A New Combinatorial Approach to Sequence Comparison

Mantaci, Sabrina; Restivo, Antonio; Rosone, Giovanna; Sciortino, Marinella

doi:10.1007/s00224-007-9078-6

Cited by 34 publications

(23 citation statements)

References 21 publications

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“…In stark contrast to the plethora of studies investigating the accuracy of alignment-based tree reconstruction, surprisingly little is known about the accuracy of alignment-free methods, due to an almost complete absence of systematic and comprehensive large-scale studies from this field. In the context of phylogenetics, studies that introduce a new method have usually characterized its accuracy by comparing at most a handful reconstructed trees to "standard" trees derived from alignments, focusing on the clustering of subgroups and the placement of taxa instead of emphasizing numerical results (even though studies may otherwise be large-scale: Li et al, 2001;Otu and Sayood, 2003;Stuart et al, 2002aStuart et al, , 2002bBerry, 2003,2004;Qi et al, 2004;Chu et al, 2004;Hao and Qi, 2004;Yu and Anh, 2004;Yang et al, 2005;Mantaci et al, 2005). This makes it difficult to extract useful generalizations from this literature, especially considering that data sets vary from paper to paper.…”

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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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confidence: 99%

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

Höhl

Ragan

2007

Systematic Biology

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“…A class of similarity measures was defined by Mantaci et al [17] over an extension of the Burrows-Wheeler transform for string collections, called eBWT [16]. Later, Yang et al [34,35] recrafted the method by Mantaci et al and introduced the Burrows-Wheeler similarity distribution (BWSD) of two strings S 1 and S 2 based on the BWT of their concatenation.…”

Section: Introductionmentioning

confidence: 99%

Algorithms to compute the Burrows-Wheeler Similarity Distribution

Louza

Telles

Gog

et al. 2019

Theoretical Computer Science

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The Burrows-Wheeler transform (BWT) is a well studied text transformation widely used in data compression and text indexing. The BWT of two strings can also provide similarity measures between them, based on the observation that the more their symbols are intermixed in the transformation, the more the strings are similar. In this article we present two new algorithms to compute similarity measures based on the BWT for string collections. In particular, we present practical and theoretical improvements to the computation of the Burrows-Wheeler similarity distribution for all pairs of strings in a collection. Our algorithms take advantage of the BWT computed for the concatenation of all strings, and use compressed data structures that allow reducing the running time with a small memory footprint, as shown by a set of experiments with real and artificial datasets.

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“…There are also some other important methods such as Lemple-Ziv (LZ) complexity, Burrows-Wheeler (BW) transform [1,[23][24][25][26][27][28] which are based on compression algorithm, but do not actually apply the compression. The Burrows-Wheeler Transform (BWT) was introduced by Burrows and Wheeler in 1994, and is recently studied also from a combinatorial point of view [24][25][26][29][30][31][32]. Loosely speaking, BWT can map any finite string (word) over an ordered alphabet to another one which can be compressed easier.…”

Section: Introductionmentioning

confidence: 99%

“…Loosely speaking, BWT can map any finite string (word) over an ordered alphabet to another one which can be compressed easier. To compare the similarity of two sequences, Mantaci et al [25,30] introduced an extension of the Burrows-Wheeler Transform (EBWT) and defined a class of dissimilarity measures. While Yang et al [1] used a Burrows-Wheeler similarity distribution (BWSD) based on Burrows-Wheeler transform to express the similarity between two protein sequences.…”

Section: Introductionmentioning

confidence: 99%

A Burrows-Wheeler Transform Based Method for DNA Sequence Comparison

Li¹

2014

CBB

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Burrows-Wheeler Transform (BWT) is an extremely useful tool for textual lossless data compression. Recently, it has found many applications to bioinformatics. In this paper, BWT is introduced from the view of combinatorics, and then an equivalence relation on words is proposed which shows that the transformation captures some common features of equivalent words. Based on the rationale that to what extent two words differ can be evaluated by the factors excluding their common features, a matrix representation for a DNA sequence is defined by means of a "subtraction operation" between the original word and its BWT word, thus a DNA sequences is converted into a 24-D vector whose components are the spectral norms of such matrices. To illustrate the use of the quantitative characterization of DNA sequences, phylogenetic trees of the full β-globin genes of 15 species and the S segments of 13 hantaviruses are constructed. The resulting monophyletic clusters agree well with the established taxonomic groups.

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A New Combinatorial Approach to Sequence Comparison

Cited by 34 publications

References 21 publications

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

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

Algorithms to compute the Burrows-Wheeler Similarity Distribution

A Burrows-Wheeler Transform Based Method for DNA Sequence Comparison

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