Software found to be accurate at identifying gene variations

Tools

Researchers from Children's Hospital of Philadelphia have created an algorithm to account for variances in DNA sequences to better identify those linked to disease.

The resulting software, called ParseCNV, looks at these variations -- copy number variations (CNVs), which occur naturally and may be deleted or duplicated. The difficulty has been in determining when they are related to disease, according to an announcement.

In searching for associations between CNVs and diseases, researchers typically look for differences in in how CNVs are overrepresented or underrepresented in DNA samples from patients and those of healthy people.

CNV-detection software often misreads the boundary of a CNV region, though, which could lead to a misclassification and result in false-positive or false-negative associations, explains senior author Hakon Hakonarson, M.D., director of the hospital's Center for Applied Genomics.

ParseCNV is designed to adjust for these variations and significantly advances the identification of gene variants associated with genetic diseases, the authors say in a study published in Nucleic Acids Research. The researchers say it has been 90 percent accurate, a much higher rate than conventional CNV association software. The researchers have made the software free and available for download.

Researchers have been racing to apply technology to gene sequencing in the fight against disease, claiming success in areas such as cancer and autism, as well as newborns with single-gene disorders.

The University of Texas M.D. Anderson Cancer Center cited advances in gene-sequencing technology among the reasons it decided to launch an ambitious $3 billion project to combat eight forms of cancer.

In September, the National Institutes of Health awarded $18.7 million in grants aimed at the creation of technology to make making gene sequencing faster, more accurate and less expensive, and Children's Hospital Boston recently joined the healthcare organizations embarking on for-profit ventures to improve the technology that can unlock genomic secrets to improve diagnosis and treatment of disease.

To learn more:
- read the announcement
- see previous research

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