Tabuchi, H. | bioRxiv Science

bioRxiv2022

Haploid-resolved and chromosome-scale genome assembly in hexa-autoploid sweetpotato (Ipomoea batatas (L.) Lam)

Sweetpotato (Ipomoea batatas (L.) Lam) is the worlds seventh most important food crop by production quantity. Cultivated sweetpotato is a hexaploid (2n = 6x = 90), and its genome (B1B1B2B2B2B2) is quite complex due to polyploidy, self-incompatibility, and high heterozygosity. Here we established a haploid-resolved and chromosome-scale de novo assembly of autohexaploid sweetpotato genome sequences. Before constructing the genome, we created chromosome-scale genome sequences in I. trifida using a highly homozygous accession, Mx23Hm, with PacBio RSII and Hi-C reads. Haploid-resolved genome assembly was performed for a sweetpotato cultivar, Xushu18 by hybrid assembly with Illumina paired-end (PE) and mate-pair (MP) reads, 10X genomics reads, and PacBio RSII reads. Then, 90 chromosome-scale pseudomolecules were generated by aligning the scaffolds onto a sweetpotato linkage map. De novo assemblies were also performed for chloroplast and mitochondrial genomes in I. trifida and sweetpotato. In total, 34,386 and 175,633 genes were identified on the assembled nucleic genomes of I. trifida and sweetpotato, respectively. Functional gene annotation and RNA-Seq analysis revealed locations of starch, anthocyanin, and carotenoid pathway genes on the sweetpotato genome. This is the first report of chromosome-scale de novo assembly of the sweetpotato genome. The results are expected to contribute to genomic and genetic analyses of sweetpotato.

genomics↗

bioRxiv2019

Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography

This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular lesions [e.g., myopic choroidal neovascularization (mCNV) and retinoschisis (RS)]. A total of 796 SS-OCT images were included in the study as follows and analyzed by k-fold cross-validation (k = 5) using DLs renowned model, Visual Geometry Group-16: nHM, 107 images; HM, 456 images; mCNV, 122 images; and RS, 111 images (n = 796). The binary classification of OCT images with or without myopic macular lesions; the binary classification of HM images and images with myopic macular lesions (i.e., mCNV and RS images); and the ternary classification of HM, mCNV, and RS images were examined. Additionally, sensitivity, specificity, and the area under the curve (AUC) for the binary classifications as well as the correct answer rate for ternary classification were examined. The classification results of OCT images with or without myopic macular lesions were as follows: AUC, 0.983; sensitivity, 0.953; specificity, 0.940. The classification results of HM images and images with myopic macular lesions were as follows: AUC, 0.976; sensitivity, 0.940; specificity, 0.941. The correct answer rate in the ternary classification of HM images, mCNV images, and RS images were as follows: HM images, 93.7%; mCNV images, 82.4%; and RS, 92.3% with mean, 91.4%. Using noninvasive, easy-to-obtain swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy. The study results suggest the possibility of conducting highly accurate screening of ocular diseases using artificial intelligence, which may improve the prevention of blindness and reduce workloads for ophthalmologists.

bioengineering↗