411 research outputs found
A representation of a compressed de Bruijn graph for pan-genome analysis that enables search
Recently, Marcus et al. (Bioinformatics 2014) proposed to use a compressed de
Bruijn graph to describe the relationship between the genomes of many
individuals/strains of the same or closely related species. They devised an
time algorithm called splitMEM that constructs this graph
directly (i.e., without using the uncompressed de Bruijn graph) based on a
suffix tree, where is the total length of the genomes and is the length
of the longest genome. In this paper, we present a construction algorithm that
outperforms their algorithm in theory and in practice. Moreover, we propose a
new space-efficient representation of the compressed de Bruijn graph that adds
the possibility to search for a pattern (e.g. an allele - a variant form of a
gene) within the pan-genome.Comment: Submitted to Algorithmica special issue of CPM201
Enhanced suffix arrays as language models: Virtual k-testable languages
In this article, we propose the use of suffix arrays to efficiently implement n-gram language models with practically unlimited size n. This approach, which is used with synchronous back-off, allows us to distinguish between alternative sequences using large contexts. We also show that we can build this kind of models with additional information for each symbol, such as part-of-speech tags and dependency information. The approach can also be viewed as a collection of virtual k-testable automata. Once built, we can directly access the results of any k-testable automaton generated from the input training data. Synchronous back- off automatically identies the k-testable automaton with the largest feasible k. We have used this approach in several classification tasks
Towards Distance-Based Phylogenetic Inference in Average-Case Linear-Time
Computing genetic evolution distances among a set of taxa dominates the running time of many phylogenetic inference methods. Most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method
phyBWT: Alignment-Free Phylogeny via eBWT Positional Clustering
Molecular phylogenetics is a fundamental branch of biology. It studies the evolutionary relationships among the individuals of a population through their biological sequences, and may provide insights about the origin and the evolution of viral diseases, or highlight complex evolutionary trajectories.
In this paper we develop a method called phyBWT, describing how to use the extended Burrows-Wheeler Transform (eBWT) for a collection of DNA sequences to directly reconstruct phylogeny, bypassing the alignment against a reference genome or de novo assembly. Our phyBWT hinges on the combinatorial properties of the eBWT positional clustering framework. We employ eBWT to detect relevant blocks of the longest shared substrings of varying length (unlike the k-mer-based approaches that need to fix the length k a priori), and build a suitable decomposition leading to a phylogenetic tree, step by step. As a result, phyBWT is a new alignment-, assembly-, and reference-free method that builds a partition tree without relying on the pairwise comparison of sequences, thus avoiding to use a distance matrix to infer phylogeny.
The preliminary experimental results on sequencing data show that our method can handle datasets of different types (short reads, contigs, or entire genomes), producing trees of quality comparable to that found in the benchmark phylogeny
Tight Upper and Lower Bounds on Suffix Tree Breadth
The suffix tree - the compacted trie of all the suffixes of a string - is the most important and widely-used data structure in string processing. We consider a natural combinatorial question about suffix trees: for a string S of length n, how many nodes nu(S)(d) can there be at (string) depth d in its suffix tree? We prove nu(n, d) = max(S) (is an element of Sigma n) nu(S)(d) is O ((n/d) log(n/d)), and show that this bound is asymptotically tight, describing strings for which nu(S)(d) is Omega((n/d)log(n/d)). (C) 2020 Elsevier B.V. All rights reserved.Peer reviewe
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