Virology Genomic Resources – Introduction
Infectious diseases and the pathogens that cause them have been at the center of attention for basic science research, medicine, pharmacy and even agriculture for centuries, posing some of the greatest challenges to mankind. Historical records show that these viral agents regularly cause epidemic outbreaks and have a global impact on every aspect of our lives (you can read more about the greatest pandemics here). With the emergence of genomic sequencing, the study of genomic sequences has led to the rising significant of virology genomic resources, which we will review in this blog post.
The current COVID-19 outbreak is not the first and based on the patterns we know, it certainly will not be the last. Neither will it be the last time a coronavirus causes concerns. This pandemic demonstrates how unprepared we are to deal with viruses because there are so many unknowns. Humans regularly monitor only a handful of viruses, mostly those that have been known to cause severe diseases for decades or longer. Other viruses like the coronavirus family as well as many others require significant research and have the potential to cause harm we are unprepared for.
Figure 1. Rate of viral full genomes published on the National Center for Biotechnology Information (NCBI).
On the other hand, this pandemic also shows how scientists, medical professionals and governments around the world are now collectively recording and sharing data like never before. (You can read more about the rate of data collection in COVID-19 pandemic here.)
Sequences of full genomes, metagenomic samples from humans and animal feces or blood, or even specifically amplified regions responsible for key aspects of viral biology can be used to study natural variation and decipher the process of viral evolution or adaptation. We now know that identifying characteristic genomic changes that emerge with time can be linked to various disease manifestations, clinical traits or other “phenotypic” attributes.
In this blog, we will highlight several resources that are available for anyone interested in virology to start learning about their origins, biology and disease manifestations. Many of these resources can be leveraged by educators to explain viruses to students and introduce them to viral genomic data.
Figure 2. Virus Explorer (https://media.hhmi.org/biointeractive/click/virus-explorer/).
The Virus explorer is the perfect tool to learn about these microscopic invaders that hijack the cellular machinery of living organisms. Students can explore the similarities and differences of a variety of viruses by sorting them based on structure, genomic make-up, host range, transmission mechanism, and vaccine availability. Each virus can be further examined to investigate its geographic distribution and prevalence, 3D structure, cross-section, replication cycle, size, and whether it causes disease or has a vaccine against it.
With this interactive interface, students gain a better understanding of viral diversity, the criteria that scientists use to classify viruses, and the global prevalence of viral infections. Check out the interactive website using this link https://media.hhmi.org/biointeractive/click/virus-explorer/
Informational resources like the viral explorer allow us to understand basic concepts about viruses and learn about their biology. However, we live in an age of data, which is increasingly available to study and understand the world around us. Thanks to wide availability and accessibility of data, especially highly detailed genomic data, hundreds of researchers and thousands of students around the world are combining their efforts to organize, mine and understand viruses and their interaction with the human immune system. This process requires reliable data sources and effective utilization of bioinformatics analysis. Here we will discuss several available virology genomic resources that you can start utilizing for virology research and education.
Public Domain Genomic Data (Virology genomic resources)
Figure 3. Multiple Sequence Alignment of the COVID-19 genome
Availability and accessibility of highly detailed data on viral genomes allows researchers and the public anywhere to learn and understand what is going on at the genomic level. To extract meaningful patterns from publicly available data, researchers turn to bioinformatics, a discipline that combines biostatistics, computer science and informatics to make sense of biological data that has been generated by experiments or collected from nature. To extract meaningful insights from such data, one must be proficient at finding patterns and associations by applying data science techniques and algorithms to these large datasets. If you are new to this discipline, take a look at this introductory course on Bioinformatics.
The first step to apply these kinds of data-driven bioinformatics approaches to the study of viruses is to learn about available and relevant data sources. Below, you will find several data collections that are freely available for anyone that wishes to study, analyze, learn, discover and interpret what is going on in the world of viruses and their interaction with a variety of hosts.
National Center for Biotechnology Information (NCBI)
NCBI is the National Center for Biotechnology Information. This database is hosted by the US federal government and is supported by the National Institutes of Health, an agency that helps annotate all of the data that has been submitted by various research groups. On the NCBI portal, you will find genomes, protein sequences and a lot of other information on almost any topic, including virology.
Figure 4. The National Center for Biotechnology Information (NCBI) is one of the one-stop-shops for almost every biology researcher on the planet, you can visit the database using this link https://www.ncbi.nlm.nih.gov/.
NCBI Virus is a community portal dedicated to viral sequence data from RefSeq, GenBank and other NCBI repositories.
Figure 5. NCBI Virus: Find genomic data about various viruses, the coronavirus family or specifically about the novel coronavirus. https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/
NCBI Virus is a great place to explore viral genomic sequences available on NCBI. Easy navigation and basic tools allow you to quickly find the genomes and associated metadata. For example, I can quickly find here genomic sequences of beta coronaviruses and host-specific collections of published genomes.
When looking for a specific set of data, it is important to think how can this data answer a question you might have. What question can you ask? Which dataset will answer the question? What kind of data collections have to be prepared to answer this question in a comprehensive and reliable way?
Figure 6. The NCBI Virus search interface. One of the ways that we can start using datasets is to ask a question. The question can lead us to select specific genomes from the available GenBank accession numbers, for example, the name of their strain, the country where they have collected the host from which host they were collected, country, groups, etc.
What kinds of questions will you ask? With more than 3000 genomics sequences available, I started asking different questions based on similarity. Since we already know about the pathogenesis, origin or replication of a similar coronavirus – say SARS or MERS from the previous outbreaks, we can ask how this novel coronavirus differs from the past outbreak-causing pathogens. By looking at the differences, we will be able to think about different impacts of the found differences on virus characteristics.
These types of questions or comparisons require detailed and complete “metadata”. NCBI is not the only repository that shares genomic sequencing data on viruses. IN fact, one of the challenges is that NCBI takes a substantial time to validate submitted sequences and make them available. This issue is addressed by the next repository in our review.
Global Epidemic Data Repository (GISAID)
The GISAID Initiative started out by promoting the international sharing of all influenza virus sequences, related clinical and epidemiological data associated with human viruses, and geographical as well as species-specific data associated with avian and other animal viruses, to help researchers understand how the viruses evolve, spread and potentially become pandemics.
This platform was launched on the occasion of the Sixty-first World Health Assembly in May 2008. Created as an alternative to the public domain sharing model, GISAID’s sharing mechanism took into account the concerns of Member States by providing a publicly accessible database designed by scientists for scientists, to improve the sharing of influenza data. Since its launch GISAID plays an essential role in the sharing of data among the WHO Collaborating Centers and National Influenza Centers for the bi-annual influenza vaccine virus recommendations by the WHO Global Influenza Surveillance and Response System (GISRS).
Figure 7. The Initiative ensures that open access to data in GISAID is provided free-of-charge and to everyone, provided individuals can identify themselves and agree to uphold the GISAID sharing mechanism governed through its Database Access Agreement.
To get access to this repository, you have to register to receive your personal access credentials to the GISAID platform. To do so, you must first identify yourself and complete the registration form. This information helps GISAID protect the use of your identity and the integrity of its user base. To start, you can register for free using this link: https://www.gisaid.org/registration/register/
Based on comparative analysis of genomic sequences from previous and current virus samples, we can derive how the current genome is different – as well as start thinking about what implications these differences in sequences might have on various properties of its proteins. These viral proteins interact with the host proteins by entering the cell and causing what we call “disease”.
There are several ways we can study host response to viral entry and replication. One way to learn about subcellular processes that are involved in this response is to generate gene expression data upon viral entry as well as timepoints after the virus entry. Gene expression data can be found in different formats (you can learn more about transcriptomic data here: https://edu.t-bio.info/collections/transcriptomics/
NCBI Gene expression omnibus (GEO)
One of the places to look for data that can provide insight and answers related to the expression of genes that can characterize the process of infection is the NCBI “Gene Expression Omnibus” (also simply known as GEO). GEO is an international public repository that archives and freely distributes microarray, next-generation sequencing, and other forms of high-throughput functional genomics data submitted by the research community. You can navigate to this repository by following this link: https://www.ncbi.nlm.nih.gov/geo/
While I was looking for places to find the data that can reveal how viruses infect cells and what is a typical immune response to viral entry, I found an interesting collection of experimental data. One such dataset was deposited on Nov 30, 2019. This dataset is from transcriptomic analysis of circRNAs/miRNAs/mRNAs in response to the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection:
Figure 8. Gene expression omnibus. The GEO database is full of interesting datasets and provides a user-friendly interface that allows users to query, locate, review and download studies and gene expression profiles of interest.
(Example dataset from MERS infection: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139516)
In this study, the authors studied the relevance of circRNAs in MERS-CoV infection and found 7 key Differentially Expressed circRNAs involved in various biological processes that can characterize MERS-CoV infection. Not having access to a lab where such viruses can be studied experimentally, we can still analyze data from past experiments. These studies can reveal important biological mechanisms the virus uses to overcome cellular immune responses.
NIAID is the National Institute for Allergies and Infectious Diseases, an agency that specializes in the topic of our discussion. Let’s take a look at the portal they developed to make virology data and tools available.
Virus Pathogen Resource (VIPR)
The Influenza Research Database (IRD) and Virus Pathogen Resource (ViPR) are freely available, NIAID-funded virology genomic resources that support the research of viral pathogens in the NIAID Category A-C Priority Pathogen lists and those causing (re)emerging infectious diseases.
Figure 9. ViPR: Virus Pathogen Resources includes information about Sequences & strains, Immune epitopes, 3D protein structures, Host Factor Data, Antiviral Drugs, Plasmid Data and tools to analyze and perform Sequence Alignment, Phylogenetic Tree, Sequence Variation (SNP), Metadata-driven Comparative Analysis, BLAST & a tool called VIGOR4 Genome Annotator.
IRD and ViPR integrate data from external sources (GenBank, UniProt, Immune Epitope Database, Protein Data Bank, etc.), direct submissions, and internal curation and analysis pipelines, and provide a suite of bioinformatics analysis and visualization tools to expedite virology research.
Omics Data Analysis tools for Virology Studies
As you saw in previous sections, many of the data repositories and databases seek to provide basic navigation and query tools to help find specific datasets. These can be visual or filtering methods. One of the challenges with analyzing such detailed data is its size and complexity that requires multiple steps to process before an analysis is meaningful.
Commonly used tools for analysis of viral genomes include multiple sequence alignment (MSA), phylogenetic analysis, analysis of global and local similarity as well as a variety of annotation and visualization tools to characterize genomic elements and the variation they contain.
Many of these tools are available in a “command-line” version only. To use these tools, one has to install executable scripts on a computer or cluster and run them by calling their commands and options. Significant computational resources are required to compare such large datasets and even small numbers of genomes can take time to process. Many of the tools were designed with specific dataset or type of comparison in mind, so it is easy to misinterpret results or arrive at incorrect conclusions by running tools with improper settings or on incorrect datasets.
Figure 10. The T-BioInfo web-based platform for bioinformatics analysis. (for non-bioinformaticians)
Several tools make this process of finding, testing and deploying such commands on large-scale datasets easy even for a non-technical user. A commonly used open-source framework is Galaxy, which has its own set of virology-related tools and can be deployed on your local machine or server cluster. Another more user-friendly tool is the T-BioInfo platform developed at the Tauber Bioinformatics Research Center at University of Haifa in Israel. This set of tools is perfect for someone looking to make the most of the virology genomic resources we already discussed.
The T- Bioinfo Platform is an AI guided platform that provides a combined hands-on learning experience during the analysis stages which enables biologists to process and analyze large-scale & complex analyses for various -omics data types, including Next Generation Sequencing (NGS), Mass-Spectroscopy (Mass-Spec), and Structural Biology. Sections of the platform offer data-type specific tools for processing, analysis and interpretation of detected signals using standard and innovative analytical methods.
Here, I will review the different sections of the platform related to virology studies. From personal experience, I believe that this cloud-based and user-friendly bioinformatics platform can be very helpful in understanding the logic of Virology related analysis even for someone with little to no background in the field of bioinformatics.
Figure 11. The Transcriptomics Data analysis pipelines. Each analysis pipeline is a path in the graph (network) across the analysis stages. The analysis stages are denoted as vertical strips containing computational procedures with the same purpose (like “mapping of reads on the genome”) but based on different algorithms.
Analysis of Transcriptomic data can help study:
- Expression of genes in response to viral infections
- Comparison between immune responses at gene and isoform level
- Analysis of pathway activation and regulation
- Changes of expression during disease progression
In the context of current outbreak, I am sharing a recent article on the architecture of SARS-Cov-2 transcriptome that revealed many #viral RNAs with unknown functions and modifications. This high-resolution map of SARS-CoV-2 sheds more light on how the virus replicates and how it escapes the human defense system. You can read the publication here : https://www.biorxiv.org/content/10.1101/2020.03.12.988865v2.full.pdf
Data Association and Integration
Figure 12. The differences in the viral genomes can be studied from an evolutionary standpoint and that means that we can study genome-wide and then a protein specific variation that can indicate a common origin or characterized relationship and even arrive at an evolutionary distance between samples which of course is very important in the context of origin.
The Data integration & Modelling section can be used for :
- Integrate multi-omics data
- Analysis of phylogenetic trees of Viral genomes
- Evolutionary studies of mutation variants
- Genome wide association studies
Modeling of Viral Replication and Infection Process
The dedicated virology section is designed for :
- Modelling viral process like replication, transfection, particle production
- Association with real data (Fitting)
- Design of models for Simulations
- Network analysis of parameters
Figure 13. T-BioInfo mathematical modeling and simulation section for the study of virus specific processes. These in silico modelling studies can help identify important factors that affect replication interference and outcomes of viral infection.
Analysis of GFP signal in Microscopy Data
The cell culture image section helps in the analysis of
- Microscopic images
- Automated detection of cell boundaries
- Automated quantification of GFP
Figure 14. Analysis of microscopy images where GFP (green fluorescent protein) signal can be used to quantify viral particles in cell culture.
This section can be used to analyze cell microscopy to find patterns and link them to other types of -omics data.
Screening, Superimposition and Docking of 3D structures
The structural biology sections were designed for:
- Superimposition and physio-chemical analysis of structures and then
- comparing the structural topologies,
- Screening and docking of large libraries of small molecules.
Figure 15: Screening and Docking of molecules on the T-BioInfo platform.
As we move from genomics to proteins and learn from these datasets, the next step will point towards the need for silico drug design where molecular structures and their physical chemical characteristics can be compared at the structural level and are going to be important for both vaccine and antiviral drug design.
Visualization of Virology Data
Visualizing complex and rich genomic data is essential for interpretation and hypothesis generation. Clear visualization tools can also be a valuable aid in communicating discoveries. A key challenge in data-driven research is to discover unexpected patterns and to formulate hypotheses in an unbiased manner in vast amounts of genomic and other associated data. Several portals utilize powerful and effective visualization tools to help link and explore data on viruses. Here are a few of them:
Nextstrain is an excellent web portal that provides a regularly updated view of publicly available data alongside powerful analytic and visualization tools to harness the scientific and public health potential of pathogen genome data. This data can be useful for the epidemiological understanding and improve outbreak response.
Figure 16. If you want to see the pandemic in action, you can visit this link https://nextstrain.org/ncov/global. This virology genomic resources also allows to download the data used in visualization.
Since viral genomes respond to pressures by accumulating mutations, these single-nucleotide or multi-nucleotide changes can be used as markers of transmission origin. Typically, closely related genomes indicate closely related origins or similar infection characteristics.
On the top right panel under the transmission, the visual shows different sequences that emerge in specific locations. Nextstrain offers us a lot of granularity about the relationships and how they can help us interpret an association between these different sequences. By reconstructing a phylogeny we can learn about important epidemiological phenomena such as spatial spread, introduction timings, and epidemic growth rate.
For example, look at this interesting paper published in PNAS. The authors analyzed the COVID-19 Genetic network to provide a ‘snapshot’ of pandemic’s origin by analyzing the first 160 complete virus genomes sequenced from human patients. In this study, the scientists have mapped some of the original spread of the new coronavirus through its mutations, which creates different viral lineages.
Figure 17. Phylogenetic network of 160 SARS-CoV-2 genomes. Read the full publication here: https://www.pnas.org/content/early/2020/04/07/2004999117
Since this study was conducted, the research team has extended its analysis to 1,001 viral genomes. While that article is yet to be peer-reviewed, the author says the latest work suggests that “ the first infection and spread among humans of COVID-19 occurred between mid-September and early December”. (https://www.sciencedaily.com/releases/2020/04/200409085644.htm)
One of the challenges is to link mutations we can find in viral genomes with their functionality. In order to link different levels of information starting from genomic variants to the protein amino acid sequence and understand the impact on protein structure, we developed a data visualization tool for Sequence Navigation (SeqNav). SeqNav is hosted on the T-BioInfo platform where it is used to visualize the outputs from evolutionary analysis using CirSeq and NGS data. The tool will show genome variants, amino acid changes, and even protein structure changes allowing for a full picture to be brought to the researcher. SeqNav is already preloaded with several virology genomic resources that illustrate the power of visualization for these types of datasets.
Figure 18. The visual interface of Seqnav. The tool is currently available free to learn the genome variations, amino acid variations, and even protein structure changes. You can access the tool by visiting this link https://t-bio.info/seqnav/
To aid with this complex analysis the interface is divided into four main sections. The menu on top, allowing one to change the bin size or the number of nucleotides present within a bin, the Rate of Mutation visualized, the Fitness type if a fitness job was performed, and the type of mutation (whether synonymous or nonsynonymous).
Several demo datasets can be seen to compare and contrast various level of mutation fitness and the impact of the genomic variation on protein structure:
Specific Resources for COVID-19 Research.
With so many tools, data repositories and virology genomic resources, you might be wondering if the best place to start is something dedicated to COVID-19. Below are a few more examples of COVID-19 specific initiatives and resources you can review:
The world has seen growth not only in data generation but also in published research. Thousands of research papers, articles and blog posts have been published on the new coronavirus in a matter of months, with over thousand appearing on the open preprint sites bioRxiv and medRxiv alone.
Thanks to the teams at the Allen Institute for AI and Semantic Scholar for the COVID-19 Open Research Dataset (CORD-19), a corpus of tens of thousands of papers related to past and present coronaviruses were assembled into a structured dataset. The CORD-19 portal continues to be updated on a weekly basis and is already being used by multiple research groups to understand what kinds of conclusions are being made about COVID-19.
Figure 19. CoViz: Helping scientists visualize and explore COVID-19 literature with AI. image source: https://allenai.org
Followed by the database the collaboration has now come up with another handy virology genomic resource “CoViz”, an AI-powered graph visualization tool that enables quick and intuitive exploration of associations between scientific concepts focusing on proteins, genes, cells, drugs, and diseases, which are fundamental to the study of the virus.
For example, recently associations between drugs and adverse drug responses have been published by mining publications using AI:
Figure 20. Boosting innovation and scientific discovery with NLP and data mining. Follow this link to read the full article.
COVID-19 DATA PORTAL by EMBL-EBI
The European Molecular Biology Laboratory (EMBL) at the Wellcome Genome Campus in Hinxton, Cambridge, UK, is one of the world’s largest concentrations of scientific and technical expertise in genomics. Recently, this European consortium launched the EBI-EMBL COVID-19 Data Portal to bring together relevant datasets and analysis tools in an effort to accelerate coronavirus research. This portal enables researchers to upload, access and analyze COVID-19 related reference data and special virology genomic resources as part of the wider European COVID-19 Data Platform.
Figure 21. A recently launched EMBL-EBI COVID-19 Data Portal allows researchers across the globe to access, analyse & share data on SARS-CoV-2.
Vinod Scaria RNA Biology: Genepi
Dr. Vinod Scaria lab at IGIB compiled a list of resources for Genomes & Genetic Epidemiology of SARS-nCoV-2. Including resources like the COVID-19 Genomepedia. The COVID-19 Genomepedia is an integrative and searchable resource systematically collecting details of SARS-nCoV-2 genomes sequenced and reported from across the globe. You can view the full list here.
Figure 22: Website with India-specific resources on COVID-19 genome testing and repositories of publicly available data from around the world.
CoronaVIR is an organized collection of information related to novel strain of coronavirus, i.e. SARS-CoV-2 prepared by the Indraprastha Institute of Information Technology, New Delhi. CoronaVIR is an integrated multi-omics repository dedicated to current genomic, proteomic, diagnostic and therapeutic knowledge about coronaviruses particularly the recent strain, i.e. SARS-CoV-2 or 2019-nCoV. This web resource will be helpful for the researchers engaged in the development of therapies and drugs for the COVID-19.
Figure 23. CoronaVIR repository by Indian Institute of Technology (I.I.T.), New Delhi. https://webs.iiitd.edu.in/raghava/coronavir/index.html
Oftentimes, the situation can become very confusing – experts and non-experts alike offer their view of events and conflicting theories can quickly emerge. In many cases, we have no other options but to take sides and hope we made the right decision. But teaching about scientific research, we have to base our opinions on available data and here virology genomic resources become invaluable. Thankfully, when it comes to viruses in general and specifically COVID-19, data is being made publicly available in greater numbers than ever before.
While it might be extremely difficult to learn new things when there are many unknowns, long-term trends suggest times like these can be an opportunity as well. As educators, researchers or students, we can take this opportunity to explore virology genomic resources and apply data analysis tools to these publicly available repositories on the current epidemic. At the same time, we should be careful simply taking interpretation into our own hands, recognizing that in-depth knowledge of virology is critical for appropriate interpretation.
For those interested to learn about bioinformatics, we welcome you to review a recent free webinar we held to illustrate how some of the mentioned tools and databases can be used to mine virology genomic resources from the current and previous outbreaks:
For a more detailed and practical learning experience, consider joining our upcoming program on “Bioinformatics for Infectious Diseases”
Key Links, Virology Genomic Resources and References:
- Tauber Bioinformatics Research Center at University of Haifa, israel. http://tauber-bioinfo.haifa.ac.il
- The T-BioInfo Bioinformatics platform https://server.t-bio.info
- Sequence Navigator (SeqNav) https://t-bio.info/seqnav/
- The architecture of SARS-CoV-2 transcriptome. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.12.988865.
- 20 of the worst epidemics and pandemics in history. By Owen Jarus – Live Science Contributor, All About History March 20, 2020. https://www.livescience.com/worst-epidemics-and-pandemics-in-history.html
- COVID-19 Genomes, Elia Brodsky on Medium. https://medium.com/@eliabrodsky/covid-19-genomics-cb99ee2b7736
- National Center for Biotechnology Information (NCBI). https://www.ncbi.nlm.nih.gov/
Other useful virology genomic resources for COVID-19
- http://genome.ucsc.edu/covid19.html at UC Santa Cruz Genomics Institute
- https://datascience.nih.gov/covid-19-open-access-resources#computational at National Institute of Health
- https://www.nih.gov/health-information/coronavirus at National Institute of Health
- https://www.ebi.ac.uk/covid-19 at European Bioinformatics Institute
- https://artic.network/ncov-2019 at Wellcome Trust The ARTIC network is making available a set of materials to assist groups in sequencing the virus including a set of primers, laboratory protocols, bioinformatics tutorials and datasets focused around the use of the portable Oxford Nanopore MinION sequencer.
- https://github.com/galaxyproject/SARS-CoV-2 at Github for Ongoing analysis of COVID-19 using Galaxy, BioConda and public research infrastructures (https://covid19.galaxyproject.org)
- https://github.com/CDCgov/SARS-CoV-2_Sequencing at Github A collection of sequencing protocols and bioinformatic resources for SARS-CoV-2 sequencing.
- https://github.com/BU-ISCIII/SARS-Cov2_analysis at Github Documentation for analyzing SARS-Cov2 NGS samples.
- http://covid.portugene.com/cgi-bin/COVid_home.cgi CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2
- https://covid.pages.uni.lu/map_curation at Université du Luxembourg Resources and practices to develop a COVID-19 disease map aimed to set up a map of mechanisms focused on host-pathogen interactions, specific to the SARS-CoV-2 virus.
- https://www.wikipathways.org/index.php/Portal:Disease/COVIDPathways at Wiki A special subset of disease pathways highlighted during the current and the content is released under a CC0 waiver to be freely used, reused and distributed.
- http://hipathia.babelomics.org/covid19/ at Clinical Bioinformatics Area a web tool that implements a mechanistic model of human signaling for the interpretation of the consequences of the combined changes of gene expression levels and/or genomic mutations.
- https://gladstone-bioinformatics.shinyapps.io/shiny-covidpathways/ at Gladstone Institutes The WikiPathways team at Gladstone Institutes is searching the literature for pathway figures related to COVID-19. This interactive tool lets you filter, search and view their findings. Current Stats : Figures: 221, Papers: 189, Total genes: 4,818, Unique genes: 1,523
- https://biit.cs.ut.ee/covid/#/trajectories at Bioinformatics, Algorithms and Data Mining Group (BIITs) A log plot based visualization of COVID-19 Positive cases.
- https://genexa.ch/sars2-bioinformatics-resources/ at genexa provides resources useful for SARS-CoV-2 (COVID-19) bioinformatics & genomics.