By John Russell
Dec. 4, 3008 | In two weeks, Ingenuity Systems will launch IPA 7.0, a major release of its popular pathway analysis software. These tools have come a long way in the past few years. With IPA 7.0, Ingenuity is introducing Contextual Data Analysis, which the company says will speed researchers’ efforts to develop relevant hypotheses. Ingenuity also added a significant amount of microRNA data and beefed up its coverage of specific diseases.
“What is unique about Contextual Data Analysis is the ability to filter not just on your analysis results, but the ability to constrain your hypothesis space upfront – to develop a relevant hypothesis/model from the beginning,” says Heidi Bullock, Ingenuity’s director of marketing.
Several pathway tools already enable filtering at the ortholog level so it is fairly straightforward to filter on Entrez Gene information. “For example, Gene A -> regulates Gene B. Today, it is not hard to find Gene A (mouse) and Gene B (mouse). What is different about IPA is how we structure information. Users can not only filter on Gene A (mouse) Gene B (mouse) (ortholog level), but they can filter on findings that show Gene A has been shown to regulate Gene B in mouse. Users can build models that will only show connections where the findings have been described in a particular species, cell type, tissue, or even disease,” she says.
Ingenuity says the enhanced functionality will help researchers save time by quickly honing in on the results that are most relevant to their experimental model or experimental conditions. For example, by applying a CNS tissue filter to customer data in IPA 7 – it is now possible to understand genes expressed in CNS tissue and clearly identify functions specific to CNS. “They can constrain hypothesis with a set of advanced filters,” says Bullock.
Some of the added constraints include:
- Molecule Filters: These focus on molecules with particular biological and chemical characteristics: species (human, mouse, rat); protein function or chemical class (transcription factor, kinase, biologic drug, chemical reagent, chemical toxicant); gene expression in tissues, cell lines (hippocampus, DU 145, etc.); Protein detection in biofluids (CSF, blood, etc.).
- Association with disease (metabolic disease, cardiovascular disease)
- Relationship Filters: These focus on relationships with specific biological and chemical characteristics: type of molecular event (binding, transcription, phosphorylation, direct or indirect interaction); observed in a certain tissue or cell line or in a particular species.
“In addition, our metabolic and signaling pathways are now organized by common processes and themes. You can more easily browse pathway libraries and quickly focus in on pathways most relevant to a particular biological question,” says Bullock.
The added microRNA content, claims Ingenuity, means that IPA can be used as a complete microRNA analysis tool, eliminating the need to plug microRNA identifiers into a separate miRNA target prediction software package, export those mRNA targets, and then visualize pathways. It’s possible to complete all steps in IPA without losing the original (and visual) connection to the microRNA that was assayed.
“Researchers can now leverage predicted and experimentally demonstrated mRNA targets of human, mouse, and rat microRNA to understand the potential impact of microRNA disregulation on cellular processes, pathways, diseases, and phenotypes,” says Bullock.
Like all pathway tool providers, Ingenuity continues to add disease-specific content and Bullock says immunology, infectious disease, cancer, and toxicology content have been added in the 7.0 release. For more on the pathway tool market see, Pathway Tools Shine. One measure of pathway tool use is the increasing number of citations they receive, and Ingenuity says its IPA tools have been cited in more than 1200 papers to date.
----------------------------------
This article first appeared in Bio-IT World’s Predictive Biomedicine newsletter. Click here for a free subscription.