Chromosome-scale genome assembly of sweet cherry(Prunus avium L.)cv.Tieton obtained using long-read and Hi-C sequencing

Sweet cherry(Prunus avium)is an economically significant fruit species in the genus Prunus.However,in contrast to other important fruit trees in this genus,only one draft genome assembly is available for sweet cherry,which was assembled using only Illumina short-read sequences.The incompleteness and low quality of the current sweet cherry draft genome limit its use in genetic and genomic studies.A high-quality chromosome-scale sweet cherry reference genome assembly is therefore needed.A total of 65.05 Gb of Oxford Nanopore long reads and 46.24 Gb of Illumina short reads were generated,representing~190x and 136x coverage,respectively,of the sweet cherry genome.The final de novo assembly resulted in a phased haplotype assembly of 344.29 Mb with a contig N50 of 3.25 Mb.Hi-C scaffolding of the genome resulted in eight pseudochromosomes containing 99.59%of the bases in the assembled genome.Genome annotation revealed that more than half of the genome(59.40%)was composed of repetitive sequences,and 40,338 protein-coding genes were predicted,75.40%of which were functionally annotated.With the chromosomescale assembly,we revealed that gene duplication events contributed to the expansion of gene families for salicylic acid/jasmonic acid carboxyl methyltransferase and ankyrin repeat-containing proteins in the genome of sweet cherry.Four auxin-responsive genes(two GH3s and two SAURs)were induced in the late stage of fruit development,indicating that auxin is crucial for the sweet cherry ripening process.In addition,772 resistance genes were identified and functionally predicted in the sweet cherry genome.The high-quality genome assembly of sweet cherry obtained in this study will provide valuable genomic resources for sweet cherry improvement and molecular breeding.

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources.Although leaf image-based methods have been widely adopted in plant species identification,they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars.Here,we propose an automatic leaf image-based cultivar identification pipeline called MFCIS(Multi-feature Combined Cultivar Identification System),which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network(CNN).Persistent homology,a multiscale and robust method,was employed to extract the topological signatures of leaf shape,texture,and venation details.A CNN-based algorithm,the Xception network,was fine-tuned for extracting high-level leaf image features.For fruit species,we benchmarked the MFCIS pipeline on a sweet cherry(Prunus avium L.)leaf dataset with>5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%.For annual crop species,we applied the MFCIS pipeline to a soybean(Glycine max L.Merr.)leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods.The identification models for each growth period were trained independently,and their results were combined using a score-level fusion strategy.The classification accuracy after score-level fusion was 91.4%,which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods.To facilitate the adoption of the proposed pipelines,we constructed a user-friendly web service,which is freely available at http://www.mfcis.online.