Literature DB >> 17326095

PhenCode: connecting ENCODE data with mutations and phenotype.

Belinda Giardine1, 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 Blumenfeld, Bruce Gottlieb, Mauno Vihinen, Jouni Väliaho, Jim Kent, Webb Miller, Ross C Hardison.   

Abstract

PhenCode (Phenotypes for ENCODE; http://www.bx.psu.edu/phencode) 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.

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Year:  2007        PMID: 17326095     DOI: 10.1002/humu.20484

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.878


  33 in total

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Authors:  Gudmundur A Thorisson; Juha Muilu; Anthony J Brookes
Journal:  Nat Rev Genet       Date:  2009-01       Impact factor: 53.242

2.  The road from next-generation sequencing to personalized medicine.

Authors:  Manuel L Gonzalez-Garay
Journal:  Per Med       Date:  2014       Impact factor: 2.512

3.  Mutation spectrum of PAH gene in phenylketonuria patients in Northwest China: identification of twenty novel variants.

Authors:  Yousheng Yan; Chuan Zhang; Xiaohua Jin; Qinhua Zhang; Lei Zheng; Xuan Feng; Shengju Hao; Huafang Gao; Xu Ma
Journal:  Metab Brain Dis       Date:  2019-02-12       Impact factor: 3.584

4.  Genetic Epidemiology of Complex Phenotypes.

Authors:  Darren D O'Rielly; Proton Rahman
Journal:  Methods Mol Biol       Date:  2021

Review 5.  Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation.

Authors:  Haiming Tang; Paul D Thomas
Journal:  Genetics       Date:  2016-06       Impact factor: 4.562

6.  Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.

Authors:  Arun Prasad Pandurangan; Tom L Blundell
Journal:  Protein Sci       Date:  2019-11-25       Impact factor: 6.725

7.  28-way vertebrate alignment and conservation track in the UCSC Genome Browser.

Authors:  Webb Miller; Kate Rosenbloom; Ross C Hardison; Minmei Hou; James Taylor; Brian Raney; Richard Burhans; David C King; Robert Baertsch; Daniel Blankenberg; Sergei L Kosakovsky Pond; Anton Nekrutenko; Belinda Giardine; Robert S Harris; Svitlana Tyekucheva; Mark Diekhans; Thomas H Pringle; William J Murphy; Arthur Lesk; George M Weinstock; Kerstin Lindblad-Toh; Richard A Gibbs; Eric S Lander; Adam Siepel; David Haussler; W James Kent
Journal:  Genome Res       Date:  2007-11-05       Impact factor: 9.043

8.  XML-based approaches for the integration of heterogeneous bio-molecular data.

Authors:  Marco Mesiti; Ernesto Jiménez-Ruiz; Ismael Sanz; Rafael Berlanga-Llavori; Paolo Perlasca; Giorgio Valentini; David Manset
Journal:  BMC Bioinformatics       Date:  2009-10-15       Impact factor: 3.169

9.  Local DNA topography correlates with functional noncoding regions of the human genome.

Authors:  Stephen C J Parker; Loren Hansen; Hatice Ozel Abaan; Thomas D Tullius; Elliott H Margulies
Journal:  Science       Date:  2009-03-12       Impact factor: 47.728

Review 10.  The cystic fibrosis gene: a molecular genetic perspective.

Authors:  Lap-Chee Tsui; Ruslan Dorfman
Journal:  Cold Spring Harb Perspect Med       Date:  2013-02-01       Impact factor: 6.915

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