NEW YORK (GenomeWeb) – Greek bioinformatics firm E-Nios is building a business around developing integrated software solutions for identifying and prioritizing gene targets from high-throughput omics data for the pharmaceutical, biotechnology, and personalized medicine domains.
The six-person, Athens-based company spun out from Greece's National Hellenic Research Foundation three years ago to commercialize a series of computational algorithms and tools developed by NHRF scientists over the last decade as part of their participation in several large-scale collaborative projects in Europe, E-Nios CEO Aristotelis Chatziioannou told GenomeWeb. He said that the researchers opted to commercialize their methods so that they could support larger projects that require high-end computing infrastructure.
"In order [to] have a solution that can support really bulky projects [that are] very demanding … in terms of computation, you really need to do that in a professional way [and] this is something that needs extra money and investments to do," Chatziioannou told GenomeWeb.
The company's software uses a combination of advanced statistical methodologies from the field of enrichment statistics and advanced concepts from the field of topological analysis of networks and graph theory to analyze various kinds of omics data to help researchers identify genes and pathways associated with the mechanisms of interest. Furthermore, the software analyzes data in a "mathematically rigorous way" so that its workflows are reproducible and simple enough for people with no prior bioinformatics experience to run, he added.
E-Nios' first product is a software-as-a-service solution called Bioinfominer was released in April this year. The software provides tools for prioritizing and ranking genes according to their likely causal roles in disease. The software uses controlled vocabularies like GO and the Human Phenotype Ontology to integrate gene, protein-protein interaction information, and phenotype levels and uses curated drug-gene interactions and protein interactome information to screen and prioritize druggable gene targets and their direct binding interactors at the protein level. The software also generates a separate list of genes that interact with the prioritized genes in protein-protein interaction networks, and includes a "correction method" for reducing noise in data as well as filtration tools for eliminating random events in the data. In addition to working with human data, Bioinfominer can also analyze data from other model organisms including mouse, rat, cow, chicken, pig, zebrafish, and fruit fly.
Chatziioannou and his colleagues have previously published details of some components of their solutions in several peer-reviewed journals. For example, they discussed the core algorithm underlying BioInfoMiner in Frontiers in Neuroscience. That paper describes a web-based statistical approach, called Stranger, for performing functional analysis of high-throughput omics datasets using controlled biological vocabularies like the Gene Ontology or pathway terms from the Kyoto Encyclopedia of Gene and Genomes. A separate paper published earlier this year in Scientific Reports highlights a study in which the company's tools were used to find that the combination of gene expression and DNA methylation profiles were most effective for predicting diseases and conditions independently known to be associated with smoking.
These papers describe some of the basics of E-Nios' platform but the commercial version expands on them and offers far more features and functionalities, according to Chatziioannou. E-Nios intends to release additional products including one that bundles Bioinfominer and cloud-based data analysis workflows for processing raw genomic data that will provide customers with tools for processing omics data through to final interpretation and target prioritization, the company said.
E-Nios targets clients in the traditional biomedical research space including pharma, research, and diagnostic labs, but it also pursuing targets in the industrial biotechnology market. Chatziioannou, who is also an NHRF research associate professor and head of the institute's metabolic engineering and bioinformatics program, told GenomeWeb that the firm has worked with academic research groups and companies on projects in metabolic engineering, synthetic biology, and metagenomics analysis.
E-Nios software has been used, for example, in research collaborations with groups at the French National Institute of Health and Medical Research and the German Cancer Research Center in Heidelberg, among others. Working with these groups has been valuable in extensively testing Bioinfominer in a wide range of biological scenarios, Chatziioannou noted.
E-Nios offers both individual and group licenses for its software. It charges around $5,000 for individual licenses while the cost of group licenses varies from several thousand to a few hundred thousand dollars depending on the customer's requirements. Customers also have a number of options by which they can access the company's software. For example, they can use the E-Nios application programming interface to run the tools on their data or the company can perform the analysis for them. Clients can also use Bioinfominer as it is or the company can develop tailored workflows specific meet clients' needs. Furthermore, E-Nios is open to collaborating more broadly with customers who need help with their gene prioritization projects including helping them design their experiments, generate the requisite datasets, and analyze data using its software, Chatziioannou said.
E-Nios' solutions compete with products from companies like Ingenuity, now owned by Qiagen, and Advaita Bioinformatics, as well as with open-source software solutions developed for integrated omics data analysis. But Chatziioannou believes E-Nios' solutions are different. "We have a very specific and powerful way of prioritizing not only pathways but [also] the master regulators in these pathways so at the end we [get] a very succinct and valuable list of gene targets," he said. The platform is also unique in the sense that it is completely automated and data driven, he added. "It doesn't need any type of prior knowledge so you don't need to put extra information regarding the type of disease or mechanism that you are analyzing."
Since it is part of Qiagen, customers of Ingenuity's products have access to a much broader pool of resources beyond just software tools for integrating omics data which could make solutions like Ingenuity Pathway Analysis more attractive to some clients. But Chatziioannou believes that the market is young and there are plenty of opportunities for newer companies to make their mark. "I really think that this area, especially the area of translational bioinformatics, is a completely emergent [field] … even the concepts have not boiled down to very well-established practice," he said. "We can contribute [for example] in building engineering best practices in the analysis of highly complex data."
Since its launch, E-Nios has raised around €120,000 (around $133,000) in seed funding from venture capitalists including the Piraeus Jeremie Technology Catalyst Fund and IQbility. The company is currently in negotiations for additional financing with several unnamed investors, Chatziioannou told GenomeWeb. He could not disclose how much the company hopes to raise as those details have not yet been finalized.