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ProCAT: a data analysis approach for protein microarrays

Xiaowei Zhu1, Mark Gerstein123 and Michael Snyder124*

Author Affiliations

1 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA

2 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA

3 Department of Computer Science, Yale University, New Haven, CT 06511, USA

4 Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA

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Genome Biology 2006, 7:R110  doi:10.1186/gb-2006-7-11-r110

Published: 16 November 2006

Abstract

Protein microarrays provide a versatile method for the analysis of many protein biochemical activities. Existing DNA microarray analytical methods do not translate to protein microarrays due to differences between the technologies. Here we report a new approach, ProCAT, which corrects for background bias and spatial artifacts, identifies significant signals, filters nonspecific spots, and normalizes the resulting signal to protein abundance. ProCAT provides a powerful and flexible new approach for analyzing many types of protein microarrays.