ALFRED: A Practical Method for Alignment-Free Distance Computation

Abstract

Alignment-free approaches are gaining persistent interest in many sequence analysis applications such as phylogenetic inference and metagenomic classification/clustering, especially for large-scale sequence datasets. Besides the widely used k-mer methods, the average common substring (ACS) approach has emerged to be one of the well-known alignment-free approaches. Two recent works further generalize this ACS approach by allowing a bounded number k of mismatches in the common substrings, relying on approximation (linear time) and exact computation, respectively. Albeit having a good worst-case time complexity [Formula: see text], the exact approach is complex and unlikely to be efficient in practice. Herein, we present ALFRED, an alignment-free distance computation method, which solves the generalized common substring search problem via exact computation. Compared to the theoretical approach, our algorithm is easier to implement and more practical to use, while still providing highly competitive theoretical performances with an expected run-time of [Formula: see text]. By applying our program to phylogenetic inference as a case study, we find that our program facilitates to exactly reconstruct the topology of the reference phylogenetic tree for a set of 27 primate mitochondrial genomes, at reasonably acceptable speed. ALFRED is implemented in C++ programming language and the source code is freely available online.

Fast and accurate phylogeny reconstruction using filtered spaced-word matches

“…Match-length approaches, in contrast, estimate phylogenetic distances from the length of substring matches between two sequences (Comin and Verzotto, 2012; Haubold et al , 2005; Thankachan et al , 2016; Ulitsky et al , 2006). Since the length of exact substring matches between two homologous sequence regions depends on the mismatch frequency, substitution rates can be estimated, in turn, from the average length of exact common substrings (Domazet-Loso and Haubold, 2009).…”

A greedy alignment-free distance estimator for phylogenetic inference

“…The algorithm is much more complicated than the original ACS method and even the k-ACS approximation by [14]. Moreover the practical variant of this algorithm can get quite slow for even moderately large values of k due to its exponential dependency on k [21]. However, this algorithm has its merit as the first sub-quadratic time algorithm for exact k-ACS computation for any positive integer k .…”

The Colored Longest Common Prefix Array Computed via Sequential Scans

“…For k = 0 kmacs exactly computes the ACS. Other algorithms besides kmacs [33,29] have been designed to compute alignment-free distances based on longest matches with mismatches, but for the special case k = 0 kmacs 332 Table 3. The first collection contains 932 genomes, the second one contains 4, 983 genomes.…”

“…To keep pace with this, several algorithms that go beyond the concept of sequence alignment have been developed, called alignment-free [35]. Alignment-free approaches have been explored in several large-scale biological applications ranging, for instance, from DNA sequence comparison [12,28,14,19,27] to whole-genome phylogeny construction [34,15,13,23,33] and the classification of protein sequences [14]. Most alignment-free approaches above mentioned require, each with its own specific approach and with the use of appropriate data structures, the computation of statistics of the sequences of the analyzed collections.…”