GeoBoost: accelerating research involving the geospatial metadata of virus GenBank records.
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ABSTRACT:
SUBMITTER: Tahsin T
PROVIDER: S-EPMC5925778 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectiveThe metadata reflecting the location of the infected host (LOIH) of virus sequences in GenBank often lacks specificity. This work seeks to enhance this metadata by extracting more specific geographic information from related full-text articles and mapping them to their latitude/longitudes using knowledge derived from external geographical databases.Materials and methodsWe developed a rule-based information extraction framework for linking GenBank records to the latitude/longitudes of the LOIH. Our system first extracts existing geospatial metadata from GenBank records and attempts to improve it by seeking additional, relevant geographic information from text and tables in related full-text PubMed Central articles. The final extracted locations of the records, based on data assimilated from these sources, are then disambiguated and mapped to their respective geo-coordinates. We evaluated our approach on a manually annotated dataset comprising of 5728 GenBank records for the influenza A virus.ResultsWe found the precision, recall, and f-measure of our system for linking GenBank records to the latitude/longitudes of their LOIH to be 0.832, 0.967, and 0.894, respectively.DiscussionOur system had a high level of accuracy for linking GenBank records to the geo-coordinates of the LOIH. However, it can be further improved by expanding our database of geospatial data, incorporating spell correction, and enhancing the rules used for extraction.ConclusionOur system performs reasonably well for linking GenBank records for the influenza A virus to the geo-coordinates of their LOIH based on record metadata and information extracted from related full-text articles.
| S-EPMC4997033 | biostudies-literature
Project description:: https://zodo.asu.edu/zoophydb/.
| S-EPMC6225896 | biostudies-literature
Project description:SummaryWe present GeoBoost2, a natural language-processing pipeline for extracting the location of infected hosts for enriching metadata in nucleotide sequences repositories like National Center of Biotechnology Information's GenBank for downstream analysis including phylogeography and genomic epidemiology. The increasing number of pathogen sequences requires complementary information extraction methods for focused research, including surveillance within countries and between borders. In this article, we describe the enhancements from our earlier release including improvement in end-to-end extraction performance and speed, availability of a fully functional web-interface and state-of-the-art methods for location extraction using deep learning.Availability and implementationApplication is freely available on the web at https://zodo.asu.edu/geoboost2. Source code, usage examples and annotated data for GeoBoost2 is freely available at https://github.com/ZooPhy/geoboost2.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:RNA sequencing of tissue samples taken from pediatric cancer patients of varying disease type. Patients represented those with terminal disease with samples taken from resection or autopsy.
2017-06-24 | GSE100427 | GEO
Project description:The Gemina system (http://gemina.igs.umaryland.edu) identifies, standardizes and integrates the outbreak metadata for the breadth of NIAID category A-C viral and bacterial pathogens, thereby providing an investigative and surveillance tool describing the Who [Host], What [Disease, Symptom], When [Date], Where [Location] and How [Pathogen, Environmental Source, Reservoir, Transmission Method] for each pathogen. The Gemina database will provide a greater understanding of the interactions of viral and bacterial pathogens with their hosts and infectious diseases through in-depth literature text-mining, integrated outbreak metadata, outbreak surveillance tools, extensive ontology development, metadata curation and representative genomic sequence identification and standards development. The Gemina web interface provides metadata selection and retrieval of a pathogen's; Infection Systems (Pathogen, Host, Disease, Transmission Method and Anatomy) and Incidents (Location and Date) along with a hosts Age and Gender. The Gemina system provides an integrated investigative and geospatial surveillance system connecting pathogens, pathogen products and disease anchored on the taxonomic ID of the pathogen and host to identify the breadth of hosts and diseases known for these pathogens, to identify the extent of outbreak locations, and to identify unique genomic regions with the DNA Signature Insignia Detection Tool.
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Project description:BACKGROUND: The lack of reference sequences from well-identified mycorrhizal fungi often poses a challenge to the inference of taxonomic affiliation of sequences from environmental samples, and many environmental sequences are thus left unidentified. Such unidentified sequences belonging to the widely distributed ectomycorrhizal fungal genus Inocybe (Basidiomycota) were retrieved from GenBank and divided into species that were identified in a phylogenetic context using a reference dataset from an ongoing study of the genus. The sequence metadata of the unidentified Inocybe sequences stored in GenBank, as well as data from the corresponding original papers, were compiled and used to explore the ecology and distribution of the genus. In addition, the relative occurrence of Inocybe was contrasted to that of other mycorrhizal genera. RESULTS: Most species of Inocybe were found to have less than 3% intraspecific variability in the ITS2 region of the nuclear ribosomal DNA. This cut-off value was used jointly with phylogenetic analysis to delimit and identify unidentified Inocybe sequences to species level. A total of 177 unidentified Inocybe ITS sequences corresponding to 98 species were recovered, 32% of which were successfully identified to species level in this study. These sequences account for an unexpectedly large proportion of the publicly available unidentified fungal ITS sequences when compared with other mycorrhizal genera. Eight Inocybe species were reported from multiple hosts and some even from hosts forming arbutoid or orchid mycorrhizae. Furthermore, Inocybe sequences have been reported from four continents and in climate zones ranging from cold temperate to equatorial climate. Out of the 19 species found in more than one study, six were found in both Europe and North America and one was found in both Europe and Japan, indicating that at least many north temperate species have a wide distribution. CONCLUSION: Although DNA-based species identification and circumscription are associated with practical and conceptual difficulties, they also offer new possibilities and avenues for research. Metadata assembly holds great potential to synthesize valuable information from community studies for use in a species and taxonomy-oriented framework.
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Project description:Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.
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Project description:Traditional methods of characterizing biodiversity are increasingly being supplemented and replaced by approaches based on DNA sequencing alone. These approaches commonly involve extraction and high-throughput sequencing of bulk samples from biologically complex communities or samples of environmental DNA (eDNA). In such cases, vouchers for individual organisms are rarely obtained, often unidentifiable, or unavailable. Thus, identifying these sequences typically relies on comparisons with sequences from genetic databases, particularly GenBank. While concerns have been raised about biases and inaccuracies in laboratory and analytical methods, comparatively little attention has been paid to the taxonomic reliability of GenBank itself. Here we analyze the metazoan mitochondrial sequences of GenBank using a combination of distance-based clustering and phylogenetic analysis. Because of their comparatively rapid evolutionary rates and consequent high taxonomic resolution, mitochondrial sequences represent an invaluable resource for the detection of the many small and often undescribed organisms that represent the bulk of animal diversity. We show that metazoan identifications in GenBank are surprisingly accurate, even at low taxonomic levels (likely <1% error rate at the genus level). This stands in contrast to previously voiced concerns based on limited analyses of particular groups and the fact that individual researchers currently submit annotated sequences to GenBank without significant external taxonomic validation. Our encouraging results suggest that the rapid uptake of DNA-based approaches is supported by a bioinformatic infrastructure capable of assessing both the losses to biodiversity caused by global change and the effectiveness of conservation efforts aimed at slowing or reversing these losses.
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Project description:Undergraduate subject pools are prevalent across disciplines in the United States. The Health Education Research Experience (HERE) Program was the first known course-based subject pool entirely managed and conducted online for online students enrolled in an introductory health education/health promotion course. The program was conducted within five semesters from Spring 2012 through Summer 2013. The HERE Program encompassed 13 studies embedded in two sections of an undergraduate online course at the University of Florida. The studies were all related to course topics and current research topics in health education/promotion (as identified through the Healthy People 2020 Framework). The topics ranged from the relatively less sensitive health aspects of college life (i.e., technology use) to studies assessing more sensitive health topics (i.e., intimate partner violence and sexual assault). In alignment with a best practice in survey design, the HERE Program's survey instruments included one metadata item embedded in each survey to identify which devices students used to complete the surveys. Understanding which devices students used for survey completion has ramifications for survey designers and survey researchers. In contrast to the relative uniformity of pen and paper surveys and control of the survey completion environment, online surveys may not look identical across personal devices and may be completed in increasingly varied environments. All studies, study procedures and protocols, and metadata collection procedures were approved by the university's Institutional Review Board. The data presented here were extracted from each survey's data files and aggregated. The aggregated metadata are available through Mendeley Data in a.csv file for widespread access. Descriptive statistics are presented in tables. The data provided in this article will benefit researchers interested in survey methodology, questionnaire design, modes of survey collection, and survey metadata. The data are hosted in the following Mendeley Data repository: https://data.mendeley.com/datasets/ht9jmd3cdt/2.
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Project description:Genetic data are a crucial and exponentially growing resource across all biological sciences, yet curated databases are scarce. The widespread occurrence of sequence and (meta)data errors in public repositories calls for comprehensive improvements of curation protocols leading to robust research and downstream analyses. We collated and curated all available GenBank cytochrome-b sequences for amphibians, a benchmark marker in this globally declining vertebrate clade. The Amphibia's Curated Database of Cytochrome-b (ACDC) consists of 36,514 sequences representing 2,309 species from 398 genera (median?=?2 with 50% interquartile ranges of 1-7 species/genus). We updated the taxonomic identity of >4,800 sequences (ca. 13%) and found 2,359 (6%) conflicting sequences with 84% of the errors originating from taxonomic misidentifications. The database (accessible at https://doi.org/10.6084/m9.figshare.9944759 ) also includes an R script to replicate our study for other loci and taxonomic groups. We provide recommendations to improve genetic-data quality in public repositories and flag species for which there is a need for taxonomic refinement in the face of increased rate of amphibian extinctions in the Anthropocene.
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