25,364 research outputs found
A two-phase approach for detecting recombination in nucleotide sequences
Genetic recombination can produce heterogeneous phylogenetic histories within
a set of homologous genes. Delineating recombination events is important in the
study of molecular evolution, as inference of such events provides a clearer
picture of the phylogenetic relationships among different gene sequences or
genomes. Nevertheless, detecting recombination events can be a daunting task,
as the performance of different recombinationdetecting approaches can vary,
depending on evolutionary events that take place after recombination. We
recently evaluated the effects of postrecombination events on the prediction
accuracy of recombination-detecting approaches using simulated nucleotide
sequence data. The main conclusion, supported by other studies, is that one
should not depend on a single method when searching for recombination events.
In this paper, we introduce a two-phase strategy, applying three statistical
measures to detect the occurrence of recombination events, and a Bayesian
phylogenetic approach in delineating breakpoints of such events in nucleotide
sequences. We evaluate the performance of these approaches using simulated
data, and demonstrate the applicability of this strategy to empirical data. The
two-phase strategy proves to be time-efficient when applied to large datasets,
and yields high-confidence results.Comment: 5 pages, 3 figures. Chan CX, Beiko RG and Ragan MA (2007). A
two-phase approach for detecting recombination in nucleotide sequences. In
Hazelhurst S and Ramsay M (Eds) Proceedings of the First Southern African
Bioinformatics Workshop, 28-30 January, Johannesburg, 9-1
Addressing the shortcomings of three recent bayesian methods for detecting interspecific recombination in DNA sequence alignments
We address a potential shortcoming of three probabilistic models for detecting interspecific recombination in DNA sequence alignments: the multiple change-point model (MCP) of Suchard et al. (2003), the dual multiple change-point model (DMCP) of Minin et al. (2005), and the phylogenetic factorial hidden Markov model (PFHMM) of Husmeier (2005). These models are based on the Bayesian paradigm, which requires the solution of an integral over the space of branch lengths. To render this integration analytically tractable, all three models make the same assumption that the vectors of branch lengths of the phylogenetic tree are independent among sites. While this approximation reduces the computational complexity considerably, we show that it leads to the systematic prediction of spurious topology changes in the Felsenstein zone, that is, the area in the branch lengths configuration space where maximum parsimony consistently infers the wrong topology due to long-branch attraction. We apply two Bayesian hypothesis tests, based on an inter- and an intra-model approach to estimating the marginal likelihood. We then propose a revised model that addresses these shortcomings, and compare it with the aforementioned models on a set of synthetic DNA sequence alignments systematically generated around the Felsenstein zone
Einstein Cluster Alignments Revisited
We have examined whether the major axes of rich galaxy clusters tend to point
toward their nearest neighboring cluster. We have used the data of Ulmer,
McMillan, and Kowalski, who used position angles based on X-ray morphology. We
also studied a subset of this sample with updated positions and distances from
the MX Northern Abell Cluster Survey (for rich clusters () with well
known redshifts). A Kolmogorov-Smirnov (KS) test showed no significant signal
for nonrandom angles on any scale Mpc. However, refining the
null hypothesis with the Wilcoxon rank-sum test, we found a high confidence
signal for alignment. Confidence levels increase to a high of 99.997% as only
near neighbors which are very close are considered. We conclude there is a
strong alignment signal in the data, consistent with gravitational instability
acting on Gaussian perturbations.Comment: Minor revisions. To be published in Ap
Distinguishing regional from within-codon rate heterogeneity in DNA sequence alignments
We present an improved phylogenetic factorial hidden Markov model (FHMM) for detecting two types of mosaic structures in DNA sequence alignments, related to (1) recombination and (2) rate heterogeneity. The focus of the present work is on improving the modelling of the latter aspect. Earlier papers have modelled different degrees of rate heterogeneity with separate hidden states of the FHMM. This approach fails to appreciate the intrinsic difference between two types of rate heterogeneity: long-range regional effects, which are potentially related to differences in the selective pressure, and the short-term periodic patterns within the codons, which merely capture the signature of the genetic code. We propose an improved model that explicitly distinguishes between these two effects, and we assess its performance on a set of simulated DNA sequence alignments
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
Sequence-based protein homology detection has been extensively studied and so
far the most sensitive method is based upon comparison of protein sequence
profiles, which are derived from multiple sequence alignment (MSA) of sequence
homologs in a protein family. A sequence profile is usually represented as a
position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and
accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This
paper presents a new homology detection method MRFalign, consisting of three
key components: 1) a Markov Random Fields (MRF) representation of a protein
family; 2) a scoring function measuring similarity of two MRFs; and 3) an
efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning
two MRFs. Compared to HMM that can only model very short-range residue
correlation, MRFs can model long-range residue interaction pattern and thus,
encode information for the global 3D structure of a protein family.
Consequently, MRF-MRF comparison for remote homology detection shall be much
more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that
MRFalign outperforms several popular HMM or PSSM-based methods in terms of both
alignment accuracy and remote homology detection and that MRFalign works
particularly well for mainly beta proteins. For example, tested on the
benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM
succeed on 48% and 52% of proteins, respectively, at superfamily level, and on
15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign
succeeds on 57.3% and 42.5% of proteins at superfamily and fold level,
respectively. This study implies that long-range residue interaction patterns
are very helpful for sequence-based homology detection. The software is
available for download at http://raptorx.uchicago.edu/download/.Comment: Accepted by both RECOMB 2014 and PLOS Computational Biolog
The impact of mutation and gene conversion on the local diversification of antigen genes in African trypanosomes
Patterns of genetic diversity in parasite antigen gene families hold important information about their potential to generate antigenic variation within and between hosts. The evolution of such gene families is typically driven by gene duplication, followed by point mutation and gene conversion. There is great interest in estimating the rates of these processes from molecular sequences for understanding the evolution of the pathogen and its significance for infection processes. In this study, a series of models are constructed to investigate hypotheses about the nucleotide diversity patterns between closely related gene sequences from the antigen gene archive of the African trypanosome, the protozoan parasite causative of human sleeping sickness in Equatorial Africa. We use a hidden Markov model approach to identify two scales of diversification: clustering of sequence mismatches, a putative indicator of gene conversion events with other lower-identity donor genes in the archive, and at a sparser scale, isolated mismatches, likely arising from independent point mutations. In addition to quantifying the respective probabilities of occurrence of these two processes, our approach yields estimates for the gene conversion tract length distribution and the average diversity contributed locally by conversion events. Model fitting is conducted using a Bayesian framework. We find that diversifying gene conversion events with lower-identity partners occur at least five times less frequently than point mutations on variant surface glycoprotein (VSG) pairs, and the average imported conversion tract is between 14 and 25 nucleotides long. However, because of the high diversity introduced by gene conversion, the two processes have almost equal impact on the per-nucleotide rate of sequence diversification between VSG subfamily members. We are able to disentangle the most likely locations of point mutations and conversions on each aligned gene pair
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
A new procedure to analyze RNA non-branching structures
RNA structure prediction and structural motifs analysis are challenging tasks in the investigation of RNA function. We propose a novel procedure to detect structural motifs shared between two RNAs (a reference and a target). In particular, we developed two core modules: (i) nbRSSP_extractor, to assign a unique structure to the reference RNA encoded by a set of non-branching structures; (ii) SSD_finder, to detect structural motifs that the target RNA shares with the reference, by means of a new score function that rewards the relative distance of the target non-branching structures compared to the reference ones. We integrated these algorithms with already existing software to reach a coherent pipeline able to perform the following two main tasks: prediction of RNA structures (integration of RNALfold and nbRSSP_extractor) and search for chains of matches (integration of Structator and SSD_finder)
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