BioBIKE: a Web-based, programmable, integrated biological knowledge base.
Ontology highlight
ABSTRACT:
SUBMITTER: Elhai J
PROVIDER: S-EPMC2703918 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The development of SARS-CoV-2 vaccines during the COVID-19 pandemic has prompted the emergence of COVID-19 vaccine data. Timely access to COVID-19 vaccine information is crucial to researchers and public. To support more comprehensive annotation, integration, and analysis of COVID-19 vaccine information, we have developed Cov19VaxKB, a knowledge-focused COVID-19 vaccine database (http://www.violinet.org/cov19vaxkb/). Cov19VaxKB features comprehensive lists of COVID-19 vaccines, vaccine formulations, clinical trials, publications, news articles, and vaccine adverse event case reports. A web-based query interface enables comparison of product information and host responses among various vaccines. The knowledge base also includes a vaccine design tool for predicting vaccine targets and a statistical analysis tool that identifies enriched adverse events for FDA-authorized COVID-19 vaccines based on VAERS case report data. To support data exchange, Cov19VaxKB is synchronized with Vaccine Ontology and the Vaccine Investigation and Online Information Network (VIOLIN) database. The data integration and analytical features of Cov19VaxKB can facilitate vaccine research and development while also serving as a useful reference for the public.
| S-EPMC8716025 | biostudies-literature
Project description:BackgroundOne in five U.S. adults lives with some kind of mental health condition and 4.6% of all U.S. adults have a serious mental illness. The Internet has become the first place for these people to seek online mental health information for help. However, online mental health information is not well-organized and often of low quality. There have been efforts in building evidence-based mental health knowledgebases curated with information manually extracted from the high-quality scientific literature. Manual extraction is inefficient. Crowdsourcing can potentially be a low-cost mechanism to collect labeled data from non-expert laypeople. However, there is not an existing annotation tool integrated with popular crowdsourcing platforms to perform the information extraction tasks. In our previous work, we prototyped a Semantic Text Annotation Tool (STAT) to address this gap.ObjectiveWe aimed to refine the STAT prototype (1) to improve its usability and (2) to enhance the crowdsourcing workflow efficiency to facilitate the construction of evidence-based mental health knowledgebase, following a user-centered design (UCD) approach.MethodsFollowing UCD principles, we conducted four design iterations to improve the initial STAT prototype. In the first two iterations, usability testing focus groups were conducted internally with 8 participants recruited from a convenient sample, and the usability was evaluated with a modified System Usability Scale (SUS). In the following two iterations, usability testing was conducted externally using the Amazon Mechanical Turk (MTurk) platform. In each iteration, we summarized the usability testing results through thematic analysis, identified usability issues, and conducted a heuristic evaluation to map identified usability issues to Jakob Nielsen's usability heuristics. We collected suggested improvements in the usability testing sessions and enhanced STAT accordingly in the next UCD iteration. After four UCD iterations, we conducted a case study of the system on MTurk using mental health related scientific literature. We compared the performance of crowdsourcing workers with two expert annotators from two aspects: efficiency and quality.ResultsThe SUS score increased from 70.3 ± 12.5 to 81.1 ± 9.8 after the two internal UCD iterations as we improved STAT's functionality based on the suggested improvements. We then evaluated STAT externally through MTurk in the following two iterations. The SUS score decreased to 55.7 ± 20.1 in the third iteration, probably because of the complexity of the tasks. After further simplification of STAT and the annotation tasks with an improved annotation guideline, the SUS score increased to 73.8 ± 13.8 in the fourth iteration of UCD. In the evaluation case study, on average, the workers spent 125.5 ± 69.2 s on the onboarding tutorial and the crowdsourcing workers spent significantly less time on the annotation tasks compared to the two experts. In terms of annotation quality, the workers' annotation results achieved average F1-scores ranged from 0.62 to 0.84 for the different sentences.ConclusionsWe successfully developed a web-based semantic text annotation tool, STAT, to facilitate the curation of semantic web knowledgebases through four UCD iterations. The lessons learned from the UCD process could serve as a guide to further enhance STAT and the development and design of other crowdsourcing-based semantic text annotation tasks. Our study also showed that a well-organized, informative annotation guideline is as important as the annotation tool itself. Further, we learned that a crowdsourcing task should consist of multiple simple microtasks rather than a complicated task.
| S-EPMC7577935 | biostudies-literature
Project description:Increasingly sophisticated knowledge about RNA structure and function requires an inclusive knowledge representation that facilitates the integration of independently -generated information arising from such efforts as genome sequencing projects, microarray analyses, structure determination and RNA SELEX experiments. While RNAML, an XML-based representation, has been proposed as an exchange format for a select subset of information, it lacks domain-specific semantics that are essential for answering questions that require expert knowledge. Here, we describe an RNA knowledge base (RKB) for structure-based knowledge using RDF/OWL Semantic Web technologies. RKB extends a number of ontologies and contains basic terminology for nucleic acid composition along with context/model-specific structural features such as sugar conformations, base pairings and base stackings. RKB (available at http://semanticscience.org/projects/rkb) is populated with PDB entries and MC-Annotate structural annotation. We show queries to the RKB using description logic reasoning, thus opening the door to question answering over independently-published RNA knowledge using Semantic Web technologies.
| S-EPMC2903721 | biostudies-literature
Project description:ObjectiveTo build a knowledge base of dietary supplement (DS) information, called the integrated DIetary Supplement Knowledge base (iDISK), which integrates and standardizes DS-related information from 4 existing resources.Materials and methodsiDISK was built through an iterative process comprising 3 phases: 1) establishment of the content scope, 2) development of the data model, and 3) integration of existing resources. Four well-regarded DS resources were integrated into iDISK: The Natural Medicines Comprehensive Database, the "About Herbs" page on the Memorial Sloan Kettering Cancer Center website, the Dietary Supplement Label Database, and the Natural Health Products Database. We evaluated the iDISK build process by manually checking that the data elements associated with 50 randomly selected ingredients were correctly extracted and integrated from their respective sources.ResultsiDISK encompasses a terminology of 4208 DS ingredient concepts, which are linked via 6 relationship types to 495 drugs, 776 diseases, 985 symptoms, 605 therapeutic classes, 17 system organ classes, and 137 568 DS products. iDISK also contains 7 concept attribute types and 3 relationship attribute types. Evaluation of the data extraction and integration process showed average errors of 0.3%, 2.6%, and 0.4% for concepts, relationships and attributes, respectively.ConclusionWe developed iDISK, a publicly available standardized DS knowledge base that can facilitate more efficient and meaningful dissemination of DS knowledge.
| S-EPMC7075538 | biostudies-literature
Project description:Virulence factors (VFs) are molecules that allow microbial pathogens to overcome host defense mechanisms and cause disease in a host. It is critical to study VFs for better understanding microbial pathogenesis and host defense mechanisms. Victors (http://www.phidias.us/victors) is a novel, manually curated, web-based integrative knowledge base and analysis resource for VFs of pathogens that cause infectious diseases in human and animals. Currently, Victors contains 5296 VFs obtained via manual annotation from peer-reviewed publications, with 4648, 179, 105 and 364 VFs originating from 51 bacterial, 54 viral, 13 parasitic and 8 fungal species, respectively. Our data analysis identified many VF-specific patterns. Within the global VF pool, cytoplasmic proteins were more common, while adhesins were less common compared to findings on protective vaccine antigens. Many VFs showed homology with host proteins and the human proteins interacting with VFs represented the hubs of human-pathogen interactions. All Victors data are queriable with a user-friendly web interface. The VFs can also be searched by a customized BLAST sequence similarity searching program. These VFs and their interactions with the host are represented in a machine-readable Ontology of Host-Pathogen Interactions. Victors supports the 'One Health' research as a vital source of VFs in human and animal pathogens.
| S-EPMC6324020 | biostudies-literature
Project description:Mass spectrometry-based proteomics starts with identifications of peptides and proteins, which provide the bases for forming the next-level hypotheses whose "validations" are often employed for forming even higher level hypotheses and so forth. Scientifically meaningful conclusions are thus attainable only if the number of falsely identified peptides/proteins is accurately controlled. For this reason, RAId continued to be developed in the past decade. RAId employs rigorous statistics for peptides/proteins identification, hence assigning accurate P-values/E-values that can be used confidently to control the number of falsely identified peptides and proteins. The RAId web service is a versatile tool built to identify peptides and proteins from tandem mass spectrometry data. Not only recognizing various spectra file formats, the web service also allows four peptide scoring functions and choice of three statistical methods for assigning P-values/E-values to identified peptides. Users may upload their own protein database or use one of the available knowledge integrated organismal databases that contain annotated information such as single amino acid polymorphisms, post-translational modifications, and their disease associations. The web service also provides a friendly interface to display, sort using different criteria, and download the identified peptides and proteins. RAId web service is freely available at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/raid.
| S-EPMC6635056 | biostudies-literature
Project description:A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein-protein interactions, protein/gene regulations, protein-small molecule interactions, protein-GO relationships, protein-pathway relationships, and pathway-disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses--the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs.
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Project description:Addiction, a disorder of maladaptive brain plasticity, is associated with changes in numerous gene expressions. Nowadays, high-throughput sequencing data on addictive substance-induced gene expression have become widely available. A resource for comprehensive annotation of genes that show differential expression in response to commonly abused substances is necessary. So, we developed AddictGene by integrating gene expression, gene-gene interaction, gene-drug interaction and epigenetic regulatory annotation for over 70,156 items of differentially expressed genes associated with 7 commonly abused substances, including alcohol, nicotine, cocaine, morphine, heroin, methamphetamine, and amphetamine, across three species (human, mouse, rat). We also collected 1,141 addiction-related experimentally validated genes by techniques such as RT-PCR, northern blot and in situ hybridization. The easy-to-use web interface of AddictGene (http://159.226.67.237/sun/addictgedb/) allows users to search and browse multidimensional data on DEGs of their interest: 1) detailed gene-specific information extracted from the original studies; 2) basic information about the specific gene extracted from NCBI; 3) SNP associated with substance dependence and other psychiatry disorders; 4) expression alteration of specific gene in other psychiatric disorders; 5) expression patterns of interested gene across 31 primary and 54 secondary human tissues; 6) functional annotation of interested gene; 7) epigenetic regulators involved in the alteration of specific genes, including histone modifications and DNA methylation; 8) protein-protein interaction for functional linkage with interested gene; 9) drug-gene interaction for potential druggability. AddictGene offers a valuable repository for researchers to study the molecular mechanisms underlying addiction, and might provide valuable insights into potential therapies for drug abuse and relapse.
| S-EPMC8113760 | biostudies-literature
Project description:BackgroundIntegrated knowledge translation (IKT) is a model of research co-production, whereby researchers partner with knowledge users throughout the research process and who can use the research recommendations in practice or policy. IKT approaches are used to improve the relevance and impact of research. As an emerging field, however, the evidence underpinning IKT is in active development. The Integrated Knowledge Translation Research Network represents a collaborative interdisciplinary team that aims to advance the state of IKT science.MethodsIn 2017, the Integrated Knowledge Translation Research Network issued a call to its members for concept papers to further define IKT, outline an IKT research agenda, and inform the Integrated Knowledge Translation Research Network's special meeting entitled, Integrated Knowledge Translation State of the Science Colloquium, in Ottawa, Canada (2018). At the colloquium, authors presented concept papers and discussed knowledge-gaps for a research agenda and implications for advancing the IKT field. We took detailed field notes, audio-recorded the meeting and analysed the data using qualitative content analysis.ResultsTwenty-four participants attended the meeting, including researchers (n = 11), trainees (n = 6) and knowledge users (n = 7). Seven overarching categories emerged from these proceedings - IKT theory, IKT methods, IKT process, promoting partnership, definitions and distinctions of key IKT terms, capacity-building, and role of funders. Within these categories, priorities identified for future IKT research included: (1) improving clarity about research co-production/IKT theories and frameworks; (2) describing the process for engaging knowledge users; and (3) identifying research co-production/IKT outcomes and methods for evaluation.ConclusionThe Integrated Knowledge Translation State of the Science Colloquium initiated a research agenda to advance IKT science and practice. Next steps will focus on building a theoretical and evidence base for IKT.
| S-EPMC6972018 | biostudies-literature
Project description:BackgroundAs a result of its simplicity and high efficiency, the CRISPR-Cas system has been widely used as a genome editing tool. Recently, CRISPR base editors, which consist of deactivated Cas9 (dCas9) or Cas9 nickase (nCas9) linked with a cytidine or a guanine deaminase, have been developed. Base editing tools will be very useful for gene correction because they can produce highly specific DNA substitutions without the introduction of any donor DNA, but dedicated web-based tools to facilitate the use of such tools have not yet been developed.ResultsWe present two web tools for base editors, named BE-Designer and BE-Analyzer. BE-Designer provides all possible base editor target sequences in a given input DNA sequence with useful information including potential off-target sites. BE-Analyzer, a tool for assessing base editing outcomes from next generation sequencing (NGS) data, provides information about mutations in a table and interactive graphs. Furthermore, because the tool runs client-side, large amounts of targeted deep sequencing data (< 1 GB) do not need to be uploaded to a server, substantially reducing running time and increasing data security. BE-Designer and BE-Analyzer can be freely accessed at http://www.rgenome.net/be-designer/ and http://www.rgenome.net/be-analyzer /, respectively.ConclusionWe develop two useful web tools to design target sequence (BE-Designer) and to analyze NGS data from experimental results (BE-Analyzer) for CRISPR base editors.
| S-EPMC6307267 | biostudies-literature