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Motivation High-throughput sequencing technologies are used increasingly not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations. Results To support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape., Bioinformatics, 37 (12), ISSN:1367-4803, ISSN:1460-2059

Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 SARS-CoV-2 genomes from Switzerland in 2020 - the 6th largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrast these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland’s border closures de-coupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 - 98% reduction in introductions during Switzerland’s strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Finally, we quantified local transmission dynamics once introductions into Switzerland occurred, using a novel phylodynamic model. We find that transmission slowed 35 – 63% upon outbreak detection in summer 2020, but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.One Sentence SummaryPhylogenetic and phylodynamic methods quantify the drop in case introductions and local transmission with implementation of public health measures.

Pathogen genomes provide insights into their evolution and epidemic spread. We sequenced 1,439 SARS-CoV-2 genomes from Switzerland, representing 3-7% of all confirmed cases per week. Using these data, we demonstrate that no one lineage became dominant, pointing against evolution towards general lower virulence. On an epidemiological level, we report no evidence of cryptic transmission before the first confirmed case. We find many early viral introductions from Germany, France, and Italy and many recent introductions from Germany and France. Over the summer, we quantify the number of non-traceable infections stemming from introductions, quantify the effective reproductive number, and estimate the degree of undersampling. Our framework can be applied to quantify evolution and epidemiology in other locations or for other pathogens based on genomic data.One Sentence SummaryWe quantify SARS-CoV-2 spread in Switzerland based on genome sequences from our nation-wide sequencing effort.

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity.Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

Although combination antiretoviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Here, we present a methodology for the comparison of mutational pathways in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational pathways from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models on a large number of resistance mutations and develop a statistical test to assess differences in the inferred mutational pathways between two groups. We apply this method to the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional data set of South African individuals living with HIV-1 subtype C, as well as a genotype data set of subtype B infections derived from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. Our results also show that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Furthermore, the maximum likelihood mutational networks for subtypes B and C share only 7 edges (Jaccard distance 0.802) and imply many different evolutionary pathways. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational pathways between any two groups.Author summaryThere is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Western Europe and Australia, and most therapeutic strategies are based on research and clinical studies on this subtype. However, non-B subtypes represent the majority of global HIV-1 infections; e.g., subtype C alone accounts for nearly half of all HIV-1 infections. We present a statistical framework enabling the comparison of patterns of accumulating mutations in different HIV-1 subtypes. Specifically, we study lopinavir resistance pathways in HIV-1 subtypes B versus C, but the methodology can be generally applied to compare the temporal ordering of genetic events in different subgroups.

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups., Author summary There is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Australia and western and central Europe, and most therapeutic strategies are based on research and clinical studies on this subtype. However, non-B subtypes represent the majority of global HIV-1 infections; e.g., subtype C alone accounts for nearly half of all HIV-1 infections. We present a statistical framework enabling the comparison of patterns of accumulating mutations in different HIV-1 subtypes. Specifically, we compare the temporal ordering of lopinavir resistance mutations in HIV-1 subtypes B versus C. To this end, we combine the Hidden Conjunctive Bayesian Network (H-CBN) model with an approximate inference scheme enabling comparisons of larger networks. We show that the development of resistance to lopinavir differs significantly between subtypes B and C, such that findings based on subtype B sequences can not always be applied to sybtype C. The described methodology is suitable for comparing different subgroups in the context of other evolutionary processes.

Advances in high-throughput sequencing (HTS) technologies have facilitated the assessment of the genetic diversity of heterogeneous virus populations at an unprecedented level of detail. However, the existence of technical errors confounds the identification of truthful variants. Here, we present a comparative approach for the identification of patterns of co-variation in deep-sequenced virus populations. In addition to sequencing errors, we account for other unknown sources of error by modeling the occurrences of patterns of mutations using the Dirichlet distribution as prior for the multinomial distribution.

Rapidly evolving RNA viruses prevail within a host as a collection of closely related variants, referred to as viral quasispecies. Advances in high-throughput sequencing (HTS) technologies have facilitated the assessment of the genetic diversity of such virus populations at an unprecedented level of detail. However, analysis of HTS data from virus populations is challenging due to short, error-prone reads. In order to account for uncertainties originating from these limitations, several computational and statistical methods have been developed for studying the genetic heterogeneity of virus population. Here, we review methods for the analysis of HTS reads, including approaches to local diversity estimation and global haplotype reconstruction. Challenges posed by aligning reads, as well as the impact of reference biases on diversity estimates are also discussed. In addition, we address some of the experimental approaches designed to improve the biological signal-to-noise ratio. In the future, computational methods for the analysis of heterogeneous virus populations are likely to continue being complemented by technological developments., Virus Research, 239, ISSN:0168-1702, ISSN:1872-7492