Data Analysis Supplement to

Replication dynamics of the yeast genome

M. K. Raghuraman1*§, Elizabeth A. Winzeler2*, David Collingwood3*, Sonia Hunt1, Lisa Wodicka4,5, Andrew Conway6, David J. Lockhart4,7, Ronald W. Davis8, Bonita J. Brewer1, and Walton L. Fangman1

*These authors contributed equally to this work

§To whom correspondence should be addressed (email: raghu@u.washington.edu)

1Department of Genetics , University of Washington, Seattle, WA 98195

2Genomics Institute of the Novartis Research Foundation, 3115 Merryfield Row, Suite 200, San Diego, CA 92121-1125

3Department of Mathematics , University of Washington, Seattle, WA 98195

4Affymetrix , 3380 Central Expressway, Santa Clara, CA 98195

5Present address: Aventa Biosciences, 4757 Nexus Centre Drive, Suite 200, San Diego, CA 92121

6Silicon Genetics, 935 Washington Street, San Carlos, CA 94070

7Present address: The Salk Institute for Biological Studies, Laboratory of Genetics, 10010 North Torrey Pines Road, La Jolla, CA 92037

8Stanford Genome Sequence and Technology Center, Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305-5307

We present an algorithm for predicting origin locations and replication timing which is applicable in a variety of microarray experiments where an ordered set of sequenced clones is available. Our algorithm applied to an Affymetrix microarray experiment produces a list of 332 predicted origins in the budding yeast Saccharomyces cerevisiae; these predictions correlate well with the location and timing of known efficient origins.

The origin prediction algorithm requires a choice of two numerical parameters and the prediction of replication timing requires one further choice. These assumptions are clearly summarized in part III below.

I. Preliminary Data Analysis

II. Secondary Data Analysis

III. Summary of Data Analysis Parameters