Literature DB >> 15759635

Exploratory visual analysis of pharmacogenomic results.

David M Reif1, Scott M Dudek, Christian M Shaffer, Janey Wang, Jason H Moore.   

Abstract

Comprehensive analysis of expansive pharmacogenomic datasets is a daunting challenge. A thorough exploration of experimental results requires both statistical and annotative information. Therefore, appropriate analysis tools must bring a readily-accessible, flexible combination of statistics and biological annotation to the user's desktop. We present the Exploratory Visual Analysis (EVA) software and database as such a tool and demonstrate its utility in replicating the findings of an earlier pharmacogenomic study as well as elucidating novel biologically plausible hypotheses. EVA brings all of the often disparate pieces of analysis together in an infinitely flexible visual display that is amenable to any type of statistical result and biological question. Here, we describe the motivations for developing EVA, detail the database and custom graphical user interface (GUI), provide an example of its application to a publicly available pharmacogenomic dataset, and discuss the broad utility of the EVA tool for the pharmacogenomics community.

Mesh:

Year:  2005        PMID: 15759635

Source DB:  PubMed          Journal:  Pac Symp Biocomput        ISSN: 2335-6928


  14 in total

1.  The Pathway Less Traveled: Moving from Candidate Genes to Candidate Pathways in the Analysis of Genome-Wide Data from Large Scale Pharmacogenetic Association Studies.

Authors:  R A Wilke; R K Mareedu; J H Moore
Journal:  Curr Pharmacogenomics Person Med       Date:  2008

2.  The role of Ifng in alterations in liver gene expression in a mouse model of fulminant autoimmune hepatitis.

Authors:  Michael W Milks; James G Cripps; Heping Lin; Jing Wang; Richard T Robinson; Jennifer L Sargent; Michael L Whitfield; James D Gorham
Journal:  Liver Int       Date:  2009-04-16       Impact factor: 5.828

3.  Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission.

Authors:  Kathleen Askland; Cynthia Read; Jason Moore
Journal:  Hum Genet       Date:  2008-12-04       Impact factor: 4.132

4.  Differential Response to High Glucose in Skin Fibroblasts of Monozygotic Twins Discordant for Type 1 Diabetes.

Authors:  M Luiza Caramori; Youngki Kim; Rama Natarajan; Jason H Moore; Stephen S Rich; Josyf C Mychaleckyj; Ryoko Kuriyama; David Kirkpatrick; Michael Mauer
Journal:  J Clin Endocrinol Metab       Date:  2015-04-22       Impact factor: 5.958

5.  Ion channels and schizophrenia: a gene set-based analytic approach to GWAS data for biological hypothesis testing.

Authors:  Kathleen Askland; Cynthia Read; Chloe O'Connell; Jason H Moore
Journal:  Hum Genet       Date:  2011-08-25       Impact factor: 4.132

Review 6.  Layers of epistasis: genome-wide regulatory networks and network approaches to genome-wide association studies.

Authors:  Richard Cowper-Sal lari; Michael D Cole; Margaret R Karagas; Mathieu Lupien; Jason H Moore
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2010-12-31

7.  Data Synthesis and Tool Development for Exploring Imaging Genomic Patterns.

Authors:  Sungeun Kim; Li Shen; Andrew J Saykin; John D West
Journal:  IEEE Symp Comput Intell Bioinforma Comput Biol Proc       Date:  2009-03-30

8.  Exploratory Visual Analysis of statistical results from microarray experiments comparing high and low grade glioma.

Authors:  David M Reif; Mark A Israel; Jason H Moore
Journal:  Cancer Inform       Date:  2007-04-01

Review 9.  Bioinformatics challenges for genome-wide association studies.

Authors:  Jason H Moore; Folkert W Asselbergs; Scott M Williams
Journal:  Bioinformatics       Date:  2010-01-06       Impact factor: 6.937

10.  Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS.

Authors:  Nora Chung Kim; Peter C Andrews; Folkert W Asselbergs; H Robert Frost; Scott M Williams; Brent T Harris; Cynthia Read; Kathleen D Askland; Jason H Moore
Journal:  BioData Min       Date:  2012-07-28       Impact factor: 2.522

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