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Related Experiment Video

Updated: Jul 12, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

The PhenoGen informatics website: tools for analyses of complex traits.

Sanjiv V Bhave¹, Cheryl Hornbaker, Tzu L Phang

¹Department of Pharmacology, University of Colorado at Denver and Health Sciences Center, Aurora, CO 80045, USA. sanjiv.bhave@uchsc.edu

|September 1, 2007

Summary

The PhenoGen Informatics website offers a comprehensive bioinformatics toolbox for managing large-scale omics data. This platform facilitates data storage, integration, and analysis, aiding in the discovery of genes linked to complex traits.

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

The rapid expansion of
omics
research (genomics, transcriptomics, proteomics, phenomics) generates vast datasets.
Effective management and integration of diverse biological data present significant challenges for bioinformatics.
There is a critical need for advanced tools to efficiently store, integrate, and analyze high-throughput omics data.

Purpose of the Study:

To introduce the PhenoGen Informatics website as a solution for managing and analyzing large-scale biological data.
To highlight the utility of the PhenoGen website in integrating various data types, including microarray, genotype, and phenotype data.
To showcase the platform's capability in facilitating the search for candidate genes associated with complex traits.

Main Methods:

Development and implementation of the PhenoGen Informatics website (http://phenogen.uchsc.edu).
Integration of microarray data with genotype and phenotype information.
Facilitation of "in-silico" experiments using both private and shared datasets.
Enabling data sharing among investigators.

Main Results:

The PhenoGen website provides a unified toolbox for storing, analyzing, and integrating omics data.
The platform supports the combination of quantitative trait loci (QTL) and microarray data for candidate gene discovery.
Investigators can perform "in-silico" experiments and share data, fostering collaborative research.

Conclusions:

The PhenoGen website offers accessible tools for high-throughput data storage, analysis, and interpretation.
The platform's architecture is adaptable for future high-throughput "omics" data types.
Easy integration of new public or PhenoGen-developed tools enhances the platform's analytical capabilities.