Strelka2: fast and accurate calling of germline and somatic variants.

We describe Strelka2 ( https://github.com/Illumina/strelka ), an open-source small-variant-calling method for research and clinical germline and somatic sequencing applications. Strelka2 introduces a novel mixture-model-based estimation of insertion/deletion error parameters from each sample, an efficient tiered haplotype-modeling strategy, and a normal sample contamination model to improve liquid tumor analysis. For both germline and somatic calling, Strelka2 substantially outperformed the current leading tools in terms of both variant-calling accuracy and computing cost.

ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions.

SUMMARY: We describe a novel computational method for genotyping repeats using sequence graphs. This method addresses the long-standing need to accurately genotype medically important loci containing repeats adjacent to other variants or imperfect DNA repeats such as polyalanine repeats. Here we introduce a new version of our repeat genotyping software, ExpansionHunter, that uses this method to perform targeted genotyping of a broad class of such loci. AVAILABILITY AND IMPLEMENTATION: ExpansionHunter is implemented in C++ and is available under the Apache License Version 2.0. The source code, documentation, and Linux/macOS binaries are available at https://github.com/Illumina/ExpansionHunter/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Social and Genetic Networks of HIV-1 Transmission in New York City.

BACKGROUND: Sexually transmitted infections spread across contact networks. Partner elicitation and notification are commonly used public health tools to identify, notify, and offer testing to persons linked in these contact networks. For HIV-1, a rapidly evolving pathogen with low per-contact transmission rates, viral genetic sequences are an additional source of data that can be used to infer or refine transmission networks. METHODS AND FINDINGS: The New York City Department of Health and Mental Hygiene interviews individuals newly diagnosed with HIV and elicits names of sexual and injection drug using partners. By law, the Department of Health also receives HIV sequences when these individuals enter healthcare and their physicians order resistance testing. Our study used both HIV sequence and partner naming data from 1342 HIV-infected persons in New York City between 2006 and 2012 to infer and compare sexual/drug-use named partner and genetic transmission networks. Using these networks, we determined a range of genetic distance thresholds suitable for identifying potential transmission partners. In 48% of cases, named partners were infected with genetically closely related viruses, compatible with but not necessarily representing or implying, direct transmission. Partner pairs linked through the genetic similarity of their HIV sequences were also linked by naming in 53% of cases. Persons who reported high-risk heterosexual contact were more likely to name at least one partner with a genetically similar virus than those reporting their risk as injection drug use or men who have sex with men. CONCLUSIONS: We analyzed an unprecedentedly large and detailed partner tracing and HIV sequence dataset and determined an empirically justified range of genetic distance thresholds for identifying potential transmission partners. We conclude that genetic linkage provides more reliable evidence for identifying potential transmission partners than partner naming, highlighting the importance and complementarity of both epidemiological and molecular genetic surveillance for characterizing regional HIV-1 epidemics.

A molecular transmission network of recent hepatitis C infection in people with and without HIV: Implications for targeted treatment strategies.

Combining phylogenetic and network methodologies has the potential to better inform targeted interventions to prevent and treat infectious diseases. This study reconstructed a molecular transmission network for people with recent hepatitis C virus (HCV) infection and modelled the impact of targeting directly acting antiviral (DAA) treatment for HCV in the network. Participants were selected from three Australian studies of recent HCV from 2004 to 2014. HCV sequence data (Core-E2) from participants at the time of recent HCV detection were analysed to infer a network by connecting pairs of sequences whose divergence was ≤.03 substitutions/site. Logistic regression was used to identify factors associated with connectivity. Impact of targeting HCV DAAs at both HIV co-infected and random nodes was simulated (1 million replicates). Among 236 participants, 21% (n=49) were connected in the network. HCV/HIV co-infected participants (47%) were more likely to be connected compared to HCV mono-infected participants (16%) (OR 4.56; 95% CI; 2.13-9.74). Simulations targeting DAA HCV treatment to HCV/HIV co-infected individuals prevented 2.5 times more onward infections than providing DAAs to randomly selected individuals. Results demonstrate that genetic distance-based network analyses can be used to identify characteristics associated with HCV transmission, informing targeted prevention and treatment strategies.

Using HIV Sequence and Epidemiologic Data to Assess the Effect of Self-referral Testing for Acute HIV Infection on Incident Diagnoses in San Diego, California.

BACKGROUND: Because recently infected individuals disproportionately contribute to the spread of human immunodeficiency virus (HIV), we evaluated the impact of a primary HIV screening program (the Early Test) implemented in San Diego. METHODS: The Early Test program used combined nucleic acid and serology testing to screen for primary infection targeting local high-risk individuals. Epidemiologic, HIV sequence, and geographic data were obtained from the San Diego County Department of Public Health and the Early Test program. Poisson regression analysis was performed to determine whether the Early Test program was temporally and geographically associated with changes in incident HIV diagnoses. Transmission chains were inferred by phylogenetic analysis of sequence data. RESULTS: Over time, a decrease in incident HIV diagnoses was observed proportional to the number primary HIV infections diagnosed in each San Diego region (P < .001). Molecular network analyses also showed that transmission chains were more likely to terminate in regions where the program was marketed (P = .002). Although, individuals in these zip codes had infection diagnosed earlier (P = .08), they were not treated earlier (P = .83). CONCLUSIONS: These findings suggests that early HIV diagnoses by this primary infection screening program probably contributed to the observed decrease in new HIV diagnoses in San Diego, and they support the expansion and evaluation of similar programs.

RELAX: detecting relaxed selection in a phylogenetic framework.

Relaxation of selective strength, manifested as a reduction in the efficiency or intensity of natural selection, can drive evolutionary innovation and presage lineage extinction or loss of function. Mechanisms through which selection can be relaxed range from the removal of an existing selective constraint to a reduction in effective population size. Standard methods for estimating the strength and extent of purifying or positive selection from molecular sequence data are not suitable for detecting relaxed selection, because they lack power and can mistake an increase in the intensity of positive selection for relaxation of both purifying and positive selection. Here, we present a general hypothesis testing framework (RELAX) for detecting relaxed selection in a codon-based phylogenetic framework. Given two subsets of branches in a phylogeny, RELAX can determine whether selective strength was relaxed or intensified in one of these subsets relative to the other. We establish the validity of our test via simulations and show that it can distinguish between increased positive selection and a relaxation of selective strength. We also demonstrate the power of RELAX in a variety of biological scenarios where relaxation of selection has been hypothesized or demonstrated previously. We find that obligate and facultative γ-proteobacteria endosymbionts of insects are under relaxed selection compared with their free-living relatives and obligate endosymbionts are under relaxed selection compared with facultative endosymbionts. Selective strength is also relaxed in asexual Daphnia pulex lineages, compared with sexual lineages. Endogenous, nonfunctional, bornavirus-like elements are found to be under relaxed selection compared with exogenous Borna viruses. Finally, selection on the short-wavelength sensitive, SWS1, opsin genes in echolocating and nonecholocating bats is relaxed only in lineages in which this gene underwent pseudogenization; however, selection on the functional medium/long-wavelength sensitive opsin, M/LWS1, is found to be relaxed in all echolocating bats compared with nonecholocating bats.

Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection.

Over the past two decades, comparative sequence analysis using codon-substitution models has been honed into a powerful and popular approach for detecting signatures of natural selection from molecular data. A substantial body of work has focused on developing a class of "branch-site" models which permit selective pressures on sequences, quantified by the ω ratio, to vary among both codon sites and individual branches in the phylogeny. We develop and present a method in this class, adaptive branch-site random effects likelihood (aBSREL), whose key innovation is variable parametric complexity chosen with an information theoretic criterion. By applying models of different complexity to different branches in the phylogeny, aBSREL delivers statistical performance matching or exceeding best-in-class existing approaches, while running an order of magnitude faster. Based on simulated data analysis, we offer guidelines for what extent and strength of diversifying positive selection can be detected reliably and suggest that there is a natural limit on the optimal parametric complexity for "branch-site" models. An aBSREL analysis of 8,893 Euteleostomes gene alignments demonstrates that over 80% of branches in typical gene phylogenies can be adequately modeled with a single ω ratio model, that is, current models are unnecessarily complicated. However, there are a relatively small number of key branches, whose identities are derived from the data using a model selection procedure, for which it is essential to accurately model evolutionary complexity.

Gene-wide identification of episodic selection.

We present BUSTED, a new approach to identifying gene-wide evidence of episodic positive selection, where the non-synonymous substitution rate is transiently greater than the synonymous rate. BUSTED can be used either on an entire phylogeny (without requiring an a priori hypothesis regarding which branches are under positive selection) or on a pre-specified subset of foreground lineages (if a suitable a priori hypothesis is available). Selection is modeled as varying stochastically over branches and sites, and we propose a computationally inexpensive evidence metric for identifying sites subject to episodic positive selection on any foreground branches. We compare BUSTED with existing models on simulated and empirical data. An implementation is available on www.datamonkey.org/busted, with a widget allowing the interactive specification of foreground branches.

Comprehensive sieve analysis of breakthrough HIV-1 sequences in the RV144 vaccine efficacy trial.

The RV144 clinical trial showed the partial efficacy of a vaccine regimen with an estimated vaccine efficacy (VE) of 31% for protecting low-risk Thai volunteers against acquisition of HIV-1. The impact of vaccine-induced immune responses can be investigated through sieve analysis of HIV-1 breakthrough infections (infected vaccine and placebo recipients). A V1/V2-targeted comparison of the genomes of HIV-1 breakthrough viruses identified two V2 amino acid sites that differed between the vaccine and placebo groups. Here we extended the V1/V2 analysis to the entire HIV-1 genome using an array of methods based on individual sites, k-mers and genes/proteins. We identified 56 amino acid sites or "signatures" and 119 k-mers that differed between the vaccine and placebo groups. Of those, 19 sites and 38 k-mers were located in the regions comprising the RV144 vaccine (Env-gp120, Gag, and Pro). The nine signature sites in Env-gp120 were significantly enriched for known antibody-associated sites (p = 0.0021). In particular, site 317 in the third variable loop (V3) overlapped with a hotspot of antibody recognition, and sites 369 and 424 were linked to CD4 binding site neutralization. The identified signature sites significantly covaried with other sites across the genome (mean = 32.1) more than did non-signature sites (mean = 0.9) (p < 0.0001), suggesting functional and/or structural relevance of the signature sites. Since signature sites were not preferentially restricted to the vaccine immunogens and because most of the associations were insignificant following correction for multiple testing, we predict that few of the genetic differences are strongly linked to the RV144 vaccine-induced immune pressure. In addition to presenting results of the first complete-genome analysis of the breakthrough infections in the RV144 trial, this work describes a set of statistical methods and tools applicable to analysis of breakthrough infection genomes in general vaccine efficacy trials for diverse pathogens.

Evolutionary origins of human herpes simplex viruses 1 and 2.

Herpesviruses have been infecting and codiverging with their vertebrate hosts for hundreds of millions of years. The primate simplex viruses exemplify this pattern of virus-host codivergence, at a minimum, as far back as the most recent common ancestor of New World monkeys, Old World monkeys, and apes. Humans are the only primate species known to be infected with two distinct herpes simplex viruses: HSV-1 and HSV-2. Human herpes simplex viruses are ubiquitous, with over two-thirds of the human population infected by at least one virus. Here, we investigated whether the additional human simplex virus is the result of ancient viral lineage duplication or cross-species transmission. We found that standard phylogenetic models of nucleotide substitution are inadequate for distinguishing among these competing hypotheses; the extent of synonymous substitutions causes a substantial underestimation of the lengths of some of the branches in the phylogeny, consistent with observations in other viruses (e.g., avian influenza, Ebola, and coronaviruses). To more accurately estimate ancient viral divergence times, we applied a branch-site random effects likelihood model of molecular evolution that allows the strength of natural selection to vary across both the viral phylogeny and the gene alignment. This selection-informed model favored a scenario in which HSV-1 is the result of ancient codivergence and HSV-2 arose from a cross-species transmission event from the ancestor of modern chimpanzees to an extinct Homo precursor of modern humans, around 1.6 Ma. These results provide a new framework for understanding human herpes simplex virus evolution and demonstrate the importance of using selection-informed models of sequence evolution when investigating viral origin hypotheses.

IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

Detecting individual sites subject to episodic diversifying selection.

The imprint of natural selection on protein coding genes is often difficult to identify because selection is frequently transient or episodic, i.e. it affects only a subset of lineages. Existing computational techniques, which are designed to identify sites subject to pervasive selection, may fail to recognize sites where selection is episodic: a large proportion of positively selected sites. We present a mixed effects model of evolution (MEME) that is capable of identifying instances of both episodic and pervasive positive selection at the level of an individual site. Using empirical and simulated data, we demonstrate the superior performance of MEME over older models under a broad range of scenarios. We find that episodic selection is widespread and conclude that the number of sites experiencing positive selection may have been vastly underestimated.

FUBAR: a fast, unconstrained bayesian approximation for inferring selection.

Model-based analyses of natural selection often categorize sites into a relatively small number of site classes. Forcing each site to belong to one of these classes places unrealistic constraints on the distribution of selection parameters, which can result in misleading inference due to model misspecification. We present an approximate hierarchical Bayesian method using a Markov chain Monte Carlo (MCMC) routine that ensures robustness against model misspecification by averaging over a large number of predefined site classes. This leaves the distribution of selection parameters essentially unconstrained, and also allows sites experiencing positive and purifying selection to be identified orders of magnitude faster than by existing methods. We demonstrate that popular random effects likelihood methods can produce misleading results when sites assigned to the same site class experience different levels of positive or purifying selection--an unavoidable scenario when using a small number of site classes. Our Fast Unconstrained Bayesian AppRoximation (FUBAR) is unaffected by this problem, while achieving higher power than existing unconstrained (fixed effects likelihood) methods. The speed advantage of FUBAR allows us to analyze larger data sets than other methods: We illustrate this on a large influenza hemagglutinin data set (3,142 sequences). FUBAR is available as a batch file within the latest HyPhy distribution (http://www.hyphy.org), as well as on the Datamonkey web server (http://www.datamonkey.org/).

Solving the MPI reconstruction problem with automatically tuned regularization parameters.

In the field of medical imaging, magnetic particle imaging (MPI) poses a promising non-ionizing tomographic technique with high spatial and temporal resolution. In MPI, iterative solvers are used to reconstruct the particle distribution out of the measured voltage signal based on a system matrix. The amount of regularization needed to reconstruct an image of good quality differs from measurement to measurement, depending on the MPI system and the measurement settings. Finding the right choice for the three major parameters controlling the regularization is commonly done by hand and requires time and experience. In this work, we study the reduction to a single regularization parameter and propose a method that enables automatic reconstruction. The method is qualitatively and quantitatively validated on several MPI data sets showing promising results.

Characterization of the clinically approved MRI tracer resotran for magnetic particle imaging in a comparison study.

Objective.The availability of magnetic nanoparticles (MNPs) with medical approval for human intervention is fundamental to the clinical translation of magnetic particle imaging (MPI). In this work, we thoroughly evaluate and compare the magnetic properties of an magnetic resonance imaging (MRI) approved tracer to validate its performance for MPI in future human trials.Approach.We analyze whether the recently approved MRI tracer Resotran is suitable for MPI. In addition, we compare Resotran with the previously approved and extensively studied tracer Resovist, with Ferrotran, which is currently in a clinical phase III study, and with the tailored MPI tracer Perimag.Main results.Initial magnetic particle spectroscopy (MPS) measurements indicate that Resotran exhibits performance characteristics akin to Resovist, but below Perimag. We provide data on four different tracers using dynamic light scattering, transmission electron microscopy, vibrating sample magnetometry measurements, MPS to derive hysteresis, point spread functions, and a serial dilution, as well as system matrix based MPI measurements on a preclinical scanner (Bruker 25/20 FF), including reconstructed images.Significance.Numerous approved MNPs used as tracers in MRI lack the necessary magnetic properties essential for robust signal generation in MPI. The process of obtaining medical approval for dedicated MPI tracers optimized for signal performance is an arduous and costly endeavor, often only justifiable for companies with a well-defined clinical business case. Resotran is an approved tracer that has become available in Europe for MRI. In this work, we study the eligibility of Resotran for MPI in an effort to pave the way for human MPI trials.

Deep sequencing reveals minor protease resistance mutations in patients failing a protease inhibitor regimen.

Standard genotypic antiretroviral resistance testing, performed by bulk sequencing, does not readily detect variants that comprise <20% of the circulating HIV-1 RNA population. Nevertheless, it is valuable in selecting an antiretroviral regimen after antiretroviral failure. In patients with poor adherence, resistant variants may not reach this threshold. Therefore, deep sequencing would be potentially valuable for detecting minority resistant variants. We compared bulk sequencing and deep sequencing to detect HIV-1 drug resistance at the time of a second-line protease inhibitor (PI)-based antiretroviral regimen failure. Eligibility criteria were virologic failure (HIV-1 RNA load of >500 copies/ml) of a first-line nonnucleoside reverse transcriptase inhibitor-based regimen, with at least the M184V mutation (lamivudine resistance), and second-line failure of a lopinavir/ritonavir (LPV/r)-based regimen. An amplicon-sequencing approach on the Roche 454 system was used. Six patients with viral loads of >90,000 copies/ml and one patient with a viral load of 520 copies/ml were included. Mutations not detectable by bulk sequencing during first- and second-line failure were detected by deep sequencing during second-line failure. Low-frequency variants (>0.5% of the sequence population) harboring major protease inhibitor resistance mutations were found in 5 of 7 patients despite poor adherence to the LPV/r-based regimen. In patients with intermittent adherence to a boosted PI regimen, deep sequencing may detect minority PI-resistant variants, which likely represent early events in resistance selection. In patients with poor or intermittent adherence, there may be low evolutionary impetus for such variants to reach fixation, explaining the low prevalence of PI resistance.

Modeling HIV-1 drug resistance as episodic directional selection.

The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

Extrapolation of System Matrices in Magnetic Particle Imaging.

Magnetic particle imaging exploits the non-linear magnetization of superparamagnetic iron-oxide particles to generate a tomographic image in a defined field-of-view. For reconstruction of the particle distribution, a time-consuming calibration step is required, in which system matrices get measured using a robot. To achieve artifact-free images, system matrices need to cover not only the field-of-view but also a larger area around it. Especially for large measurements- inevitable for future clinical application- this leads to long calibration time and high consumption of persistent memory. In this work, we analyze the signal in the outer part of the system matrix and motivate the usage of extrapolation methods to computationally expand the system matrix after restricting the calibration to the field-of-view. We propose a suitable extrapolation method and show its applicability on measured 2D and 3D data. In doing so, we achieve a considerable reduction of calibration time and consumption of persistent memory while preserving an artifact-free result.

Optimization of magnetic nanoparticles for engineering erythrocytes as theranostic agents.

The application of superparamagnetic iron oxide nanoparticles (SPIONs) in drug delivery, magnetic resonance imaging, cell tracking, and hyperthermia has been long exploited regarding their inducible magnetic properties. Nevertheless, SPIONs remain rapidly cleared from the circulation by the reticuloendothelial system (RES) or mononuclear phagocyte system, with uptake dependent on several factors such as the hydrodynamic diameter, electrical charge and surface coating. This rapid clearance of SPION-based theranostic agents from circulation is one of the main challenges hampering the medical applications that differ from RES targeting. This work proposes a strategy to render biocompatible SPIONs through their encapsulation in the red blood cells (RBCs). In this work, the research has been focused on the multi-step optimization of chemical synthesis of magnetic nanoparticles (MNPs), precisely iron oxide nanoparticles (IONPs) and zinc manganese-ferrite nanoparticles (Zn/Mn FNPs), for encapsulation in human and murine RBCs. The encapsulation through the transient opening of RBC membrane pores requires extensive efforts to deliver high-quality nanoparticles in terms of chemical properties, morphology, stability and biocompatibility. After reaching this goal, in vitro experiments were performed with selected nanomaterials to investigate the potential of engineered MNP-RBC constructs in theranostic approaches.

A random effects branch-site model for detecting episodic diversifying selection.

Adaptive evolution frequently occurs in episodic bursts, localized to a few sites in a gene, and to a small number of lineages in a phylogenetic tree. A popular class of "branch-site" evolutionary models provides a statistical framework to search for evidence of such episodic selection. For computational tractability, current branch-site models unrealistically assume that all branches in the tree can be partitioned a priori into two rigid classes--"foreground" branches that are allowed to undergo diversifying selective bursts and "background" branches that are negatively selected or neutral. We demonstrate that this assumption leads to unacceptably high rates of false positives or false negatives when the evolutionary process along background branches strongly deviates from modeling assumptions. To address this problem, we extend Felsenstein's pruning algorithm to allow efficient likelihood computations for models in which variation over branches (and not just sites) is described in the random effects likelihood framework. This enables us to model the process at every branch-site combination as a mixture of three Markov substitution models--our model treats the selective class of every branch at a particular site as an unobserved state that is chosen independently of that at any other branch. When benchmarked on a previously published set of simulated sequences, our method consistently matched or outperformed existing branch-site tests in terms of power and error rates. Using three empirical data sets, previously analyzed for episodic selection, we discuss how modeling assumptions can influence inference in practical situations.