Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering

All biological species undergo change over time due to the evolutionary process. These changes can occur rapidly and unpredictably. Due to their high potential to spread quickly, it is critical to be able to monitor changes and detect viral variants. Phylogenetic trees serve as good methods to study evolutionary relationships. Complex big data in biomedicine is plentiful in regards to viral data. In this paper, we analyze phylogenetic trees with reference to viruses and conduct dynamic programming using the Smith-Waterman algorithm, followed by hierarchical clustering. This methodology constitutes an intelligent approach for data mining, paving the way for examining variations in SARS-Cov-2, which in turn can help to discover knowledge potentially useful in biomedicine. © 2022 IEEE.

ViralFlow: A Versatile Automated Workflow for SARS-CoV-2 Genome Assembly, Lineage Assignment, Mutations and Intrahost Variant Detection.

The COVID-19 pandemic is driven by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) that emerged in 2019 and quickly spread worldwide. Genomic surveillance has become the gold standard methodology used to monitor and study this fast-spreading virus and its constantly emerging lineages. The current deluge of SARS-CoV-2 genomic data generated worldwide has put additional pressure on the urgent need for streamlined bioinformatics workflows. Here, we describe a workflow developed by our group to process and analyze large-scale SARS-CoV-2 Illumina amplicon sequencing data. This workflow automates all steps of SARS-CoV-2 reference-based genomic analysis: data processing, genome assembly, PANGO lineage assignment, mutation analysis and the screening of intrahost variants. The pipeline is capable of processing a batch of around 100 samples in less than half an hour on a personal laptop or in less than five minutes on a server with 50 threads. The workflow presented here is available through Docker or Singularity images, allowing for implementation on laptops for small-scale analyses or on high processing capacity servers or clusters. Moreover, the low requirements for memory and CPU cores and the standardized results provided by ViralFlow highlight it as a versatile tool for SARS-CoV-2 genomic analysis.

Circulation and Evolution of SARS-CoV-2 in India: Let the Data Speak.

The COVID-19 pandemic is a global challenge that impacted 200+ countries. India ranks in the second and third positions in terms of number of reported cases and deaths. Being a populous country with densely packed cities, SARS-CoV-2 spread exponentially. India sequenced ≈0.14% isolates from confirmed cases for pandemic surveillance and contributed ≈1.58% of complete genomes sequenced globally. This study was designed to map the circulating lineage diversity and to understand the evolution of SARS-CoV-2 in India using comparative genomics and population genetics approaches. Despite varied sequencing coverage across Indian States and Union Territories, isolates belonging to variants of concern (VoC) and variants of interest (VoI) circulated, persisted, and diversified during the first seventeen months of the pandemic. Delta and Kappa lineages emerged in India and spread globally. The phylogenetic tree shows lineage-wise monophyletic clusters of VoCs/VoIs and diversified tree topologies for non-VoC/VoI lineages designated as 'Others' in this study. Evolutionary dynamics analyses substantiate a lack of spatio-temporal clustering, which is indicative of multiple global and local introductions. Sites under positive selection and significant variations in spike protein corroborate with the constellation of mutations to be monitored for VoC/VoI as well as substitutions that are characteristic of functions with implications in virus-host interactions, differential glycosylation, immune evasion, and escape from neutralization.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Genomic and proteomic mutation landscapes of SARS-CoV-2.

The ongoing pandemic caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), affects thousands of people every day worldwide. Hence, drugs and vaccines effective against all variants of SARS-CoV-2 are crucial today. Viral genome mutations exist commonly which may impact the encoded proteins, possibly resulting to varied effectivity of detection tools and disease treatment. Thus, this study surveyed the SARS-CoV-2 genome and proteome and evaluated its mutation characteristics. Phylogenetic analyses of SARS-CoV-2 genes and proteins show three major clades and one minor clade (P6810S; ORF1ab). The overall frequency and densities of mutations in the genes and proteins of SARS-CoV-2 were observed. Nucleocapsid exhibited the highest mutation density among the structural proteins while the spike D614G was the most common, occurring mostly in genomes outside China and United States. ORF8 protein had the highest mutation density across all geographical areas. Moreover, mutation hotspots neighboring and at the catalytic site of RNA-dependent RNA polymerase were found that might challenge the binding and effectivity of remdesivir. Mutation coldspots may present as conserved diagnostic and therapeutic targets were found in ORF7b, ORF9b, and ORF14. These findings suggest that the virion's genotype and phenotype in a specific population should be considered in developing diagnostic tools and treatment options.

The International Virus Bioinformatics Meeting 2020.

The International Virus Bioinformatics Meeting 2020 was originally planned to take place in Bern, Switzerland, in March 2020. However, the COVID-19 pandemic put a spoke in the wheel of almost all conferences to be held in 2020. After moving the conference to 8-9 October 2020, we got hit by the second wave and finally decided at short notice to go fully online. On the other hand, the pandemic has made us even more aware of the importance of accelerating research in viral bioinformatics. Advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks. The International Virus Bioinformatics Meeting 2020 has attracted approximately 120 experts in virology and bioinformatics from all over the world to join the two-day virtual meeting. Despite concerns being raised that virtual meetings lack possibilities for face-to-face discussion, the participants from this small community created a highly interactive scientific environment, engaging in lively and inspiring discussions and suggesting new research directions and questions. The meeting featured five invited and twelve contributed talks, on the four main topics: (1) proteome and RNAome of RNA viruses, (2) viral metagenomics and ecology, (3) virus evolution and classification and (4) viral infections and immunology. Further, the meeting featured 20 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.

Understanding evolution of SARS-CoV-2: A perspective from analysis of genetic diversity of RdRp gene.

Coronavirus disease 2019 emerged as the first example of "Disease X", a hypothetical disease of humans caused by an unknown infectious agent that was named as novel coronavirus and subsequently designated as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The origin of the outbreak at the animal market in Wuhan, China implies it as a case of zoonotic spillover. The study was designed to understand evolution of Betacoronaviruses and in particular diversification of SARS-CoV-2 using RNA dependent RNA polymerase (RdRp) gene, a stable genetic marker. Phylogenetic and population stratification analyses were carried out using maximum likelihood and Bayesian methods, respectively. Molecular phylogeny using RdRp showed that SARS-CoV-2 isolates cluster together. Bat-CoV isolate RaTG13 and Pangolin-CoVs are observed to branch off prior to SARS-CoV-2 cluster. While SARS-CoV form a single cluster, Bat-CoVs form multiple clusters. Population-based analyses revealed that both SARS-CoV-2 and SARS-CoV form separate clusters with no admixture. Bat-CoVs were found to have single and mixed ancestry and clustered as four sub-populations. Population-based analyses of Betacoronaviruses using RdRp revealed that SARS-CoV-2 is a homogeneous population. SARS-CoV-2 appears to have evolved from Bat-CoV isolate RaTG13, which diversified from a common ancestor from which Pangolin-CoVs have also evolved. The admixed Bat-CoV sub-populations indicate that bats serve as reservoirs harboring virus ensembles that are responsible for zoonotic spillovers such as SARS-CoV and SARS-CoV-2. The extent of admixed isolates of Bat-CoVs observed in population diversification studies underline the need for periodic surveillance of bats and other animal reservoirs for potential spillovers as a measure towards preparedness for emergence of zoonosis.