbstract
Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome. To map the full extent of structural variation in the human genome, detection methods are needed that improve on short-read approaches. This Review discusses how ensemble algorithms and emerging sequencing technologies are helping to resolve the full spectrum of structural variations.
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Cites
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Cited by
Opportunities and challenges in long-read sequencing data analysis
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Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing
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Pan-genome analysis of 33 genetically diverse rice accessions reveals hidden genomic variations
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The barley pan-genome reveals the hidden legacy of mutation breeding
Jayakodi, M., Padmarasu, S., Haberer, G., Bonthala, V. S., Gundlach, H., Monat, C., Lux, T., Kamal, N., Lang, D., Himmelbach, A., et al. (2020) · Nature · doi:10.1038/s41586-020-2947-8 · Cited by 565
Metadata
- Authors
- Ho, S. S., Urban, A. E., & Mills, R. E.
- Venue
- Nature Reviews Genetics
- Published
- 2019-11-15
- Volume
- 21
- Issue
- 3
- Pages
- 171-189
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- OpenAlex
- W2986448893
- Cited by
- 615
- References
- 215
- FWCI
- 10.10
- Type
- journal article
- Language
- en
- Open access
- green
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