Evolutionary distances in the twilight zone -- a rational kernel approach

Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons and avoid potential alignment problems. However, in general they are not biologically motivated and ignore our knowledge about the evolution of sequences. Thus, it is still a major open question how to define an evolutionary distance metric between divergent sequences that makes use of indel information and known substitution models without the need for a multiple alignment. Here we propose a new evolutionary distance metric to close this gap. It uses finite-state transducers to create a biologically motivated similarity score which models substitutions and indels, and does not depend on a multiple sequence alignment. The sequence similarity score is defined in analogy to pairwise alignments and additionally has the positive semi-definite property. We describe its derivation and show in simulation studies and real-world examples that it is more accurate in reconstructing phylogenies than competing methods. The result is a new and accurate way of determining evolutionary distances in and beyond the twilight zone of sequence alignments that is suitable for large datasets.Comment: to appear in PLoS ON

Antiretroviral therapy partially improves the abnormalities of dendritic cells and lymphoid and myeloid regulatory populations in recently infected HIV patients

This study aimed to evaluate the effects of antiretroviral therapy on plasmacytoid (pDC) and myeloid (mDC) dendritic cells as well as regulatory T (Treg) and myeloid-derived suppressor (MDSC) cells in HIVinfected patients. Forty-five HIV-infected patients (20 of them with detectable HIV load −10 recently infected and 10 chronically infected patients-, at baseline and after antiretroviral therapy, and 25 with undetectable viral loads) and 20 healthy controls were studied. The influence of HIV load, bacterial translocation (measured by 16S rDNA and lipopolysaccharide-binding protein) and immune activation markers (interleukin –IL- 6, soluble CD14, activated T cells) was analyzed. The absolute numbers and percentages of pDC and mDC were significantly increased in patients. Patients with detectable viral load exhibited increased intracellular expression of IL-12 by mDCs and interferon -IFN- α by pDCs. Activated population markers were elevated, and the proportion of Tregs was significantly higher in HIV-infected patients. The MDSC percentage was similar in patients and controls, but the intracellular expression of IL-10 was significantly higher in patients. The achievement of undetectable HIV load after therapy did not modify bacterial translocation parameters, but induce an increase in pDCs, mDCs and MDSCs only in recently infected patients. Our data support the importance of early antiretroviral therapy to preserve dendritic and regulatory cell function in HIV-infected individuals

Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2

Spatiotemporal bias in genome sampling can severely confound discrete trait phylogeographic inference. This has impeded our ability to accurately track the spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, despite the availability of unprecedented numbers of SARS-CoV-2 genomes. Here, we present an approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2. We demonstrate that including travel history data yields i) more realistic hypotheses of virus spread and ii) higher posterior predictive accuracy compared to including only sampling location. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled locations. Our reconstructions reinforce specific transmission hypotheses suggested by the inclusion of travel history data, but also suggest alternative routes of virus migration that are plausible within the epidemiological context but are not apparent with current sampling efforts.status: publishe

MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics

<p>Abstract</p> <p>Background</p> <p>The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most comparative studies involving molecular sequences. Still, the choice of a phylogeny inference software is often dictated by a combination of parameters not related to the raw performance of the implemented algorithm(s) but rather by practical issues such as ergonomics and/or the availability of specific functionalities.</p> <p>Results</p> <p>Here, we present MetaPIGA v2.0, a robust implementation of several stochastic heuristics for large phylogeny inference (under maximum likelihood), including a Simulated Annealing algorithm, a classical Genetic Algorithm, and the Metapopulation Genetic Algorithm (metaGA) together with complex substitution models, discrete Gamma rate heterogeneity, and the possibility to partition data. MetaPIGA v2.0 also implements the Likelihood Ratio Test, the Akaike Information Criterion, and the Bayesian Information Criterion for automated selection of substitution models that best fit the data. Heuristics and substitution models are highly customizable through manual batch files and command line processing. However, MetaPIGA v2.0 also offers an extensive graphical user interface for parameters setting, generating and running batch files, following run progress, and manipulating result trees. MetaPIGA v2.0 uses standard formats for data sets and trees, is platform independent, runs in 32 and 64-bits systems, and takes advantage of multiprocessor and multicore computers.</p> <p>Conclusions</p> <p>The metaGA resolves the major problem inherent to classical Genetic Algorithms by maintaining high inter-population variation even under strong intra-population selection. Implementation of the metaGA together with additional stochastic heuristics into a single software will allow rigorous optimization of each heuristic as well as a meaningful comparison of performances among these algorithms. MetaPIGA v2.0 gives access both to high customization for the phylogeneticist, as well as to an ergonomic interface and functionalities assisting the non-specialist for sound inference of large phylogenetic trees using nucleotide sequences. MetaPIGA v2.0 and its extensive user-manual are freely available to academics at <url>http://www.metapiga.org</url>.</p

Genetic Signature of Rapid IHHNV (Infectious Hypodermal and Hematopoietic Necrosis Virus) Expansion in Wild Penaeus Shrimp Populations

Infectious hypodermal and hematopoietic necrosis virus (IHHNV) is a widely distributed single-stranded DNA parvovirus that has been responsible for major losses in wild and farmed penaeid shrimp populations on the northwestern Pacific coast of Mexico since the early 1990's. IHHNV has been considered a slow-evolving, stable virus because shrimp populations in this region have recovered to pre-epizootic levels, and limited nucleotide variation has been found in a small number of IHHNV isolates studied from this region. To gain insight into IHHNV evolutionary and population dynamics, we analyzed IHHNV capsid protein gene sequences from 89 Penaeus shrimp, along with 14 previously published sequences. Using Bayesian coalescent approaches, we calculated a mean rate of nucleotide substitution for IHHNV that was unexpectedly high (1.39×10−4 substitutions/site/year) and comparable to that reported for RNA viruses. We found more genetic diversity than previously reported for IHHNV isolates and highly significant subdivision among the viral populations in Mexican waters. Past changes in effective number of infections that we infer from Bayesian skyline plots closely correspond to IHHNV epizootiological historical records. Given the high evolutionary rate and the observed regional isolation of IHHNV in shrimp populations in the Gulf of California, we suggest regular monitoring of wild and farmed shrimp and restriction of shrimp movement as preventative measures for future viral outbreaks

Fast and accurate protein substructure searching with simulated annealing and GPUs

<p>Abstract</p> <p>Background</p> <p>Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching.</p> <p>Results</p> <p>We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU).</p> <p>Conclusions</p> <p>The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.</p

Mitogenomic phylogenetic analyses of the Delphinidae with an emphasis on the Globicephalinae

BACKGROUND: Previous DNA-based phylogenetic studies of the Delphinidae family suggest it has undergone rapid diversification, as characterised by unresolved and poorly supported taxonomic relationships (polytomies) for some of the species within this group. Using an increased amount of sequence data we test between alternative hypotheses of soft polytomies caused by rapid speciation, slow evolutionary rate and/or insufficient sequence data, and hard polytomies caused by simultaneous speciation within this family. Combining the mitogenome sequences of five new and 12 previously published species within the Delphinidae, we used Bayesian and maximum-likelihood methods to estimate the phylogeny from partitioned and unpartitioned mitogenome sequences. Further ad hoc tests were then conducted to estimate the support for alternative topologies. RESULTS: We found high support for all the relationships within our reconstructed phylogenies, and topologies were consistent between the Bayesian and maximum-likelihood trees inferred from partitioned and unpartitioned data. Resolved relationships included the placement of the killer whale (Orcinus orca) as sister taxon to the rest of the Globicephalinae subfamily, placement of the Risso's dolphin (Grampus griseus) within the Globicephalinae subfamily, removal of the white-beaked dolphin (Lagenorhynchus albirostris) from the Delphininae subfamily and the placement of the rough-toothed dolphin (Steno bredanensis) as sister taxon to the rest of the Delphininae subfamily rather than within the Globicephalinae subfamily. The additional testing of alternative topologies allowed us to reject all other putative relationships, with the exception that we were unable to reject the hypothesis that the relationship between L. albirostris and the Globicephalinae and Delphininae subfamilies was polytomic. CONCLUSION: Despite their rapid diversification, the increased sequence data yielded by mitogenomes enables the resolution of a strongly supported, bifurcating phylogeny, and a chronology of the divergences within the Delphinidae family. This highlights the benefits and potential application of large mitogenome datasets to resolve long-standing phylogenetic uncertainties

Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs

Background A standard procedure in many areas of bioinformatics is to use a single multiple sequence alignment (MSA) as the basis for various types of analysis. However, downstream results may be highly sensitive to the alignment used, and neglecting the uncertainty in the alignment can lead to significant bias in the resulting inference. In recent years, a number of approaches have been developed for probabilistic sampling of alignments, rather than simply generating a single optimum. However, this type of probabilistic information is currently not widely used in the context of downstream inference, since most existing algorithms are set up to make use of a single alignment. Results In this work we present a framework for representing a set of sampled alignments as a directed acyclic graph (DAG) whose nodes are alignment columns; each path through this DAG then represents a valid alignment. Since the probabilities of individual columns can be estimated from empirical frequencies, this approach enables sample-based estimation of posterior alignment probabilities. Moreover, due to conditional independencies between columns, the graph structure encodes a much larger set of alignments than the original set of sampled MSAs, such that the effective sample size is greatly increased. Conclusions The alignment DAG provides a natural way to represent a distribution in the space of MSAs, and allows for existing algorithms to be efficiently scaled up to operate on large sets of alignments. As an example, we show how this can be used to compute marginal probabilities for tree topologies, averaging over a very large number of MSAs. This framework can also be used to generate a statistically meaningful summary alignment; example applications show that this summary alignment is consistently more accurate than the majority of the alignment samples, leading to improvements in downstream tree inference. Implementations of the methods described in this article are available at http://statalign.github.io/WeaveAlign webcite