ENCODE Comes to Ingenuity Variant Analysis

Ingenuity is pleased to announce the incorporation of ENCODE content into its market-leading Variant Analysis application. With this addition, researchers can now filter for variants that fall within ENCODE transcription factor binding sites, providing enhanced capabilities to identify biologically relevant variants.

ENCODE, which stands for the Encyclopedia Of DNA Elements, is a public research consortium that was launched by the National Human Genome Research Institute (NHGRI) in September 2003 to identify all functional elements in the human genome sequence. After an initial pilot phase, ENCODE scientists started applying their methods in a production phase to the entire genome in 2007. In September 2012, that led to the publication of 30 papers in leading journals.  Scientists continue their efforts to complete the mapping, and ENCODE is committed to rapidly releasing data into the public domain.

Now researchers working with Ingenuity Variant Analysis can access ENCODE findings directly within the application through the “Predicted Deleterious Filter,” as shown below:

ENCODE in Predicted Deleterious Filter

Variants that fall within ENCODE transcription factor binding sites can now be selected in the Predicted Deleterious filter.

Users of Variant Analysis can also see ENCODE annotations in the “Regulatory Site” column of the “Variants” details view, as shown:

ENCODE in Regulatory Columns

A new “ENCODE TFBS” Regulatory Site indicates regions observed to be bound by a transcription factor

This content enhancement in Variant Analysis reflects Ingenuity’s commitment to providing a world-class Knowledge Base. The Ingenuity Knowledge Base is supported by a robust set of people, processes, and technology for curating high-quality scientific relationships from peer-reviewed journals and both public and private biomedical databases.

  • Click here to learn more about the Ingenuity Knowledge Base
  • Click here to learn more about the most recent release of Ingenuity Variant Analysis