We’re honored to learn that a paper we published in Bioinformatics last year was one of the journal’s 10 most cited articles of 2014!
This paper is especially important to us because it was our first description and published evaluation of four important new gene expression tools we introduced to the Ingenuity Pathway Analysis web application: Upstream Regulator Analysis, Mechanistic Networks, Causal Network Analysis, and Downstream Effects Analysis.
“Causal analysis approaches in Ingenuity Pathway Analysis” was written by Andreas Kramer, Jeff Green, and Stuart Tugendreich from QIAGEN Bioinformatics, along with Jack Pollard, Jr, of Sanofi-Aventis. In it, the authors describe the new tools, and also present results from experiments using the tools on new datasets.
As IPA users know, the four tools described in the paper are based on Ingenuity Knowledge Base, described in the publication as “a large structured collection of observations in various experimental contexts with nearly 5 million findings manually curated from the biomedical literature or integrated from third-party databases.” Some 40,000 nodes in the network represent mammalian genes and the products, compounds, microRNA molecules, and functions associated with them. Those nodes are linked by nearly 1.5 million edges, which represent cause-effect relationships such as transcription, molecular modification, or binding events. The new tools, like all other features of the Ingenuity applications, reflect our commitment to the idea that new data is made more meaningful when it is interpreted in a context of prior biological knowledge.
Here’s a bit more about the tools, as described in the paper:
- Upstream Regulator Analysis: Determines likely upstream regulators connected to genes in a given dataset through a set of direct or indirect relationships
- Mechanistic Networks: Builds on predicted upstream regulators by connecting regulators expected to be part of the same signaling or causal mechanism
- Causal Network Analysis: Connects upstream regulators to molecules in a given dataset and generates a more complete picture by taking advantage of paths with more than one link
- Downstream Effects Analysis: Infers the impact on disease or biological functions downstream of genes with up- or down-regulated expression in a dataset.
Many thanks to all the scientists who have been using the new IPA tools and citing our paper as they publish. This honor from the Bioinformatics journal is a great indicator that the tools have been every bit as helpful as we’d hoped!