Software

GenMAPP 2: new features and resources for pathway analysis

Nathan Salomonis^1,2 , Kristina Hanspers¹ , Alexander C Zambon¹ , Karen Vranizan^1,3 , Steven C Lawlor¹ , Kam D Dahlquist⁴ , Scott W Doniger⁵ , Josh Stuart⁶ , Bruce R Conklin^1,2,7,8 and Alexander R Pico¹

¹Gladstone Institute of Cardiovascular Disease, 1650 Owens Street, San Francisco, CA 94158 USA

²Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, 513 Parnassus Avenue, San Francisco, CA 94143, USA

³Functional Genomics Laboratory, University of California, Berkeley, CA 94720 USA

⁴Department of Biology, Loyola Marymount University, 1 LMU Drive, MS 8220, Los Angeles, CA 90045 USA

⁵Computational Biology Graduate Program, Washington University School of Medicine, St. Louis, MO 63108 USA

⁶Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064 USA

⁷Department of Medicine, University of California, San Francisco, CA 94143 USA

⁸Department of Molecular and Cellular Pharmacology, University of California, San Francisco, CA 94143 USA

author email corresponding author email

BMC Bioinformatics 2007, 8:217doi:10.1186/1471-2105-8-217

Published:	24 June 2007

Abstract

Background

Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses.

Results

To address the growing needs of the microarray community we have released version 2 of Gene Map Annotator and Pathway Profiler (GenMAPP), a new GenMAPP database schema, and integrated resources for pathway analysis. We have redesigned the GenMAPP database to support multiple gene annotations and species as well as custom species database creation for a potentially unlimited number of species. We have expanded our pathway resources by utilizing homology information to translate pathway content between species and extending existing pathways with data derived from conserved protein interactions and coexpression. We have implemented a new mode of data visualization to support analysis of complex data, including time-course, single nucleotide polymorphism (SNP), and splicing. GenMAPP version 2 also offers innovative ways to display and share data by incorporating HTML export of analyses for entire sets of pathways as organized web pages.

Conclusion

GenMAPP version 2 provides a means to rapidly interrogate complex experimental data for pathway-level changes in a diverse range of organisms.