English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Efficient Algorithms for the Computational Design of Optimal Tiling Arrays

Schliep, A., & Krause, R. (2008). Efficient Algorithms for the Computational Design of Optimal Tiling Arrays. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 5(4), 557-567. Retrieved from http://www2.computer.org/portal/web/csdl/doi/10.1109/TCBB.2008.50.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7EE4-B Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7EE5-9
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Schliep, Alexander1, Author              
Krause, Roland1, Author              
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              

Content

show
hide
Free keywords: Biology and genetics Graph Theory
 Abstract: The representation of a genome by oligonucleotide probes is a prerequisite for the analysis of many of its basic properties, such as transcription factor binding sites, chromosomal breakpoints, gene expression of known genes and detection of novel genes, in particular those coding for small RNAs. An ideal representation would consist of a high density set of oligonucleotides with similar melting temperatures that do not cross-hybridize with other regions of the genome and are equidistantly spaced. The implementation of such design is typically called a tiling array or genome array. We formulate the minimal cost tiling path problem for the selection of oligonucleotides from a set of candidates. Computing the selection of probes requires multi-criterion optimization, which we cast into a shortest path problem. Standard algorithms running in linear time allow us to compute globally optimal tiling paths from millions of candidate oligonucleotides on a standard desktop computer for most problem variants. The solutions to this multi-criterion optimization are spatially adaptive to the problem instance. Our formulation incorporates experimental constraints with respect to specific regions of interest and trade offs between hybridization parameters, probe quality and tiling density easily.

Details

show
hide
Language(s): eng - English
 Dates: 2008-10-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 5 (4) Sequence Number: - Start / End Page: 557 - 567 Identifier: -