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BioTech
Special Issues
Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19
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Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19

A special issue of BioTech (ISSN 2673-6284). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 21406

Special Issue Editors

Prof. Dr. Mario Cannataro

E-Mail Website
Guest Editor
Department of Medical and Surgical Sciences, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
Interests: bioinformatics, parallel computing, artificial intelligence, data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Agapito

E-Mail Website
Guest Editor
Department of Legal, Historical, Economic and SocialSciences, University "Magna Græcia" of Catanzaro, 88100 Catanzaro, Italy
Interests: distributed computing; bioinformatics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

COVID-19 poses many challenges at biological, medical, and epidemiological levels, such as: investigating the molecular basis of the disease, tracing virus mutations, SARS-CoV-2 genomes and variants databases, host–virus interactions, vaccine development, drug repurposing, the integrated collection and analysis of basic epidemiological data about the spread of the infection, collaborative infrastructures to analyze electronic health records (EHRs) on a large scale, large-scale testing and tracing of people, infectious disease modelling, COVID-19 epidemiology and public health, effects of the pandemic at the emotional and behavioral level, the impact of the pandemic on remote working, etc.

Each of these challenges may benefit from high-performance computing infrastructures and novel software pipelines, including bioinformatics for basic research, computer simulation for epidemiology and disease modelling, and big data integration, for example, for connecting disease data with environmental and climate data, mobile applications, wearable sensors to trace people or to collect health data, telemedicine infrastructures to collect health data and to remotely assist COVID-19 patients with mild symptoms, data science and data analytics solutions for statistical and data mining analysis of data at several levels, including mood and sentiment analysis of long-time quarantined people as well as care givers and healthcare personnel.

This Special Issue invites submissions from bioinformaticians, data scientists, biologists, as well as medical doctors and epidemiologists, to present (high-performance) computing methods for deciphering COVID-19 and interdisciplinary applications for fighting the COVID-19 pandemic. Position papers discussing emerging solutions and future directions in the computer-based management of pandemic are also welcomed. Topics including but not limited to:

  • SARS-CoV-2 databases (genome, variants, structures, host–virus interactions)
  • Bioinformatics pipelines for SARS-CoV-2 virus data analysis (sequences, structures, interactions, infection mechanisms)
  • Bioinformatics pipelines for COVID-19 drugs repurposing and vaccines design
  • Computing infrastructures for enabling COVID-19 collaborative research
  • Computing methods for tracing and tracking COVID-19 patients and their contacts
  • Exploiting electronic health records data for COVID-19 research
  • Data science for COVID-19 clinical processes (diagnosis, treatment, prognosis, follow-up)
  • Computing infrastructures for COVID-19 data collection, integration, sharing, and visualization
  • Telemedicine infrastructures and sensors for collecting public health citizen data
  • Telemedicine for the remote support of COVID-19 patients (monitoring, diagnosis, treatment, follow-up, tele-presence)
  • Data science for public health decision making
  • Data science for relating COVID-19 data with environmental, pollution, and climate data
  • Modelling and simulation of SARS-CoV-2 virus diffusion
  • Epidemiology, virology, public health, and COVID-19
  • Network-based analysis for epidemics
  • Computing infrastructures for collecting quarantined citizen emotion data
  • Sentiment analysis software pipelines for mood and emotion analysis during the COVID-19 pandemic

Prof. Dr. Mario Cannataro
Dr. Giuseppe Agapito
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. BioTech is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Editorial

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3 pages, 180 KiB  
Editorial
Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19—Editorial
by Mario Cannataro and Giuseppe Agapito
BioTech 2022, 11(4), 47; https://doi.org/10.3390/biotech11040047 - 15 Oct 2022
Viewed by 1795
Abstract
The COVID-19 disease (Coronavirus Disease 19), caused by the SARS-CoV-2 virus (Severe Acute Respiratory Syndrome Coronavirus 2), has posed many challenges worldwide at various levels, with special focus to the biological, medical, and epidemiological ones [...] Full article
(This article belongs to the Special Issue Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19)

Research

Jump to: Editorial

14 pages, 787 KiB  
Article
Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration
by Ileana Scarpino, Chiara Zucco, Rosarina Vallelunga, Francesco Luzza and Mario Cannataro
BioTech 2022, 11(3), 41; https://doi.org/10.3390/biotech11030041 - 03 Sep 2022
Cited by 9 | Viewed by 3848
Abstract
Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study different topic modeling techniques are compared, as Latent Dirichlet Allocation (LDA) [...] Read more.
Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study different topic modeling techniques are compared, as Latent Dirichlet Allocation (LDA) and topic modeling based on BERT transformer, to extract meaningful insights in the Italian narration of COVID-19 pandemic. In particular, the main focus was the characterization of Post-acute Sequelae of COVID-19, (i.e., PASC) writings as opposed to writings by health professionals and general reflections on COVID-19, (i.e., non-PASC) writings, modeled as a semi-supervised task. The results show that the BERTopic-based approach outperforms the LDA-base approach by grouping in the same cluster the 97.26% of analyzed documents, and reaching an overall accuracy of 91.97%. Full article
(This article belongs to the Special Issue Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19)
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22 pages, 2770 KiB  
Article
Application of CCTV Methodology to Analyze COVID-19 Evolution in Italy
by Marianna Milano, Giuseppe Agapito and Mario Cannataro
BioTech 2022, 11(3), 33; https://doi.org/10.3390/biotech11030033 - 11 Aug 2022
Cited by 1 | Viewed by 2274
Abstract
Italy was one of the European countries most afflicted by the COVID-19 pandemic. From 2020 to 2022, Italy adopted strong containment measures against the COVID-19 epidemic and then started an important vaccination campaign. Here, we extended previous work by applying the COVID-19 Community [...] Read more.
Italy was one of the European countries most afflicted by the COVID-19 pandemic. From 2020 to 2022, Italy adopted strong containment measures against the COVID-19 epidemic and then started an important vaccination campaign. Here, we extended previous work by applying the COVID-19 Community Temporal Visualizer (CCTV) methodology to Italian COVID-19 data related to 2020, 2021, and five months of 2022. The aim of this work was to evaluate how Italy reacted to the pandemic in the first two waves of COVID-19, in which only containment measures such as the lockdown had been adopted, in the months following the start of the vaccination campaign, the months with the mildest weather, and the months affected by the new COVID-19 variants. This assessment was conducted by observing the behavior of single regions. CCTV methodology allows us to map the similarities in the behavior of Italian regions on a graph and use a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. The results depict that the communities formed by Italian regions change with respect to the ten data measures and time. Full article
(This article belongs to the Special Issue Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19)
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15 pages, 12941 KiB  
Article
Characterisation of Omicron Variant during COVID-19 Pandemic and the Impact of Vaccination, Transmission Rate, Mortality, and Reinfection in South Africa, Germany, and Brazil
by Carolina Ribeiro Xavier, Rafael Sachetto Oliveira, Vinícius da Fonseca Vieira, Marcelo Lobosco and Rodrigo Weber dos Santos
BioTech 2022, 11(2), 12; https://doi.org/10.3390/biotech11020012 - 26 Apr 2022
Cited by 18 | Viewed by 4467
Abstract
Several variants of SARS-CoV-2 have been identified in different parts of the world, including Gamma, detected in Brazil, Delta, detected in India, and the recent Omicron variant, detected in South Africa. The emergence of a new variant is a cause of great concern. [...] Read more.
Several variants of SARS-CoV-2 have been identified in different parts of the world, including Gamma, detected in Brazil, Delta, detected in India, and the recent Omicron variant, detected in South Africa. The emergence of a new variant is a cause of great concern. This work considers an extended version of an SIRD model capable of incorporating the effects of vaccination, time-dependent transmissibility rates, mortality, and even potential reinfections during the pandemic. We use this model to characterise the Omicron wave in Brazil, South Africa, and Germany. During Omicron, the transmissibility increased by five for Brazil and Germany and eight for South Africa, whereas the estimated mortality was reduced by three-fold. We estimated that the reported cases accounted for less than 25% of the actual cases during Omicron. The mortality among the nonvaccinated population in these countries is, on average, three to four times higher than the mortality among the fully vaccinated. Finally, we could only reproduce the observed dynamics after introducing a new parameter that accounts for the percentage of the population that can be reinfected. Reinfection was as high as 40% in South Africa, which has only 29% of its population fully vaccinated and as low as 13% in Brazil, which has over 70% and 80% of its population fully vaccinated and with at least one dose, respectively. The calibrated models were able to estimate essential features of the complex virus and vaccination dynamics and stand as valuable tools for quantifying the impact of protocols and decisions in different populations. Full article
(This article belongs to the Special Issue Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19)
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15 pages, 1906 KiB  
Article
High Performance Integration Pipeline for Viral and Epitope Sequences
by Tommaso Alfonsi, Pietro Pinoli and Arif Canakoglu
BioTech 2022, 11(1), 7; https://doi.org/10.3390/biotech11010007 - 21 Mar 2022
Cited by 3 | Viewed by 2730
Abstract
With the spread of COVID-19, sequencing laboratories started to share hundreds of sequences daily. However, the lack of a commonly agreed standard across deposition databases hindered the exploration and study of all the viral sequences collected worldwide in a practical and homogeneous way. [...] Read more.
With the spread of COVID-19, sequencing laboratories started to share hundreds of sequences daily. However, the lack of a commonly agreed standard across deposition databases hindered the exploration and study of all the viral sequences collected worldwide in a practical and homogeneous way. During the first months of the pandemic, we developed an automatic procedure to collect, transform, and integrate viral sequences of SARS-CoV-2, MERS, SARS-CoV, Ebola, and Dengue from four major database institutions (NCBI, COG-UK, GISAID, and NMDC). This data pipeline allowed the creation of the data exploration interfaces VirusViz and EpiSurf, as well as ViruSurf, one of the largest databases of integrated viral sequences. Almost two years after the first release of the repository, the original pipeline underwent a thorough refinement process and became more efficient, scalable, and general (currently, it also includes epitopes from the IEDB). Thanks to these improvements, we constantly update and expand our integrated repository, encompassing about 9.1 million SARS-CoV-2 sequences at present (March 2022). This pipeline made it possible to design and develop fundamental resources for any researcher interested in understanding the biological mechanisms behind the viral infection. In addition, it plays a crucial role in many analytic and visualization tools, such as ViruSurf, EpiSurf, VirusViz, and VirusLab. Full article
(This article belongs to the Special Issue Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19)
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13 pages, 4698 KiB  
Article
VirusLab: A Tool for Customized SARS-CoV-2 Data Analysis
by Pietro Pinoli, Anna Bernasconi, Anna Sandionigi and Stefano Ceri
BioTech 2021, 10(4), 27; https://doi.org/10.3390/biotech10040027 - 06 Nov 2021
Cited by 4 | Viewed by 4626
Abstract
Since the beginning of 2020, the COVID-19 pandemic has posed unprecedented challenges to viral data analysis and connected host disease diagnostic methods. We propose VirusLab, a flexible system for analysing SARS-CoV-2 viral sequences and relating them to metadata or clinical information about the [...] Read more.
Since the beginning of 2020, the COVID-19 pandemic has posed unprecedented challenges to viral data analysis and connected host disease diagnostic methods. We propose VirusLab, a flexible system for analysing SARS-CoV-2 viral sequences and relating them to metadata or clinical information about the host. VirusLab capitalizes on two existing resources: ViruSurf, a database of public SARS-CoV-2 sequences supporting metadata-driven search, and VirusViz, a tool for visual analysis of search results. VirusLab is designed for taking advantage of these resources within a server-side architecture that: (i) covers pipelines based on approaches already in use (ARTIC, Galaxy) but entirely cutomizable upon user request; (ii) predigests analysis of raw sequencing data from different platforms (Oxford Nanopore and Illumina); (iii) gives access to public archives datasets; (iv) supplies user-friendly reporting – making it a tool that can also be integrated into a business environment. VirusLab can be installed and hosted within the premises of any organization where information about SARS-CoV-2 sequences can be safely integrated with information about hosts (e.g., clinical metadata). A system such as VirusLab is not currently available in the landscape of similar providers: our results show that VirusLab is a powerful tool to generate tabular/graphical and machine readable reports that can be integrated in more complex pipelines. We foresee that the proposed system can support many research-oriented and therapeutic scenarios within hospitals or the tracing of viral sequences and their mutational processes within organizations for viral surveillance. Full article
(This article belongs to the Special Issue Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19)
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