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SummaryPhylogenetics plays a crucial role in the interpretation of genomic data1. Phylogenetic analyses of SARS-CoV-2 genomes have allowed the detailed study of the virus’s origins2, of its international3,4 and local4–9 spread, and of the emergence10 and reproductive success11 of new variants, among many applications. These analyses have been enabled by the unparalleled volumes of genome sequence data generated and employed to study and help contain the pandemic12. However, preferred model-based phylogenetic approaches including maximum likelihood and Bayesian methods, mostly based on Felsenstein’s ‘pruning’ algorithm13,14, cannot scale to the size of the datasets from the current pandemic4,15, hampering our understanding of the virus’s evolution and transmission16. We present new approaches, based on reworking Felsenstein’s algorithm, for likelihood-based phylogenetic analysis of epidemiological genomic datasets at unprecedented scales. We exploit near-certainty regarding ancestral genomes, and the similarities between closely related and densely sampled genomes, to greatly reduce computational demands for memory and time. Combined with new methods for searching amongst candidate evolutionary trees, this results in our MAPLE (‘MAximum Parsimonious Likelihood Estimation’) software giving better results than popular approaches such as FastTree 217, IQ-TREE 218, RAxML-NG19 and UShER15. Our approach therefore allows complex and accurate proba-bilistic phylogenetic analyses of millions of microbial genomes, extending the reach of genomic epidemiology. Future epidemiological datasets are likely to be even larger than those currently associated with COVID-19, and other disciplines such as metagenomics and biodiversity science are also generating huge numbers of genome sequences20–22. Our methods will permit continued use of preferred likelihood-based phylogenetic analyses.

MotivationPhylogeny inference via maximum likelihood is NP-hard. Current methods make simplifying assumptions such as stationarity, homogeneity, and time-reversibility for computational ease. The stationarity assumption is violated by empirical observations of GC content evolution, and might systematically bias phylogeny inference. The general Markov model (GM) is a suitable alternative to stationary models because it allows for the evolution of GC content. Related work on the GM model has predominantly focused on inferring unrooted trees using either the log-det distance or phylogenetic invariants.MethodsWe adapted the structural EM framework to perform tree search under the GM model (SEM-GM). Additionally, we implemented a minimum spanning tree framework called MST-backbone to improve the scalability of SEM-GM by constraining search through tree space. MST-backbone(SEM-GM) was used to infer unrooted trees, which are subsequently rooted under the GM model; the latter procedure is called rSEM-GM. We compared our method with RAxML-NG, IQ-TREE, and FastTree on simulated data. We validated our methods on six empirical datasets.ResultsEstimated experimental phylogenies are rooted with high accuracy under the GM model (recall ranging from 80% to 94%). However, virus phylogenies are not realistically rooted, suggesting that the GM model may be overtrained on some empirical datasets. The comparative analysis of simulated data suggests that MST-backbone(SEM-GM) and FastTree scale linearly whereas rSEM-GM, RAxML-NG, and IQ-TREE scale quadratically. The results on empirical data suggest that it is not necessary to use the general time-reversible model for computational ease.Availabilityhttps://github.com/prabhavk/mst-backbone-sem-gmContactprabhav.kalaghatgi@molgen.mpg.deSupplementary informationSupplementary data are available online

The widely used model for evolutionary relationships is a bifurcating tree with all taxa/observations placed at the leaves. This is not appropriate if the taxa have been densely sampled across evolutionary time and may be in a direct ancestral relationship, or if there is not enough information to fully resolve all the branching points in the evolutionary tree. In this article, we present a fast distance-based agglomeration method called family-joining (FJ) for constructing so-called generally labeled trees in which taxa may be placed at internal vertices and the tree may contain polytomies. FJ constructs such trees on the basis of pairwise distances and a distance threshold. We tested three methods for threshold selection, FJ-AIC, FJ-BIC, and FJ-CV, which minimize Akaike information criterion, Bayesian information criterion, and cross-validation error, respectively. When compared with related methods on simulated data, FJ-BIC was among the best at reconstructing the correct tree across a wide range of simulation scenarios. FJ-BIC was applied to HIV sequences sampled from individuals involved in a known transmission chain. The FJ-BIC tree was found to be compatible with almost all transmission events. On average, internal branches in the FJ-BIC tree have higher bootstrap support than branches in the leaf-labeled bifurcating tree constructed using RAxML. 36% and 25% of the internal branches in the FJ-BIC tree and RAxML tree, respectively, have bootstrap support greater than 70%. To the best of our knowledge the method presented here is the first attempt at modeling evolutionary relationships using generally labeled trees.

Background HCV infections can now be completely cured, thanks to the currently marketed direct-acting antivirals (DAAs). It is known that HCV patients carry viral populations with baseline polymorphisms and/or mutations that make them resistant against some of these DAAs, which can negatively impact the patient's treatment outcome. Using complete HCV coding sequences isolated from 1,306 treatment-naive patients of genotypes (GTs) 1, 2, 3, 4 and 6 from around the globe, we studied the prevalence of baseline resistance-associated polymorphisms (RAPs) and resistance mutations (RMs) against DAAs that are currently on the market or in clinical trials. Methods The HCV genome sequences used in this study were retrieved from the NCBI database. RAPs and RMs, with reference to HCV GT1a, were identified using the HCV Geno2pheno web server. Results Nearly 50% of the total amino acid positions (including NS3 protease, NS5A and NS5B) studied are baseline polymorphisms that differentiated one GT from the rest. A proportion of these baseline polymorphisms and baseline non-polymorphic RMs could confer a significant increase in resistance against DAAs. Conclusions In this study, we show the presence and prevalence of RAPs and RMs in DAA treatment-naive patients against currently used DAAs or DAAs in clinical trials. Our study suggests that RAPs and RMs profiling of HCV patients should be performed before the start of the therapy. Our results should be relevant especially in low- and middle-income countries, where the patients have a large variation of GTs and subtypes, and where the generic HCV treatment is now increasingly available.

Hepatitis C virus (HCV) causes a major health burden and can be effectively treated by direct-acting antivirals (DAAs). The non-structural protein 5A (NS5A), which plays a role in the viral genome replication, is one of the DAAs&rsquo, targets. Resistance-associated viruses (RAVs) harbouring NS5A resistance-associated mutations (RAMs) have been described at baseline and after therapy failure. A mutation from glutamine to arginine at position 30 (Q30R) is a characteristic RAM for the HCV sub/genotype (GT) 1a, but arginine corresponds to the wild type in the GT-1b, still, GT-1b strains are susceptible to NS5A-inhibitors. In this study, we show that GT-1b strains with R30Q often display other specific NS5A substitutions, particularly in positions 24 and 34. We demonstrate that in GT-1b secondary substitutions usually happen after initial R30Q development in the phylogeny, and that the chemical properties of the corresponding amino acids serve to restore the positive charge in this region, acting as compensatory mutations. These findings may have implications for RAVs treatment.

Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations in minority populations. Thus, genotypic resistance testing based on NGS data can offer novel, treatment-relevant insights. Since existing web services for analyzing resistance in NGS samples are subject to long processing times and follow strictly rules-based approaches, we developed geno2pheno[ngs-freq], a web service for rapidly identifying drug resistance in HIV-1 and HCV samples. By relying on frequency files that provide the read counts of nucleotides or codons along a viral genome, the time-intensive step of processing raw NGS data is eliminated. Once a frequency file has been uploaded, consensus sequences are generated for a set of user-defined prevalence cutoffs, such that the constructed sequences contain only those nucleotides whose codon prevalence exceeds a given cutoff. After locally aligning the sequences to a set of references, resistance is predicted using the well-established approaches of geno2pheno[resistance] and geno2pheno[hcv]. geno2pheno[ngs-freq] can assist clinical decision making by enabling users to explore resistance in viral populations with different abundances and is freely available at http://ngs.geno2pheno.org.

A temporal increase in non-B subtypes has earlier been described in Sweden by us and we hypothesized that this increased viral heterogeneity may become a hotspot for the development of more complex and unique recombinant forms (URFs) if the epidemics converge. In the present study, we performed subtyping using four automated tools and phylogenetic analysis by RAxML of pol gene sequences (n = 5246) and HIV-1 near full-length genome (HIV-NFLG) sequences (n = 104). A CD4+ T-cell decline trajectory algorithm was used to estimate time of HIV infection. Transmission clusters were identified using the family-joining method. The analysis of HIV-NFLG and pol gene described 10.6% (11/104) and 2.6% (137/5246) of the strains as URFs, respectively. An increasing trend of URFs was observed in recent years by both approaches (p = 0·0082; p pol gene of all URFs identified 14 clusters with two to eight sequences. Larger transmission clusters of URFs (BF1 and 01B) were observed among MSM who mostly were sero-diagnosed in recent time. Understanding the increased appearance and transmission of URFs in recent years could have importance for public health interventions and the use of HIV-NFLG would provide better statistical support for such assessments.

The face of hepatitis C virus (HCV) therapy is changing dramatically. Direct-acting antiviral agents (DAAs) specifically targeting HCV proteins have been developed and entered clinical practice in 2011. However, despite high sustained viral response (SVR) rates of more than 90%, a fraction of patients do not eliminate the virus and in these cases treatment failure has been associated with the selection of drug resistance mutations (RAMs). RAMs may be prevalent prior to the start of treatment, or can be selected under therapy, and furthermore they can persist after cessation of treatment. Additionally, certain DAAs have been approved only for distinct HCV genotypes and may even have subtype specificity. Thus, sequence analysis before start of therapy is instrumental for managing DAA-based treatment strategies. We have created the interpretation system geno2pheno[HCV] (g2p[HCV]) to analyse HCV sequence data with respect to viral subtype and to predict drug resistance. Extensive reviewing and weighting of literature related to HCV drug resistance was performed to create a comprehensive list of drug resistance rules for inhibitors of the HCV protease in non-structural protein 3 (NS3-protease: Boceprevir, Paritaprevir, Simeprevir, Asunaprevir, Grazoprevir and Telaprevir), the NS5A replicase factor (Daclatasvir, Ledipasvir, Elbasvir and Ombitasvir), and the NS5B RNA-dependent RNA polymerase (Dasabuvir and Sofosbuvir). Upon submission of up to eight sequences, g2p[HCV] aligns the input sequences, identifies the genomic region(s), predicts the HCV geno- and subtypes, and generates for each DAA a drug resistance prediction report. g2p[HCV] offers easy-to-use and fast subtype and resistance analysis of HCV sequences, is continuously updated and freely accessible under http://hcv.geno2pheno.org/index.php. The system was partially validated with respect to the NS3-protease inhibitors Boceprevir, Telaprevir and Simeprevir by using data generated with recombinant, phenotypic cell culture assays obtained from patients' virus variants.

Motivation: Homology detection is a long-standing challenge in computational biology. To tackle this problem, typically all-versus-all BLAST results are coupled with data partitioning approaches resulting in clusters of putative homologous proteins. One of the main problems, however, has been widely neglected: all clustering tools need a density parameter that adjusts the number and size of the clusters. This parameter is crucial but hard to estimate without gold standard data at hand. Developing a gold standard, however, is a difficult and time consuming task. Having a reliable method for detecting clusters of homologous proteins between a huge set of species would open opportunities for better understanding the genetic repertoire of bacteria with different lifestyles. Results: Our main contribution is a method for identifying a suitable and robust density parameter for protein homology detection without a given gold standard. Therefore, we study the core genome of 89 actinobacteria. This allows us to incorporate background knowledge, i.e. the assumption that a set of evolutionarily closely related species should share a comparably high number of evolutionarily conserved proteins (emerging from phylum-specific housekeeping genes). We apply our strategy to find genes/proteins that are specific for certain actinobacterial lifestyles, i.e. different types of pathogenicity. The whole study was performed with transitivity clustering, as it only requires a single intuitive density parameter and has been shown to be well applicable for the task of protein sequence clustering. Note, however, that the presented strategy generally does not depend on our clustering method but can easily be adapted to other clustering approaches. Availability: All results are publicly available at http://transclust.mmci.uni-saarland.de/actino_core/ or as Supplementary Material of this article. Contact: roettger@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Pathogenic Escherichia coli, such as Enterohemorrhagic E. coli (EHEC) and Enteroaggregative E. coli (EAEC), are globally widespread bacteria. Some may cause the hemolytic uremic syndrome (HUS). Varying strains cause epidemics all over the world. Recently, we observed an epidemic outbreak of a multi-resistant EHEC strain in Western Europe, mainly in Germany. The Robert Koch Institute reports >4300 infections and >50 deaths (July, 2011). Farmers lost several million EUR since the origin of infection was unclear. Here, we contribute to the currently ongoing research with a computer-aided study of EHEC transcriptional regulatory interactions, a network of genetic switches that control, for instance, pathogenicity, survival and reproduction of bacterial cells. Our strategy is to utilize knowledge of gene regulatory networks from the evolutionary relative E. coli K-12, a harmless strain mainly used for wet lab studies. In order to provide high-potential candidates for human pathogenic E. coli bacteria, such as EHEC, we developed the integrated online database and an analysis platform EhecRegNet. We utilize 3489 known regulations from E. coli K-12 for predictions of yet unknown gene regulatory interactions in 16 human pathogens. For these strains we predict 40913 regulatory interactions. EhecRegNet is based on the identification of evolutionarily conserved regulatory sites within the DNA of the harmless E. coli K-12 and the pathogens. Identifying and characterizing EHEC's genetic control mechanism network on a large scale will allow for a better understanding of its survival and infection strategies. This will support the development of urgently needed new treatments. EhecRegNet is online viahttp://www.ehecregnet.de.

THE ECDC reports around 100,000 new infections in Europe and Central Asia in 2010 with large variance in incidence and dominant mode of transmission across different countries. This study reconstructed HIV transmission networks and investigated patterns of epidemic growth and spread from a Europe wide dataset of 30,000 patients. We find that the reconstructed transmission graph, where vertices represent patients and edges, a transmission between them, has has assortativity coeffienct (~ 0.6) which suggests endogenous growth of the HIV epidemic in Europe. Temporal evolution of the epidemic, which is estimated from reconstructed phylogenetic trees, indicates episodic growth which probably reflects the community structure of the contact network. This poster will be presented at ECCB 2012.

For neuroblastoma, the most common extracranial tumour of childhood, identification of new biomarkers and potential therapeutic targets is mandatory to improve risk stratification and survival rates. MicroRNAs are deregulated in most cancers, including neuroblastoma. In this study, we analysed 430 miRNAs in 69 neuroblastomas by stem-loop RT-qPCR. Prediction of event-free survival (EFS) with support vector machines (SVM) and actual survival times with Cox regression-based models (CASPAR) were highly accurate and were independently validated. SVM-accuracy for prediction of EFS was 88.7% (95% CI: 88.5-88.8%). For CASPAR-based predictions, 5y-EFS probability was 0.19% (95% CI: 0-38%) in the CASPAR-predicted short survival group compared with 0.78% (95%CI: 64-93%) in the CASPAR-predicted long survival group. Both classifiers were validated on an independent test set yielding accuracies of 94.74% (SVM) and 5y-EFS probabilities as 0.25 (95% CI: 0.0-0.55) for short versus 1 ± 0.0 for long survival (CASPAR), respectively. Amplification of the MYCN oncogene was highly correlated with deregulation of miRNA expression. In addition, 37 miRNAs correlated with TrkA expression, a marker of excellent outcome, and 6 miRNAs further analysed in vitro were regulated upon TrkA transfection, suggesting a functional relationship. Expression of the most significant TrkA-correlated miRNA, miR-542-5p, also discriminated between local and metastatic disease and was inversely correlated with MYCN amplification and event-free survival. We conclude that neuroblastoma patient outcome prediction using miRNA expression is feasible and effective. Studies testing miRNA-based predictors in comparison to and in combination with mRNA and aCGH information should be initiated. Specific miRNAs (e.g., miR-542-5p) might be important in neuroblastoma tumour biology, and qualify as potential therapeutic targets.