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Applied Biosystems Software Accelerates the Identification of Genetic Variants Used in Biomedical Research
Product News

Applied Biosystems Software Accelerates the Identification of Genetic Variants Used in Biomedical Research

Applied Biosystems Software Accelerates the Identification of Genetic Variants Used in Biomedical Research
Product News

Applied Biosystems Software Accelerates the Identification of Genetic Variants Used in Biomedical Research


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Applied Biosystems has announced a software application designed to accelerate the analysis of genetic variation data. The new Variant Reporter™ Software is designed to reduce manual review time of these data by automating the detection of genetic variants and streamlining the data analysis process.

Applied Biosystems expects that the software can reduce by up to half the time currently required to evaluate genetic variation data generated during medical sequencing projects.

Medical sequencing, also known as resequencing, is widely used to identify genetic variations in sample populations. This analysis technique is central to understanding genetic causes of disease and individual response to drugs. Automated DNA sequencing technologies have made it possible for researchers to perform detection and screening studies of genetic variants in large sample populations. These production-scale projects are generating substantial data sets that require robust bioinformatics tools that can facilitate the rapid analysis and management of these data.

Up to 70% of the medical sequencing process - from DNA extraction to data analysis - can be spent in data analysis. To streamline the analysis process Variant Reporter Software uses proprietary algorithms to identify genetic variations based on standardized or user-defined parameters. Results are then presented for validation in a visual format that allows researchers to simultaneously compare multiple quality control metrics. These features are expected to enable researchers to more efficiently study complex diseases across multiple genes and process data sets larger than current software applications are capable of managing.

Dr. Nicholas Marini, Ph.D., a research scientist at the University of California at Berkeley, is resequencing the coding regions in 19 folate metabolic genes from a population of 500 newborns to explain why women who use vitamins containing folic acid in early pregnancy are at a lower risk for delivering infants with neural tube defects. Neural tube defects are birth defects of the brain and spinal cord that can cause conditions such as spina bifida.

“The ability to rapidly evaluate resequencing data could significantly advance research focused on the molecular basis of disease or an individual’s potential drug response,” said Dr. Marini. “The Variant Reporter software addresses a major log jam that currently exists in this process by providing a better way to rapidly evaluate variation data and identify targets of interest. This could save us literally hundreds of hours in time spent evaluating these large data sets.”

Variant Reporter software identifies genetic variations by using proprietary algorithms that have been tested for precision against large reference data sets. The software uses these algorithms to accelerate the discovery of genetic variants, determine the genotypes and then calculate project quality statistics.

The results are then presented in a single window which allows users to compare them against multiple quality control metrics including targeted electropherograms. Results can be captured for further study in reports which provide information such as high-level project statistics, comprehensive details on project quality or individual specimen genotypes.

“Medical sequencing projects are contributing to a significant increase in the number of samples run on Sanger-based sequencing systems worldwide,” said Kim Caple, vice president and general manager for Applied Biosystems’ genetic analysis business. “The Variant Reporter software enables our customers engaged in medical sequencing projects to better understand the data generated by our genetic analysis instruments systems so they can be more successful in their work.”

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