BCM collaborates with Roche NimbleGen for high throughput exome capture technology

The Baylor College of Medicine Human Genome Sequencing Center (BCM-HGSC) has standardized its target enrichment human disease research studies on Roche NimbleGen (SIX: RO, ROG; OTCQX: RHHBY) Sequence Capture Exome technology. The Baylor HGSC will sequence over 5,000 exomes in the next two years to identify genetic variants underlying multiple human diseases and will employ NimbleGen SeqCap EZ Exome and customized NimbleGen exome designs as the exome capture technology of choice. More than 15 different diseases will be investigated by Baylor HGSC, including brain, liver, pancreatic, colon, ovarian and bladder cancers, heart disease, diabetes, autism, and other inherited diseases with the goal of better understanding causative mutations and their impact on these diseases. These studies are supported by multiple funds from the National Institutes of Health (NIH) and other research consortia. Through collaboration with Roche NimbleGen, BCM- HGSC has established and optimized their pipeline for high throughput exome capture and sequencing with multiple next-generation sequencing platforms. Baylor has already captured and sequenced over 2,000 samples, with roughly 1,000 of these using exome sequencing.

“We believe studies like these carried out by leading research centers like the Baylor Human Genome Sequencing Center will provide insights on the missing heritability of many genetic diseases, hopefully leading to future breakthroughs in diagnostic and therapeutic tools.”

Because the majority of known disease-causing mutations occur in the coding regions of the human genome (the exome), sequencing the exome allows researchers to focus on this highly important 1% of the genome and efficiently identify many of the genetic variants that are important for genetic diseases. The cost savings gained by exome sequencing in comparison to whole-genome sequencing allows researchers to investigate more biological samples and to do so more rapidly. It is also especially important for cancer studies, as the heterogeneous tumor tissues often require very deep sequencing, which is made economically possible by targeted enrichment.

"We have now been working with scientists from Roche NimbleGen for more than three years developing these optimized capture methods, said Dr. Richard Gibbs, director of HGSC and Wofford Cain Professor from Department of Molecular and Human Genetics at Baylor. "The different configurations of the SeqCap EZ Exome technology have superb performance in our hands, and will have profound influence on human disease studies"

Roche NimbleGen was the first company to offer a commercial solution for sequence capture and exome capture. Based on the success of the NimbleGen Sequence Capture 2.1M Exome Arrays which have enabled researchers worldwide to sequence thousands of exomes since late 2008, Roche NimbleGen launched SeqCap EZ Exome technology at the end of 2009. This new technology employs an in-solution method that simplifies the workflow, provides high-quality targeted capture/enrichment, and is scalable to easily facilitate research studies of any size.

In a recent study published in Genome Biology, scientists from the HGSC at Baylor and at Roche NimbleGen used SeqCap EZ Exome to demonstrate that as little as 3 Gb of raw sequence data is needed to discover >95% of expected heterozygous single base variants. In this study, SeqCap EZ Exome technology was coupled with multiple high throughput sequencing platforms for efficient resequencing of protein coding regions within the human genome.

"Roche NimbleGen is committed to providing the best exome capture technology for researchers worldwide," stated Dr. Andreas Görtz, VP of Marketing at Roche NimbleGen. "We believe studies like these carried out by leading research centers like the Baylor Human Genome Sequencing Center will provide insights on the missing heritability of many genetic diseases, hopefully leading to future breakthroughs in diagnostic and therapeutic tools."

Source:

Roche NimbleGen

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