Confidently Estimating the Number of DNA Replication Origins

Bhaskar Anand and Keich Uri
Additional contact information
Bhaskar Anand: Computer Science Division, University of California, Berkeley
Keich Uri: School of Mathematics and Statistics, University of Sydney

Statistical Applications in Genetics and Molecular Biology, 2010, vol. 9, issue 1, 23

Abstract: We present a method for estimating and providing a confidence interval for the number of DNA replication origins in the genome of the yeast Kluyveromyces lactis. The method requires an initial set of verified sites from which a position specific frequency matrix (PSFM) can be constructed. We further assume that we have access to a sparingly used experimental procedure which can verify the functionality of a few, but not all, computationally predicted sites. While our motivation comes from estimating the number of autonomously replicating sequences (ARSs), our method can also be applied to estimating the genome-wide number of "functional" transcription factor binding sites, where functionality is determined by experimental verification of the transcription factor binding event using, for example, ChIP data. The reliability of our method is demonstrated by correctly predicting the known number of Saccharomyces cerevisiae ARSs as well as the number of S. cerevisiae probes that bind to the transcription factor ABF1.

Keywords: PSFM; confidence interval; ARS; K. lactis; S. cerevisiae (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1544 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:28

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1544

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().