Using recurrent neural networks to detect supernumerary chromosomes in fungal strains causing blast diseases.

Abstract

The genomes of the fungus Magnaporthe oryzae that causes blast diseases on diverse grass species, including major crops, have indispensable core-chromosomes and may contain supernumerary chromosomes, also known as mini-chromosomes. These mini-chromosomes are speculated to provide effector gene mobility, and may transfer between strains. To understand the biology of mini-chromosomes, it is valuable to be able to detect whether a M. oryzae strain possesses a mini-chromosome. Here, we applied recurrent neural network models for classifying DNA sequences as arising from core- or mini-chromosomes. The models were trained with sequences from available core- and mini-chromosome assemblies, and then used to predict the presence of mini-chromosomes in a global collection of M. oryzae isolates using short-read DNA sequences. The model predicted that mini-chromosomes were prevalent in M. oryzae isolates. Interestingly, at least one mini-chromosome was present in all recent wheat isolates, but no mini-chromosomes were found in early isolates collected before 1991, indicating a preferential selection for strains carrying mini-chromosomes in recent years. The model was also used to identify assembled contigs derived from mini-chromosomes. In summary, our study has developed a reliable method for categorizing DNA sequences and showcases an application of recurrent neural networks in predictive genomics.