Database Curation Research Articles

Nematodes as bioindicators of polluted sediments using metabarcoding and microscopic taxonomy

The use of bioindicator species is a widely applied approach to evaluate ecological conditions, and several indices have been designed for this purpose. To assess the impact of pollution, especially in sediments, a pollution-sensitive index based on nematodes, one of the most abundant and species-rich groups of metazoa, was developed. The NemaSPEAR[%] index in its original form relies on the morphological inspection of nematode species. The application of a morphologically based NemaSPEAR[%] at the genus-level was previously validated. The present study evaluated a NemaSPEAR[%] index based on metabarcoding of nematode communities and tested the potential of fragments from the 28S rDNA, 18S rDNA and cytochrome c oxidase subunit I (COI) genes. In general, molecular-based results tended to show a poorer condition than morphology-based results for the investigated sites. At the genus level, NemaSPEAR[%] values based on morphological data strongly correlated with those based on molecular data for both the 28S rDNA and the 18S rDNA gene fragments (R2 = 0.86 and R2 = 0.74, respectively). Within the dominant genera (>3%) identified by morphology, 68% were detected by at least one of the two ribosomal markers. At the species level, however, concordance was less pronounced, as there were several deviations of the molecular from the morphological data. These differences could mostly be attributed to shortcomings in the reference database used in the molecular-based assignments. Our pilot study shows that a molecularly based, genus-level NemaSPEAR[%] can be successfully applied to evaluate polluted sediment. Future studies need to validate this approach further, e.g. with bulk extractions of whole meiofaunal communities in order to circumvent time-consuming nematode isolation. Further database curation with abundant NemaSPEAR[%] species will also increase the applicability of this approach.

CORDITE: The Curated CORona Drug InTERactions Database for SARS-CoV-2.

SummarySince the outbreak in 2019, researchers are trying to find effective drugs against the SARS-CoV-2 virus based on de novo drug design and drug repurposing. The former approach is very time consuming and needs extensive testing in humans, whereas drug repurposing is more promising, as the drugs have already been tested for side effects, etc. At present, there is no treatment for COVID-19 that is clinically effective, but there is a huge amount of data from studies that analyze potential drugs.We developed CORDITE to efficiently combine state-of-the-art knowledge on potential drugs and make it accessible to scientists and clinicians. The web interface also provides access to an easy-to-use API that allows a wide use for other software and applications, e.g., for meta-analysis, design of new clinical studies, or simple literature search. CORDITE is currently empowering many scientists across all continents and accelerates research in the knowledge domains of virology and drug design.

Pathways to urban health and well-being: measuring and modelling of community services' in a medium size city.

Social and natural capital are fundamental to people's wellbeing, often within the context of local community. Developing communities and linking people together provide benefits in terms of mental well-being, physical activity and other associated health outcomes. The research presented here was carried out in Christchurch - Ōtautahi, New Zealand, a city currently re-building, after a series of devastating earthquakes in 2010 and 2011. Poor mental health has been shown to be a significant post-earthquake problem, and social connection has been postulated as part of a solution. By curating a disparate set of community services, activities and facilities, organised into a Geographic Information Systems (GIS) database, we created i) an accessibility analysis of 11 health and well-being services, ii) a mobility scenario analysis focusing on 4 general well-being services and iii) a location-allocation model focusing on 3 primary health care and welfare location optimisation. Our results demonstrate that overall, the majority of neighbourhoods in Christchurch benefit from a high level of accessibility to almost all the services; but with an urban-rural gradient (the further away from the centre, the less services are available, as is expected). The noticeable exception to this trend, is that the more deprived eastern suburbs have poorer accessibility, suggesting social inequity in accessibility. The findings presented here show the potential of optimisation modelling and database curation for urban and community facility planning purposes.

Evaluating shortfalls and spatial accuracy of biodiversity documentation in the Atlantic Forest, the most diverse and threatened Brazilian phytogeographic domain

AbstractDigital accessible knowledge of biodiversity data is an increasingly important source of information in studies of biogeography and conservation. These databases can also reveal temporal, spatial and taxonomical gaps in biodiversity documentation, even in areas that have been intensively studied and from where accurate species lists are available. Therefore, revealing these gaps may help allocating collecting efforts, conservation priorities and strategies for improving database curation. Here, we evaluate potential shortfalls for flowering plants in a tropical hotspot, the Brazilian Atlantic Forest, by cross‐referencing two online repositories of biodiversity data (the Global Biodiversity Information Facility – GBIF – and the Brazilian Flora 2020 floristic database – BFG). We aimed to evaluate the congruence between those repositories, highlighting tendencies in current documentation for this area. We found that from the 7220 reported flowering plant species endemics to the Atlantic Forest, 1573 (22%) have no valid spatial data in GBIF, and 75% of all of the 605,951 records do not present valid spatial information. Most of the missing information is related to species known only from few and old collections with absent or inaccurately georeferenced data. This lack of information may cause a large impact in spatial studies, especially for rare and threatened species. Nevertheless, our analysis also shows that spatial information for the filtered data is highly congruent between GBIF and BFG data, indicating relatively high availability of quality data in large repositories after standard and automatized cleaning procedures. Still, good practices to decrease the impact of losing data are recommended, including more investment in field collections, targeting poorly known species and returning cleaned spatial datasets to online repositories after taxonomic revisions.

Making LiGHT Work of Large Area Survey? Developing Approaches to Rapid Archaeological Mapping and the Creation of Systematic National-scaled Heritage Data

The characteristics and form of heritage data are fundamental to its utility in a range of applications, particularly so for heritage agencies who have a remit in management, policy, and the creation and curation of national databases of monuments, sites, and landscapes. Written from the perspective of an archaeological survey function in a national heritage agency, this paper draws on preliminary outcomes from a research and development project that aims to proof protocols for creating systematic data across large areas drawing heavily on remotely sensed data. This recognises that a systemic consideration of the implications of changing technology and data is sometimes desirable, rather than gradual assimilation of developments into existing practice. In particular, the issues being addressed relate to the challenges and opportunities of proliferating remote sensed data and digital workflows. These include the strategic assessment of threat, consideration of fitness for purpose of different datasets relative to landscape characteristics, the documentation of processes and sources of information, the suitability of data structures, and the mechanisms for automating site detection and data creation.

Comparison of morphological, DNA barcoding, and metabarcoding characterizations of freshwater nematode communities.

Biomonitoring approaches and investigations of many ecological questions require assessments of the biodiversity of a given habitat. Small organisms, ranging from protozoans to metazoans, are of great ecological importance and comprise a major share of the planet's biodiversity but they are extremely difficult to identify, due to their minute body sizes and indistinct structures. Thus, most biodiversity studies that include small organisms draw on several methods for species delimitation, ranging from traditional microscopy to molecular techniques. In this study, we compared the efficiency of these methods by analyzing a community of nematodes. Specifically, we evaluated the performances of traditional morphological identification, single‐specimen barcoding (Sanger sequencing), and metabarcoding in the identification of 1500 nematodes from sediment samples. The molecular approaches were based on the analysis of the 28S ribosomal large and 18S small subunits (LSU and SSU). The morphological analysis resulted in the determination of 22 nematode species. Barcoding identified a comparable number of operational taxonomic units (OTUs) based on 28S rDNA (n = 20) and fewer OTUs based on 18S rDNA (n = 12). Metabarcoding identified a higher OTU number but fewer amplicon sequence variants (AVSs) (n = 48 OTUs, n = 17 ASVs for 28S rDNA, and n = 31 OTUs, n = 6 ASVs for 18S rDNA). Between the three approaches (morphology, barcoding, and metabarcoding), only three species (13.6%) were shared. This lack of taxonomic resolution hinders reliable community identifications to the species level. Further database curation will ensure the effective use of molecular species identification.

AVADA: toward automated pathogenic variant evidence retrieval directly from the full-text literature

PurposeBoth monogenic pathogenic variant cataloging and clinical patient diagnosis start with variant-level evidence retrieval followed by expert evidence integration in search of diagnostic variants and genes. Here, we try to accelerate pathogenic variant evidence retrieval by an automatic approach. MethodsAutomatic VAriant evidence DAtabase (AVADA) is a novel machine learning tool that uses natural language processing to automatically identify pathogenic genetic variant evidence in full-text primary literature about monogenic disease and convert it to genomic coordinates. ResultsAVADA automatically retrieved almost 60% of likely disease-causing variants deposited in the Human Gene Mutation Database (HGMD), a 4.4-fold improvement over the current best open source automated variant extractor. AVADA contains over 60,000 likely disease-causing variants that are in HGMD but not in ClinVar. AVADA also highlights the challenges of automated variant mapping and pathogenicity curation. However, when combined with manual validation, on 245 diagnosed patients, AVADA provides valuable evidence for an additional 18 diagnostic variants, on top of ClinVar’s 21, versus only 2 using the best current automated approach. ConclusionAVADA advances automated retrieval of pathogenic monogenic variant evidence from full-text literature. Far from perfect, but much faster than PubMed/Google Scholar search, careful curation of AVADA-retrieved evidence can aid both database curation and patient diagnosis.

Integrating image caption information into biomedical document classification in support of biocuration.

Gathering information from the scientific literature is essential for biomedical research, as much knowledge is conveyed through publications. However, the large and rapidly increasing publication rate makes it impractical for researchers to quickly identify all and only those documents related to their interest. As such, automated biomedical document classification attracts much interest. Such classification is critical in the curation of biological databases, because biocurators must scan through a vast number of articles to identify pertinent information within documents most relevant to the database. This is a slow, labor-intensive process that can benefit from effective automation.We present a document classification scheme aiming to identify papers containing information relevant to a specific topic, among a large collection of articles, for supporting the biocuration classification task. Our framework is based on a meta-classification scheme we have introduced before; here we incorporate into it features gathered from figure captions, in addition to those obtained from titles and abstracts. We trained and tested our classifier over a large imbalanced dataset, originally curated by the Gene Expression Database (GXD). GXD collects all the gene expression information in the Mouse Genome Informatics (MGI) resource. As part of the MGI literature classification pipeline, GXD curators identify MGI-selected papers that are relevant for GXD. The dataset consists of ~60 000 documents (5469 labeled as relevant; 52 866 as irrelevant), gathered throughout 2012–2016, in which each document is represented by the text of its title, abstract and figure captions. Our classifier attains precision 0.698, recall 0.784, f-measure 0.738 and Matthews correlation coefficient 0.711, demonstrating that the proposed framework effectively addresses the high imbalance in the GXD classification task. Moreover, our classifier’s performance is significantly improved by utilizing information from image captions compared to using titles and abstracts alone; this observation clearly demonstrates that image captions provide substantial information for supporting biomedical document classification and curation. Database URL:

AAgAtlas 1.0: A Database of Human Autoantigens Extracted from Biomedical Literature.

Autoantibodies are antibodies against host self-proteins (autoantigens), which play significant roles in homeostasis maintenance and diseases with autoimmune disorders. Numerous papers were published in the past decade on the identification of human autoantigens in different human diseases. However, there is no consensus collection with all the reported autoantigens yet. To address this need, previously we developed a human autoantigen database, AAgAtlas 1.0, by text-mining and manual curation, which collects 1126 autoantigens associated with 1071 human diseases. AAgAtlas 1.0 provides a user-friendly interface to conveniently browse, retrieve, and download human autoantigen genes, their functional annotation, related diseases, and the evidence from the literature. AAgAtlas is freely available online http://biokb.ncpsb.org/aagatlas/ . In this chapter, we make an introduction and provide a guide to the users of AAgAtlas 1.0 database.

CRISPR sequences are sometimes erroneously translated and can contaminate public databases with spurious proteins containing spaced repeats.

The genomics era is resulting in the generation of a plethora of biological sequences that are usually stored in public databases. There are many computational tools that facilitate the annotation of these sequences, but sometimes they produce mistakes that enter the databases and can be propagated when erroneous data are used for secondary analyses, such as gene prediction or homology searching. While developing a computational gene finder based on protein-coding sequences, we discovered that the reference UniProtKB protein database is contaminated with some spurious sequences translated from DNA containing clustered regularly interspaced short palindromic repeats. We therefore encourage developers of prokaryotic computational gene finders and protein database curators to consider this source of error.

MicroRNA Applications in Marine Biology

MicroRNAs (miRNAs) are single-stranded, short (~ 22 nt) non-coding RNAs that control gene expression in most metazoan taxa. These vital post-transcriptional regulators are emerging as a novel class of relatively well-conserved biomarkers useful to molecular ecologists working on non-model marine organisms. The purpose of this review is to provide researchers with a brief background on miRNAs and to explore recent applications in marine biology. MiRNA datasets have been broadly employed in studies concerning commercially important species (oysters and crustaceans), phylogenetics (particularly deep evolutionary splits), and environmental stressor responses (temperature and salinity). Most progress has been made in the characterization of cnidarian miRNAs and bivalve and crustacean immune-related miRNAs. The use of miRNAs in phylogenetics is still under debate due to the secondary loss of miRNAs in some lineages, but they have been successfully applied in the resolution of deep evolutionary splits. Finally, miRNAs have been investigated in abiotic stress responses, but data interpretation is limited by the high number of species-specific miRNAs detected in these studies. Improvements in miRNA database curation and functional annotation should provide more confidence in their use. Due to their evolutionary conservation, resilience to degradation, and amenable bioinformatics workflows, miRNAs are a powerful molecular tool in marine genomics. MiRNA investigations regarding environmental stress response will be particularly useful due to their potential to reveal physiological alterations and disease. Thus, they may be ultimately utilized as bio-indicators of environmental health.

Report from the STRAND Working Group on the 2019 STR sequence nomenclature meeting

This report summarizes topics discussed at the STR sequence nomenclature meeting hosted by the STRAND Working Group in April 2019. Invited attendees for this meeting included researchers known-to-us to be developing STR sequence-based nomenclature schemata, scientific representatives from vendors developing STR sequence bioinformatic methods, DNA intelligence database curators, and academic experts in STR genomics. The goal of this meeting was to provide a forum for individuals developing nomenclature schemata to present and discuss their ideas, encouraging mutual awareness, identification of differences in approaches, opposing aspects, and opportunities for parallelization while some approaches are still under development.

Checklist for gene/disease-specific variation database curators to enable ethical data management.

Databases with variant and phenotype information are essential for advancing research and improving the health and welfare of individuals. These resources require data to be collected, curated, and shared among relevant specialties to maximize impact. The increasing generation of data which must be shared both nationally and globally for maximal effect presents important ethical and privacy concerns. Database curators need to ensure that their work conform to acceptable ethical standards. A Working Group of the Human Variome Project had the task of updating and streamlining ethical guidelines for locus-specific/gene variant database curators. In this article, we present practical and achievable steps which should assist database curators in carrying out their responsibilities within acceptable ethical norms.

Assessing the Multi-level Knowledge Prominence Perceived by the Authors as Revealed on their Writings

Automatic identification of widely-known facts not only plays a crucial role in automatic curation of databases of common-sense knowledge, but also benefits many applications of natural language processing such as searching, question answering, and common-sense reasoning. For better performance of such automatic identification, it is important to understand the linguistic characteristics of the writings about widely-known facts, if there are such characteristics. In order to investigate these linguistic characteristics, we juxtapose the sentences that report an event and the database entries that contain information about that event. We designed a task of identifying the number of databases in which the event of interest appears. From the experiment, we found that the number of databases to which an event is registered is strongly correlated with the writing style in which the event is described in the literature. This means that the style of writing reveals multi-level understanding of the individual authors on the status of knowledge prominence. To the best our knowledge, our work is the first that suggests an extrinsic evidence supporting the existence of such multi-level cognition. (KAIST)

BioNorm: deep learning-based event normalization for the curation of reaction databases.

A biochemical reaction, bio-event, depicts the relationships between participating entities. Current text mining research has been focusing on identifying bio-events from scientific literature. However, rare efforts have been dedicated to normalize bio-events extracted from scientific literature with the entries in the curated reaction databases, which could disambiguate the events and further support interconnecting events into biologically meaningful and complete networks. In this paper, we propose BioNorm, a novel method of normalizing bio-events extracted from scientific literature to entries in the bio-molecular reaction database, e.g. IntAct. BioNorm considers event normalization as a paraphrase identification problem. It represents an entry as a natural language statement by combining multiple types of information contained in it. Then, it predicts the semantic similarity between the natural language statement and the statements mentioning events in scientific literature using a long short-term memory recurrent neural network (LSTM). An event will be normalized to the entry if the two statements are paraphrase. To the best of our knowledge, this is the first attempt of event normalization in the biomedical text mining. The experiments have been conducted using the molecular interaction data from IntAct. The results demonstrate that the method could achieve F-score of 0.87 in normalizing event-containing statements. The source code is available at the gitlab repository https://gitlab.com/BioAI/leen and BioASQvec Plus is available on figshare https://figshare.com/s/45896c31d10c3f6d857a.

Allele frequency analysis of variants reported to cause autosomal dominant inherited retinal diseases question the involvement of 19% of genes and 10% of reported pathogenic variants

BackgroundNext generation sequencing (NGS) generates a large amount of genetic data that can be used to better characterise disease-causing variants. Our aim was to examine allele frequencies of sequence variants...

Molecular versus morphological data for benthic diatoms biomonitoring in Northern Europe freshwater and consequences for ecological status

Diatoms are known to be efficient bioindicators for water quality assessment because of their rapid response to environmental pressures and their omnipresence in water bodies. The identification of benthic diatoms communities in the biofilm, coupled with quality indices such as the Indice de polluosensibilité spécifique (IPS) can be used for biomonitoring purposes in freshwater. However, the morphological identification and counting of diatoms species under the microscope is time-consuming and requires extensive expertise to deal with a constantly evolving taxonomy. In response, a molecular-based and potentially more cost-effective method has been developed, coupling high-throughput sequencing and DNA metabarcoding. The method has already been tested for water quality assessment with diatoms in Central Europe. In this study, we applied both the traditional and molecular methods on 180 biofilms samples from Northern Europe (rivers and lakes of Fennoscandia and Iceland). The DNA metabarcoding data were obtained on two different DNA markers, the 18S-V4 and rbcL barcodes, with the NucleoSpin Soil kit for DNA extraction and sequenced on an Ion Torrent PGM platform. We assessed the ability of the molecular method to produce species inventories, IPS scores and ecological status class comparable to the ones generated by the traditional morphology-based approach. The two methods generated correlated but significantly different IPS scores and ecological status assessment. The observed deviations are explained by presence/absence and abundance discrepancies in the species inventories, mainly due to the incompleteness of the barcodes reference databases, primer bias and strictness of the bioinformatic pipeline. Abundance discrepancies are less common than presence/absence discrepancies but have a greater effect on the ecological assessment. Missing species in the reference databases are mostly acidophilic benthic diatoms species, typical of the low pH waters of Northern Europe. The two different DNA markers also generated significantly different ecological status assessments. The use of the 18S-V4 marker generates more species inventories discrepancies, but achieves an ecological assessment more similar to the traditional morphology-based method. Further development of the metabarcoding method is needed for its use in environmental assessment. For its application in Northern Europe, completion and curation of reference databases are necessary, as well as evaluation of the currently available bioinformatics pipelines. New indices, fitted for environmental biomonitoring, should also be developed directly from molecular data.

BioCyc: A Genomic and Metabolic Web Portal with Multiple Omics Analytical Tools

BioCyc.org is a web portal that contains Pathway/Genome Databases for over 14,500 organisms, including 2400 human microbiome members. The BioCyc databases are unique in integrating a diverse range of data and providing a high level of curation for important microbes as well as model eukaryotic organisms and Homo sapiens. Each Pathway/Genome Database describes the genome and proteome of an organism, as well as its biochemical pathways and (for a small number of organisms) its regulatory network. BioCyc bioinformatics tools combine unparalleled breadth and user friendliness and include a unique set of visualization tools to speed comprehension of its extensive and complex data. BioCyc also includes MetaCyc, the largest collection of metabolic pathways and enzymes currently available, with over 2600 pathways. It contains the equivalent of about 9,400 textbook-pages of mini-review summaries for enzymes and pathways. The data in MetaCyc has been manually curated from over 58,000 published papers. Analysis tools in BioCyc include comparisons of pathways and genomes among multiple organisms, alignment of orthologous genes in a multi-genome browser, and searching for minimal-cost metabolic network routes that connect two substrates. Tools are also provided for visualizing omics data – such as metabolomics and gene expression data – on individual pathway diagrams, on personalized groups of multiple pathway diagrams called pathway collages, and on diagrams depicting the full metabolic network of an organism. A new visualization tool in BioCyc is the Omics Dashboard, an interactive tool for exploration and analysis of gene-expression and metabolomics datasets through a hierarchy of cellular systems and subsystems. This highly visual tool enables the user to survey their data at a very high level of abstraction, and to drill down to examine details at the pathway, subsystem, and gene levels. Patterns in the data can often be perceived quickly and easily. Another new tool is the MultiOmics Explainer, which helps researchers understand and interpret the results of their omics experiments in the context of what is known about an organism's metabolic and regulatory network. At this time this tool is not available online and requires installation of the software on a local computer. Another recent addition to BioCyc involves manual curation of databases for important pathogens. So far, the project includes Mycobacterium tuberculosis (H37Rv), Staphylococcus aureus (NCTC 8325) and Salmonella enterica (14028S) The BioCyc databases can be accessed through the BioCyc.org web site and can be downloaded. In addition, the Pathway Tools software can be downloaded and installed locally to create BioCyc-like databases for more genomes, curate existing databases, or perform additional analyses that are not available online. Access to MetaCyc and EcoCyc is unrestricted. Unlimited access to other databases requires a subscription. Support or Funding Information National Institute of General Medical Sciences of the National Institutes of Health (NIH) [GM080746, GM077678, GM75742] A portion of the initial display of the Omics Dashboard, showing the average gene expression levels of genes, at different time points, in pathways from different subsystems. Another example output of the Omics Dashboard, showing gene-expression profiles at the regulon level. A typical pathway diagram from a BioCyc database. The Multi-Genome Browser enables comparison of orthologous regions from different organisms. Genes in the Genome Browser are color-coded to indicate transcription units. Reaction diagrams provide color-coded atom mapping between reactants and products. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Automatic mapping of atoms across both simple and complex chemical reactions

Mapping atoms across chemical reactions is important for substructure searches, automatic extraction of reaction rules, identification of metabolic pathways, and more. Unfortunately, the existing mapping algorithms can deal adequately only with relatively simple reactions but not those in which expert chemists would benefit from computer’s help. Here we report how a combination of algorithmics and expert chemical knowledge significantly improves the performance of atom mapping, allowing the machine to deal with even the most mechanistically complex chemical and biochemical transformations. The key feature of our approach is the use of few but judiciously chosen reaction templates that are used to generate plausible “intermediate” atom assignments which then guide a graph-theoretical algorithm towards the chemically correct isomorphic mappings. The algorithm performs significantly better than the available state-of-the-art reaction mappers, suggesting its uses in database curation, mechanism assignments, and – above all – machine extraction of reaction rules underlying modern synthesis-planning programs.

BioReader: a text mining tool for performing classification of biomedical literature

BackgroundScientific data and research results are being published at an unprecedented rate. Many database curators and researchers utilize data and information from the primary literature to populate databases, form hypotheses, or as the basis for analyses or validation of results. These efforts largely rely on manual literature surveys for collection of these data, and while querying the vast amounts of literature using keywords is enabled by repositories such as PubMed, filtering relevant articles from such query results can be a non-trivial and highly time consuming task.ResultsWe here present a tool that enables users to perform classification of scientific literature by text mining-based classification of article abstracts. BioReader (Biomedical Research Article Distiller) is trained by uploading article corpora for two training categories - e.g. one positive and one negative for content of interest - as well as one corpus of abstracts to be classified and/or a search string to query PubMed for articles. The corpora are submitted as lists of PubMed IDs and the abstracts are automatically downloaded from PubMed, preprocessed, and the unclassified corpus is classified using the best performing classification algorithm out of ten implemented algorithms.ConclusionBioReader supports data and information collection by implementing text mining-based classification of primary biomedical literature in a web interface, thus enabling curators and researchers to take advantage of the vast amounts of data and information in the published literature. BioReader outperforms existing tools with similar functionalities and expands the features used for mining literature in database curation efforts. The tool is freely available as a web service at http://www.cbs.dtu.dk/services/BioReader