3,013 research outputs found
Accurate reconstruction of insertion-deletion histories by statistical phylogenetics
The Multiple Sequence Alignment (MSA) is a computational abstraction that
represents a partial summary either of indel history, or of structural
similarity. Taking the former view (indel history), it is possible to use
formal automata theory to generalize the phylogenetic likelihood framework for
finite substitution models (Dayhoff's probability matrices and Felsenstein's
pruning algorithm) to arbitrary-length sequences. In this paper, we report
results of a simulation-based benchmark of several methods for reconstruction
of indel history. The methods tested include a relatively new algorithm for
statistical marginalization of MSAs that sums over a stochastically-sampled
ensemble of the most probable evolutionary histories. For mammalian
evolutionary parameters on several different trees, the single most likely
history sampled by our algorithm appears less biased than histories
reconstructed by other MSA methods. The algorithm can also be used for
alignment-free inference, where the MSA is explicitly summed out of the
analysis. As an illustration of our method, we discuss reconstruction of the
evolutionary histories of human protein-coding genes.Comment: 28 pages, 15 figures. arXiv admin note: text overlap with
arXiv:1103.434
Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires
The adaptive immune system recognizes antigens via an immense array of
antigen-binding antibodies and T-cell receptors, the immune repertoire. The
interrogation of immune repertoires is of high relevance for understanding the
adaptive immune response in disease and infection (e.g., autoimmunity, cancer,
HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the
quantitative and molecular-level profiling of immune repertoires thereby
revealing the high-dimensional complexity of the immune receptor sequence
landscape. Several methods for the computational and statistical analysis of
large-scale AIRR-seq data have been developed to resolve immune repertoire
complexity in order to understand the dynamics of adaptive immunity. Here, we
review the current research on (i) diversity, (ii) clustering and network,
(iii) phylogenetic and (iv) machine learning methods applied to dissect,
quantify and compare the architecture, evolution, and specificity of immune
repertoires. We summarize outstanding questions in computational immunology and
propose future directions for systems immunology towards coupling AIRR-seq with
the computational discovery of immunotherapeutics, vaccines, and
immunodiagnostics.Comment: 27 pages, 2 figure
Probabilistic Graphical Model Representation in Phylogenetics
Recent years have seen a rapid expansion of the model space explored in
statistical phylogenetics, emphasizing the need for new approaches to
statistical model representation and software development. Clear communication
and representation of the chosen model is crucial for: (1) reproducibility of
an analysis, (2) model development and (3) software design. Moreover, a
unified, clear and understandable framework for model representation lowers the
barrier for beginners and non-specialists to grasp complex phylogenetic models,
including their assumptions and parameter/variable dependencies.
Graphical modeling is a unifying framework that has gained in popularity in
the statistical literature in recent years. The core idea is to break complex
models into conditionally independent distributions. The strength lies in the
comprehensibility, flexibility, and adaptability of this formalism, and the
large body of computational work based on it. Graphical models are well-suited
to teach statistical models, to facilitate communication among phylogeneticists
and in the development of generic software for simulation and statistical
inference.
Here, we provide an introduction to graphical models for phylogeneticists and
extend the standard graphical model representation to the realm of
phylogenetics. We introduce a new graphical model component, tree plates, to
capture the changing structure of the subgraph corresponding to a phylogenetic
tree. We describe a range of phylogenetic models using the graphical model
framework and introduce modules to simplify the representation of standard
components in large and complex models. Phylogenetic model graphs can be
readily used in simulation, maximum likelihood inference, and Bayesian
inference using, for example, Metropolis-Hastings or Gibbs sampling of the
posterior distribution
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
DM-PhyClus: A Bayesian phylogenetic algorithm for infectious disease transmission cluster inference
Background. Conventional phylogenetic clustering approaches rely on arbitrary
cutpoints applied a posteriori to phylogenetic estimates. Although in practice,
Bayesian and bootstrap-based clustering tend to lead to similar estimates, they
often produce conflicting measures of confidence in clusters. The current study
proposes a new Bayesian phylogenetic clustering algorithm, which we refer to as
DM-PhyClus, that identifies sets of sequences resulting from quick transmission
chains, thus yielding easily-interpretable clusters, without using any ad hoc
distance or confidence requirement. Results. Simulations reveal that DM-PhyClus
can outperform conventional clustering methods, as well as the Gap procedure, a
pure distance-based algorithm, in terms of mean cluster recovery. We apply
DM-PhyClus to a sample of real HIV-1 sequences, producing a set of clusters
whose inference is in line with the conclusions of a previous thorough
analysis. Conclusions. DM-PhyClus, by eliminating the need for cutpoints and
producing sensible inference for cluster configurations, can facilitate
transmission cluster detection. Future efforts to reduce incidence of
infectious diseases, like HIV-1, will need reliable estimates of transmission
clusters. It follows that algorithms like DM-PhyClus could serve to better
inform public health strategies
Reconstructing phylogeny from RNA secondary structure via simulated evolution
DNA sequences of genes encoding functional RNA molecules (e.g., ribosomal RNAs) are commonly used in phylogenetics (i.e. to infer evolutionary history). Trees derived from ribosomal RNA (rRNA) sequences, however, are inconsistent with other molecular data in investigations of deep branches in the tree of life. Since much of te functional constraints on the gene products (i.e. RNA molecules) relate to three-dimensional structure, rather than their actual sequences, accumulated mutations in the gene sequences may obscure phylogenetic signal over very large evolutionary time-scales. Variation in structure, however, may be suitable for phylogenetic inference even under extreme sequence divergence. To evaluate qualitatively the manner in which structural evolution relates to sequence change, we simulated the evolution of RNA sequences under various constraints on structural change
Consistency and convergence rate of phylogenetic inference via regularization
It is common in phylogenetics to have some, perhaps partial, information
about the overall evolutionary tree of a group of organisms and wish to find an
evolutionary tree of a specific gene for those organisms. There may not be
enough information in the gene sequences alone to accurately reconstruct the
correct "gene tree." Although the gene tree may deviate from the "species tree"
due to a variety of genetic processes, in the absence of evidence to the
contrary it is parsimonious to assume that they agree. A common statistical
approach in these situations is to develop a likelihood penalty to incorporate
such additional information. Recent studies using simulation and empirical data
suggest that a likelihood penalty quantifying concordance with a species tree
can significantly improve the accuracy of gene tree reconstruction compared to
using sequence data alone. However, the consistency of such an approach has not
yet been established, nor have convergence rates been bounded. Because
phylogenetics is a non-standard inference problem, the standard theory does not
apply. In this paper, we propose a penalized maximum likelihood estimator for
gene tree reconstruction, where the penalty is the square of the
Billera-Holmes-Vogtmann geodesic distance from the gene tree to the species
tree. We prove that this method is consistent, and derive its convergence rate
for estimating the discrete gene tree structure and continuous edge lengths
(representing the amount of evolution that has occurred on that branch)
simultaneously. We find that the regularized estimator is "adaptive fast
converging," meaning that it can reconstruct all edges of length greater than
any given threshold from gene sequences of polynomial length. Our method does
not require the species tree to be known exactly; in fact, our asymptotic
theory holds for any such guide tree.Comment: 34 pages, 5 figures. To appear on The Annals of Statistic
Using Avida to test the effects of natural selection on phylogenetic reconstruction methods
Phylogenetic trees group organisms by their ancestral relationships. There are a number of distinct algorithms used to reconstruct these trees from molecular sequence data, but different methods sometimes give conflicting results. Since there are few precisely known phylogenies, simulations are typically used to test the quality of reconstruction algorithms. These simulations randomly evolve strings of symbols to produce a tree, and then the algorithms are run with the tree leaves as inputs. Here we use Avida to test two widely used reconstruction methods, which gives us the chance to observe the effect of natural selection on tree reconstruction. We find that if the organisms undergo natural selection between branch points, the methods will be successful even on very large time scales. However, these algorithms often falter when selection is absent
On the role of metaheuristic optimization in bioinformatics
Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics
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