WholePathwayScope: a comprehensive pathway-based analysis tool for high-throughput data

doi:10.1186/1471-2105-7-30

BMC Bioinformatics
Volume 7

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WholePathwayScope: a comprehensive pathway-based analysis tool for high-throughput data

Ming Yi¹ , Jay D Horton² , Jonathan C Cohen²^,3 , Helen H Hobbs²^,3^,4 and Robert M Stephens¹

¹Advanced Biomedical Computing Center, National Cancer Institute-Frederick/SAIC-Frederick Inc., Frederick, MD 21702, USA

²McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center at Dallas, TX 75390-9046, USA

³Departments of Internal Medicine and Molecular Genetics, University of Texas Southwestern Medical Center at Dallas, TX 75390-9046, USA

⁴The Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, TX 75390-9046, USA

author email corresponding author email

BMC Bioinformatics 2006, 7:30doi:10.1186/1471-2105-7-30


Published:	19 January 2006

Abstract

Background

Analysis of High Throughput (HTP) Data such as microarray and proteomics data has provided a powerful methodology to study patterns of gene regulation at genome scale. A major unresolved problem in the post-genomic era is to assemble the large amounts of data generated into a meaningful biological context. We have developed a comprehensive software tool, WholePathwayScope (WPS), for deriving biological insights from analysis of HTP data.

Result

WPS extracts gene lists with shared biological themes through color cue templates. WPS statistically evaluates global functional category enrichment of gene lists and pathway-level pattern enrichment of data. WPS incorporates well-known biological pathways from KEGG (Kyoto Encyclopedia of Genes and Genomes) and Biocarta, GO (Gene Ontology) terms as well as user-defined pathways or relevant gene clusters or groups, and explores gene-term relationships within the derived gene-term association networks (GTANs). WPS simultaneously compares multiple datasets within biological contexts either as pathways or as association networks. WPS also integrates Genetic Association Database and Partial MedGene Database for disease-association information. We have used this program to analyze and compare microarray and proteomics datasets derived from a variety of biological systems. Application examples demonstrated the capacity of WPS to significantly facilitate the analysis of HTP data for integrative discovery.

Conclusion

This tool represents a pathway-based platform for discovery integration to maximize analysis power. The tool is freely available at http://www.abcc.ncifcrf.gov/wps/wps_index.php webcite.