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

BACKGROUND: Recent years have seen the emergence of genome annotation methods based on the phylo-grammar, a probabilistic model combining continuous-time Markov chains and stochastic grammars. Previously, phylo-grammars have required considerable effort to implement, limiting their adoption by computational biologists. RESULTS: We have developed an open source software tool, xrate, for working with reversible, irreversible or parametric substitution models combined with stochastic context-free grammars. xrate efficiently estimates maximum-likelihood parameters and phylogenetic trees using a novel "phylo-EM" algorithm that we describe. The grammar is specified in an external configuration file, allowing users to design new grammars, estimate rate parameters from training data and annotate multiple sequence alignments without the need to recompile code from source. We have used xrate to measure codon substitution rates and predict protein and RNA secondary structures. CONCLUSION: Our results demonstrate that xrate estimates biologically meaningful rates and makes predictions whose accuracy is comparable to that of more specialized tools

Estimating rate constants in Hidden Markov Models by the EM algorithm

The EM algorithm, e.g. as Baum-Welch reestimation, is an important tool for parameter estimation in discrete-time Hidden Markov Models. We present a direct reestimation of rate constants for applications in which the underlying Markov process is continuous in time. Previous estimation of discrete-time transition probabilities is not necessary. Keywords continuous-time Hidden Markov Model, parameterized Hidden Markov Model, EM algorithm, maximum likelihood estimate, parameter estimation SP EDICS number: SP 3.10 computational algorithms 2 I. Introduction Hidden Markov Models (HMM) were successfully applied in various fields of time series analysis, e.g. in speech recognition [1] or ion channel analysis [2], [3], [4]. For discretetime HMM the EM algorithm for maximum likelihood parameter estimation is well known [1], [5]. In some fields, however, the formulation as a discrete-time process does not appear to be completely adequate for the dynamics to be described. In the analysis of io..