Nutritional metabolic diseases in fish frequently arise in the setting of intensive aquaculture. The etiology and pathogenesis of these conditions involve energy metabolic disorders influenced by both internal genetic factors and external environmental conditions. The exploration of genes associated with nutritional and metabolic disorder has sparked considerable interest within both the aquaculture scientific community and the industry. High-throughput sequencing technology offers researchers extensive genetic information. Effectively mining, analyzing, and securely storing this data is crucial, especially for advancing disease prevention and treatment strategies. Presently, the exploration and application of gene databases concerning nutritional and metabolic disorders in fish are at a nascent stag. Therefore, this study focused on the model organism zebrafish and five primary economic fish species as the subjects of investigation. Using information from KEGG, OMIM, and existing literature, a novel gene database associated with nutritional metabolic diseases in fish was meticulously constructed. This database encompassed 4583 genes for Danio rerio, 6287 for Cyprinus carpio, 3289 for Takifugu rubripes, 3548 for Larimichthys crocea, 3816 for Oreochromis niloticus, and 5708 for Oncorhynchus mykiss. Through a comparative systems biology approach, we discerned a relatively high conservation of genes linked to nutritional metabolic diseases across these fish species, with over 54.9 % of genes being conserved throughout all six species. Additionally, the analysis pinpointed the existence of 13 species-specific genes within the genomes of large yellow croaker, tilapia, and rainbow trout. These genes exhibit the potential to serve as novel candidate targets for addressing nutritional metabolic diseases.

While taxonomy is an often underappreciated branch of science, it serves very important roles. Bacteriophage taxonomy has evolved from a discipline based mainly on morphology, characterized by the work of David Bradley and Hans-Wolfgang Ackermann, to the sequence-based approach that is taken today. The Bacterial Viruses Subcommittee of the International Committee on Taxonomy of Viruses (ICTV) takes a holistic approach to classifying prokaryote viruses by measuring overall DNA and protein similarity and phylogeny before making decisions about the taxonomic position of a new virus. The huge number of complete genomes being deposited with the National Center for Biotechnology Information (NCBI) and other public databases has resulted in a reassessment of the taxonomy of many viruses, and the future will see the introduction of new viral families and higher orders.

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OBJECTIVE: Pollen is a useful tool for identifying the provenance and complex ecosystems surrounding honey production in Malaysian forests. As native key pollinators in Malaysia, Apis dorsata and Heterotrigona itama forage on various plant/pollen species to collect honey. This study aims to generate a dataset that uncovers the presence of these plant/pollen species and their relative abundance in the honey of A. dorsata and H. itama. The information gathered from this study can be used to determine the geographical and botanical origin and authenticity of the honey produced by these two species.
RESULTS: Sequence data were obtained for both A. dorsata and H. itama. The raw sequence data for A. dorsata was 5 Mb, which was assembled into 5 contigs with a size of 6,098,728 bp, an N50 of 15,534, and a GC average of 57.42. Similarly, the raw sequence data for H. itama was 6.3 Mb, which was assembled into 11 contigs with a size of 7,642,048 bp, an N50 of 17,180, and a GC average of 55.38. In the honey sample of A. dorsata, we identified five different plant/pollen species, with only one of the five species exhibiting a relative abundance of less than 1%. For H. itama, we identified seven different plant/pollen species, with only three of the species exhibiting a relative abundance of less than 1%. All of the identified plant species were native to Peninsular Malaysia, especially the East Coast area of Terengganu.
METHODS: Our data offers valuable insights into honey\'s geographical and botanical origin and authenticity. Metagenomic studies could help identify the plant species that honeybees forage and provide preliminary data for researchers studying the biological development of A. dorsata and H. itama. The identification of various flowers from the eDNA of honey that are known for their medicinal properties could aid in regional honey with accurate product origin labeling, which is crucial for guaranteeing product authenticity to consumers.

Autism spectrum disorder (ASD) is a developmental disability caused by differences in the brain regions. Analysis of differential expression (DE) of transcriptomic data allows for genome-wide analysis of gene expression changes related to ASD. De-novo mutations may play a vital role in ASD, but the list of genes involved is still far from complete. Differentially expressed genes (DEGs) are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches like machine learning and statistical analysis. In this study, we employed a machine learning-based approach to identify the differential gene expression between ASD and Typical Development (TD). The gene expression data of 15 ASD and 15 TD were obtained from the NCBI GEO database. Initially, we extracted the data and used a standard pipeline to pre-process the data. Further, Random Forest (RF) was used to discriminate genes between ASD and TD. We identified the top 10 prominent differential genes and compared them with the statistical test results. Our results show that the proposed RF model yields 5-fold cross-validation accuracy, sensitivity and specificity of 96.67%. Further, we obtained precision and F-measure scores of 97.5% and 96.57%, respectively. Moreover, we found 34 unique DEG chromosomal locations having influential contributions in identifying ASD from TD. We have also identified chr3:113322718-113322659 as the most significant contributing chromosomal location in discriminating ASD and TD. Our machine learning-based method of refining DE analysis is promising for finding biomarkers from gene expression profiles and prioritizing DEGs. Moreover, our study reported top 10 gene signatures for ASD may facilitate the development of reliable diagnosis and prognosis biomarkers for screening ASD.

Bookshelf is a database maintained by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine that contains freely accessible online biomedical documents, including systematic reviews, technical reports, textbooks, and reference books. The database allows users to browse and search across all content and within individual books, and it is linked to other NCBI content. This article provides an overview of Bookshelf and demonstrates its usage in a sample search. The resources available in Bookshelf are useful for students, researchers, healthcare professionals, and librarians.

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.

UNASSIGNED: A recent global outbreak of COVID-19 caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) created a pandemic and emerged as a potential threat to humanity. The analysis of virus genetic composition has revealed that the spike protein, one of the major structural proteins, facilitates the entry of the virus to host cells.
UNASSIGNED: The spike protein has become the main target for prophylactics and therapeutics studies. Here, we compared the spike proteins of SARS-CoV-2 variants using bioinformatics tools.
UNASSIGNED: The spike protein sequences of wild-type SARS-CoV-2 and its 6 variants-D614G, alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2), gamma (P.1), and omicron (B.1.1.529)-were retrieved from the NCBI database. The ClustalX program was used to sequence multiple alignment and perform mutational analysis. Several online bioinformatics tools were used to predict the physiological, immunological, and structural features of the spike proteins of SARS-CoV-2 variants. A phylogenetic tree was constructed using CLC software. Statistical analysis of the data was done using jamovi 2 software.
UNASSIGNED: Multiple sequence analysis revealed that the P681R mutation in the delta variant, which changed an amino acid from histidine (H) to arginine (R), made the protein more alkaline due to arginine\'s high pKa value (12.5) compared to histidine\'s (6.0). Physicochemical properties revealed the relatively higher isoelectric point (7.34) and aliphatic index (84.65) of the delta variant compared to other variants. Statistical analysis of the isoelectric point, antigenicity, and immunogenicity of all the variants revealed significant correlation, with P values ranging from <.007 to .04. The generation of a 2D gel map showed the separation of the delta spike protein from a grouping of the other variants. The phylogenetic tree of the spike proteins showed that the delta variant was close to and a mix of the Rousettus bat coronavirus and MERS-CoV.
UNASSIGNED: The comparative analysis of SARS-CoV-2 variants revealed that the delta variant is more aliphatic in nature, which provides more stability to it and subsequently influences virus behavior.

GenBank® and the Sequence Read Archive (SRA) are comprehensive databases of publicly available DNA sequences. GenBank contains data for 480,000 named organisms, more than 176,000 within the embryophyta, obtained through submissions from individual laboratories and batch submissions from large-scale sequencing projects. SRA contains reads from next-generation sequencing studies from over 110,000 species. Daily data exchange with the European Nucleotide Archive (ENA) in Europe and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage for both databases. GenBank and SRA data are accessible through the NCBI Entrez retrieval system that integrates these data with other data at NCBI, such as genomes, taxonomy, and the biomedical literature. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. Usage scenarios for both GenBank and SRA ranging from local and cloud analyses to online analyses supported by the NCBI web-based tools are discussed. Both GenBank and SRA, along with their related retrieval and analysis services, are available from the NCBI homepage at www.ncbi.nlm.nih.gov .

Accurate species-level identification of an etiological agent is crucial for disease diagnosis and management because knowing the agent\'s identity connects it with what is known about its host range, geographic distribution, and toxin production potential. This is particularly true in publishing peer-reviewed disease reports, where imprecise and/or incorrect identifications weaken the public knowledge base. This can be a daunting task for phytopathologists and other applied biologists that need to identify Fusarium in particular, because published and ongoing multilocus molecular systematic studies have highlighted several confounding issues. Paramount among these are: (i) this agriculturally and clinically important genus is currently estimated to comprise more than 400 phylogenetically distinct species (i.e., phylospecies), with more than 80% of these discovered within the past 25 years; (ii) approximately one-third of the phylospecies have not been formally described; (iii) morphology alone is inadequate to distinguish most of these species from one another; and (iv) the current rapid discovery of novel fusaria from pathogen surveys and accompanying impact on the taxonomic landscape is expected to continue well into the foreseeable future. To address the critical need for accurate pathogen identification, our research groups are focused on populating two web-accessible databases (FUSARIUM-ID v.3.0 and the nonredundant National Center for Biotechnology Information nucleotide collection that includes GenBank) with portions of three phylogenetically informative genes (i.e., TEF1, RPB1, and RPB2) that resolve at or near the species level in every Fusarium species. The objectives of this Special Report, and its companion in this issue (Torres-Cruz et al. 2022), are to provide a progress report on our efforts to populate these databases and to outline a set of best practices for DNA sequence-based identification of fusaria.