DNA sequence and shape are predictive for meiotic crossovers throughout the plant kingdom. (21st June 2018)

• Record Type: Journal Article
• Authors: Demirci, Sevgin; Peters, Sander A.; de Ridder, Dick; van Dijk, Aalt D.J.
• Is Part Of: Plant journal. Volume 95:Number 4(2018)
• Page Start: 686
• Page End: 699
• Publication Date: 2018-06-21
• Subjects: meiotic recombination -- crossover -- machine learning -- prediction -- genome accessibility -- DNA shape -- tomato -- Arabidopsis thaliana -- maize -- rice; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.13979
• Languages: English
• ISSNs: 0960-7412

Abstract

Summary: A better understanding of genomic features influencing the location of meiotic crossovers (COs) in plant species is both of fundamental importance and of practical relevance for plant breeding. Using CO positions with sufficiently high resolution from four plant species [ Arabidopsis thaliana, Solanum lycopersicum (tomato), Zea mays (maize) and Oryza sativa (rice)] we have trained machine‐learning models to predict the susceptibility to CO formation. Our results show that CO occurrence within various plant genomes can be predicted by DNA sequence and shape features. Several features related to genome content and to genomic accessibility were consistently either positively or negatively related to COs in all four species. Other features were found as predictive only in specific species. Gene annotation‐related features were especially predictive for maize, whereas in tomato and Arabidopsis propeller twist and helical twist (DNA shape features) and AT/TA dinucleotides were found to be the most important. In rice, high roll (another DNA shape feature) and low CA dinucleotide frequency in particular were found to be associated with CO occurrence. The accuracy of our models was sufficient for Arabidopsis and rice (area under receiver operating characteristic curve, AUROC > 0.5), and was high for tomato and maize (AUROC ≫ 0.5), demonstrating that DNA sequence and shape are predictive for meiotic COs throughout the plant kingdom. Significance Statement: Understanding the genomic features influencing the location of crossovers is of fundamental importance for many areas of plant biology, but a consistent, simultaneous analysis of multiple plant species in order to compare the genomic determinants of crossovers is lacking. We apply machine learning to crossover datasets from four different plant species in order to develop predictive models for the occurrence of crossovers, and to learn about relevant and important features correlated with CO formation.