The PhD project Bioinformatic analyses of meiotic recombination in tomato hybrids and related species specifically focuses on a multidisciplinary approach to analyze
- the genetic variation in recombination,
- CO sequence features and types,
- the frequency of recombination, and
- the synteny and structural homology involved in recombination cold and hot spots.
The various types of sequence- and genome-topology data will be integrated to predict recombination cold spots and hot spots.
CO junctions were identified by using SNP data from S. lycopersicum, S. pimpinellifolium and 60 Recombinant Inbred Lines (RILs) derived from their crosses. The frequency of recombination, gene feature variation, and nucleotide composition of recombination sites were analysed and reported in the paper "Distribution..... between S. lycopersicum and S. pimpinellifolium" in the Plant Journal.
Furthermore, using CO positions with sufficiently high resolution from four plant species (Arabidopsis thaliana, tomato, maize, and 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. Propeller twist, a DNA shape characteristic that is known to be related to nucleosome occupancy, was found predictive for 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, helical twist (a DNA shape feature) and AT/TA dinucleotides were found as most important. In rice, high roll (another DNA shape feature) and low CA dinucleotide frequency in particular were found associated with CO occurrence. The accuracy of our models was sufficient for Arabidopsis and rice (AUROC > 0.5) and high for tomato and maize (AUROC>>0.5), demonstrating that DNA sequence and shape are predictive for meiotic crossovers throughout the plant kingdom.
|Effective start/end date||1/01/15 → 31/12/18|