@article{fdi:010086378,
  title = {{M}ethylation in the {CHH} context allows to predict recombination in rice},
  author = {{P}enuela, {M}. and {G}allo-{F}ranco, {J}. {J}. and {F}inke, {J}. and {R}ocha, {C}. and {G}kanogiannis, {A}. and {G}hneim-{H}errera, {T}. and {L}orieux, {M}athias},
  editor = {},
  language = {{ENG}},
  abstract = {{DNA} methylation is the most studied epigenetic trait. {I}t is considered a key factor in regulating plant development and physiology, and has been associated with the regulation of several genomic features, including transposon silencing, regulation of gene expression, and recombination rates. {N}onetheless, understanding the relation between {DNA} methylation and recombination rates remains a challenge. {T}his work explores the association between recombination rates and {DNA} methylation for two commercial rice varieties. {T}he results show negative correlations between recombination rates and methylated cytosine counts for all contexts tested at the same time, and for {CG} and {CHG} contexts independently. {I}n contrast, a positive correlation between recombination rates and methylated cytosine count is reported in {CHH} contexts. {S}imilar behavior is observed when considering only methylated cytosines within genes, transposons, and retrotransposons. {M}oreover, it is shown that the centromere region strongly affects the relationship between recombination rates and methylation. {F}inally, machine learning regression models are applied to predict recombination using the count of methylated cytosines in the {CHH} context as the entrance feature. {T}hese findings shed light on the understanding of the recombination landscape of rice and represent a reference framework for future studies in rice breeding, genetics, and epigenetics.},
  keywords = {epigenetic ; {DNA} methylation ; bisulfite sequencing ; machine learning ; modeling},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {M}olecular {S}ciences},
  volume = {23},
  numero = {20},
  pages = {12505 [14 p.]},
  year = {2022},
  DOI = {10.3390/ijms232012505},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086378},
}