Use of the Burrows–Wheeler similarity distribution to the comparison of the proteins

Abstract

In this paper, we present an approach based on Burrows-Wheeler transform to compare the protein sequences. The strings representing amino acid sequences do not reflect the chemical physical properties better, and it is very hard to extract any key features by reading these long character strings directly. The use of the Burrows-Wheeler similarity distribution needs a suitable representation which can reflect some interesting properties of the proteins. For the comparison of the primary protein sequences we convert the protein sequences into digital codes by the Ponnuswamy hydrophobicity index, and for the comparison of the structure of the proteins we adjust the topology of protein structure strings, which are simple but useful representation of the secondary structure of proteins to match the Burrows-Wheeler similarity distribution. At last, some experiments show that the approach proposed in this paper is a powerful and useful tool for the comparison of proteins.

Algorithms to compute the Burrows-Wheeler Similarity Distribution

“…Yang et al [34,35] defined the following similarity measures based on the BWSD to compare S 1 and S 2 .…”

“…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. The authors evaluated similarity measures based on the expectation and Shannon entropy of the BWSD to efficiently construct phylogenetic trees for DNA and protein sequences, thus contributing to an alternative to alignment-based similarity measure among biological sequences.…”

“…In this article we present two new algorithms to compute the Burrows-Wheeler similarity distribution and we show how to efficiently compute BWSDbased distances among all pairs of strings in a collection. Our algorithms compute the BWT for the concatenation of all strings only once, instead of the pairwise construction of BWTs proposed by Yang [34,35], and use compressed data structures that allow reductions of the running time while still keeping a small memory usage, as shown by a set of experiments with real and artificial datasets. We also present both space-efficient alternatives and parallel versions of our algorithms, that achieved good time/space trade-off and speedup factors in our experiments, thus enabling the evaluation of the measure at larger scales.…”

Using Markov model to improve word normalization algorithm for biological sequence comparison

“…Sequence comparison helps us to find both similarities and differences among biological sequences, and assign the function of those sequences (Mitrophanov and Borodovsky 2006;Altschul et al 1997;Dai et al 2008;Pham and Zuegg 2004;Pham 2007;Felsenstein 1996;Huelsenbeck and Ronquist 2001;Komatsu et al 2001;Kumar et al 2004;Li et al 2001;Otu and Sayood 2003;Ronquist and Huelsenbeck 2003;Waddell et al 2001;Mohseni-Zadeh et al 2004;Pipenbacher et al 2002;Yao et al 2008;Yang et al 2010). For example, to identify genes and functionally related regulatory sequences in newly sequenced genomes, one must discern functional similarity among candidate subsequences based on their similarity/dissimilarity.…”

Sorting Conjugates and Suffixes of Words in a Multiset

“…The ebwt of a multiset S is a word obtained by a letters permutation of the words in S induced by the sorting of the conjugates of words in S according with an order relation (denoted by ≺ ω ) defined by using lexicographic order among infinite words. The ebwt is a reversible transformation, that has been used for circular words comparison [18,20] and for circular pattern matching [13]. It is also used as preprocessing of compressors on a single text, where the ebwt is applied to the words obtained by a factorization of the text.…”