Major new features

Contextual Data Analysis: A powerful set of capabilities that help you focus on analysis and search results that are most closely aligned with your experimental model or question.

• Save time by quickly honing in on the results that are most relevant to your experimental model or experimental conditions.
  
  
• Generate targeted and relevant hypotheses by using only those findings related to your experimental models.

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.

Pathways have updated descriptions and summaries – These quickly clarify why the given results are relevant to your dataset or search query and help you identify key players and biological processes that are downstream of a pathway.

More intuitive high level immunological functions – You can more easily interpret Functional Analysis results because IPA will quickly identifying the major immunological themes present in your data and search results.

Apply molecule or relationship filters – Using these filters you can generate a targeted hypothesis or to analyze your data in a particular context:

MOLECULE FILTERS 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 focus on relationships with specific biological and chemical characteristics: Type of molecular event (binding, transcription, phosphorylation, direct or indirect interaction) Original source of knowledge (Ingenuity Knowledge Base, BIND, DIP, Cognia) Observed in a certain tissue or cell line Observed in a particular species

New Reporting Tools: Useful and easy-to-use tools that will help you rapidly assess information and share it with colleagues.

Interactive Analysis Summary: You can send an interactive IPA Analysis Summary in .pdf format in order to easily share analysis results and insights with colleagues and project team members.

Content:

New Signaling pathways: Additional pathways have been added to enrich analyses in the areas of immunology, infectious disease, oncology, and toxicology.

microRNA Content: IPA can now be used as a complete microRNA analysis tool, eliminating the need to plug microRNA identifiers into a separate microRNA target prediction software package, export those mRNA targets, and then visualize pathways. You can now complete all steps in IPA without losing the original (and visual) connection to the microRNA that was assayed.

Predicted and experimentally demonstrated mRNA targets of human, mouse, and rat microRNA help you understand the potential impact of microRNA disregulation on cellular processes, pathways, diseases, and phenotypes.

• Identify the experimentally demonstrated or predicted mRNA targets of the microRNAs that are upregulated/downregulated in tissue samples.
  
  
• Identify signaling pathways that the targets of the respective microRNA are involved in. This helps you quickly synthesize the biological impact of otherwise problematic “one to many” microRNA to mRNA relationships by anchoring collections of mRNA targets to pathways, as opposed to one understanding the implications one target at a time, thus avoiding data integration issues.
  
  
• Narrow in on the cellular processes and disease phenotypes that microRNA targets are involved in.

Additional Chemical Classes: These provide deeper understanding of the effects of chemicals on biological systems and rapidly understand the specificity of a particular chemical by browsing the set of proteins a particular chemical acts on.

• Chemical Reagents: You can find novel intervention points in pathways by searching for chemical reagents that affect the activity of key players in pathways, networks, and cellular processes.

 

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