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.

Rfam 14: expanded coverage of metagenomic, viral and microRNA families

Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.

Molecular basis of gene regulation by the THI-box riboswitch

Riboswitches are genetic control elements located mainly within the 5' untranslated regions of messenger RNAs. These RNA elements undergo conformational changes that modulate gene expression upon binding of regulatory signals including vitamins, amino acids, nucleobases and uncharged tRNA. The thiamin pyrophosphate (TPP)-binding riboswitch (THI-box) is found in all three kingdoms of life and can regulate gene expression at the levels of premature termination of transcription, initiation of translation and mRNA splicing. The THI-box is composed of two parallel stacked helices bound by another helix in a three-way junction. We performed an in vivo expression analysis of mutants with substitutions in conserved bases located at the interior and terminal loops of the Escherichia coli thiM THI-box, which is translationally regulated, and observed two different phenotypic classes. One class exhibited high expression during growth in the presence or absence of thiamin, while the second class exhibited low expression regardless of the presence of thiamin. Accessibility of the Shine-Dalgarno region of the RNA following the addition of TPP was monitored by means of an oligonucleotide-dependent RNase H cleavage assay, and binding of 30S ribosomal subunits. These studies showed that high- and low-expression mutant RNAs are locked in the non-repressive and repressive conformations respectively.

Conserved regulatory motifs in bacteria: riboswitches and beyond

We present a computational approach that identifies regulatory elements conserved across phylogenetically distant organisms. Intergenic regulatory regions were clustered by orthology of the adjacent genes, and an iterative process was applied to search for significant motifs, enabling new elements of the putative regulon to be added in each cycle. With this approach, we identified highly conserved riboswitches and the Gram positive T-box. Interestingly, we identified many other regulatory systems that appear to depend on conserved RNA structures.