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

The genomes of the fungus Magnaporthe oryzae that causes blast diseases on diverse grass species, including major crop plants, have indispensable core-chromosomes and may contain one or more additional supernumerary chromosomes, also known as mini-chromosomes. The mini-chromosome is speculated to play a role in fungal biology, provide effector gene mobility, and may transfer between strains. To understand and study the biological function of mini-chromosomes, it is crucial to be able to identify whether a given strain of M. oryzae possesses a mini-chromosome. In this study, we applied recurrent neural network models, more specifically, Bidirectional Long Short-Term Models (Bi-LSTM), for classifying DNA sequences as core-or mini-chromosomes. The models were trained with sequences from multiple available core- and mini-chromosome assemblies. The trained model was then used to predict the presence of the mini-chromosome in a global collection of M. oryzae isolates using short-read DNA sequences. The model predicted that the mini-chromosome was prevalent in M. oryzae isolates, including those isolated from rice, wheat, Lolium and many other grass species. Interestingly, 23 recent wheat strains collected since 2005 all carried the mini-chromosome, but none of nine early strains collected before 1991 had the mini-chromosome, indicating the preferential selection for strains carrying the mini-chromosome in recent years. Based on the limited sample size, we found the presence of the mini-chromosome in isolates of pathotype Eleusine was not as high as isolates of other pathotypes. The deep learning model was also used to identify assembled sequence contigs that were derived from the mini-chromosome and partial regions on core-chromosomes potentially translocated from a mini-chromosome. In summary, our study has developed a reliable method for categorizing DNA sequences and showcases an application of recurrent neural networks in the field of predictive genomics.