Daniel Dulgerian

GenoLead

We are researching how the use of the web based technologies can aid cytogeneticists with identifying and prioritizing specific gene/drug research.

Alden Huang

University of California, Los Angeles

We would like to utilize PharmGKB annotations in our interpretation of next-generation sequencing data in studying the genetics of various neurodegenerative disorders.

Michael Linderman

Mt. Sinai School of Medicine

The Institute for Genomics and Multiscale Biology at Mt. Sinai is using PharmGKB data for annotations and interpretation of whole exomes and genomes.

Maricel Kann

University of Maryland, Baltimore

Our goal is to study the functional and structural environment of protein mutations known to be associated with a drug. Just as we did for disease mutations in DMDM (Domain Mapping to Disease Mutations), we plan to aggregate mutations with pharmacogenomic activity at the protein domain level.

Lawrence Hunter

University of Colorado School of Medicine

This data will be used as part of the 2012 IEEE BioVis contest (http://www.biovis.net/contest). The contest is for teams to visualize complex biological data in innovative ways that facilitate analysis and understanding. The initial part of the contest regards eQTL analysis. A bit over 50,000 SNPs are identified as relevant to a synthetic QTL analysis. The second part of the competition will be to visualize the known clinical implications of those SNPs (and the specific alleles indicated in the analysis). The PharmGKB data will be used to support the production of such visualizations.

Merve Cakir

Bilkent University, Turkey

We will use this dataset to annotate genome sequence data and to predict the effects of structural variations and SNPs on the drug response on an individual.

Jinlu Cai

Albert Einstein College of Medicine

We would like to integrate PharmGKB information into our SNP annotation pipeline for next-gen seq analysis. After identifying SNPs from next-gen seq data. The annotation from PharmGKB will be added to each SNP. If the information is available by perl programing/MgSQL database query. Currently, we have an annotation pipeline for exome-seq analysis. First, we use ANNOVAR to do the basic annotation and then add allele frequency from dbSNP and 1000 genome project, we would like to extend the annotation to functional level. Thus, we would like to integrate SNP annotation from PharmGKB database.

Gurkan Ustunkar

Izmir University of Economics

In my Ph.D. study, I have designed and developed a Java-based integrated desktop application specifically designed for the prioritization of SNP biomarkers and discovery of genes and pathways related to diseases via analysis of GWAS data. We are currently working on a web-based version of the system that will be open for academic purposes. Data obtained from PharmGKB will be used to improve the performance of our system. Additionally it will be used in a second project for developing a clinical DSS.

Brooke Fridley

Mayo Clinic

The Mayo Clinic is conducting multiple genomic research projects related to response to therapies (pharmacogenomics). These studies are beginning to utilize next generation sequence technology in which the annotation of variants, genes and pathways is critical for the interpretation of the results. We plan on using the data available in PharmGKB to annotate variants found from research related sequencing studies.

Benjamin Darbro

University of Iowa

We will use the PharmGKB annotations to aid in the identification and prioritization of genomic variants identified by exome sequencing. We are currently investigating the appropriate clinical use of exome sequencing and pharmacogenomics is a big part of that evaluation.

Michel Dumontier

Carleton University

We are investigating the use of Semantic Web technologies for pharmacogenomic-based clinical decision support.

Noam Shomron

Tel Aviv University

The PharmGKB data will be used to interpret diseased and healthy individuals' exome sequencing information generated by our team.

Marta Bleda Latorre

Centro de Investigacion Principe Felipe

We are working with deep sequencing data, and PharmGKB information can help us to identify and prioritize genomic variantw that could play an imortant role in drug metabolism.

James Elliott

Quantigen

I will use this dataset to aid in the development of an open-source translation table; specifically, the data will be used for algorithm development and training.

Ali Torkemani

Scripps Genomic Medicine

We are building an annotation pipeline for genome sequencing data and would like to include PharmGKB annotations.

Teh Lay Kek

Universiti Teknologi MARA

The data obtained from PharmGKB will be used to annotate the structural variation identified using whole genome sequencing techniques. We have a complete sequence of an individual and will predict the phenotypes based on the database. We are performing whole genome sequencing and metabolomics study of Malaysian Aborigines and will further use the database for phenotype prediction, and findings obtained may be of useful enrichment to the database.

John West

Personalis

Personalis combines world class expertise in the technology of genome sequencing and interpretation with an extensive track record of peer-reviewed publications and commercial success.

Yonghui Wu

Vanderbilt University

Our group is working on literature mining for Pharmacogenomic knowledge discovery. The PharmGKB data will be used as a gold standard for evaluation of our methods and models.

Raf Winand

K.U. Leuven

My work is to investigate the feasibility of preventative genomics.

Yoon Jun-Hee

Seoul National University

We will use data for research sequencing data and predicting personal drug response.

Gokhan Karakulah

Medical University of Vienna

We are investigating pharmacogenomics for clinical decision support systems.

Seo MiKyoung

Personal Genomics Institute, Korea

Recently our institute is conducting the sequencing and analysis research on the genome of cancer and mendelian disease, improving the health and welfare of Koreans. We are seeking to apply pharmacogenomics to genome sequencing and analysis.

Brian Wilson

Brigham and Women's Hospital

I will use the annotations for supporting the review of genomic data within the i2b2 framework

Yan Zhang

Virginia Bioinformatics Institute

I would like to use PharmGKB data to study the association between genetics variation of the host and host response for pathogen infection.

Jonathan Mosley

Roden Lab, Vanderbilt University

We will apply pharmacogenomic annotations to publicly available genome sequence data.

Michael Hsing

Children's Hospital, Boston

I am doing postdoctoral research at Kong's lab at Children's Hospital Boston to analyze data from whole genome sequencing (WGS) and SNP arrays.

Stephane Plaisance

BITS-VIB Bioinformatics Training and Service Facility

My main work consists of setting up analysis methods...combine your data together with probably similar content obtained from public repositories... in order to build a wider coverage list of known disease variations.

Amanda R. Elsey

University of Florida, Julie Johnson's lab

Variant and Clinical Annotations are to be used by Dr. Julie Johnson and Amanda Elsey for the UF and Shands Personalized Medicine Program.

Bahram Namjou

Cincinnati Children's Hospital Medical Center

We are using PharmGKB annotations and SNP variations to build a comprehensive database in order to identify individuals with potential risk genotypes. These variations are previously proven to be important and play major roles in drug metabolism or efficacy.

Christopher Cassa

MIT

We plan to explore how many PharmGKB variant associations consider family
history or ethnographic data (not as a criticism or analysis, but to
know whether that enrichment in GWAS datasets would be valuable.)

Rachel Karchin

Johns Hopkins

My group is working on computational methods to predict the pharmacogenomic impacts of variants. We are using the PharmGKB data as a source of variants of known impact to train and validate our models.

Sean Mooney

Buck Institute

The Mooney laboratory is evaluating supervised machine learning algorithms for the prioritization of amino acid substitutions that are likely to be associated with pharmacogenetic (PGx) phenotypes. They are using attributes derived from protein sequence and structural properties and classification tools to evaluate cross validation accuracy to discriminate pharmacokinetic and pharmacodynamics attributes. Further, protein based attributes are being used to identify genomic and proteomic attributes to prioritize genes and proteins associated with PGx with the goal of integrating variant and gene level attributes for genome annotation.

Helio Costa/Carlos Bustamante

Stanford University,Genetics Dept.

We are using PharmGKB annotations to predict and assess individual drug response and toxicity.

Emidio Capriotti

Stanford University

I am working on analysis of single nucleotide variants and development
of machine learning methods for the prediction of their effect on human health.