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1.
Sensors (Basel) ; 21(21)2021 Oct 21.
Article in English | MEDLINE | ID: covidwho-1512556

ABSTRACT

This paper proposes a cloud-based software architecture for fully automated point-of-care molecular diagnostic devices. The target system operates a cartridge consisting of an extraction body for DNA extraction and a PCR chip for amplification and fluorescence detection. To facilitate control and monitoring via the cloud, a socket server was employed for fundamental molecular diagnostic functions such as DNA extraction, amplification, and fluorescence detection. The user interface for experimental control and monitoring was constructed with the RESTful application programming interface, allowing access from the terminal device, edge, and cloud. Furthermore, it can also be accessed through any web-based user interface on smart computing devices such as smart phones or tablets. An emulator with the proposed software architecture was fabricated to validate successful operation.


Subject(s)
Cloud Computing , Point-of-Care Systems , Computers , Pathology, Molecular , Software
2.
BMC Bioinformatics ; 22(Suppl 15): 544, 2021 Nov 08.
Article in English | MEDLINE | ID: covidwho-1506889

ABSTRACT

BACKGROUND: Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of "on-demand" Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server's initial configuration and deployment. RESULTS: "Laniakea@ReCaS", the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform. CONCLUSIONS: During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers' work.


Subject(s)
COVID-19 , Cloud Computing , Computational Biology , Humans , SARS-CoV-2 , Software
4.
Sci Rep ; 11(1): 20866, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1479816

ABSTRACT

A causal relationship between plasma ceramide concentration and respiratory distress symptoms in COVID-19 patients is inferred. In this study, plasma samples of 52 individuals infected with COVID-19 were utilized in a lipidomic analysis. Lipids belonging to the ceramide class exhibited a 400-fold increase in total plasma concentration in infected patients. Further analysis led to the demonstration of concentration dependency for severe COVID-19 respiratory symptoms in a subclass of ceramides. The subclasses Cer(d18:0/24:1), Cer(d18:1/24:1), and Cer(d18:1/22:0) were shown to be increased by 48-, 40-, and 33-fold, respectively, in infected plasma samples and to 116-, 91- and 50-fold, respectively, in plasma samples with respiratory distress. Hence, monitoring plasma ceramide concentration, can be a valuable tool for measuring effects of therapies on COVID-19 respiratory distress patients.


Subject(s)
COVID-19/blood , COVID-19/complications , Ceramides/blood , Respiratory Distress Syndrome/blood , Respiratory Distress Syndrome/complications , Adult , Aged , Aged, 80 and over , Chromatography, Liquid , Drug Design , Female , Humans , Ions , Lipids/chemistry , Male , Metabolomics , Middle Aged , Principal Component Analysis , Software , Tandem Mass Spectrometry , Translational Medical Research/methods , Virus Diseases , Young Adult
5.
Sensors (Basel) ; 21(19)2021 Oct 07.
Article in English | MEDLINE | ID: covidwho-1473714

ABSTRACT

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners' physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners' smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.


Subject(s)
Education, Distance , Wearable Electronic Devices , Humans , Smartphone , Software , Students
6.
Mol Syst Biol ; 17(10): e10387, 2021 10.
Article in English | MEDLINE | ID: covidwho-1478718

ABSTRACT

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.


Subject(s)
COVID-19/immunology , Computational Biology/methods , Databases, Factual , SARS-CoV-2/immunology , Software , Antiviral Agents/therapeutic use , COVID-19/drug therapy , COVID-19/genetics , COVID-19/virology , Computer Graphics , Cytokines/genetics , Cytokines/immunology , Data Mining/statistics & numerical data , Gene Expression Regulation , Host Microbial Interactions/genetics , Host Microbial Interactions/immunology , Humans , Immunity, Cellular/drug effects , Immunity, Humoral/drug effects , Immunity, Innate/drug effects , Lymphocytes/drug effects , Lymphocytes/immunology , Lymphocytes/virology , Metabolic Networks and Pathways/genetics , Metabolic Networks and Pathways/immunology , Myeloid Cells/drug effects , Myeloid Cells/immunology , Myeloid Cells/virology , Protein Interaction Mapping , SARS-CoV-2/drug effects , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Signal Transduction , Transcription Factors/genetics , Transcription Factors/immunology , Viral Proteins/genetics , Viral Proteins/immunology
7.
BMC Pharmacol Toxicol ; 22(1): 61, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1477468

ABSTRACT

BACKGROUND: The emergence and rapid spread of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) in thelate 2019 has caused a devastating global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19). Although vaccines have been and are being developed, they are not accessible to everyone and not everyone can receive these vaccines. Also, it typically takes more than 10 years until a new therapeutic agent is approved for usage. Therefore, repurposing of known drugs can lend itself well as a key approach for significantly expediting the development of new therapies for COVID-19. METHODS: We have incorporated machine learning-based computational tools and in silico models into the drug discovery process to predict Adsorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiles of 90 potential drugs for COVID-19 treatment identified from two independent studies mainly with the purpose of mitigating late-phase failures because of inferior pharmacokinetics and toxicity. RESULTS: Here, we summarize the cardiotoxicity and general toxicity profiles of 90 potential drugs for COVID-19 treatment and outline the risks of repurposing and propose a stratification of patients accordingly. We shortlist a total of five compounds based on their non-toxic properties. CONCLUSION: In summary, this manuscript aims to provide a potentially useful source of essential knowledge on toxicity assessment of 90 compounds for healthcare practitioners and researchers to find off-label alternatives for the treatment for COVID-19. The majority of the molecules discussed in this manuscript have already moved into clinical trials and thus their known pharmacological and human safety profiles are expected to facilitate a fast track preclinical and clinical assessment for treating COVID-19.


Subject(s)
Antiviral Agents/toxicity , COVID-19/drug therapy , Drug Discovery , Drug Repositioning , Animals , Antiviral Agents/adverse effects , Captopril/therapeutic use , Cardiotoxins/toxicity , Catechols/therapeutic use , Computational Biology , Cytochrome P-450 Enzyme System/metabolism , Drug Discovery/methods , Humans , Indomethacin/therapeutic use , Linezolid/therapeutic use , Liver/drug effects , Mice , Models, Biological , Nitriles/therapeutic use , Rats , Reproduction/drug effects , Software , Valproic Acid/therapeutic use
8.
Cells ; 10(10)2021 10 14.
Article in English | MEDLINE | ID: covidwho-1470797

ABSTRACT

Prediction of linear B cell epitopes is of interest for the production of antigen-specific antibodies and the design of peptide-based vaccines. Here, we present BCEPS, a web server for predicting linear B cell epitopes tailored to select epitopes that are immunogenic and capable of inducing cross-reactive antibodies with native antigens. BCEPS implements various machine learning models trained on a dataset including 555 linearized conformational B cell epitopes that were mined from antibody-antigen protein structures. The best performing model, based on a support vector machine, reached an accuracy of 75.38% ± 5.02. In an independent dataset consisting of B cell epitopes retrieved from the Immune Epitope Database (IEDB), this model achieved an accuracy of 67.05%. In BCEPS, predicted epitopes can be ranked according to properties such as flexibility, accessibility and hydrophilicity, and with regard to immunogenicity, as judged by their predicted presentation by MHC II molecules. BCEPS also detects if predicted epitopes are located in ectodomains of membrane proteins and if they possess N-glycosylation sites hindering antibody recognition. Finally, we exemplified the use of BCEPS in the SARS-CoV-2 Spike protein, showing that it can identify B cell epitopes targeted by neutralizing antibodies.


Subject(s)
COVID-19/prevention & control , Computational Biology/methods , Databases, Factual , Epitopes, B-Lymphocyte/chemistry , SARS-CoV-2 , Animals , Antigens , COVID-19/immunology , Cross Reactions , Glycosylation , Histocompatibility Antigens Class II , Humans , Hydrophobic and Hydrophilic Interactions , Internet , Machine Learning , Mice , Peptides/chemistry , Protein Domains , Proteins/chemistry , Reproducibility of Results , Software , Spike Glycoprotein, Coronavirus/chemistry
9.
Sensors (Basel) ; 21(19)2021 Oct 07.
Article in English | MEDLINE | ID: covidwho-1463797

ABSTRACT

COVID-19 tracing applications have been launched in several countries to track and control the spread of viruses. Such applications utilize Bluetooth Low Energy (BLE) transmissions, which are short range and can be used to determine infected and susceptible persons near an infected person. The COVID-19 risk estimation depends on an epidemic model for the virus behavior and Machine Learning (ML) model to classify the risk based on time series distance of the nodes that may be infected. The BLE technology enabled smartphones continuously transmit beacons and the distance is inferred from the received signal strength indicators (RSSI). The educational activities have shifted to online teaching modes due to the contagious nature of COVID-19. The government policy makers decide on education mode (online, hybrid, or physical) with little technological insight on actual risk estimates. In this study, we analyze BLE technology to debate the COVID-19 risks in university block and indoor class environments. We utilize a sigmoid based epidemic model with varying thresholds of distance to label contact data with high risk or low risk based on features such as contact duration. Further, we train multiple ML classifiers to classify a person into high risk or low risk based on labeled data of RSSI and distance. We analyze the accuracy of the ML classifiers in terms of F-score, receiver operating characteristic (ROC) curve, and confusion matrix. Lastly, we debate future research directions and limitations of this study. We complement the study with open source code so that it can be validated and further investigated.


Subject(s)
COVID-19 , Humans , SARS-CoV-2 , Smartphone , Software , Wireless Technology
10.
Molecules ; 26(19)2021 Oct 05.
Article in English | MEDLINE | ID: covidwho-1463768

ABSTRACT

Choanoflagellates are single-celled eukaryotes with complex signaling pathways. They are considered the closest non-metazoan ancestors to mammals and other metazoans and form multicellular-like states called rosettes. The choanoflagellate Monosiga brevicollis contains over 150 PDZ domains, an important peptide-binding domain in all three domains of life (Archaea, Bacteria, and Eukarya). Therefore, an understanding of PDZ domain signaling pathways in choanoflagellates may provide insight into the origins of multicellularity. PDZ domains recognize the C-terminus of target proteins and regulate signaling and trafficking pathways, as well as cellular adhesion. Here, we developed a computational software suite, Domain Analysis and Motif Matcher (DAMM), that analyzes peptide-binding cleft sequence identity as compared with human PDZ domains and that can be used in combination with literature searches of known human PDZ-interacting sequences to predict target specificity in choanoflagellate PDZ domains. We used this program, protein biochemistry, fluorescence polarization, and structural analyses to characterize the specificity of A9UPE9_MONBE, a M. brevicollis PDZ domain-containing protein with no homology to any metazoan protein, finding that its PDZ domain is most similar to those of the DLG family. We then identified two endogenous sequences that bind A9UPE9 PDZ with <100 µM affinity, a value commonly considered the threshold for cellular PDZ-peptide interactions. Taken together, this approach can be used to predict cellular targets of previously uncharacterized PDZ domains in choanoflagellates and other organisms. Our data contribute to investigations into choanoflagellate signaling and how it informs metazoan evolution.


Subject(s)
Choanoflagellata/chemistry , Choanoflagellata/metabolism , Computational Biology/methods , PDZ Domains , Protein Binding , Amino Acid Sequence , Evolution, Molecular , Humans , Phylogeny , Protein Conformation , Signal Transduction , Software , Substrate Specificity
11.
Nat Commun ; 12(1): 3023, 2021 05 21.
Article in English | MEDLINE | ID: covidwho-1454758

ABSTRACT

Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of ß-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.


Subject(s)
Computational Biology/methods , Deep Learning , Proteins/chemistry , Proteins/metabolism , Algorithms , Computer Simulation , Molecular Dynamics Simulation , Myosins , Protein Conformation , Software
12.
BMC Med Res Methodol ; 20(1): 7, 2020 01 13.
Article in English | MEDLINE | ID: covidwho-1455915

ABSTRACT

BACKGROUND: Systematic reviews are vital to the pursuit of evidence-based medicine within healthcare. Screening titles and abstracts (T&Ab) for inclusion in a systematic review is an intensive, and often collaborative, step. The use of appropriate tools is therefore important. In this study, we identified and evaluated the usability of software tools that support T&Ab screening for systematic reviews within healthcare research. METHODS: We identified software tools using three search methods: a web-based search; a search of the online "systematic review toolbox"; and screening of references in existing literature. We included tools that were accessible and available for testing at the time of the study (December 2018), do not require specific computing infrastructure and provide basic screening functionality for systematic reviews. Key properties of each software tool were identified using a feature analysis adapted for this purpose. This analysis included a weighting developed by a group of medical researchers, therefore prioritising the most relevant features. The highest scoring tools from the feature analysis were then included in a user survey, in which we further investigated the suitability of the tools for supporting T&Ab screening amongst systematic reviewers working in medical research. RESULTS: Fifteen tools met our inclusion criteria. They vary significantly in relation to cost, scope and intended user community. Six of the identified tools (Abstrackr, Colandr, Covidence, DRAGON, EPPI-Reviewer and Rayyan) scored higher than 75% in the feature analysis and were included in the user survey. Of these, Covidence and Rayyan were the most popular with the survey respondents. Their usability scored highly across a range of metrics, with all surveyed researchers (n = 6) stating that they would be likely (or very likely) to use these tools in the future. CONCLUSIONS: Based on this study, we would recommend Covidence and Rayyan to systematic reviewers looking for suitable and easy to use tools to support T&Ab screening within healthcare research. These two tools consistently demonstrated good alignment with user requirements. We acknowledge, however, the role of some of the other tools we considered in providing more specialist features that may be of great importance to many researchers.


Subject(s)
Abstracting and Indexing/methods , Software , Systematic Reviews as Topic/methods , Biomedical Research , Delivery of Health Care , Evidence-Based Medicine/methods , Humans , Surveys and Questionnaires
13.
Health Secur ; 19(5): 532-540, 2021.
Article in English | MEDLINE | ID: covidwho-1450358

ABSTRACT

Emergency preparedness systems plan for antibiotic distribution and vaccine administration to respond to public health threats. The arrival of a COVID-19 vaccine underscores the importance of organized logistics for rapid administration to populations. The US Centers for Disease Control and Prevention Cities Readiness Initiative encourages frontline responders from 72 US cities and metropolitan statistical areas to use planning software, such as RealOpt-POD-v8.0.2, to design dispensing operations and predict staffing needs. However, planning can be difficult for local jurisdictions given uncertainty about how long it may take to complete various processes during a dispensing operation, including assessment of countermeasure needs for each person (eg, based on age or pregnancy status) and the careful dispensing of countermeasures and accompanying education. The Union County Health Department in Ohio gathered data on the timing of typical processes for an anthrax medical countermeasures distribution site through a small-scale drill and used these data to parameterize a RealOpt model capable of serving the rural county's population of just over 50,000 people within 24 hours. Results help fill a gap in parameterizing RealOpt-based planning models by highlighting the use of a small-scale drill to inform time estimates, which can be applied to RealOpt as part of county-level planning in advance of larger-scale drills to evaluate dispensing capabilities and effectiveness. The findings provide a methodological basis of future resource typing for adaptable and scalable dispensing, particularly for rural areas. Both the approach and resulting antibiotics dispensing schematic presented here could be tailored to support planning for population-based countermeasure administration to combat emerging pandemics.


Subject(s)
COVID-19 , Disaster Planning , Medical Countermeasures , COVID-19 Vaccines , Female , Humans , Pregnancy , SARS-CoV-2 , Software
14.
Am J Epidemiol ; 190(4): 611-620, 2021 04 06.
Article in English | MEDLINE | ID: covidwho-1447566

ABSTRACT

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.


Subject(s)
Disease Outbreaks/statistics & numerical data , Infections/epidemiology , Basic Reproduction Number , Global Health , Humans , Morbidity/trends , Software
15.
Nat Commun ; 12(1): 5757, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1447304

ABSTRACT

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.


Subject(s)
Data Science/methods , Medical Records Systems, Computerized , Big Data , Computer Security , Data Analysis , Health Information Interoperability , Humans , Information Storage and Retrieval , Software
16.
Gigascience ; 10(9)2021 09 28.
Article in English | MEDLINE | ID: covidwho-1443047

ABSTRACT

BACKGROUND: B-cell immunoglobulin repertoires with paired heavy and light chain can be determined by means of 10X single-cell V(D)J sequencing. Precise and quick analysis of 10X single-cell immunoglobulin repertoires remains a challenge owing to the high diversity of immunoglobulin repertoires and a lack of specialized software that can analyze such diverse data. FINDINGS: In this study, specialized software for 10X single-cell immunoglobulin repertoire analysis was developed. SCIGA (Single-Cell Immunoglobulin Repertoire Analysis) is an easy-to-use pipeline that performs read trimming, immunoglobulin sequence assembly and annotation, heavy and light chain pairing, statistical analysis, visualization, and multiple sample integration analysis, which is all achieved by using a 1-line command. Then SCIGA was used to profile the single-cell immunoglobulin repertoires of 9 patients with coronavirus disease 2019 (COVID-19). Four neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified from these repertoires. CONCLUSIONS: SCIGA provides a complete and quick analysis for 10X single-cell V(D)J sequencing datasets. It can help researchers to interpret B-cell immunoglobulin repertoires with paired heavy and light chain.


Subject(s)
Immunoglobulins/metabolism , Single-Cell Analysis/methods , Software , Antibodies, Monoclonal/chemistry , Antibodies, Monoclonal/genetics , Antibodies, Monoclonal/metabolism , COVID-19/pathology , COVID-19/virology , Humans , Immunoglobulins/chemistry , Immunoglobulins/genetics , SARS-CoV-2/immunology , SARS-CoV-2/isolation & purification
17.
Database (Oxford) ; 20212021 09 29.
Article in English | MEDLINE | ID: covidwho-1443040

ABSTRACT

EpiSurf is a Web application for selecting viral populations of interest and then analyzing how their amino acid changes are distributed along epitopes. Viral sequences are searched within ViruSurf, which stores curated metadata and amino acid changes imported from the most widely used deposition sources for viral databases (GenBank, COVID-19 Genomics UK (COG-UK) and Global initiative on sharing all influenza data (GISAID)). Epitopes are searched within the open source Immune Epitope Database or directly proposed by users by indicating their start and stop positions in the context of a given viral protein. Amino acid changes of selected populations are joined with epitopes of interest; a result table summarizes, for each epitope, statistics about the overlapping amino acid changes and about the sequences carrying such alterations. The results may also be inspected by the VirusViz Web application; epitope regions are highlighted within the given viral protein, and changes can be comparatively inspected. For sequences mutated within the epitope, we also offer a complete view of the distribution of amino acid changes, optionally grouped by the location, collection date or lineage. Thanks to these functionalities, EpiSurf supports the user-friendly testing of epitope conservancy within selected populations of interest, which can be of utmost relevance for designing vaccines, drugs or serological assays. EpiSurf is available at two endpoints. Database URL: http://gmql.eu/episurf/ (for searching GenBank and COG-UK sequences) and http://gmql.eu/episurf_gisaid/ (for GISAID sequences).


Subject(s)
Amino Acid Substitution , Antigens, Viral/chemistry , Epitopes/chemistry , Internet , Metadata , SARS-CoV-2/chemistry , Search Engine , Software , Amino Acids/chemistry , Amino Acids/immunology , Antigens, Viral/immunology , COVID-19/virology , Epitopes/immunology , Humans , SARS-CoV-2/immunology
18.
Database (Oxford) ; 20212021 09 29.
Article in English | MEDLINE | ID: covidwho-1443039

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 that causes coronavirus disease 2019 (COVID-19) disrupted the normal functioning throughout the world since early 2020 and it continues to do so. Nonetheless, the global pandemic was taken up as a challenge by researchers across the globe to discover an effective cure, either in the form of a drug or vaccine. This resulted in an unprecedented surge of experimental and computational data and publications, which often translated their findings in the form of databases (DBs) and tools. Over 160 such DBs and more than 80 software tools were developed, which are uncharacterized, unannotated, deployed at different universal resource locators and are challenging to reach out through a normal web search. Besides, most of the DBs/tools are present on preprints and are either underutilized or unrecognized because of their inability to make it to top Google search hits. Henceforth, there was a need to crawl and characterize these DBs and create a compendium for easy referencing. The current article is one such concerted effort in this direction to create a COVID-19 resource compendium (COVIDium) that would facilitate the researchers to find suitable DBs and tools for their research studies. COVIDium tries to classify the DBs and tools into 11 broad categories for quick navigation. It also provides end-users some generic hit terms to filter the DB entries for quick access to the resources. Additionally, the DB provides Tracker Dashboard, Neuro Resources, references to COVID-19 datasets and protein-protein interactions. This compendium will be periodically updated to accommodate new resources. Database URL: The COVIDium is accessible through http://kraza.in/covidium/.


Subject(s)
COVID-19 , Databases, Factual , Software , Humans , SARS-CoV-2
19.
Int J Med Inform ; 156: 104599, 2021 12.
Article in English | MEDLINE | ID: covidwho-1440101

ABSTRACT

BACKGROUND: An image sharing framework is important to support downstream data analysis especially for pandemics like Coronavirus Disease 2019 (COVID-19). Current centralized image sharing frameworks become dysfunctional if any part of the framework fails. Existing decentralized image sharing frameworks do not store the images on the blockchain, thus the data themselves are not highly available, immutable, and provable. Meanwhile, storing images on the blockchain provides availability/immutability/provenance to the images, yet produces challenges such as large-image handling, high viewing latency while viewing images, and software inconsistency while storing/loading images. OBJECTIVE: This study aims to store chest x-ray images using a blockchain-based framework to handle large images, improve viewing latency, and enhance software consistency. BASIC PROCEDURES: We developed a splitting and merging function to handle large images, a feature that allows previewing an image earlier to improve viewing latency, and a smart contract to enhance software consistency. We used 920 publicly available images to evaluate the storing and loading methods through time measurements. MAIN FINDINGS: The blockchain network successfully shares large images up to 18 MB and supports smart contracts to provide code immutability, availability, and provenance. Applying the preview feature successfully shared images 93% faster than sharing images without the preview feature. PRINCIPAL CONCLUSIONS: The findings of this study can guide future studies to generalize our framework to other forms of data to improve sharing and interoperability.


Subject(s)
Blockchain , Diagnostic Imaging , Humans , Software , X-Rays
20.
J Mass Spectrom ; 56(10): e4782, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1410026

ABSTRACT

The human respiratory system is a highly complex matrix that exhales many volatile organic compounds (VOCs). Breath-exhaled VOCs are often "unknowns" and possess low concentrations, which make their analysis, peak digging and data processing challenging. We report a new methodology, applied in a proof-of-concept experiment, for the detection of VOCs in breath. For this purpose, we developed and compared four complementary analysis methods based on solid-phase microextraction and thermal desorption (TD) tubes with two GC-mass spectrometer (MS) methods. Using eight model compounds, we obtained an LOD range of 0.02-20 ng/ml. We found that in breath analysis, sampling the exhausted air from Tedlar bags is better when TD tubes are used, not only because of the preconcentration but also due to the stability of analytes in the TD tubes. Data processing (peak picking) was based on two data retrieval approaches with an in-house script written for comparison and differentiation between two populations: sick and healthy. We found it best to use "raw" AMDIS deconvolution data (.ELU) rather than its NIST (.FIN) identification data for comparison between samples. A successful demonstration of this method was conducted in a pilot study (n = 21) that took place in a closed hospital ward (Covid-19 ward) with the discovery of four potential markers. These preliminary findings, at the molecular level, demonstrate the capabilities of our method and can be applied in larger and more comprehensive experiments in the omics world.


Subject(s)
Breath Tests/methods , COVID-19/diagnosis , Gas Chromatography-Mass Spectrometry/methods , Volatile Organic Compounds/analysis , Biomarkers/analysis , COVID-19 Testing/methods , Female , Humans , Male , Pilot Projects , SARS-CoV-2/isolation & purification , Software , Solid Phase Microextraction/methods
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