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Statistical Applications in Genetics and Molecular Biology

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Volume 13, Issue 2


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Scan statistics analysis for detection of introns in time-course tiling array data

Anat Reiner-Benaim / Ronald W. Davis
  • Stanford Genome Technology Center, Department of Biochemistry, Stanford University School of Medicine, Palo Alto, CA 94304, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Kara Juneau
  • Corresponding author
  • Stanford Genome Technology Center, Department of Biochemistry, Stanford University School of Medicine, Palo Alto, CA 94304, USA
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2014-02-27 | DOI: https://doi.org/10.1515/sagmb-2013-0038


A tiling array yields a series of abundance measurements across the genome using evenly spaced probes. These data can be used for detecting sequences that exhibit a particular behavior. Scanning window statistics are often employed for testing each probe while accounting for local correlation and smoothing noisy measurements. However, window testing may yield false probe discoveries around the sequences and false non-discoveries within the sequences, resulting in biased predicted intervals. We propose to avoid this problem by stipulating that a sequence of interest can appear at most once within a defined region, such as a gene; thus, only one window statistic is considered per region. This substantially reduces the number of tests and hence, is potentially more powerful. We compare this approach to a genome-wise scan that does not require pre-defined search regions, but considers clumps of adjacent probe discoveries. Simulations show that the gene-wise search maintains the nominal FDR level, while the genome-wise scan yields FDR that exceeds the nominal level for low interval effects, and achieves slightly less power. Using arrays to map introns in yeast, we identified 71% of the previously published introns, detected nine previously undiscovered introns, and observed no false intron discoveries by either method.

Keywords: gene-wise search; introns; meiosis; Saccharomyces cerevisiae; scan statistic; tiling arrays


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About the article

Corresponding authors: Anat Reiner-Benaim, Department of Statistics, University of Haifa, Mount Carmel, Haifa 3498838, Israel, e-mail: ; and Kara Juneau, Stanford Genome Technology Center, Department of Biochemistry, Stanford University School of Medicine, Palo Alto, CA 94304, USA, e-mail:

aPresent address: Ariosa Diagnostics, 5945 Optical Court, San Jose, CA 95138, USA.

Published Online: 2014-02-27

Published in Print: 2014-04-01

Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 13, Issue 2, Pages 173–190, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2013-0038.

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