Authors:
Michael Seifert
1
;
Ali Banaei
1
;
Jens Keilwagen
1
;
Michael Florian Mette
1
;
Andreas Houben
1
;
François Roudier
2
;
Vincent Colot
2
;
Ivo Grosse
3
and
Marc Strickert
4
Affiliations:
1
Leibniz Institute of Plant Genetics and Crop Plant Research, Germany
;
2
Ecole Normale Superieure, France
;
3
Martin Luther University, Germany
;
4
Institute of Plant Genetics and Crop Plant Research, Germany
Keyword(s):
Array-CGH, Comparative Genomics, Arabidopsis Ecotypes, Hidden Markov Model (HMM).
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
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 ( ACGH ) can be used to quantify the natural variation of different ecotypes at the DNA level. Besides, such ACGH 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 ACGH 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.