2015/06/16 by Maxime Crochemore, Gabriele Fici, Crochemore, Maxime +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Algorithms and Data Compression #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Formal Languages and Automata Theory (cs.FL) #Genomics and Phylogenetic Studies #Machine Learning in Bioinformatics
paper · pdf · doi:10.48550/arxiv.1506.04917
openalex publication_date 2015/06/16 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
Sequence comparison is a prerequisite to virtually all comparative genomic analyses. It is often realized by sequence alignment techniques, which are computationally expensive. This has led to increased research into alignment-free techniques, which are based on measures referring to the composition of sequences in terms of their constituent patterns. These measures, such as q-gram distance, are usually computed in time linear with respect to the length of the sequences. In this article, we focus on the complementary idea: how two sequences can be efficiently compared based on information that does not occur in the sequences. A word is an \em absent word of some sequence if it does not occur in the sequence. An absent word is \em minimal if all its proper factors occur in the sequence. Here we present the first linear-time and linear-space algorithm to compare two sequences by considering \em all their minimal absent words. In the process, we present results of combinatorial interest, and also extend the proposed techniques to compare circular sequences.