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Software Detects DNA Mutations in Single Cells

Genomics graphic

(National Human Genome Research Institute, NIH)

19 April 2016. A new computer program detects genetic variations in individual cells, rather than current methods that require analyzing DNA in millions of cells. The program, named Monovar, is described by geneticists and bioinformatics specialists at M.D. Anderson Cancer Center in the 18 April issue of the journal Nature Methods (paid subscription required).

Monovar is designed to provide more precise depictions of genetic alterations, including those leading to cancer, than next generation sequencing, or NGS, the current state of the art in genomic analysis. “NGS technologies have vastly improved our understanding of the human genome and its variation in diseases such as cancer,” says Ken Chen, professor of bioinformatics and a lead author of the paper in an M.D. Anderson statement. “However, because NGS measures large numbers of cells, genomic variations within tissue samples are often masked.”

The emergence of single-cell sequencing, say the authors, offers better tools for understanding characteristics of tumors, and has already made an impact on cancer research and other areas of biology. Monovar provides computational tools for single-cell sequencing to detect slight alterations in individual cells, known as single nucleotide variants or SNVs. Co-lead author Nicholas Navin, a genetics professor at M.D. Anderson, adds that “Monovar is a novel statistical method able to leverage data from multiple single cells to discover SNVs and provides highly detailed genetic data.”

In their paper, Chen, Navin, and colleagues tested Monovar against standard algorithms on three human tumor data sets. The researchers report Monovar performed better at identifying driver mutations — genetic variations implicated in cancer development — and the structure of cloned DNA, than the standard algorithms.

The M.D. Anderson team, based in Houston, believes Monovar can be applied directly to cancer diagnosis and treatment, particularly in precision medicine, where identification of small genetic variations is important for an individual patient’s care. The software can also be applied to pre-natal genetic diagnosis, and with further refinements, to diseases other than cancer.

The Monovar software is available from Bitbucket.org, with pricing determined by the size of team.

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