Product Update: IPA Winter Release Goes Beyond Pathway Analysis




Cited in more than 12,000 peer-reviewed journal articles, QIAGEN’s Ingenuity Pathway Analysis (IPA) continues to be the gold standard for researchers who need to quickly extract the biological meaning from the obscurity of their molecular data.

The winter release of IPA is now available and we wanted to take a minute to update you on its latest enhancements, including the expanded support for RNA-Seq. These updates are part of our ongoing commitment to innovation in data interpretation and keeping pace with the rapid advances happening in biomedical research.

Here are some of the highlights:

Enhanced RNA-Seq support includes human Ensembl-based isoforms: Take advantage of RefSeq and Ensembl by toggling between them in Gene Views to visualize associated isoforms. In addition, understand which isoforms from your dataset are most significantly differentially expressed for additional follow-up. 

Find the significant isoforms in your RNA-seq data with new human isoform visualizations: Quickly identify which genes in your human RNA-seq dataset have expression data for one or more isoforms. On networks and pathways, nodes with multiple isoforms in your dataset are surrounded with a yellow glow. This helps you quickly identify significant differentially expressed genes that may have splicing patterns of interest. Double click a node of interest to see which isoforms are differentially expressed in your dataset. 

Quickly identify significant connections with new layout options for networks: You can change the default layout of networks and My Pathways using the new Layout Options which emphasize different aspects of the network depending on which layout you select: Organic, Circular, Radial, Hierarchical or Subcellular giving you more versatility than ever before for how you look at your data.

To learn more please visit our IPA product page where you will find more information on how IPA can take you beyond pathway analysis and allow you to dig deeper into your RNA-Sequencing data.