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A comprehensive search system for the bioscience databases is in progress. We constructed a search service, Life science database cross search system (https://biosciencedbc.jp/dbsearch/index. php?lang=en) by integrating numerous biomedical databases using database crawling algorithms. The described system integrates 600 databases containing over 90 million entries indexed for biomedical research and development.

Allergens originating from fungal components abundantly exist in and around human life. We constructed a data portal specific for fungal allergens that includes genomic data from four Aspergillus species used by beverage industries. The fungal database contains the information of nucleotide sequences, which are similar to the coding region of already known allergens in the public database. The database will accelerate allergen identification and prediction in the fungal research field.

The circumstances in which organisms live induce polymorphism in their genes, including fungal allergen genes, leading to altered structures and functions of proteins, related to their pathogenicity. Major allergen genes of Aspergillus fumigatus, Asp f 1, Asp f 2, and Asp f 3, were examined in 59 strains [environment and animal/human-body origin] to determine their nucleotide sequences, and then categorized. The location and number of IgE epitopes on the allergen molecules were predicted using a computer software. The Asp f 1 gene was classified into two groups (f1-1 and f1-2). One of the groups possessed one-nucleotide mutation point with one amino-acid substitution. The mutated Asp f 2 gene accompanying 6-amino acid substitution was classified into 7 groups (f2-1 to f2-7). Six of the groups possessed a newborn IgE epitope. The Asp f 3 gene contained two mutations, resulted in three groups (f3-1 to f3-3) without any amino-acid substitutions. Category E, consisting of groups f1-1, f2-5, and f3-2, was specific to an environmental origin. Our findings suggest that nucleotide mutation of the fungal allergen genes, associated with the origin of the fungus, modifies the structure of proteins, and affects their pathogenic properties, such as the localization of IgE epitopes.

Biological databases represent an extraordinary collective volume of work. Diligently built up over decades and comprising many millions of contributions from the biomedical research community, biological databases provide worldwide access to a massive number of records (also known as entries) [1]. Starting from individual laboratories, genomes are sequenced, assembled, annotated, and ultimately submitted to primary nucleotide databases such as GenBank [2], European Nucleotide Archive (ENA) [3], and DNA Data Bank of Japan (DDBJ) [4] (collectively known as the International Nucleotide Sequence Database Collaboration, INSDC). Protein records, which are the translations of these nucleotide records, are deposited into central protein databases such as the UniProt KnowledgeBase (UniProtKB) [5] and the Protein Data Bank (PDB) [6]. Sequence records are further accumulated into different databases for more specialized purposes: RFam [7] and PFam [8] for RNA and protein families, respectively; DictyBase [9] and PomBase [10] for model organisms; as well as ArrayExpress [11] and Gene Expression Omnibus (GEO) [12] for gene expression profiles. These databases are selected as examples; the list is not intended to be exhaustive. However, they are representative of biological databases that have been named in the “golden set” of the 24th Nucleic Acids Research database issue (in 2016). The introduction of that issue highlights the databases that “consistently served as authoritative, comprehensive, and convenient data resources widely used by the entire community and offer some lessons on what makes a successful database” [13]. In addition, the associated information about sequences is also propagated into non-sequence databases, such as PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) for scientific literature or Gene Ontology (GO) [14] for function annotations. These databases in turn benefit individual studies, many of which use these publicly available records as the basis for their own research.

Fumonisins are well known as mycotoxins produced by various Fusarium species. Recently Aspergillus niger has been reported to be a fumonisin B2 (FB2) producer. Aspergillus niger is a member of Aspergillus section Nigri. Members of this section are common food contaminants and are also distributed widely in the environment. This study aimed to determine 1) optimum culture conditions of A. niger for fumonisin production including growth medium, temperature and incubation period and 2) fumonisin production among isolates of Aspergillus section Nigri and closely related species isolated from Japanese food and environmental samples. Growth on Czapek yeast extract broth +5% NaCl (CYBS) at 28°C for 7 days resulted in the highest levels of FB2 production as determined by quantitative LC-MS/MS of culture extracts. Sixty-two isolates were collected from various foods in domestic markets as well as from soil and air. The isolates principally separated into two groups; A. niger and A. luchuensis/A. piperis/A. tubingensis, following molecular phylogenetic analysis. ELISA using the tip culture method was shown to be suitable for screening of the fumonisin-producing strains. Phylogenic analysis of Aspergillus section Nigri isolates from food and environmental samples indicated that fumonisin producing strains could be grouped into the A. niger clade. Nineteen of 35 (54%) isolates classified as A. niger were FB2 producers. The current study suggests that FB2-producing A. niger are distributed throughout several regions of Japan.

"Life Science Database Cross Search" is a knowledge infrastructure for life science researchers. This service was originally developed in Database Center for Life Science (DBCLS), and was transferred to National Bioscience Database Center (NBDC) in 2011. It provides a web search service across more than 670 public databases (containing 15 million entries). This open infrastructure supports the research data to be "FAIR". For instance, on the point of principle F2 (provided rich meta data) and F3 (indexed in searchable resource), which are difficult to achieve by each data provider or general search engine, our search system can fulfill the terms. Our infrastructure also accelerates the utilization of semantically integrated life science data.

Background The molecular basis of the incipient stage of speciation is still poorly understood. Cichlid fish species in Lake Victoria are a prime example of recent speciation events and a suitable system to study the adaptation and reproductive isolation of species. Results Here, we report the pattern of genomic differentiation between two Lake Victoria cichlid species collected in sympatry, Haplochromis pyrrhocephalus and H. sp. ‘macula,’ based on the pooled genome sequences of 20 individuals of each species. Despite their ecological differences, population genomics analyses demonstrate that the two species are very close to a single panmictic population due to extensive gene flow. However, we identified 21 highly differentiated short genomic regions with fixed nucleotide differences. At least 15 of these regions contained genes with predicted roles in adaptation and reproductive isolation, such as visual adaptation, circadian clock, developmental processes, adaptation to hypoxia, and sexual selection. The nonsynonymous fixed differences in one of these genes, LWS, were reported as substitutions causing shift in absorption spectra of LWS pigments. Fixed differences were found in the promoter regions of four other differentially expressed genes, indicating that these substitutions may alter gene expression levels. Conclusions These diverged short genomic regions may have contributed to the differentiation of two ecologically different species. Moreover, the origins of adaptive variants within the differentiated regions predate the geological formation of Lake Victoria; thus Lake Victoria cichlid species diversified via selection on standing genetic variation.

Figure S1. (A) Site frequency spectrum of Lake Victoria cichlids. The white and black bars represent H. pyrrhocephalus and H. sp. ‘macula,’ respectively. (B) Demographic model of Lake Victoria cichlids. See the section Demographic Model and Parameter Estimation for details. (C) Nucleotide and indel frequencies within five DRs. We amplified and determined the sequences, including fixed differences, of four DRs from 20 individuals each of H. sp. ‘macula’ and H. pyrrhocephalus. Positions indicate the positions from the first nucleotides of the determined sequences. The frequencies of nucleotides in the coding region of LWS were verified in a previous study (14). (PDF 155 kb)

Figure S2. Population genomic analyses using only male individuals. (A) Site frequency spectrum in H. pyrrhocephalus (white bars; 126,843 SNPs) and H. sp. ‘macula’ (black bars; 146,456 SNPs). Nucleotide diversity and Tajima’s D values were almost same as those in all samples. (B) Average FST values (±1 SD) against coverage for Pool-seq data as in Fig. 1b (5,662,990 SNPs). The blue and orange dots represent observed and simulated values under panmixia. (C) The spatial patterns of average nucleotide diversity within each species (πW; pink), average pairwise nucleotide divergence between species (πB; blue), and FST (green) in and around LWS gene as in Fig. 2b. The green arrows represent fixed nucleotide differences between species. (PDF 279 kb)

Figure S4. The origins of mutations in DRs. Three phylogenetic trees represent the accumulation of mutations in the common ancestral species of (A) Lake Victoria species, (B) Lake Victoria and riverine Haplochromis species, and (C) tribe Tropheini in Lake Tanganyika, Lakes Malawi, Victoria, and riverine Haplochromis species. The tree topologies constructed from sequences of each DR were consistent with (D) “Riverine origin” or (E) “Modern haplochromine origin.” Scale bars indicate the number of substitutions per site. (F) The LWS sequences were determined from three riverine species: H. sp. ‘katonga’ from Katonga, H. sp. ‘kitilda-rukwa’ from Kitilda-Rukwa, and H. sp. ‘muzu’ from Muzu. (PDF 554 kb)

Text S1. Supporting Text. (DOCX 572 kb)

Figure S3. The spatial patterns of average nucleotide diversity within species (πW; pink), average pairwise nucleotide divergence between species (πB; blue), and FST (green) in and around DRs. The green arrows represent fixed nucleotide differences between species. (PDF 1356 kb)

National Bioscience Database Center (NBDC; http://biosciencedbc.jp/en/) was founded in 2011, as the Japanese central infrastructure for data repository and portal for open, reusable and integrated life science research data. We took over our mission from Integrated Database Project (2006-2010). For integration and usage promotion of life science databases scattered in many research institutes, we have conducted a lot of activities. To bring Japanese life science databases into compliance with the FAIR principles, we have also released, expanded and sophisticated services by semantic web technology.

Bov-A2 is a retroposon that is widely distributed among the genomes of ruminants (e.g., cow, deer, giraffe, pronghorn, musk deer, and chevrotain). This retroposon is composed of two monomers, called Bov-A units, which are joined by a linker sequence. The structure and origin of Bov-A2 has been well characterized but a genome-level exploration of this retroposon has not been implemented. In this study we performed an extensive search for Bov-A2 using all available genome sequence data on Bos taurus. We found unique Bov-A2-derived sequences that were longer than Bov-A2 due to amplification of three to six Bov-A units arranged in tandem. Detailed analysis of these elongated Bov-A2-derived sequences revealed that they originated through unequal crossing-over of Bov-A2. We found a large number of these elongated Bov-A2-derived sequences in cattle genomes, indicating that unequal crossing-over of Bov-A2 occurred very frequently. We found that this type of elongation is not observed in wild bovine and is therefore specific to the domesticated cattle genome. Furthermore, at specific loci, the number of Bov-A units was also polymorphic between alleles, implying that the elongation of Bov-A units might have occurred very recently. For these reasons, we speculate that genomic instability in bovine genomes can lead to extensive unequal crossing-over of Bov-A2 and levels of polymorphism might be generated in part by repeated outbreeding.