Cofactor Genomics, a leading biotechnology company, has announced the launch of its ActiveSite Expression Viewer. The product, rooted in more than a decade of expression analysis work, is designed to bring a more intuitive approach to discovery for RNA-seq projects.
Cofactor’s ActiveSite Expression Viewer gathers all of a project’s raw, alignment, and expression data into a single web interface, allowing the data to be viewed in intuitive and reductionist ways across an unlimited number of samples. The interface enables a researcher to interrogate data for hypothesis driven, gene based, or hypothesis neutral discovery of differentially expressed loci.
Researchers can quickly and easily choose the stringency of coverage and log2 cutoffs, exclude the data that does not fit the objectives of the experiment, and then extract the data directly from the ActiveSite Expression Viewer into a spreadsheet application for in-depth review and analysis.
By working closely with a handful of their largest clients as beta testers, Cofactor has produced an RNA-seq results interface in its ActiveSite viewer focused on identifying the most interesting differentially expressed loci, which are often less than one percent of the total genes expressed in a given set of samples.
Cofactor recently began bundling the ActiveSite Expression Viewer with all of its RNA-seq projects in an effort to enhance the efficiency of those conducting RNA-seq research. The company will soon open the Viewer to researchers with prior sequencing data to allow them to benefit from its features. The Expression Viewer is part of Cofactor’s ActiveSite, Cofactor’s online suite designed to enhance discovery in next-gen sequencing.
David Messina, director of analysis at Cofactor, noted, “Cofactor’s ActiveSite Expression Viewer enables researchers to focus on results and not worry so much about data analysis. Using our interactive data browser, an experiment with millions or even billions of data points can be reduced to just a few candidates. We’re excited to not only produce high quality analysis, but also enhance the way our researchers interact with their data.”
