Comparative evaluation of word composition distances for the recognition of SCOP relationships

Vinga, Susana; Gouveia-Oliveira, Rodrigo; Almeida, Jonas S.

doi:10.1093/bioinformatics/btg392

Cited by 41 publications

(43 citation statements)

References 26 publications

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“…The W-metric (Vinga et al, 2004) weighs differences between all pairs of amino acids by their entries in matrix W. Here, we use BLOSUM62 (Henikoff and Henikoff, 1992).…”

Section: Alignment-free Methodsmentioning

confidence: 99%

Is Multiple-Sequence Alignment Required for Accurate Inference of Phylogeny?

Höhl

Ragan

2007

Systematic Biology

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The process of inferring phylogenetic trees from molecular sequences almost always starts with a multiple alignment of these sequences but can also be based on methods that do not involve multiple sequence alignment. Very little is known about the accuracy with which such alignment-free methods recover the correct phylogeny or about the potential for increasing their accuracy. We conducted a large-scale comparison of ten alignment-free methods, among them one new approach that does not calculate distances and a faster variant of our pattern-based approach; all distance-based alignment-free methods are freely available from http://www.bioinformatics.org.au (as Python package decaf+py). We show that most methods exhibit a higher overall reconstruction accuracy in the presence of high among-site rate variation. Under all conditions that we considered, variants of the pattern-based approach were significantly better than the other alignment-free methods. The new pattern-based variant achieved a speed-up of an order of magnitude in the distance calculation step, accompanied by a small loss of tree reconstruction accuracy. A method of Bayesian inference from k-mers did not improve on classical alignment-free (and distance-based) methods but may still offer other advantages due to its Bayesian nature. We found the optimal word length k of word-based methods to be stable across various data sets, and we provide parameter ranges for two different alphabets. The influence of these alphabets was analyzed to reveal a trade-off in reconstruction accuracy between long and short branches. We have mapped the phylogenetic accuracy for many alignment-free methods, among them several recently introduced ones, and increased our understanding of their behavior in response to biologically important parameters. In all experiments, the pattern-based approach emerged as superior, at the expense of higher resource consumption. Nonetheless, no alignment-free method that we examined recovers the correct phylogeny as accurately as does an approach based on maximum-likelihood distance estimates of multiply aligned sequences.

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“…The W-metric (Vinga et al, 2004) weighs differences between all pairs of amino acids by their entries in matrix W. Here, we use BLOSUM62 (Henikoff and Henikoff, 1992).…”

Section: Alignment-free Methodsmentioning

confidence: 99%

Is Multiple-Sequence Alignment Required for Accurate Inference of Phylogeny?

Höhl

Ragan

2007

Systematic Biology

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“…Vinga et al (2004) found their results virtually the same for different matrices (BLOSUM62, BLOSUM50, BLOSUM40 and PAM250); we use BLOSUM62 ( Henikoff and Henikoff 1992). …”

Section: Previous Workmentioning

confidence: 89%

“…Vinga et al (2004) introduced the W-metric which they categorise as “word-based” but we note that for amino acid sequences (to which it was applied originally), it effectively operates on 1-mers only:…”

Section: Previous Workmentioning

confidence: 99%

Pattern-Based Phylogenetic Distance Estimation and Tree Reconstruction

Höhl

Rigoutsos

Ragan

2006

Evol Bioinform Online

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We have developed an alignment-free method that calculates phylogenetic distances using a maximum-likelihood approach for a model of sequence change on patterns that are discovered in unaligned sequences. To evaluate the phylogenetic accuracy of our method, and to conduct a comprehensive comparison of existing alignment-free methods (freely available as Python package at http://www.bioinformatics.org.au), we have created a data set of reference trees covering a wide range of phylogenetic distances. Amino acid sequences were evolved along the trees and input to the tested methods; from their calculated distances we infered trees whose topologies we compared to the reference trees.We find our pattern-based method statistically superior to all other tested alignment-free methods. We also demonstrate the general advantage of alignment-free methods over an approach based on automated alignments when sequences violate the assumption of collinearity. Similarly, we compare methods on empirical data from an existing alignment benchmark set that we used to derive reference distances and trees. Our pattern-based approach yields distances that show a linear relationship to reference distances over a substantially longer range than other alignment-free methods. The pattern-based approach outperforms alignment-free methods and its phylogenetic accuracy is statistically indistinguishable from alignment-based distances.

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“…King and Guda [48] presented an n-gram-based Bayesian classifier that predicts the localization of a protein sequence. The classification accuracy of n-gram composition metrics was reviewed by Vinga and Almeida [8,49], together with a new definition of distance between protein sequences. Volkovich et al [50] applied n-grams to the classification of DNA sequences considered as text over the four-letter alphabet {A, C, G, T}.…”

Section: N-gramsmentioning

confidence: 99%

n-Gram characterization of genomic islands in bacterial genomes

Pavlović-Laetić

Mitić

Beljanski³

2009

Computer Methods and Programs in Biomedicine

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The paper presents a novel, n-gram-based method for analysis of bacterial genome segments known as genomic islands (GIs). Identification of GIs in bacterial genomes is an important task since many of them represent inserts that may contribute to bacterial evolution and pathogenesis. In order to characterize and distinguish GIs from rest of the genome, binary classification of islands based on n-gram frequency distribution have been performed. It consists of testing the agreement of islands n-gram frequency distributions with the complete genome and backbone sequence. In addition, a statistic based on the maximal order Markov model is used to identify significantly overrepresented and underrepresented n-grams in islands. The results may be used as a basis for Zipf-like analysis suggesting that some of the n-grams are overrepresented in a subset of islands and underrepresented in the backbone, or vice versa, thus complementing the binary classification. The method is applied to strain-specific regions in the Escherichia coli O157:H7 EDL933 genome (O-islands), resulting in two groups of O-islands with different n-gram characteristics. It refines a characterization based on other compositional features such as G+C content and codon usage, and may help in identification of GIs, and also in research and development of adequate drugs targeting virulence genes in them.

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Comparative evaluation of word composition distances for the recognition of SCOP relationships

Abstract: All MATLAB code used to generate the data is available upon request to the authors. Additional material available at http://bioinformatics.musc.edu/wmetric

Cited by 41 publications

References 26 publications

Is Multiple-Sequence Alignment Required for Accurate Inference of Phylogeny?

Is Multiple-Sequence Alignment Required for Accurate Inference of Phylogeny?

Pattern-Based Phylogenetic Distance Estimation and Tree Reconstruction

n-Gram characterization of genomic islands in bacterial genomes

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