XRate: a fast prototyping, training and annotation tool for phylo-grammars

Klosterman, P. S.; Uzilov, Andrew; Bendaña, Yuri R.; Bradley, Robert K.; Chao, Sharon; Kosiol, Carolin; Goldman, Nick; Holmes, Ian

doi:10.1186/1471-2105-7-428

Cited by 53 publications

(34 citation statements)

References 84 publications

(132 reference statements)

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“…We believe tools such as phylogrammers (Siepel and Haussler 2004a;Klosterman et al 2006), which use standard multiple alignments and rely on the patterns of substitutions, are likely to be joined by potentially richer methods using transducers (Holmes 2003) and other indel-aware evolutionary models (Diallo et al 2007). However, these methods are computationally demanding, and just as fixed alignments are normally assumed by substitutionbased methods, it seems likely that fixed indel histories or complete ancestral reconstructions will be assumed by these techniques.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide nucleotide-level mammalian ancestor reconstruction

Paten¹,

Herrero²,

Fitzgerald³

et al. 2008

Genome Res.

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179

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Recently attention has been turned to the problem of reconstructing complete ancestral sequences from large multiple alignments. Successful generation of these genome-wide reconstructions will facilitate a greater knowledge of the events that have driven evolution. We present a new evolutionary alignment modeler, called "Ortheus," for inferring the evolutionary history of a multiple alignment, in terms of both substitutions and, importantly, insertions and deletions. Based on a multiple sequence probabilistic transducer model of the type proposed by Holmes, Ortheus uses efficient stochastic graph-based dynamic programming methods. Unlike other methods, Ortheus does not rely on a single fixed alignment from which to work. Ortheus is also more scaleable than previous methods while being fast, stable, and open source. Large-scale simulations show that Ortheus performs close to optimally on a deep mammalian phylogeny. Simulations also indicate that significant proportions of errors due to insertions and deletions can be avoided by not assuming a fixed alignment. We additionally use a challenging hold-out cross-validation procedure to test the method; using the reconstructions to predict extant sequence bases, we demonstrate significant improvements over using closest extant neighbor sequences. Accompanying this paper, a new, public, and genome-wide set of Ortheus ancestor alignments provide an intriguing new resource for evolutionary studies in mammals. As a first piece of analysis, we attempt to recover "fossilized" ancestral pseudogenes. We confidently find 31 cases in which the ancestral sequence had a more complete sequence than any of the extant sequences.

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Section: Discussionmentioning

confidence: 99%

Genome-wide nucleotide-level mammalian ancestor reconstruction

Paten¹,

Herrero²,

Fitzgerald³

et al. 2008

Genome Res.

Self Cite

179

173

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“…The eigenvalues and eigenvectors might be complex, but they come in complex conjugate pairs and the final result is always real; for more information we refer to the Supplementary Information in [2]. If the CTMC is reversible, the decomposition can be done on a symmetric matrix obtained from Q (e.g.…”

Section: Resultsmentioning

confidence: 99%

“…[1] describes how the expectation-maximization (EM) algorithm can be applied to estimate the rate matrix from DNA sequence data observed in the leaves of an evolutionary tree. The EM algorithm is implemented in the software XRate [2] and has been applied in [3] for estimating empirical codon rate matrices. [1] uses the eigenvalue decomposition of the rate matrix to calculate the expected time spent in a state and the expected number of jumps between states.…”

Section: Introductionmentioning

confidence: 99%

Comparison of methods for calculating conditional expectations of sufficient statistics for continuous time Markov chains

Tataru

Hobolth

2011

BMC Bioinformatics

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BackgroundContinuous time Markov chains (CTMCs) is a widely used model for describing the evolution of DNA sequences on the nucleotide, amino acid or codon level. The sufficient statistics for CTMCs are the time spent in a state and the number of changes between any two states. In applications past evolutionary events (exact times and types of changes) are unaccessible and the past must be inferred from DNA sequence data observed in the present.ResultsWe describe and implement three algorithms for computing linear combinations of expected values of the sufficient statistics, conditioned on the end-points of the chain, and compare their performance with respect to accuracy and running time. The first algorithm is based on an eigenvalue decomposition of the rate matrix (EVD), the second on uniformization (UNI), and the third on integrals of matrix exponentials (EXPM). The implementation in R of the algorithms is available at http://www.birc.au.dk/~paula/.ConclusionsWe use two different models to analyze the accuracy and eight experiments to investigate the speed of the three algorithms. We find that they have similar accuracy and that EXPM is the slowest method. Furthermore we find that UNI is usually faster than EVD.

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“…To date, the most promising approach to infer ncRNA ancestors has been proposed in 2009 by D. Bradley and I. Holmes, who introduced an algorithm to calculate ancestral RNA secondary structures from an alignment [15], and use these structures to infer ancestral sequences using a maximum-likelihood approach on stochastic grammars [16]. Still, the time complexity of inferring ancestral structures can be prohibitive, and the specificity of the functional structure may not accommodate sufficiently large variations of this (secondary) structure to take advantage of this model.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of ancestral RNA sequences under multiple structural constraints

2016

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BackgroundSecondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA) families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors.MethodsIn this paper, we introduce achARNement, a maximum parsimony approach that, given two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree, simultaneously calculates ancestral RNA sequences for these two families.ResultsWe test our methodology on simulated data sets, and show that achARNement outperforms classical maximum parsimony approaches in terms of accuracy, but also reduces by several orders of magnitude the number of candidate sequences. To conclude this study, we apply our algorithms on the Glm clan and the FinP-traJ clan from the Rfam database.ConclusionsOur results show that our methods reconstruct small sets of high-quality candidate ancestors with better agreement to the two target structures than with classical approaches. Our program is freely available at: http://csb.cs.mcgill.ca/acharnement.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3105-4) contains supplementary material, which is available to authorized users.

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XRate: a fast prototyping, training and annotation tool for phylo-grammars

Cited by 53 publications

References 84 publications

Genome-wide nucleotide-level mammalian ancestor reconstruction

Genome-wide nucleotide-level mammalian ancestor reconstruction

Comparison of methods for calculating conditional expectations of sufficient statistics for continuous time Markov chains

Reconstruction of ancestral RNA sequences under multiple structural constraints

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