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CODA (crossover distribution analyzer): quantitative characterization of crossover position patterns along chromosomes

Authors :
Franck Gauthier
Olivier C. Martin
Matthieu Falque
Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon)
Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA)
Agence nationale de la recherche [ANR-07-BLANC-COPATH, ANR-09-GENM-022-003]
Falque, Matthieu
Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)
Institut de Chimie Organique et Analytique (ICOA)
Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)
Source :
BMC Bioinformatics, BMC Bioinformatics, BioMed Central, 2011, 12, ⟨10.1186/1471-2105-12-27⟩, BMC Bioinformatics, Vol 12, Iss 1, p 27 (2011), BMC Bioinformatics (12), . (2011), BMC Bioinformatics, 2011, 12 (1), pp.27. ⟨10.1186/1471-2105-12-27⟩
Publication Year :
2011
Publisher :
HAL CCSD, 2011.

Abstract

Background During meiosis, homologous chromosomes exchange segments via the formation of crossovers. This phenomenon is highly regulated; in particular, crossovers are distributed heterogeneously along the physical map and rarely arise in close proximity, a property referred to as "interference". Crossover positions form patterns that give clues about how crossovers are formed. In several organisms including yeast, tomato, Arabidopsis, and mouse, it is believed that crossovers form via at least two pathways, one interfering, the other not. Results We have developed a software package - "CODA", for CrossOver Distribution Analyzer - which allows one to quantitatively characterize crossover patterns by fitting interference models to experimental data. Two families of interfering models are provided: the "gamma" model and the "beam-film" model. The user can specify single or two-pathways modeling, and the software package infers the model's parameters and their confidence intervals. CODA can handle data produced from measurements on bivalents or gametes, in the form of continuous crossover positions or marker genotyping. We illustrate the possibilities on data from Wheat, corn and mouse. Conclusions CODA extends the kind of crossover data that could be analyzed so far to include gametic data (rather than only bivalents/tetrads) when using two-pathways modeling. It will also enable users to perform analyses based on the beam-film model. CODA implements that model's complex physics and mathematics, and uses a summary statistic to overcomes the lack of a computable likelihood which has hampered its use till now.

Details

Language :
English
ISSN :
14712105
Database :
OpenAIRE
Journal :
BMC Bioinformatics, BMC Bioinformatics, BioMed Central, 2011, 12, ⟨10.1186/1471-2105-12-27⟩, BMC Bioinformatics, Vol 12, Iss 1, p 27 (2011), BMC Bioinformatics (12), . (2011), BMC Bioinformatics, 2011, 12 (1), pp.27. ⟨10.1186/1471-2105-12-27⟩
Accession number :
edsair.doi.dedup.....3208155091db9065059755564b4042b9
Full Text :
https://doi.org/10.1186/1471-2105-12-27⟩