Being able to link clinical outcomes to SARS‐CoV‐2 virus strains is a critical component of understanding COVID‐19. Here we discuss how current processes hamper sustainable data collection to enable meaningful analysis and insights. Following the ‘Fast Healthcare Interoperable Resource’ (FHIR) implementation guide, we introduce an ontology‐based standard questionnaire to overcome these shortcomings and describe patient “journeys” in coordination with the World Health Organization’s recommendations. We identify steps in the clinical health data acquisition cycle and workflows that likely have the biggest impact in the data‐driven understanding of this virus. Specifically, we recommend detailed symptoms and medical history using FHIR standards. We have taken the first steps towards this by making patient status mandatory in GISAID immediately resulting in a measurable increase in the fraction of cases with useful patient information. The main remaining limitation is the lack of controlled vocabulary or a medical ontology.

In silico predictions combined with in vitro, in vivo, and in situ observations collectively suggest that mouse adaptation of the severe acute respiratory syndrome 2 virus requires an aromatic substitution in position 501 or position 498 (but not both) of the spike protein’s receptor binding domain. This effect could be enhanced by mutations in positions 417, 484, and 493 (especially K417N, E484K, Q493K, and Q493R), and to a lesser extent by mutations in positions 486 and 499 (such as F486L and P499T). Such enhancements, due to more favorable binding interactions with residues on the complementary angiotensin-converting enzyme 2 interface, are, however, unlikely to sustain mouse infectivity on their own based on theoretical and experimental evidence to date. Our current understanding thus points to the Alpha, Beta, Gamma, and Omicron variants of concern infecting mice, whereas Delta and “Delta Plus” lack a similar biomolecular basis to do so. This paper identifies 11 countries (Brazil, Chile, Djibouti, Haiti, Malawi, Mozambique, Reunion, Suriname, Trinidad and Tobago, Uruguay, and Venezuela) where targeted local field surveillance of mice is encouraged because they may have come in contact with humans who had the virus with adaptive mutation(s). It also provides a systematic methodology to analyze the potential for other animal reservoirs and their likely locations.

Natural variations in a genome can drastically alter the CRISPR-Cas9 off-target landscape by creating or removing sites. Despite the resulting potential side-effects from such unaccounted for sites, current off-target detection pipelines are not equipped to include variant information. To address this, we developed VARiant-aware detection and SCoring of Off-Targets (VARSCOT). VARSCOT identifies only 0.6% of off-targets to be common between 4 individual genomes and the reference, with an average of 82% of off-targets unique to an individual. VARSCOT is the most sensitive detection method for off-targets, finding 40 to 70% more experimentally verified off-targets compared to other popular software tools and its machine learning model allows for CRISPR-Cas9 concentration aware off-target activity scoring. VARSCOT allows researchers to take genomic variation into account when designing individual or population-wide targeting strategies. VARSCOT is available from https://github.com/BauerLab/VARSCOT .

Detecting viral and vector integration events is a key step when investigating interactions between viral and host genomes. This is relevant in several fields, including virology, cancer research and gene therapy. For example, investigating integrations of wild-type viruses such as human papillomavirus and hepatitis B virus has proven to be crucial for understanding the role of these integrations in cancer. Furthermore, identifying the extent of vector integration is vital for determining the potential for genotoxicity in gene therapies. To address these questions, we developed isling, the first tool specifically designed for identifying viral integrations in both wild-type and vector from next-generation sequencing data. Isling addresses complexities in integration behaviour including integration of fragmented genomes and integration junctions with ambiguous locations in a host or vector genome, and can also flag possible vector recombinations. We show that isling is up to 1.6-fold faster and up to 170% more accurate than other viral integration tools, and performs well on both simulated and real datasets. Isling is therefore an efficient and application-agnostic tool that will enable a broad range of investigations into viral and vector integration. These include comparisons between integrations of wild-type viruses and gene therapy vectors, as well as assessing the genotoxicity of vectors and understanding the role of viruses in cancer.

External DNA sequences can be inserted into an organism’s genome either through natural processes such as gene transfer, or through targeted genome engineering strategies. Being able to robustly identify such foreign DNA is a crucial capability for health and biosecurity applications, such as anti-microbial resistance (AMR) detection or monitoring gene drives. This capability does not exist for poorly characterised host genomes or with limited information about the integrated sequence. To address this, we developed the INserted Sequence Information DEtectoR (INSIDER). INSIDER analyses whole genome sequencing data and identifies segments of potentially foreign origin by their significant shift in k-mer signatures. We demonstrate the power of INSIDER to separate integrated DNA sequences from normal genomic sequences on a synthetic dataset simulating the insertion of a CRISPR-Cas gene drive into wild-type yeast. As a proof-of-concept, we use INSIDER to detect the exact AMR plasmid in whole genome sequencing data from a Citrobacter freundii patient isolate. INSIDER streamlines the process of identifying integrated DNA in poorly characterised wild species or when the insert is of unknown origin, thus enhancing the monitoring of emerging biosecurity threats.

The increased development of functionally diverse and highly specialized genome editors has created the need for comparative analytics tools that are able to profile the mutational outcomes, particularly rare and complex outcomes, to assess the editor's applicability to different domains. To address this need, we have developed Generalizable On-target activity ANAlyzer (GOANA), a high-throughput web-based software for determining editing efficiency and cataloguing rare outcomes from next-generation sequencing data. GOANA calculates mutation frequency and outcomes relative to a supplied control sample. It is scalable to thousands of target sites across the entire genome and is 4,000% faster than CRISPResso2. Mutations are reported on a “per-read” level rather than individually, enabling the identification of co-occurring mutations. GOANA is editor agnostic and can be applied to data generated from any targeted editing experiment, including base editors. Requiring only that control and treated reads are aligned to the same reference, GOANA can handle data from any library preparation method, including pooled amplicon and whole-genome sequencing. As a proof of principle, we analyze two large data sets of CRISPR-Cas9 and CRISPR-Cas12a editing, demonstrating the power of GOANA and highlighting several key differences between the two enzymes. GOANA is available for use at https://gt-scan.csiro.au/goana/ and as a command line tool from https://github.com/BauerLab/GOANA .