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SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis.

TitleSPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis.
Publication TypeJournal Article
Year of Publication2014
AuthorsChampeimont, R, Carbone, A
JournalBMC Bioinformatics
Volume15
Pagination391
Date Published2014
ISSN1471-2105
KeywordsBase Composition, Chromosomes, DNA Breaks, Double-Stranded, Genomics, Meiosis, Models, Biological, Recombination, Genetic, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Yeasts
Abstract

BACKGROUND: Meiotic recombination between homologous chromosomes provides natural combinations of genetic variations and is a main driving force of evolution. It is initiated via programmed DNA double-strand breaks (DSB) and involves a specific axial chromosomal structure. So far, recombination regions have been mainly determined by experiments, both expensive and time-consuming.

RESULTS: SPoRE is a mathematical model that describes the non-uniform localisation of DSB and axis proteins sites, and distinguishes high versus low protein density. It is based on a combination of genomic signals, based on what is known from wet-lab experiments, whose contribution is precisely quantified. It models axis proteins accumulation at gene 5'-ends with a discrete approximation of their diffusion and convection along genes. It models DSB accumulation at approximated gene promoter positions with intergenic region length and GC-content. SPoRE can be used for prediction and it is parameterised in an obvious way that makes it easy to understand from a biological viewpoint.

CONCLUSIONS: When compared to Saccharomyces cerevisiae experimental data, SPoRE predicts axis protein and DSB positions with high sensitivity and precision, axis protein density with an average local correlation r = 0.63 and DSB density with an average local correlation r = 0.62. SPoRE outbreaks previous DSB predictors, which are based on nucleotide patterning, and it reaches 85% of success rate in DSB prediction compared to 54% obtained by available tools on a benchmarked dataset.SPoRE is available at the address http://www.lcqb.upmc.fr/SPoRE/.

DOI10.1186/s12859-014-0391-1
Alternate JournalBMC Bioinformatics
PubMed ID25495332
PubMed Central IDPMC4268827

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