Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Software

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

Franck Gauthier, Olivier C Martin and Matthieu Falque*

Author Affiliations

UMR de Génétique Végétale, INRA - Univ Paris-Sud - CNRS - AgroParisTech, Ferme du Moulon, F-91190 Gif-sur-Yvette, France

For all author emails, please log on.

BMC Bioinformatics 2011, 12:27  doi:10.1186/1471-2105-12-27

Published: 20 January 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.