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Following Proof of Concept for Algorithm, Omicia Preps Launch of Clinical Genome Analysis Platform


By Julia Karow

Following a proof of principle for its disease-gene finding algorithm, genome interpretation startup Omicia plans to launch a platform for the clinical analysis of human genomes later this year.

Last week, the company, together with collaborator Mark Yandell at the University of Utah, published a description of the Variant Annotation, Analysis, and Search Tool, or VAAST, in Genome Research. In that paper, they applied VAAST to several published genome datasets to demonstrate that it can identify both common and rare disease-causing variants.

Separately, a team led by Gholson Lyon at the Children’s Hospital of Philadelphia, in collaboration with the University of Utah, the National Human Genome Research Institute, and others, published a study in the American Journal of Human Genetics last week in which they used VAAST to identify the causative gene for a novel X-linked genetic disease from X chromosome exon sequencing data.

"For us, it is a very nice proof of principle that VAAST really works, and the beautiful study that Gholson did really shows the clinical utility of VAAST," said Martin Reese, CEO and CSO of Omicia.

According to Omicia's paper, VAAST is a probabilistic search tool that identifies disease-causing variants in genome sequence data. It combines elements from existing amino acid substitution and aggregative approaches that increase accuracy and make it easy to use. The tool can score both coding and non-coding variants, and evaluate rare and common variants, giving it "much greater scope of use than any existing methodology," the authors write.

The researchers tested VAAST on a variety of published datasets to gauge how successful the algorithm is in identifying the correct disease gene. For example, they applied it to exome-sequencing datasets from two siblings affected by Miller syndrome from studies published last year, and were able to identify the two disease genes known to be involved as likely candidates. Including the parents' exomes rendered the two genes as the only candidate genes.

They then analyzed the exomes of six unrelated individuals in which different disease-causing variants from Miller syndrome patients were artificially inserted and found that with only three exomes, the correct disease-causing gene ranked first in the analysis. This, Reese said, showed that genomic information from family members is not necessary to pinpoint a disease gene.

In another example, they tested VAAST on a single published exome of a patient suffering from congenital chloride diarrhea and found that the disease-causing gene ranked 21st. After adding another exome containing the same mutation, the gene ranked first.

The scientists also looked at how VAAST scored 100 different known disease-causing genes that they artificially inserted into whole-genome sequences under different disease scenarios, for example dominant or recessive. They also applied VAAST to search for genes causing common multigenic diseases in published datasets and concluded that the tool "will prove useful for re-analyses of existing GWAS and linkage studies."

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Lyon's team at CHOP and their colleagues used VAAST, as well as another approach, to identify an unknown disease gene in a novel X-linked genetic disorder, applying it to exon sequencing data of the X chromosome. This lethal disease, tentatively named "Ogden syndrome," starts in infancy and leads to aged appearance, craniofacial anomalies, hypotonia, global developmental delays, cryptorchidism, and cardiac arrhythmias.

The researchers sequenced the X chromosome exons of one affected boy, his mother, maternal grandmother, unaffected brother, and unaffected uncle. Using VAAST, they first analyzed variants from the patient only, which led to a list of three to five candidate genes, depending on the variant-calling method used. After adding data from the mother and grandmother, the NAA10 gene ranked first as a disease candidate. This gene encodes the catalytic subunit of the main N-terminal acetyltransferase in humans, and subsequent functional studies, as well as the fact that the same gene is mutated in an unrelated family with the disease, suggests that it causes the disorder.

Omicia, which co-developed VAAST with Yandell’s group and has the sole commercial rights to it, is now working on integrating the algorithm into a platform called the Genome Analysis System, which it plans to release commercially at the end of the third quarter.

The company has grown to seven full-time employees to work on the product, funded mainly through government grants. It is currently looking for other sources of funding, Reese said, including institutional or corporate investors.

The platform, to be used for clinical annotations of both whole genomes and more targeted data such as exomes or gene panels, is currently in beta testing with several undisclosed collaborators. Besides VAAST, which generates disease candidate lists, it will also include annotation tools that will provide additional information about the role of the genes. "[Users] can submit their genomes, and it puts all the clinical annotations on top of it. It has a very nice interface that you can then [use to] link and relate to diseases," Reese said.

The software will run on a server or in the cloud, and will be able to analyze multiple genomes in parallel. Pricing for its use, under a "software as a service" model, has not been determined. Reese said Omicia will target both academic researchers and clinical testing labs as customers, or "anybody interested in clinical annotations" of genomes.

In parallel to Omicia's commercialization effort, Yandell's group is using VAAST in at least two academic collaborations and will continue to do so, Reese said.

Omicia is not the only firm who sees a business opportunity in human genome annotation for clinical research. Knome, for example, offers human genome interpretation services to pharmaceutical and academic clinical researchers to identify gene variants involved in drug response, cancer, and other diseases, using its kGAP genome interpretation engine.

Also, GenomeQuest earlier this month launched a "clinical decision-support system," called GQ-DxSM, for whole-genome diagnostics that analyzes information about variations and changes in genes and proteins to improve disease treatment, according to the firm.

Have topics you'd like to see covered in In Sequence? Contact the editor at jkarow [at] genomeweb [.] com