Detecting Selection in the HIV-1 Genome during Sexual Transmission Events.

Little is known about whether and how variation in the HIV-1 genome affects its transmissibility. Assessing which genomic features of HIV-1 are under positive or negative selection during transmission is challenging, because very few virus particles are typically transmitted, and random genetic drift can dilute genetic signals in the recipient virus population. We analyzed 30 transmitter-recipient pairs from the Zurich Primary HIV Infection Study and the Swiss HIV Cohort Study using near full-length HIV-1 genomes. We developed a new statistical test to detect selection during transmission, called Selection Test in Transmission (SeTesT), based on comparing the transmitter and recipient virus population and accounting for the transmission bottleneck. We performed extensive simulations and found that sensitivity of detecting selection during transmission is limited by the strong population bottleneck of few transmitted virions. When pooling individual test results across patients, we found two candidate HIV-1 genomic features for affecting transmission, namely amino acid positions 3 and 18 of Vpu, which were significant before but not after correction for multiple testing. In summary, SeTesT provides a general framework for detecting selection based on genomic sequencing data of transmitted viruses. Our study shows that a higher number of transmitter-recipient pairs is required to improve sensitivity of detecting selection.

V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput data.

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. AVAILABILITYAND IMPLEMENTATION: V-pipe is freely available at https://github.com/cbg-ethz/V-pipe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Intra- and Interchain Interactions in (Cu1/2Au1/2)CN, (Ag1/2Au1/2)CN, and (Cu1/3Ag1/3Au1/3)CN and Their Effect on One-, Two-, and Three-Dimensional Order.

Mixed-metal cyanides (Cu1/2Au1/2)CN, (Ag1/2Au1/2)CN, and (Cu1/3Ag1/3Au1/3)CN adopt an AuCN-type structure in which metal-cyanide chains pack on a hexagonal lattice with metal atoms arranged in sheets. The interactions between and within the metal-cyanide chains are investigated using density functional theory (DFT) calculations, 13C solid-state NMR (SSNMR), and X-ray pair distribution function (PDF) measurements. Long-range metal and cyanide order is found within the chains: (-Cu-NC-Au-CN-)∞, (-Ag-NC-Au-CN-)∞, and (-Cu-NC-Ag-NC-Au-CN-)∞. Although Bragg diffraction studies establish that there is no long-range order between chains, X-ray PDF results show that there is local order between chains. In (Cu1/2Au1/2)CN and (Ag1/2Au1/2)CN, there is a preference for unlike metal atoms occurring as nearest neighbors within the metal sheets. A general mathematical proof shows that the maximum average number of heterometallic nearest-neighbor interactions on a hexagonal lattice with two types of metal atoms is four. Calculated energies of periodic structural models show that those with four unlike nearest neighbors are most favorable. Of these, models in space group Immm give the best fits to the X-ray PDF data out to 8 Å, providing good descriptions of the short- and medium-range structures. This result shows that interactions beyond those of nearest neighbors must be considered when determining the structures of these materials. Such interactions are also important in (Cu1/3Ag1/3Au1/3)CN, leading to the adoption of a structure in Pmm2 containing mixed Cu-Au and Ag-only sheets arranged to maximize the numbers of Cu···Au nearest- and next-nearest-neighbor interactions.

Recent advances in inferring viral diversity from high-throughput sequencing data.

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.

Estimating Fitness of Viral Quasispecies from Next-Generation Sequencing Data.

The quasispecies model is ubiquitous in the study of viruses. While having lead to a number of insights that have stood the test of time, the quasispecies model has mostly been discussed in a theoretical fashion with little support of data. With next-generation sequencing (NGS), this situation is changing and a wealth of data can now be produced in a time- and cost-efficient manner. NGS can, after removal of technical errors, yield an exceedingly detailed picture of the viral population structure. The widespread availability of cross-sectional data can be used to study fitness landscapes of viral populations in the quasispecies model. This chapter highlights methods that estimate the strength of selection in selective sweeps, assesses marginal fitness effects of quasispecies, and finally infers the fitness landscape of a viral quasispecies, all on the basis of NGS data.

A Comprehensive Analysis of Primer IDs to Study Heterogeneous HIV-1 Populations.

Determining the composition of viral populations is becoming increasingly important in the field of medical virology. While recently developed computational tools for viral haplotype analysis allow for correcting sequencing errors, they do not always allow for the removal of errors occurring in the upstream experimental protocol, such as PCR errors. Primer IDs (pIDs) are one method to address this problem by harnessing redundant template resampling for error correction. By using a reference mixture of five HIV-1 strains, we show how pIDs can be useful for estimating key experimental parameters, such as the substitution rate of the PCR process and the reverse transcription (RT) error rate. In addition, we introduce a hidden Markov model for determining the recombination rate of the RT PCR process. We found no strong sequence-specific bias in pID abundances (the same RT efficiencies as compared to commonly used short, specific RT primers) and no effects of pIDs on the estimated distribution of the references viruses.

A framework for inferring fitness landscapes of patient-derived viruses using quasispecies theory.

Fitness is a central quantity in evolutionary models of viruses. However, it remains difficult to determine viral fitness experimentally, and existing in vitro assays can be poor predictors of in vivo fitness of viral populations within their hosts. Next-generation sequencing can nowadays provide snapshots of evolving virus populations, and these data offer new opportunities for inferring viral fitness. Using the equilibrium distribution of the quasispecies model, an established model of intrahost viral evolution, we linked fitness parameters to the composition of the virus population, which can be estimated by next-generation sequencing. For inference, we developed a Bayesian Markov chain Monte Carlo method to sample from the posterior distribution of fitness values. The sampler can overcome situations where no maximum-likelihood estimator exists, and it can adaptively learn the posterior distribution of highly correlated fitness landscapes without prior knowledge of their shape. We tested our approach on simulated data and applied it to clinical human immunodeficiency virus 1 samples to estimate their fitness landscapes in vivo. The posterior fitness distributions allowed for differentiating viral haplotypes from each other, for determining neutral haplotype networks, in which no haplotype is more or less credibly fit than any other, and for detecting epistasis in fitness landscapes. Our implemented approach, called QuasiFit, is available at http://www.cbg.ethz.ch/software/quasifit.