Peanut (Arachis hypogaea) consists of two subspecies, hypogaea and fastigiata, and has been cultivated worldwide for hundreds of years. Here, 158 peanut accessions were selected to dissect the molecular footprint of agronomic traits related to domestication using specific-locus amplified fragment sequencing (SLAF-seq method). Then, a total of 17,338 high-quality single nucleotide polymorphisms (SNPs) in the whole peanut genome were revealed. Eleven agronomic traits in 158 peanut accessions were subsequently analyzed using genome-wide association studies (GWAS). Candidate genes responsible for corresponding traits were then analyzed in genomic regions surrounding the peak SNPs, and 1,429 genes were found within 200 kb windows centerd on GWAS-identified peak SNPs related to domestication. Highly differentiated genomic regions were observed between hypogaea and fastigiata accessions using FST values and sequence diversity (π) ratios. Among the 1,429 genes, 662 were located on chromosome A3, suggesting the presence of major selective sweeps caused by artificial selection during long domestication. These findings provide a promising insight into the complicated genetic architecture of domestication-related traits in peanut, and reveal whole-genome SNP markers of beneficial candidate genes for marker-assisted selection (MAS) in future breeding programs.

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AbsractPeanut (Arachis hypogaea L.) is an important oilseed and cash crop worldwide. Wild Arachis spp. are potental sources of novel genes for the genetic improvement of cultivated peanut. Understanding the genetic relationships with cultivated peanut is important for the efficient use of wild species in breeding programmes. However, for this genus, only a few genetic resources have been explored so far. In this study, new chloroplast genomic resources have been developed for the genus Arachis based on whole chloroplast genomes from seven species that were sequenced using next-generation sequencing technologies. The chloroplast genomes ranged in length from 156,275 to 156,395 bp, and their gene contents, gene orders, and GC contents were similar to those for other Fabaceae species. Comparative analyses among the seven chloroplast genomes revealed 643 variable sites that included 212 singletons and 431 parsimony-informative sites. We also identified 101 SSR loci and 85 indel mutation events. Thirty-seven SSR loci were found to be polymorphic by in silico comparative analyses. Eleven highly divergent DNA regions, suitable for phylogenetic and species identification, were detected in the seven chloroplast genomes. A molecular phylogeny based on the complete chloroplast genome sequences provided the best resolution of the seven Arachis species.

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Molecular structures are commonly depicted in 2D printed forms in scientific documents such as journal papers and patents. However, these 2D depictions are not machine readable. Due to a backlog of decades and an increasing amount of printed literatures, there is a high demand for translating printed depictions into machinereadable formats, which is known as Optical Chemical Structure Recognition (OCSR). Most OCSR systems developed over the last three decades use a rule-based approach, which vectorizes the depiction based on the interpretation of vectors and nodes as bonds and atoms. Here, we present a practical software called MolMiner, which is primarily built using deep neural networks originally developed for semantic segmentation and object detection to recognize atom and bond elements from documents. These recognized elements can be easily connected as a molecular graph with a distance-based construction algorithm. MolMiner gave state-of-the-art performance on four benchmark data sets and a self-collected external data set from scientific papers. As MolMiner performed similarly well in real-world OCSR tasks with a user-friendly interface, it is a useful and valuable tool for daily applications. The free download links of Mac and Windows versions are available at https://github.com/iipharma/pharmamind-molminer.