4,113 research outputs found
Detection of recombination in DNA multiple alignments with hidden markov models
CConventional phylogenetic tree estimation methods assume that all sites in a DNA multiple alignment have the same evolutionary history. This assumption is violated in data sets from certain bacteria and viruses due to recombination, a process that leads to the creation of mosaic sequences from different strains and, if undetected, causes systematic errors in phylogenetic tree estimation. In the current work, a hidden Markov model (HMM) is employed to detect recombination events in multiple alignments of DNA sequences. The emission probabilities in a given state are determined by the branching order (topology) and the branch lengths of the respective phylogenetic tree, while the transition probabilities depend on the global recombination probability. The present study improves on an earlier heuristic parameter optimization scheme and shows how the branch lengths and the recombination probability can be optimized in a maximum likelihood sense by applying the expectation maximization (EM) algorithm. The novel algorithm is tested on a synthetic benchmark problem and is found to clearly outperform the earlier heuristic approach. The paper concludes with an application of this scheme to a DNA sequence alignment of the argF gene from four Neisseria strains, where a likely recombination event is clearly detected
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
The EM Algorithm and the Rise of Computational Biology
In the past decade computational biology has grown from a cottage industry
with a handful of researchers to an attractive interdisciplinary field,
catching the attention and imagination of many quantitatively-minded
scientists. Of interest to us is the key role played by the EM algorithm during
this transformation. We survey the use of the EM algorithm in a few important
computational biology problems surrounding the "central dogma"; of molecular
biology: from DNA to RNA and then to proteins. Topics of this article include
sequence motif discovery, protein sequence alignment, population genetics,
evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Developing and applying heterogeneous phylogenetic models with XRate
Modeling sequence evolution on phylogenetic trees is a useful technique in
computational biology. Especially powerful are models which take account of the
heterogeneous nature of sequence evolution according to the "grammar" of the
encoded gene features. However, beyond a modest level of model complexity,
manual coding of models becomes prohibitively labor-intensive. We demonstrate,
via a set of case studies, the new built-in model-prototyping capabilities of
XRate (macros and Scheme extensions). These features allow rapid implementation
of phylogenetic models which would have previously been far more
labor-intensive. XRate's new capabilities for lineage-specific models,
ancestral sequence reconstruction, and improved annotation output are also
discussed. XRate's flexible model-specification capabilities and computational
efficiency make it well-suited to developing and prototyping phylogenetic
grammar models. XRate is available as part of the DART software package:
http://biowiki.org/DART .Comment: 34 pages, 3 figures, glossary of XRate model terminolog
A Seeded Genetic Algorithm for RNA Secondary Structural Prediction with Pseudoknots
This work explores a new approach in using genetic algorithm to predict RNA secondary structures with pseudoknots. Since only a small portion of most RNA structures is comprised of pseudoknots, the majority of structural elements from an optimal pseudoknot-free structure are likely to be part of the true structure. Thus seeding the genetic algorithm with optimal pseudoknot-free structures will more likely lead it to the true structure than a randomly generated population. The genetic algorithm uses the known energy models with an additional augmentation to allow complex pseudoknots. The nearest-neighbor energy model is used in conjunction with Turner’s thermodynamic parameters for pseudoknot-free structures, and the H-type pseudoknot energy estimation for simple pseudoknots. Testing with known pseudoknot sequences from PseudoBase shows that it out performs some of the current popular algorithms
Genomics and proteomics: a signal processor's tour
The theory and methods of signal processing are becoming increasingly important in molecular biology. Digital filtering techniques, transform domain methods, and Markov models have played important roles in gene identification, biological sequence analysis, and alignment. This paper contains a brief review of molecular biology, followed by a review of the applications of signal processing theory. This includes the problem of gene finding using digital filtering, and the use of transform domain methods in the study of protein binding spots. The relatively new topic of noncoding genes, and the associated problem of identifying ncRNA buried in DNA sequences are also described. This includes a discussion of hidden Markov models and context free grammars. Several new directions in genomic signal processing are briefly outlined in the end
The Mathematics of Phylogenomics
The grand challenges in biology today are being shaped by powerful
high-throughput technologies that have revealed the genomes of many organisms,
global expression patterns of genes and detailed information about variation
within populations. We are therefore able to ask, for the first time,
fundamental questions about the evolution of genomes, the structure of genes
and their regulation, and the connections between genotypes and phenotypes of
individuals. The answers to these questions are all predicated on progress in a
variety of computational, statistical, and mathematical fields.
The rapid growth in the characterization of genomes has led to the
advancement of a new discipline called Phylogenomics. This discipline results
from the combination of two major fields in the life sciences: Genomics, i.e.,
the study of the function and structure of genes and genomes; and Molecular
Phylogenetics, i.e., the study of the hierarchical evolutionary relationships
among organisms and their genomes. The objective of this article is to offer
mathematicians a first introduction to this emerging field, and to discuss
specific mathematical problems and developments arising from phylogenomics.Comment: 41 pages, 4 figure
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PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.
Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions
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