The rapid advancement of sequencing technologies poses challenges in managing the large volume and exponential growth of sequence data efficiently and on time. To address this issue, we present GenBase (https://ngdc.cncb.ac.cn/genbase), an open-access data repository that follows the International Nucleotide Sequence Database Collaboration (INSDC) data standards and structures, for efficient nucleotide sequence archiving, searching, and sharing. As a core resource within the National Genomics Data Center (NGDC), of the China National Center for Bioinformation (CNCB; https://ngdc.cncb.ac.cn), GenBase offers bilingual submission pipeline and services, as well as local submission assistance in China. GenBase also provides a unique Excel format for metadata description and feature annotation of nucleotide sequences, along with a real-time data validation system to streamline sequence submissions. As of April 23, 2024, GenBase received 68,251 nucleotide sequences and 689,574 annotated protein sequences across 414 species from 2319 submissions. Out of these, 63,614 (93%) nucleotide sequences and 620,640 (90%) annotated protein sequences have been released and are publicly accessible through GenBase\'s web search system, File Transfer Protocol (FTP), and Application Programming Interface (API). Additionally, in collaboration with INSDC, GenBase has constructed an effective data exchange mechanism with GenBank and started sharing released nucleotide sequences. Furthermore, GenBase integrates all sequences from GenBank with daily updates, demonstrating its commitment to actively contributing to global sequence data management and sharing.

This chapter describes procedures for the use of DNA sequence data to obtain and compare taxonomic identification using the public databases GenBank and Barcode of Life Data System (BOLD). The chapter begins by describing procedures used to prepare quality sequences for uploading into GenBank and BOLD. Next, steps used to query the DNA sequences against the public databases are described using GenBank BLAST and BOLD identification engines. Interpretation guidelines for the taxonomic identification assignments are presented. Finally, a procedure for evaluating the accuracy and reliability of sequences from GenBank and BOLD is provided.

BACKGROUND: The reconstruction of the evolutionary history of organisms has been greatly influenced by the advent of molecular techniques, leading to a significant increase in studies utilizing genomic data from different species. However, the lack of standardization in gene nomenclature poses a challenge in database searches and evolutionary analyses, impacting the accuracy of results obtained.
RESULTS: To address this issue, a Python class for standardizing gene nomenclatures, SynGenes, has been developed. It automatically recognizes and converts different nomenclature variations into a standardized form, facilitating comprehensive and accurate searches. Additionally, SynGenes offers a web form for individual searches using different names associated with the same gene. The SynGenes database contains a total of 545 gene name variations for mitochondrial and 2485 for chloroplasts genes, providing a valuable resource for researchers.
CONCLUSIONS: The SynGenes platform offers a solution for standardizing gene nomenclatures of mitochondrial and chloroplast genes and providing a standardized search solution for specific markers in GenBank. Evaluation of SynGenes effectiveness through research conducted on GenBank and PubMedCentral demonstrated its ability to yield a greater number of outcomes compared to conventional searches, ensuring more comprehensive and accurate results. This tool is crucial for accurate database searches, and consequently, evolutionary analyses, addressing the challenges posed by non-standardized gene nomenclature.

Assembled genome sequences are being generated at an exponential rate. Here we present FCS-GX, part of NCBI\'s Foreign Contamination Screen (FCS) tool suite, optimized to identify and remove contaminant sequences in new genomes. FCS-GX screens most genomes in 0.1-10 min. Testing FCS-GX on artificially fragmented genomes demonstrates high sensitivity and specificity for diverse contaminant species. We used FCS-GX to screen 1.6 million GenBank assemblies and identified 36.8 Gbp of contamination, comprising 0.16% of total bases, with half from 161 assemblies. We updated assemblies in NCBI RefSeq to reduce detected contamination to 0.01% of bases. FCS-GX is available at https://github.com/ncbi/fcs/ or https://doi.org/10.5281/zenodo.10651084 .

Staphylinidae, or rove beetles, are one of the mega-diverse and abundant families of the ground-living terrestrial arthropods that is taxonomically poorly known even in the regions adjacent to Europe where the fauna has been investigated for the longest time. Since DNA barcoding is a tool to accelerate biodiversity research, here we explored if the currently-available COI barcode libraries are representative enough for the study of rove beetles of West Siberia. This is a vast region adjacent to Europe with poorly-known fauna of rove beetles and from where not a single DNA barcode has hitherto been produced for Staphylinidae. First, we investigated the faunal similarity between the rove beetle faunas of the climatically compatible West Siberia in Asia, Fennoscandia in Europe and Canada and Alaska in North America. Second, we investigated barcodes available for Staphylinidae from the latter two regions in BOLD and GenBank, the world\'s largest DNA barcode libraries. We conclude that the rather different rove beetle faunas of Fennoscandia, on the one hand and Canada and Alaska on the other hand, are well covered in both barcode libraries that complement each other. We also find that even without any barcodes originating from specimens collected in West Siberia, this coverage is helpful for the study of rove beetles there due to the significant number of widespread species shared between West Siberia and Fennoscandia and due to the even larger number of shared genera amongst all three investigated regions. For the first time, we compiled a literature-based checklist for 726 species of the West Siberian Staphylinidae supplemented by their occurrence dataset submitted to GBIF. Our script written for mining unique (i.e. not redundant) barcodes for a given geographic area across global libraries is made available here and can be adopted for any other regions.

Genetic component assembly is key in the simulation and implementation of genetic circuits. Automating this process, thus accelerating prototyping, is a necessity. We present pyBrick-DNA, a software written in Python, that assembles components for the construction of genetic circuits. pyBrick-DNA (colab.pyBrick.com) is a user-friendly environment where scientists can select genetic sequences or input custom sequences to build genetic assemblies. All components are modularly fused to generate a ready-to-go single DNA fragment. It includes Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and plant gene-editing components. Hence, pyBrick-DNA can generate a functional CRISPR construct composed of a single-guided RNA integrated with Cas9, promoters, and terminator elements. The outcome is a DNA sequence, along with a graphical representation, composed of user-selected genetic parts, ready to be synthesized and cloned in vivo. Moreover, the sequence can be exported as a GenBank file allowing its use with other synthetic biology tools.

Scientific names permit humans and search engines to access knowledge about the biodiversity that surrounds us, and names linked to DNA sequences are playing an ever-greater role in search-and-match identification procedures. Here, we analyze how users and curators of the National Center for Biotechnology Information (NCBI) are flagging and curating sequences derived from nomenclatural type material, which is the only way to improve the quality of DNA-based identification in the long run. For prokaryotes, 18,281 genome assemblies from type strains have been curated by NCBI staff and improve the quality of prokaryote naming. For Fungi, type-derived sequences representing over 21,000 species are now essential for fungus naming and identification. For the remaining eukaryotes, however, the numbers of sequences identifiable as type-derived are minuscule, representing only 739 species of arthropods, 1542 vertebrates, and 125 embryophytes. An increase in the production and curation of such sequences will come from (i) sequencing of types or topotypic specimens in museum collections, (ii) the March 2023 rule changes at the International Nucleotide Sequence Database Collaboration requiring more metadata for specimens, and (iii) efforts by data submitters to facilitate curation, including informing NCBI curators about a specimen\'s type status. We illustrate different type-data submission journeys and provide best-practice examples from a range of organisms. Expanding the number of type-derived sequences in DNA databases, especially of eukaryotes, is crucial for capturing, documenting, and protecting biodiversity.

Collaborations between scientists from the global north and global south (N-S collaborations) are a key driver of the \"fourth paradigm of science\" and have proven crucial to addressing global crises like COVID-19 and climate change. However, despite their critical role, N-S collaborations on datasets are not well understood. Science of science studies tend to rely on publications and patents to examine N-S collaboration patterns. To this end, the rise of global crises requiring N-S collaborations to produce and share data presents an urgent need to understand the prevalence, dynamics, and political economy of N-S collaborations on research datasets. In this paper, we employ a mixed methods case study research approach to analyze the frequency of and division of labor in N-S collaborations on datasets submitted to GenBank over 29 years (1992-2021). We find: (1) there is a low representation of N-S collaborations over the 29-year period. When they do occur, N-S collaborations display \"burstiness\" patterns, suggesting that N-S collaborations on datasets are formed and maintained reactively in the wake of global health crises such as infectious disease outbreaks; (2) The division of labor between datasets and publications is disproportionate to the global south in the early years, but becomes more overlapping after 2003. An exception in the case of countries with lower S&T capacity but high income, where these countries have a higher prevalence on datasets (e.g., United Arab Emirates). We qualitatively inspect a sample of N-S dataset collaborations to identify leadership patterns in dataset and publication authorship. The findings lead us to argue there is a need to include N-S dataset collaborations in measures of research outputs to nuance the current models and assessment tools of equity in N-S collaborations. The paper contributes to the SGDs objectives to develop data-driven metrics that can inform scientific collaborations on research datasets.

In biological science, the study of DNA sequences is considered an important factor because it carries the genomic details that can be used by researchers and doctors for the early prediction of disease using DNA classification. The NCBI has the world\'s largest database of genetic sequences, but the security of this massive amount of data is currently the greatest issue. One of the options is to encrypt these genetic sequences using blockchain technology. As a result, this paper presents a survey on healthcare data breaches, the necessity for blockchain in healthcare, and the number of research studies done in this area. In addition, the report suggests DNA sequence classification for earlier disease identification and evaluates previous work in the field.

Assembled genome sequences are being generated at an exponential rate. Here we present FCS-GX, part of NCBI\'s Foreign Contamination Screen (FCS) tool suite, optimized to identify and remove contaminant sequences in new genomes. FCS-GX screens most genomes in 0.1-10 minutes. Testing FCS-GX on artificially fragmented genomes demonstrates sensitivity >95% for diverse contaminant species and specificity >99.93%. We used FCS-GX to screen 1.6 million GenBank assemblies and identified 36.8 Gbp of contamination (0.16% of total bases), with half from 161 assemblies. We updated assemblies in NCBI RefSeq to reduce detected contamination to 0.01% of bases. FCS-GX is available at https://github.com/ncbi/fcs/.