Biology subjects

Kant, R.

Publications and source records attributed to Kant, R..

4 recordsLinked to original sources

bioRxiv2021

Zoonotic spillover of SARS-CoV-2: mink-adapted virus in humans

The COVID-19 pandemic caused by SARS-CoV-2 started in fall 2019. A range of different mammalian species, including farmed mink, have been confirmed as susceptible to infection with this virus. We report here the spillover of mink-adapted SARS-CoV-2 from farmed mink to humans after extensive adaptation that lasted at least 3 months. We found the presence of four mutations in the S gene (that gave rise to variant: G75V, M177T, Y453F and C1247F) and others in an isolate obtained from SARS-CoV-2 positive patient.

microbiology↗

bioRxiv2021

HaVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences

BackgroundSARS-CoV-2 related research has increased in importance worldwide since December 2019. Several new variants of SARS-CoV-2 have emerged globally, of which the most notable and concerning currently are the UK variant B.1.1.7, the South African variant B1.351 and the Brazilian variant P.1. Detecting and monitoring novel variants is essential in SARS-CoV-2 surveillance. While there are several tools for assembling virus genomes and performing lineage analyses to investigate SARS-CoV-2, each is limited to performing singular or a few functions separately. ResultsDue to the lack of publicly available pipelines, which could perform fast reference-based assemblies on raw SARS-CoV-2 sequences in addition to identifying lineages to detect variants of concern, we have developed an open source bioinformatic pipeline called HaVoC (Helsinki university Analyzer for Variants Of Concern). HaVoC can reference assemble raw sequence reads and assign the corresponding lineages to SARS-CoV-2 sequences. ConclusionsHaVoC is a pipeline utilizing several bioinformatic tools to perform multiple necessary analyses for investigating genetic variance among SARS-CoV-2 samples. The pipeline is particularly useful for those who need a more accessible and fast tool to detect and monitor the spread of SARS-CoV-2 variants of concern during local outbreaks. HaVoC is currently being used in Finland for monitoring the spread of SARS-CoV-2 variants. HaVoC user manual and source code are available at https://www.helsinki.fi/en/projects/havoc and https://bitbucket.org/auto_cov_pipeline/havoc, respectively.

bioinformatics↗

bioRxiv2020

Detection and molecular characterisation of SARS-CoV-2 in farmed mink (Neovision vision) in Poland

SARS-CoV-2 is the aetiological agent of COVID-19 disease and has been spreading worldwide since December 2019. The virus has been shown to infect different animal species under experimental conditions. Also, minks have been found to be susceptible to SARS-CoV-2 infection in fur farms in Europe and the USA. Here we investigated 91 individual minks from a farm located in Northern Poland. Using RT-PCR, antigen detection and NGS, we confirmed 15 animals positive for SARS-CoV-2. The result was verified by sequencing of full viral genomes, confirming SARS-CoV-2 infection in Polish mink. Country-scale monitoring conducted by veterinary inspection so far has not detected the presence of SARS-CoV-2 on other mink farms. Taking into consideration that Poland has a high level of positive diagnostic tests among its population, there is a high risk that more Polish mink farms become a source for SARS-CoV-2. Findings reported here and from other fur producing countries urge the assessment of SARS-CoV-2 prevalence in animals bred in Polish fur farms.

microbiology↗

bioRxiv2020

Novel NGS Pipeline for Virus Discovery from a Wide Spectrum of Hosts and Sample Types

The study of the microbiome data holds great potential for elucidating the biological and metabolic functioning of living organisms and their role in the environment. Metagenomic analyses have shown that humans, along with e.g. domestic animals, wildlife and arthropods, are colonized by an immense community of viruses. The current Coronavirus pandemic (COVID-19) heightens the need to rapidly detect previously unknown viruses in an unbiased way. The increasing availability of metagenomic data in this era of next-generation sequencing (NGS), along with increasingly affordable sequencing technologies, highlight the need for reliable and comprehensive methods to manage such data. In this article, we present a novel stand-alone pipeline called LAZYPIPE for identifying both previously known and novel viruses in host-associated or environmental samples and give examples of virus discovery based on it. LAZYPIPE is a Unix-based pipeline for automated assembling and taxonomic profiling of NGS libraries implemented as a collection of C++, Perl, and R scripts.

bioinformatics↗