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Bioinformatics Advance Access published online on December 12, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl606
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Published by Oxford University Press 2006
Received May 2, 2006
Revised November 8, 2006
Accepted November 23, 2006

Article

Functional genomics via multiscale analysis: application to gene expression and ChIP-on-chip data

Gilad Lerman 1 *, Joseph McQuown 2, Alexandre Blais 3, Brian D. Dynlacht 4, Guangliang Chen 1, and Bud Mishra 5

1 Department of Mathematics, University of Minnesota, 127 Vincent Hall, 206 Church St. S.E., Minneapolis, MN 55455, USA
2 Department of Applied Mathematics and Statistics, State University of New York, Stony Brook, NY 11794., USA
3 NYU School of Medicine, 550 First Avenue, New York, NY, USA 10016
4 NYU School of Medicine, 550 First Avenue, New York, NY, USA 10016; NYU Cancer Institute, 550 First Avenue, New York, NY, USA 10016
5 Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY, USA. 10012; NYU School of Medicine, 550 First Avenue, New York, NY, USA 10016

* To whom correspondence should be addressed.
Gilad Lerman, E-mail: lerman{at}umn.edu


  Abstract

We present a fast, versatile, and adaptive-multiscale algorithm for analyzing a wide variety of DNA microarray data. Its primary application is in normalization of array data as well as subsequent identification of "enriched targets", e.g. differentially expressed genes in expression profiling arrays and enriched sites in ChIP-on-chip experimental data.

We show how to accommodate the unique characteristics of ChIP-on-chip data, where the set of "enriched targets" is large, asymmetric and whose proportion to the whole data varies locally.

Our software as well as our raw DNA microarray data with PCR validations are freely available at http://www.math.umn.edu/~lerman/supp/bioinfo06.

Associate Editor: John Quackenbush
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