PhenCode: Connecting ENCODE data with mutations and phenotype

Belinda Giardine, Cathy Riemer, Tim Hefferon, Daryl Thomas, Fan Hsu, Julian Zielenski, Yunhua Sang, Laura Elnitski, Garry Cutting, Heather Trumbower, Andrew Kern, Robert Kuhn, George P. Patrinos, Jim Hughes, Doug Higgs, David Chui, Charles Scriver, Manyphong Phommarinh, Santosh K. Patnaik, Olga BlumenfeldBruce Gottlieb, Mauno Vihinen, Jouni Väliaho, Jim Kent, Webb Miller, Ross C. Hardison

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


PhenCode (Phenotypes for ENCODE; is a collaborative, exploratory project to help understand phenotypes of human mutations in the context of sequence and functional data from genome projects. Currently, it connects human phenotype and clinical data in various locus-specific databases (LSDBs) with data on genome sequences, evolutionary history, and function from the ENCODE project and other resources in the UCSC Genome Browser. Initially, we focused on a few selected LSDBs covering genes encoding alpha- and beta-globins (HBA, HBB), phenylalanine hydroxylase (PAH), blood group antigens (various genes), androgen receptor (AR), cystic fibrosis transmembrane conductance regulator (CFTR), and Bruton's tyrosine kinase (BTK), but we plan to include additional loci of clinical importance, ultimately genomewide. We have also imported variant data and associated OMIM links from Swiss-Prot. Users can find interesting mutations in the UCSC Genome Browser (in a new Locus Variants track) and follow links back to the LSDBs for more detailed information. Alternatively, they can start with queries on mutations or phenotypes at an LSDB and then display the results at the Genome Browser to view complementary information such as functional data (e.g., chromatin modifications and protein binding from the ENCODE consortium), evolutionary constraint, regulatory potential, and/or any other tracks they choose. We present several examples illustrating the power of these connections for exploring phenotypes associated with functional elements, and for identifying genomic data that could help to explain clinical phenotypes.

Original languageEnglish (US)
Pages (from-to)554-562
Number of pages9
JournalHuman mutation
Issue number6
StatePublished - Jun 2007

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)


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