High-resolution estimates of crossover and noncrossover recombination from a captive baboon colony

Homologous recombination has been extensively studied in humans and a handful of model organisms. Much less is known about recombination in other species, including non-human primates. Here we present a study of crossovers and non-crossover (NCO) recombination in olive baboons (Papio anubis) from two pedigrees containing a total of 20 paternal and 17 maternal meioses, and compare these results to linkage-disequlibrium (LD) based recombination estimates from 36 unrelated olive baboons. We demonstrate how crossovers, combined with LD-based recombination estimates, can be used to identify genome assembly errors. We also quantify sex-specific differences in recombination rates, including elevated male crossover and reduced female crossover rates near telomeres. Finally, we add to the increasing body of evidence suggesting that while most NCO recombination tracts in mammals are short (e.g., < 500 bp), there are a non-negligible fraction of longer (e.g., > 1 Kb) NCO tracts. We fit a mixture-of-two-geometric distributions model to the NCO tract length distribution and estimate that >99% of all NCO tracts are very short (mean 24 bp), but the remaining tracts can be quite long (mean 11 Kb). A single geometric distribution model for NCO tract lengths is incompatible with the data, suggesting that LD-based methods for estimating NCO recombination rates that make this assumption may need to be modified.

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Hand preferences for unimanual and bimanual coordinated actions in olive baboons (Papio anubis): Consistency over time and across populations.

Journal of Comparative Psychology ◽

10.1037/com0000040 ◽

2016 ◽

Vol 130 (4) ◽

pp. 341-350 ◽

Cited By ~ 9

Author(s):

Sandra Molesti ◽

Jacques Vauclair ◽

Adrien Meguerditchian

Keyword(s):

Papio Anubis ◽

Olive Baboons ◽

Hand Preferences ◽

Over Time

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BREC: an R package/Shiny app for automatically identifying heterochromatin boundaries and estimating local recombination rates along chromosomes

BMC Bioinformatics ◽

10.1186/s12859-021-04233-1 ◽

2021 ◽

Vol 22 (S6) ◽

Author(s):

Yasmine Mansour ◽

Annie Chateau ◽

Anna-Sophie Fiston-Lavier

Keyword(s):

Data Quality ◽

Data Science ◽

Fruit Fly ◽

R Package ◽

Model Organisms ◽

Data Quality Control ◽

Recombination Rates ◽

Functional Dynamics ◽

Shiny App ◽

User Friendly

Abstract Background Meiotic recombination is a vital biological process playing an essential role in genome's structural and functional dynamics. Genomes exhibit highly various recombination profiles along chromosomes associated with several chromatin states. However, eu-heterochromatin boundaries are not available nor easily provided for non-model organisms, especially for newly sequenced ones. Hence, we miss accurate local recombination rates necessary to address evolutionary questions. Results Here, we propose an automated computational tool, based on the Marey maps method, allowing to identify heterochromatin boundaries along chromosomes and estimating local recombination rates. Our method, called BREC (heterochromatin Boundaries and RECombination rate estimates) is non-genome-specific, running even on non-model genomes as long as genetic and physical maps are available. BREC is based on pure statistics and is data-driven, implying that good input data quality remains a strong requirement. Therefore, a data pre-processing module (data quality control and cleaning) is provided. Experiments show that BREC handles different markers' density and distribution issues. Conclusions BREC's heterochromatin boundaries have been validated with cytological equivalents experimentally generated on the fruit fly Drosophila melanogaster genome, for which BREC returns congruent corresponding values. Also, BREC's recombination rates have been compared with previously reported estimates. Based on the promising results, we believe our tool has the potential to help bring data science into the service of genome biology and evolution. We introduce BREC within an R-package and a Shiny web-based user-friendly application yielding a fast, easy-to-use, and broadly accessible resource. The BREC R-package is available at the GitHub repository https://github.com/GenomeStructureOrganization.

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