9,286 research outputs found
Circumstances in which parsimony but not compatibility will be provably misleading
Phylogenetic methods typically rely on an appropriate model of how data
evolved in order to infer an accurate phylogenetic tree. For molecular data,
standard statistical methods have provided an effective strategy for extracting
phylogenetic information from aligned sequence data when each site (character)
is subject to a common process. However, for other types of data (e.g.
morphological data), characters can be too ambiguous, homoplastic or saturated
to develop models that are effective at capturing the underlying process of
change. To address this, we examine the properties of a classic but neglected
method for inferring splits in an underlying tree, namely, maximum
compatibility. By adopting a simple and extreme model in which each character
either fits perfectly on some tree, or is entirely random (but it is not known
which class any character belongs to) we are able to derive exact and explicit
formulae regarding the performance of maximum compatibility. We show that this
method is able to identify a set of non-trivial homoplasy-free characters, when
the number of taxa is large, even when the number of random characters is
large. By contrast, we show that a method that makes more uniform use of all
the data --- maximum parsimony --- can provably estimate trees in which {\em
none} of the original homoplasy-free characters support splits.Comment: 37 pages, 2 figure
Louse (Insecta : Phthiraptera) mitochondrial 12S rRNA secondary structure is highly variable
Lice are ectoparasitic insects hosted by birds and mammals. Mitochondrial 12S rRNA sequences obtained from lice show considerable length variation and are very difficult to align. We show that the louse 12S rRNA domain III secondary structure displays considerable variation compared to other insects, in both the shape and number of stems and loops. Phylogenetic trees constructed from tree edit distances between louse 12S rRNA structures do not closely resemble trees constructed from sequence data, suggesting that at least some of this structural variation has arisen independently in different louse lineages. Taken together with previous work on mitochondrial gene order and elevated rates of substitution in louse mitochondrial sequences, the structural variation in louse 12S rRNA confirms the highly distinctive nature of molecular evolution in these insects
Parametric inference of recombination in HIV genomes
Recombination is an important event in the evolution of HIV. It affects the
global spread of the pandemic as well as evolutionary escape from host immune
response and from drug therapy within single patients. Comprehensive
computational methods are needed for detecting recombinant sequences in large
databases, and for inferring the parental sequences.
We present a hidden Markov model to annotate a query sequence as a
recombinant of a given set of aligned sequences. Parametric inference is used
to determine all optimal annotations for all parameters of the model. We show
that the inferred annotations recover most features of established hand-curated
annotations. Thus, parametric analysis of the hidden Markov model is feasible
for HIV full-length genomes, and it improves the detection and annotation of
recombinant forms.
All computational results, reference alignments, and C++ source code are
available at http://bio.math.berkeley.edu/recombination/.Comment: 20 pages, 5 figure
Tree Contractions and Evolutionary Trees
An evolutionary tree is a rooted tree where each internal vertex has at least
two children and where the leaves are labeled with distinct symbols
representing species. Evolutionary trees are useful for modeling the
evolutionary history of species. An agreement subtree of two evolutionary trees
is an evolutionary tree which is also a topological subtree of the two given
trees. We give an algorithm to determine the largest possible number of leaves
in any agreement subtree of two trees T_1 and T_2 with n leaves each. If the
maximum degree d of these trees is bounded by a constant, the time complexity
is O(n log^2(n)) and is within a log(n) factor of optimal. For general d, this
algorithm runs in O(n d^2 log(d) log^2(n)) time or alternatively in O(n d
sqrt(d) log^3(n)) time
RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.
Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]
Quantifying evolutionary constraints on B cell affinity maturation
The antibody repertoire of each individual is continuously updated by the
evolutionary process of B cell receptor mutation and selection. It has recently
become possible to gain detailed information concerning this process through
high-throughput sequencing. Here, we develop modern statistical molecular
evolution methods for the analysis of B cell sequence data, and then apply them
to a very deep short-read data set of B cell receptors. We find that the
substitution process is conserved across individuals but varies significantly
across gene segments. We investigate selection on B cell receptors using a
novel method that side-steps the difficulties encountered by previous work in
differentiating between selection and motif-driven mutation; this is done
through stochastic mapping and empirical Bayes estimators that compare the
evolution of in-frame and out-of-frame rearrangements. We use this new method
to derive a per-residue map of selection, which provides a more nuanced view of
the constraints on framework and variable regions.Comment: Previously entitled "Substitution and site-specific selection driving
B cell affinity maturation is consistent across individuals
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