Skip to main content
Premium Trial:

Request an Annual Quote

GenomeNext Launches New SaaS Product that Offers Reproducible, Cost-Effective Variant Analysis, Annotation


NEW YORK (GenomeWeb) – GenomeNext has launched a new product for the next-generation sequencing market, a proprietary cloud-based data analysis pipeline that scientists in research and clinical contexts can use to process, analyze, and annotate variants in whole-genome, exome, and targeted panel data.

It's the first product for the company, which officially opened its doors last March to commercialize computational technology for quickly and efficiently analyzing large quantities of genomic information developed by researchers at the Nationwide Children's Hospital in Columbus, OH. The company licensed the technology from the hospital and has used it to develop a secure, automated software-as-a-service platform that offers access to standard tools for identifying and annotating variants.

It's a simple to use system that does not require specialized personnel to set up and manage the data processing pipelines, as some other commercial solutions do, James Hirmas, the company's co-founder and CEO, told GenomeWeb. Customers simply create accounts, upload Fastq files containing their whole-genome or exome or targeted panel data to the GenomeNext system, select the sort of analysis they want, and then run the pipeline. They get back a recalibrated variant call file (VCF), a fully annotated and filtered variant file, and an annotated database file of results. Moreover, the company developed its system with the clinical market in mind and offers a standardized pipeline that is well suited to clinical settings where customers have to meet regulatory requirements, he said.

Also, since it runs on Amazon's cloud, it's a more cost-effective option for clients that need to analyze large quantities of data but don't want to invest in large, local high-performance compute clusters or storage solutions, Hirmas said. It also means that the system does not have to deal with the computational bottlenecks that locally installed systems sometimes run into; as the datasets grow, it simply leverages additional compute resources to speed up analysis. The platform also includes tools for organizing and managing data, for creating and organizing multiple research teams and projects, and for user access control options.

The company charges on a per-sample basis. For commercial entities, it costs $250 to analyze data from a targeted panel, $500 to analyze data from a whole exome, and $800 to analyze data from a whole genome. Those prices include unlimited free storage in the cloud, and the company does not charge for data uploads. There is special pricing offered for academics, which is determined on an individual basis depending on the scope of the projects to be performed, Hirmas said. According to the company, it takes about 30 minutes to analyze data from a single panel, an hour to analyze whole exomes, and 180 minutes to analyze a whole genome.

GenomeNext is entering a saturated market where it will have to compete with multiple firms, including more established players like Omicia and Knome, as well as smaller startups like iBinom, that also offer solutions for identifying and annotating variants in data from panels as well as from whole-genome and exome sequencing experiments. However, what primarily sets GenomeNext's solution apart from the competition is its underlying deterministic strategy, which ensures that even if samples are run repeatedly through the system, it produces the same result each time ensuring that results are reproducible, according to Hirmas. There are no other solutions currently available on the market that can make this claim, he said.

Underlying the company's platform is a program called Churchill, which was developed by a team of researchers led by Peter White, the director of molecular bioinformatics at Nationwide's Research Institute and GenomeNext's chief scientific advisor. Churchill, as described in a detailed paper that was published this week in Genome Biology, employs "novel deterministic parallelization techniques" that support computationally efficient analysis of genomic data using cloud resources. Its approach "enables division of the workflow across many genomic regions with fixed boundaries" that "utilizes both an artificial chromosome, where interchromosomal or boundary-spanning read pairs are processed, and overlapping subregional boundaries that together maintain data integrity and enables significant performance improvements." Furthermore, the alignment, post-alignment processing, and genotyping and variant calling steps are optimized to "significantly reduce analysis time without downsampling and without sacrific[ing] data integrity or quality."

The GenomeNext platform is currently being used by clients in a number of research institutes and commercial clinical laboratories — though the firm has declined to name those clients — and the company continues to receive requests from potential customers for demonstrations, according to Hirmas.