Database Curation Research Articles

O (DES)CONHECIMENTO DA BIODIVERSIDADE: UMA SISTEMATIZAÇÃO SOBRE LACUNAS, LIMITAÇÕES, VIESES, DÉFICITS E RUÍDOS

With the advancements in bioinformatics and ecoinformatics tools, it has become feasible to utilize vast amounts of biodiversity information to describe biological patterns and attempt to assess the ecological and evolutionary processes underlying them, as well as to establish more effective and comprehensive conservation actions. However, existing information regarding biodiversity remains incomplete, with gaps, limitations, biases, deficits, and noise. In this regard, we acknowledge the need to discuss issues related to the use of large biodiversity databases and their quality, discussions that need to be conceptually structured. In this text, we will systematically describe the gaps, limitations, and biases in biodiversity knowledge, issues that are directly associated with the acquisition of species information in nature. Subsequently, we will outline the types of deficits and noise, which are problems associated with biodiversity databases. This is because the information present in primary records and large databases is frequently used inadequately. Additionally, we also point out perspectives on how to address these deficiencies in ecological/biogeographical work through appropriate database curation and the construction of maps of ignorance. Finally, we highlight future challenges, such as the need for new data collections, the training of new taxonomists, and the provision of financial support for the infrastructure and digitization of biological collections.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities.

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Advancing Source Tracking: Systematic Review and Source-Specific Genome Database Curation of Fecally Shed Prokaryotes.

Advancements within fecal source tracking (FST) studies are complicated by a lack of knowledge regarding the genetic content and distribution of fecally shed microbial populations. To address this gap, we performed a systematic literature review and curated a large collection of genomes (n = 26,018) representing fecally shed prokaryotic species across broad and narrow source categories commonly implicated in FST studies of recreational waters (i.e., cats, dogs, cows, seagulls, chickens, pigs, birds, ruminants, human feces, and wastewater). We find that across these sources the total number of prokaryotic genomes recovered from materials meeting our initial inclusion criteria varied substantially across fecal sources: from none in seagulls to 9,085 in pigs. We examined genome sequences recovered from these metagenomic and isolation-based studies extensively via comparative genomic approaches to characterize trends across source categories and produce a finalized genome database for each source category which is available online (n = 12,730). On average, 81% of the genomes representing species-level populations occur only within a single source. Using fecal slurries to test the performance of each source database, we report read capture rates that vary with fecal source alpha diversity and database size. We expect this resource to be useful to FST-related objectives, One Health research, and sanitation efforts globally.

Companion Animal Owners' Knowledge, Attitudes and Perceptions Regarding Antibiotic Use in Portugal.

While multiple studies have focused on the motivations surrounding antibiotic prescription among veterinarians, little is known about companion animal owners' knowledge, attitude and perception (KAP) regarding the topic. A nationwide survey directed toward Portuguese dog and cat owners was conducted online and at veterinary practices to characterize their KAP regarding antibiotics. After database curation, a total of 423 valid submissions were considered. Although 97.9% of respondents stated that they knew what an antibiotic was, 23.5% and 19.2% answered that they were used to treat viral and fungal infections, respectively. Antimicrobial effectiveness was favored over cost when 87.7% of owners agreed they would prefer to spend more money to identify the appropriate antibiotic. Around 87% of respondents recognized antibiotic resistance as a significant health problem and 74.6% strongly agreed/somewhat agreed that antibiotic use in pets may contribute to resistance development. However, only 25.3% recognized that this could promote resistance dissemination, showing little awareness of the interconnection between human and animal health. Moreover, 55.6% of respondents were neutral when asked whether antibiotics used in veterinary medicine were also important for humans. These findings suggest that communication between veterinarians and pet owners can still be improved to further clarify the impact that antibiotic use has in pets from a One-Health perspective, also enabling antimicrobial stewardship interventions.

A nineteenth-century urban Ottoman population micro dataset: Data extraction and relational database curation from the 1840s pre-census Bursa population registers

In recent decades, the “big microdata revolution” has transformed access to transcribed historical census data for social science research. However, the population records of the Ottoman Empire, spanning Southeastern Europe, Western Asia, and Northern Africa, remained inaccessible to the big microdata ecosystem due to their prolonged unavailability. This publication marks the inaugural release of complete population data for an Ottoman urban center, Bursa, derived from the 1839 population registers. The dataset presents originally non-tabulated register data in a tabular format integrated into a relational Microsoft Access database. Thus, we showcase the extensive and diverse data found in the Ottoman population registers, demonstrating a level of quality and sophistication akin to the censuses conducted worldwide in the nineteenth century. This valuable resource, whose potential has been massively underexploited, is now presented in an accessible format compatible with global microdata repositories. Our aim with this dataset is to enable historical demographic studies for the Ottoman realm and beyond, while also broadening access to the datasets constructed by our large research team.

Ten common issues with reference sequence databases and how to mitigate them.

Metagenomic sequencing has revolutionized our understanding of microbiology. While metagenomic tools and approaches have been extensively evaluated and benchmarked, far less attention has been given to the reference sequence database used in metagenomic classification. Issues with reference sequence databases are pervasive. Database contamination is the most recognized issue in the literature; however, it remains relatively unmitigated in most analyses. Other common issues with reference sequence databases include taxonomic errors, inappropriate inclusion and exclusion criteria, and sequence content errors. This review covers ten common issues with reference sequence databases and the potential downstream consequences of these issues. Mitigation measures are discussed for each issue, including bioinformatic tools and database curation strategies. Together, these strategies present a path towards more accurate, reproducible and translatable metagenomic sequencing.

Benchmarking informatics approaches for virus discovery: caution is needed when combining in silico identification methods.

The identification of viruses from environmental metagenomes using informatics tools has offered critical insights in microbial ecology. However, it remains difficult for researchers to know which tools optimize viral recovery for their specific study. In an attempt to recover more viruses, studies are increasingly combining the outputs from multiple tools without validating this approach. After benchmarking combinations of six viral identification tools against mock metagenomes and environmental samples, we found that these tools should only be combined cautiously. Two to four tool combinations maximized viral recovery and minimized non-viral contamination compared with either the single-tool or the five- to six-tool ones. By providing a rigorous overview of the behavior of in silico viral identification strategies and a pipeline to replicate our process, our findings guide the use of existing viral identification tools and offer a blueprint for feature engineering of new tools that will lead to higher-confidence viral discovery in microbiome studies.

MarkerScan: Separation and assembly of cobionts sequenced alongside target species in biodiversity genomics projects.

Contamination of public databases by mislabelled sequences has been highlighted for many years and the avalanche of novel sequencing data now being deposited has the potential to make databases difficult to use effectively. It is therefore crucial that sequencing projects and database curators perform pre-submission checks to remove obvious contamination and avoid propagating erroneous taxonomic relationships. However, it is important also to recognise that biological contamination of a target sample with unexpected species' DNA can also lead to the discovery of fascinating biological phenomena through the identification of environmental organisms or endosymbionts. Here, we present a novel, integrated method for detection and generation of high-quality genomes of all non-target genomes co-sequenced in eukaryotic genome sequencing projects. After performing taxonomic profiling of an assembly from the raw data, and leveraging the identity of small rRNA sequences discovered therein as markers, a targeted classification approach retrieves and assembles high-quality genomes. The genomes of these cobionts are then not only removed from the target species' genome but also available for further interrogation. Source code is available from https://github.com/CobiontID/MarkerScan. MarkerScan is written in Python and is deployed as a Docker container.

Comparative analysis of metagenomic classifiers for long-read sequencing datasets

BackgroundLong reads have gained popularity in the analysis of metagenomics data. Therefore, we comprehensively assessed metagenomics classification tools on the species taxonomic level. We analysed kmer-based tools, mapping-based tools and two general-purpose long reads mappers. We evaluated more than 20 pipelines which use either nucleotide or protein databases and selected 13 for an extensive benchmark. We prepared seven synthetic datasets to test various scenarios, including the presence of a host, unknown species and related species. Moreover, we used available sequencing data from three well-defined mock communities, including a dataset with abundance varying from 0.0001 to 20% and six real gut microbiomes.ResultsGeneral-purpose mappers Minimap2 and Ram achieved similar or better accuracy on most testing metrics than best-performing classification tools. They were up to ten times slower than the fastest kmer-based tools requiring up to four times less RAM. All tested tools were prone to report organisms not present in datasets, except CLARK-S, and they underperformed in the case of the high presence of the host’s genetic material. Tools which use a protein database performed worse than those based on a nucleotide database. Longer read lengths made classification easier, but due to the difference in read length distributions among species, the usage of only the longest reads reduced the accuracy. The comparison of real gut microbiome datasets shows a similar abundance profiles for the same type of tools but discordance in the number of reported organisms and abundances between types. Most assessments showed the influence of database completeness on the reports.ConclusionThe findings indicate that kmer-based tools are well-suited for rapid analysis of long reads data. However, when heightened accuracy is essential, mappers demonstrate slightly superior performance, albeit at a considerably slower pace. Nevertheless, a combination of diverse categories of tools and databases will likely be necessary to analyse complex samples. Discrepancies observed among tools when applied to real gut datasets, as well as a reduced performance in cases where unknown species or a significant proportion of the host genome is present in the sample, highlight the need for continuous improvement of existing tools. Additionally, regular updates and curation of databases are important to ensure their effectiveness.

Discovery of Streptomyces species CS-62, a novel producer of the Acinetobacter baumannii selective antibiotic factumycin.

Narrow-spectrum antibiotics are of great interest given their ability to spare the microbiome and decrease widespread antibiotic resistance compared to broad-spectrum antibiotics. Herein, we screened an in-house library of Actinobacteria strains for selective activity against Acinetobacter baumannii and successfully identified Streptomyces sp. CS-62 as a producer of a natural product with this valuable activity. Analysis of the cultures via high-resolution mass spectrometry and tandem mass spectrometry, followed by comparison with molecules in the Natural Product Atlas and the Global Natural Products Social Molecular Networking platform, suggested a novel natural product. Genome mining analysis initially supported the production of a novel kirromycin derivative. Isolation and structure elucidation via mass spectrometry and Nuclear Magnetic Resonance (NMR) analyses revealed that the active natural product was the known natural product factumycin, exposing omissions and errors in the consulted databases. While public databases are generally very useful for avoiding rediscovery of known molecules, rediscovery remains a problem due to public databases either being incomplete or having errors that result in failed dereplication. Overall, the work describes the ongoing problem of dereplication and the continued need for public database curation.

Improper data practices erode the quality of global ecological databases and impede the progress of ecological research.

The scientific community has entered an era of big data. However, with big data comes big responsibilities, and best practices for how data are contributed to databases have not kept pace with the collection, aggregation, and analysis of big data. Here, we rigorously assess the quantity of data for specific leaf area (SLA) available within the largest and most frequently used global plant trait database, the TRY Plant Trait Database, exploring how much of the data were applicable (i.e., original, representative, logical, and comparable) and traceable (i.e., published, cited, and consistent). Over three-quarters of the SLA data in TRY either lacked applicability or traceability, leaving only 22.9% of the original data usable compared with the 64.9% typically deemed usable by standard data cleaning protocols. The remaining usable data differed markedly from the original for many species, which led to altered interpretation of ecological analyses. Though the data we consider here make up only 4.5% of SLA data within TRY, similar issues of applicability and traceability likely apply to SLA data for other species as well as other commonly measured, uploaded, and downloaded plant traits. We end with suggested steps forward for global ecological databases, including suggestions for both uploaders to and curators of databases with the hope that, through addressing the issues raised here, we can increase data quality and integrity within the ecological community.

Considerations needed for analysing data from the Swedish Electrofishing RegiSter (SERS), with special reference to the RivFishTIME database of long-term riverine fish surveys

The published database RivFishTIME (Comte et al. 2021, Global Ecology and Biogeography, doi: 10.1111/geb.13210) includes a large section of time-series data on fish abundance in Swedish rivers from the Swedish Electrofishing RegiSter, SERS. Knowledge about the limitations of the source data are important when extracting and analyzing data and with this brief note we provide some details that may be helpful for interpreting the Swedish time-series. The note highlights the importance of linking vital metadata to extracted focal data when constructing new databases, especially concerning time series data from monitoring programs conducted in non-randomly selected sites with human environmental impacts. Many of the SERS data come from rivers that have been affected by human impact, e.g. liming to mitigate environmental acidification and hydropower dams, since before monitoring was initiated. Data in SERS are also biased towards shallow salmonid habitats, due to the configuration of Swedish monitoring programs. Hence, data from many rivers are not representative of their fish biodiversity in general. This information is vital for appropriate interpretation of fish biodiversity trends. For RivFishTIME analyses considerations are important since Swedish data constitutes a large proportion of the database. We also provide background information about SERS and references to other Swedish databases containing complementary information. Finally, we provide contact information of the SERS database curators, who can assist prospective analysts with data extraction from SERS.

RCRUX: A Rapid and Versatile Tool for Generating Metabarcoding Reference libraries in R.

The sequencing revolution requires accurate taxonomic classification of DNA sequences. Key to making accurate taxonomic assignments are curated, comprehensive reference barcode databases. However, the generation and curation of such databases has remained challenging given the large and continuously growing volumes of both DNA sequence data and novel reference barcode targets. Monitoring and research applications require a greater diversity of specialized gene regions and targeted taxa then are currently curated by professional staff. Thus there is a growing need for an easy to implement computational tool that can generate comprehensive metabarcoding reference libraries for any bespoke locus. We address this need by reimagining CRUX from the Anacapa Toolkit and present the rCRUX package in R which, like it's predecessor, relies on sequence homology and PCR primer compatibility instead of keyword-searches to avoid limitations of user-defined metadata. The typical workflow involves searching for plausible seed amplicons (get_seeds_local() or get_seeds_remote()) by simulating in silico PCR to acquire a set of sequences analogous to PCR products containing a user-defined set of primer sequences. Next, these seeds are used to iteratively blast search seed sequences against a local copy of the National Center for Biotechnology Information (NCBI) formatted nt database using a taxonomic-rank based stratified random sampling approach ( blast_seeds() ). This results in a comprehensive set of sequence matches. This database is dereplicated and cleaned (derep_and_clean_db()) by identifying identical reference sequences and collapsing the taxonomic path to the lowest taxonomic agreement across all matching reads. This results in a curated, comprehensive database of primer-specific reference barcode sequences from NCBI. Databases can then be compared (compare_db()) to determine read and taxonomic overlap. We demonstrate that rCRUX provides more comprehensive reference databases for the MiFish Universal Teleost 12S, Taberlet trnl, fungal ITS, and Leray CO1 loci than CRABS, MetaCurator, RESCRIPt, and ecoPCR reference databases. We then further demonstrate the utility of rCRUX by generating 24 reference databases for 20 metabarcoding loci, many of which lack dedicated reference database curation efforts. The rCRUX package provides a simple to use tool for the generation of curated, comprehensive reference databases for user-defined loci, facilitating accurate and effective taxonomic classification of metabarcoding and DNA sequence efforts broadly.

PEDL+: protein-centered relation extraction from PubMed at your fingertip.

Relation extraction (RE) from large text collections is an important tool for database curation, pathway reconstruction, or functional omics data analysis. In practice, RE often is part of a complex data analysis pipeline requiring specific adaptations like restricting the types of relations or the set of proteins to be considered. However, current systems are either non-programmable web sites or research code with fixed functionality. We present PEDL+, a user-friendly tool for extracting protein-protein and protein-chemical associations from PubMed articles. PEDL+ combines state-of-the-art NLP technology with adaptable ranking and filtering options and can easily be integrated into analysis pipelines. We evaluated PEDL+ in two pathway curation projects and found that 59% to 80% of its extractions were helpful. PEDL+ is freely available at https://github.com/leonweber/pedl.

Public Biological Databases and the Sui Generis Database Right

The sui generis database right is an intellectual property right created in the European Union to stimulate investment in the curation of databases. Since its inception, communities engaged in research and development efforts have questioned its potential to incentivise database production, and posit that it stifles productive downstream uses of existing datasets. European courts have restricted the right’s ambit through a restrictive interpretation of the circumstances in which it applies, which we argue, enables downstream use of biological databases. Nonetheless, residual ambiguities about potential infringement of the right exist. The prospect of unintentional infringement can frustrate downstream innovation. These ambiguities are compounded because the criteria that determine whether or not the right applies are reliant on information that is not available to the prospective downstream users of public datasets. Repealing the sui generis database right is recommended. Legislatures are advised to refrain from the implementation of broad novel intellectual property rights in the future, without first adopting safeguards that mitigate the potential for such rights to frustrate the reuse of available intangibles to the detriment of pro-social innovation.

Functional unknomics: Systematic screening of conserved genes of unknown function.

The human genome encodes approximately 20,000 proteins, many still uncharacterised. It has become clear that scientific research tends to focus on well-studied proteins, leading to a concern that poorly understood genes are unjustifiably neglected. To address this, we have developed a publicly available and customisable "Unknome database" that ranks proteins based on how little is known about them. We applied RNA interference (RNAi) in Drosophila to 260 unknown genes that are conserved between flies and humans. Knockdown of some genes resulted in loss of viability, and functional screening of the rest revealed hits for fertility, development, locomotion, protein quality control, and resilience to stress. CRISPR/Cas9 gene disruption validated a component of Notch signalling and 2 genes contributing to male fertility. Our work illustrates the importance of poorly understood genes, provides a resource to accelerate future research, and highlights a need to support database curation to ensure that misannotation does not erode our awareness of our own ignorance.

Biomolecular Event Extraction using Natural Language Processing

Biomedical research and discoveries are communicated through scholarly publications and this literature is voluminous, rich in scientific text and growing exponentially by the day. Biomedical journals publish nearly three thousand research articles daily, making literature search a challenging proposition for researchers. Biomolecular events involve genes, proteins, metabolites, and enzymes that provide invaluable insights into biological processes and explain the physiological functional mechanisms. Text mining (TM) or extraction of such events automatically from big data is the only quick and viable solution to gather any useful information. Such events extracted from biological literature have a broad range of applications like database curation, ontology construction, semantic web search and interactive systems. However, automatic extraction has its challenges on account of ambiguity and the diverse nature of natural language and associated linguistic occurrences like speculations, negations etc., which commonly exist in biomedical texts and lead to erroneous elucidation. In the last decade, many strategies have been proposed in this field, using different paradigms like Biomedical natural language processing (BioNLP), machine learning and deep learning. Also, new parallel computing architectures like graphical processing units (GPU) have emerged as possible candidates to accelerate the event extraction pipeline. This paper reviews and provides a summarization of the key approaches in complex biomolecular big data event extraction tasks and recommends a balanced architecture in terms of accuracy, speed, computational cost, and memory usage towards developing a robust GPU-accelerated BioNLP system.

An automated workflow to assess completeness and curate GenBank for environmental DNA metabarcoding: The marine fish assemblage as case study

AbstractTo successfully implement environmental DNA‐based (eDNA) diversity monitoring, the completeness and accuracy of reference databases used for taxonomic assignment of eDNA sequences are among the challenges to be tackled. Here, we have developed a workflow that evaluates the current status of GenBank for marine fishes. For a given combination of species and barcodes, a gap analysis is performed and potentially erroneous sequences are identified. Our gap analysis based on the four most used genes (cytochrome c oxidase subunit 1, 12S rRNA, 16S rRNA, and cytochrome b) for fish eDNA metabarcoding found that COI, the universal choice for metazoans, is the gene covering the highest number of Northeast Atlantic marine fishes (70%), while 12S rRNA, the preferred region for fish‐targeting studies, only covers about 50% of the species. The presence of too close and too distant barcode sequences as expected by their taxonomic classification confirms the existence of erroneous sequences in GenBank that our workflow can detect and eliminate. Comparing taxonomic assignments of real marine eDNA samples with raw and clean reference databases for the most used 12S rRNA barcodes (teleo and MiFish), we confirmed that both barcodes perform differently and demonstrated that the application of the database cleaning workflow can result in drastic changes in community composition. Besides providing a tool for reference database curation, this study confirms the need to increase 12S rRNA reference sequences for European marine fishes and evidences the dangers of taxonomic assignments by directly querying GenBank. We have developed a workflow that evaluates the current status of GenBank for marine fishes. For a given combination of species and barcodes, a gap analysis is performed and potentially erroneous sequences are identified.

“A Patchwork of Data Systems”: Quilting as an Analytic Lens and Stabilizing Practice for Knowledge Infrastructures

Museums and archives rely on databases and similar technologies to manage their collections, but even when tailor-made for memory institutions, databases require considerable adaptation to remain usable over long periods of time. To better understand how collection staff maintain and migrate databases over multiple years and decades, we talked to archivists from the US-based Archon User Collaborative and collection managers from the University of Michigan Research Museums. We found that the collection staff uses terms taken from quilting for database curation: they “tie” and “weave” a “patchwork of data systems” together. We extend their quilting metaphor as an analytical lens and show what can be gained through a shift in framing database work as a craft. We describe database curation as a process of creating a quilted infrastructure: a long-lived knowledge system that is sustained by the use of multiple “digital surfaces,” a reliance on a community of practice, intergenerational transfer of “quilts,” and by leveraging invisibility to conduct work. We argue that this nonnormative mode of computing needs better support from both software developers and administrators. We also show that although the invisibility of craft practices offers practitioners independence, it also can increase their precarity.

Successful Development of a Natural Language Processing Algorithm for Pancreatic Neoplasms and Associated Histologic Features.

Natural language processing (NLP) algorithms can interpret unstructured text for commonly used terms and phrases. Pancreatic pathologies are diverse and include benign and malignant entities with associated histologic features. Creating a pancreas NLP algorithm can aid in electronic health record coding as well as large database creation and curation. Text-based pancreatic anatomic and cytopathologic reports for pancreatic cancer, pancreatic ductal adenocarcinoma, neuroendocrine tumor, intraductal papillary neoplasm, tumor dysplasia, and suspicious findings were collected. This dataset was split 80/20 for model training and development. A separate set was held out for testing purposes. We trained using convolutional neural network to predict each heading. Over 14,000 reports were obtained from the Mass General Brigham Healthcare System electronic record. Of these, 1252 reports were used for algorithm development. Final accuracy and F1 scores relative to the test set ranged from 95% and 98% for each queried pathology. To understand the dependence of our results to training set size, we also generated learning curves. Scoring metrics improved as more reports were submitted for training; however, some queries had high index performance. Natural language processing algorithms can be used for pancreatic pathologies. Increased training volume, nonoverlapping terminology, and conserved text structure improve NLP algorithm performance.