Array-based genome comparison of arabidopsis ecotypes using hidden markov models

Michael Seifert*, Ali Banaei, Jens Keilwagen, Michael Florian Mette, Andreas Houben, François Roudier, Vincent Colot, Ivo Grosse, Marc Strickert

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Arabidopsis thaliana is an important model organism in plant biology with a broad geographic distribution including ecotypes from Africa, America, Asia, and Europe. The natural variation of different ecotypes is expected to be reflected to a substantial degree in their genome sequences. Array comparative genomic hybridization (Array-CGH) can be used to quantify the natural variation of different ecotypes at the DNA level. Besides, such Array-CGH data provides the basics to establish a genome-wide map of DNA copy number variation for different ecotypes. Here, we present a new approach based on Hidden Markov Models (HMMs) to predict copy number variations in Array-CGH experiments. Using this approach, an improved genome-wide characterization of DNA segments with decreased or increased copy numbers is obtained in comparison to the routinely used segMNT algorithm. The software and the data set used in this case study can be downloaded from http://dig.ipk-gatersleben.de/HMMs/ACGH/ACGH.html.

Original languageEnglish (US)
Title of host publicationBIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing
Pages3-11
Number of pages9
StatePublished - 2009
Externally publishedYes
Event2nd International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2009 - Porto, Portugal
Duration: Jan 14 2009Jan 17 2009

Other

Other2nd International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2009
CountryPortugal
CityPorto
Period01/14/0901/17/09

Keywords

  • Arabidopsis Ecotypes
  • Array-CGH
  • Comparative Genomics
  • Hidden Markov Model(HMM)

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Control and Systems Engineering

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