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Summary The fungal pathogen,Magnaporthe oryzae Triticumpathotype, causing wheat blast disease was first identified in South America and recently spread across continents to South Asia and Africa. Here, we studied the genetic relationship among isolates found on the three continents.Magnaporthe oryzaestrains closely related to a South American field isolate B71 were found to have caused the wheat blast outbreaks in South Asia and Africa. Genomic variation among isolates from the three continents was examined using an improved B71 reference genome and whole‐genome sequences.We found strong evidence to support that the outbreaks in Bangladesh and Zambia were caused by the introductions of genetically separated isolates, although they were all close to B71 and, therefore, collectively referred to as the B71 branch. In addition, B71 branch strains carried at least one supernumerary mini‐chromosome. Genome assembly of a Zambian strain revealed that its mini‐chromosome was similar to the B71 mini‐chromosome but with a high level of structural variation.Our findings show that while core genomes of the multiple introductions are highly similar, the mini‐chromosomes have undergone marked diversification. The maintenance of the mini‐chromosome and rapid genomic changes suggest the mini‐chromosomes may serve important virulence or niche adaptation roles under diverse environmental conditions.
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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.
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- PAR ID:
- 10535801
- Publisher / Repository:
- Oxford
- Date Published:
- Journal Name:
- NAR Genomics and Bioinformatics
- Volume:
- 6
- Issue:
- 3
- ISSN:
- 2631-9268
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/nargab/lqae108
