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Word-based or 'alignment-free' methods for phylogeny inference have become popular in recent years. These methods are much faster than traditional, alignment-based approaches, but they are generally less accurate. Most alignment-free methods calculate 'pairwise' distances between nucleic-acid or protein sequences; these distance values can then be used as input for tree-reconstruction programs such as neighbor-joining. In this paper, we propose the first word-based phylogeny approach that is based on 'multiple' sequence comparison and 'maximum likelihood'. Our algorithm first samples small, gap-free alignments involving four taxa each. For each of these alignments, it then calculates a quartet tree and, finally, the program 'Quartet MaxCut' is used to infer a super tree for the full set of input taxa from the calculated quartet trees. Experimental results show that trees produced with our approach are of high quality., (© The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Motivation: Most methods for pairwise and multiple genome alignment use fast local homology search tools to identify anchor points, i.e. high-scoring local alignments of the input sequences. Sequence segments between those anchor points are then aligned with slower, more sensitive methods. Finding suitable anchor points is therefore crucial for genome sequence comparison; speed and sensitivity of genome alignment depend on the underlying anchoring methods., Results: In this article, we use filtered spaced word matches to generate anchor points for genome alignment. For a given binary pattern representing match and don't-care positions, we first search for spaced-word matches, i.e. ungapped local pairwise alignments with matching nucleotides at the match positions of the pattern and possible mismatches at the don't-care positions. Those spaced-word matches that have similarity scores above some threshold value are then extended using a standard X-drop algorithm; the resulting local alignments are used as anchor points. To evaluate this approach, we used the popular multiple-genome-alignment pipeline Mugsy and replaced the exact word matches that Mugsy uses as anchor points with our spaced-word-based anchor points. For closely related genome sequences, the two anchoring procedures lead to multiple alignments of similar quality. For distantly related genomes, however, alignments calculated with our filtered-spaced-word matches are superior to alignments produced with the original Mugsy program where exact word matches are used to find anchor points., Availability and Implementation: http://spacedanchor.gobics.de., Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: Word-based or 'alignment-free' algorithms are increasingly used for phylogeny reconstruction and genome comparison, since they are much faster than traditional approaches that are based on full sequence alignments. Existing alignment-free programs, however, are less accurate than alignment-based methods., Results: We propose Filtered Spaced Word Matches (FSWM) , a fast alignment-free approach to estimate phylogenetic distances between large genomic sequences. For a pre-defined binary pattern of match and don't-care positions, FSWM rapidly identifies spaced word-matches between input sequences, i.e. gap-free local alignments with matching nucleotides at the match positions and with mismatches allowed at the don't-care positions. We then estimate the number of nucleotide substitutions per site by considering the nucleotides aligned at the don't-care positions of the identified spaced-word matches. To reduce the noise from spurious random matches, we use a filtering procedure where we discard all spaced-word matches for which the overall similarity between the aligned segments is below a threshold. We show that our approach can accurately estimate substitution frequencies even for distantly related sequences that cannot be analyzed with existing alignment-free methods; phylogenetic trees constructed with FSWM distances are of high quality. A program run on a pair of eukaryotic genomes of a few hundred Mb each takes a few minutes., Availability and Implementation: The program source code for FSWM including a documentation, as well as the software that we used to generate artificial genome sequences are freely available at http://fswm.gobics.de/., Contact: chris.leimeister@stud.uni-goettingen.de., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press.)

In this article, we present a user-friendly web interface for two alignment-free sequence-comparison methods that we recently developed. Most alignment-free methods rely on exact word matches to estimate pairwise similarities or distances between the input sequences. By contrast, our new algorithms are based on inexact word matches. The first of these approaches uses the relative frequencies of so-called spaced words in the input sequences, i.e. words containing 'don't care' or 'wildcard' symbols at certain pre-defined positions. Various distance measures can then be defined on sequences based on their different spaced-word composition. Our second approach defines the distance between two sequences by estimating for each position in the first sequence the length of the longest substring at this position that also occurs in the second sequence with up to k mismatches. Both approaches take a set of deoxyribonucleic acid (DNA) or protein sequences as input and return a matrix of pairwise distance values that can be used as a starting point for clustering algorithms or distance-based phylogeny reconstruction. The two alignment-free programmes are accessible through a web interface at 'Göttingen Bioinformatics Compute Server (GOBICS)': http://spaced.gobics.de http://kmacs.gobics.de and the source codes can be downloaded., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)