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Computational biology

Facilitating genome structural variation analysis

Nature Methods volume 20, pages 491–492 (2023)Cite this article

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Although structural variation is less explored than single-nucleotide variation, recent studies have shown it to be associated with several human diseases. Three fresh computational methods might help to elucidate this inadequately understood part of our genetic makeup.

Research on genetic variations has mainly focused on single-nucleotide variants (SNVs) — changes in a single nucleotide at a specific position in the genome. SNV is a general term that includes both rare mutations and single-nucleotide polymorphisms (SNPs), which are point variations that are present in at least 1% of the population. Analyzing several million SNPs in a typical human genome has led us to important genetic markers for some diseases. Sometimes markers consist of a single SNP, but often we can find groups of SNPs associated with disease development (for example, in cancer).

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Acknowledgements

This article has been supported in part by the Croatian Science Foundation under the project Single genome and metagenome assembly (IP-2018-01-5886) and the Genome Institute of Singapore, A*STAR core funding.

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  1. Laboratory of AI in Genomics, Genome Institute of Singapore, A*STAR, Singapore, Singapore

    Mile Sikic

  2. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Mile Sikic

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  1. Mile Sikic
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Correspondence to Mile Sikic.

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Sikic, M. Facilitating genome structural variation analysis. Nat Methods 20, 491–492 (2023). https://doi.org/10.1038/s41592-023-01767-5

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