Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses (preprint)

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learned. As a component of the Platform, the SARS-CoV-2 Data Hubs enabled the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.

Towards increased accuracy and reproducibility in SARS-CoV-2 next generation sequence analysis for public health surveillance (preprint)

During the COVID-19 pandemic, SARS-CoV-2 surveillance efforts integrated genome sequencing of clinical samples to identify emergent viral variants and to support rapid experimental examination of genome-informed vaccine and therapeutic designs. Given the broad range of methods applied to generate new viral genomes, it is critical that consensus and variant calling tools yield consistent results across disparate pipelines. Here we examine the impact of sequencing technologies (Illumina and Oxford Nanopore) and 7 different downstream bioinformatic protocols on SARS-CoV-2 variant calling as part of the NIH Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Tracking Resistance and Coronavirus Evolution (TRACE) initiative, a public-private partnership established to address the COVID-19 outbreak. Our results indicate that bioinformatic workflows can yield consensus genomes with different single nucleotide polymorphisms, insertions, and/or deletions even when using the same raw sequence input datasets. We introduce the use of a specific suite of parameters and protocols that greatly improves the agreement among pipelines developed by diverse organizations. Such consistency among bioinformatic pipelines is fundamental to SARS-CoV-2 and future pathogen surveillance efforts. The application of analysis standards is necessary to more accurately document phylogenomic trends and support data-driven public health responses.

Selection analysis identifies unusual clustered mutational changes in Omicron lineage BA.1 that likely impact Spike function (preprint)

Among the 30 non-synonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (i) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (ii) interactions of Spike with ACE2 receptors, and (iii) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any virus within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and, in combination with other mutations, adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron over all previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected.

Freely accessible ready to use global infrastructure for SARS-CoV-2 monitoring (preprint)

The COVID-19 pandemic is the first global health crisis to occur in the age of big genomic data. Although data generation capacity is well established and sufficiently standardized, analytical capacity is not. To establish analytical capacity it is necessary to pull together global computational resources and deliver the best open source tools and analysis workflows within a ready to use, universally accessible resource. Such a resource should not be controlled by a single research group, institution, or country. Instead it should be maintained by a community of users and developers who ensure that the system remains operational and populated with current tools. A community is also essential for facilitating the types of discourse needed to establish best analytical practices. Bringing together public computational research infrastructure from the USA, Europe, and Australia, we developed a distributed data analysis platform that accomplishes these goals. It is immediately accessible to anyone in the world and is designed for the analysis of rapidly growing collections of deep sequencing datasets. We demonstrate its utility by detecting allelic variants in high-quality existing SARS-CoV-2 sequencing datasets and by continuous reanalysis of COG-UK data. All workflows, data, and documentation is available at https://covid19.galaxyproject.org.

The landscape of SARS-CoV-2 RNA modifications (preprint)

In 2019 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the first documented cases of severe lung disease COVID-19. Since then, SARS-CoV-2 has been spreading around the globe resulting in a severe pandemic with over 500.000 fatalities and large economical and social disruptions in human societies. Gaining knowledge on how SARS-Cov-2 interacts with its host cells and causes COVID-19 is crucial for the intervention of novel therapeutic strategies. SARS-CoV-2, like other coronaviruses, is a positive-strand RNA virus. The viral RNA is modified by RNA-modifying enzymes provided by the host cell. Direct RNA sequencing (DRS) using nanopores enables unbiased sensing of canonical and modified RNA bases of the viral transcripts. In this work, we used DRS to precisely annotate the open reading frames and the landscape of SARS-CoV-2 RNA modifications. We provide the first DRS data of SARS-CoV-2 in infected human lung epithelial cells. From sequencing three isolates, we derive a robust identification of SARS-CoV-2 modification sites within a physiologically relevant host cell type. A comparison of our data with the DRS data from a previous SARS-CoV-2 isolate, both raised in monkey renal cells, reveals consistent RNA modifications across the viral genome. Conservation of the RNA modification pattern during progression of the current pandemic suggests that this pattern is likely essential for the life cycle of SARS-CoV-2 and represents a possible target for drug interventions.