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

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 regi...

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Autores principales: Champeimont, Raphaël, Carbone, Alessandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4268827/
https://www.ncbi.nlm.nih.gov/pubmed/25495332
http://dx.doi.org/10.1186/s12859-014-0391-1
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author Champeimont, Raphaël
Carbone, Alessandra
author_facet Champeimont, Raphaël
Carbone, Alessandra
author_sort Champeimont, Raphaël
collection PubMed
description 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0391-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-42688272014-12-17 SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis Champeimont, Raphaël Carbone, Alessandra BMC Bioinformatics Methodology Article 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0391-1) contains supplementary material, which is available to authorized users. BioMed Central 2014-12-11 /pmc/articles/PMC4268827/ /pubmed/25495332 http://dx.doi.org/10.1186/s12859-014-0391-1 Text en © Champeimont and Carbone; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Champeimont, Raphaël
Carbone, Alessandra
SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis
title SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis
title_full SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis
title_fullStr SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis
title_full_unstemmed SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis
title_short SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis
title_sort spore: a mathematical model to predict double strand breaks and axis protein sites in meiosis
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4268827/
https://www.ncbi.nlm.nih.gov/pubmed/25495332
http://dx.doi.org/10.1186/s12859-014-0391-1
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