Background Alignment-free (AF) sequence comparison is attracting persistent interest driven by data-intensive applications. Hence, many AF procedures have been proposed in recent years, but a lack of a clearly defined benchmarking consensus hampers their performance assessment. Results Here, we present a community resource ( http://afproject.org ) to establish standards for comparing alignment-free approaches across different areas of sequence-based research. We characterize 74 AF methods available in 24 software tools for five research applications, namely, protein sequence classification, gene tree inference, regulatory element detection, genome-based phylogenetic inference, and reconstruction of species trees under horizontal gene transfer and recombination events. Conclusion The interactive web service allows researchers to explore the performance of alignment-free tools relevant to their data types and analytical goals. It also allows method developers to assess their own algorithms and compare them with current state-of-the-art tools, accelerating the development of new, more accurate AF solutions. Electronic supplementary material The online version of this article (10.1186/s13059-019-1755-7) contains supplementary material, which is available to authorized users.

2 Alignment-free (AF) sequence comparison is attracting persistent interest driven by data-intensive applications. Hence, many AF procedures have been proposed in recent years, but a lack of a clearly defined benchmarking consensus hampers their performance assessment. Here, we present a community resource (http://afproject.org) to establish standards for comparing alignment-free approaches across different areas of sequence-based research. We characterize 74 AF methods available in 24 software tools for five research applications, namely, protein sequence classification, gene tree inference, regulatory element detection, genome-based phylogenetic inference and reconstruction of species trees under horizontal gene transfer and recombination events.The interactive web service allows researchers to explore the performance of alignmentfree tools relevant to their data types and analytical goals. It also allows method developers to assess their own algorithms and compare them with current state-of-theart tools, accelerating the development of new, more accurate AF solutions. BACKGROUNDComparative analysis of DNA and amino acid sequences is of fundamental importance in biological research, particularly in molecular biology and genomics. It is the first and key step in molecular evolutionary analysis, gene function and regulatory region prediction, sequence assembly, homology searching, molecular structure prediction, gene discovery and protein structure-function relationships analysis. Traditionally, sequence comparison was based on pairwise or multiple sequence alignment (MSA). Software tools for sequence alignment, such as BLAST [1] and CLUSTAL [2], are the most widely used bioinformatics methods.Although alignment-based approaches generally remain the references for sequence

We study the number N k of length-k word matches between pairs of evolutionarily related DNA sequences, as a function of k. We show that the Jukes-Cantor distance between two genome sequences-i.e. the number of substitutions per site that occurred since they evolved from their last common ancestor-can be estimated from the slope of a function F that depends on N k and that is affine-linear within a certain range of k. Integers k min and k max can be calculated depending on the length of the input sequences, such that the slope of F in the relevant range can be estimated from the values F(k min ) and F(k max ). This approach can be generalized to so-called Spaced-word Matches (SpaM), where mismatches are allowed at positions specified by a user-defined binary pattern. Based on these theoretical results, we implemented a prototype software program for alignment-free sequence comparison called Slope-SpaM. Test runs on real and simulated sequence data show that Slope-SpaM can accurately estimate phylogenetic distances for distances up to around 0.5 substitutions per position. The statistical stability of our results is improved if spaced words are used instead of contiguous words. Unlike previous alignment-free methods that are based on the number of (spaced) word matches, Slope-SpaM produces accurate results, even if sequences share only local homologies. OPEN ACCESSCitation: Röhling S, Linne A, Schellhorn J, Hosseini M, Dencker T, Morgenstern B (2020) The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances. PLoS ONE 15(2): e0228070. https://doi.org/10. Data Availability Statement:The source code of our software is freely available through GitHub Traditionally, phylogenetic distances are inferred from pairwise or multiple sequence alignments. For the huge amounts of sequence data that are now available, however, sequence alignment has become too slow. Therefore, considerable efforts have been made in recent years, to develop fast alignment-free approaches that can estimate phylogenetic distances without the need to calculate full alignments of the input sequences, see [3][4][5][6][7] for recent review articles. Alignment-free approaches are not only used in phylogeny reconstruction, but are also important in metagenomics [8][9][10], to find genome rearrangements [11] and in epidemiology [12] and other medical applications, for example to identify drug-resistant bacteria [13] or to classify viruses [14,15]. In all these applications, it is crucial to rapidly estimate pairwise similarity or dissimilarity values in large sets of sequence data.Some alignment-free approaches are based on word frequencies [16,17] or on the length of common substrings [18][19][20]. Other methods use variants of the D 2 distance which is defined as the number of word matches of a pre-defined length between two sequences [15,[21][22][23]; a review focusing on these methods is given in [24]. kWIP [25] is a further development of this concept that uses information-theoretical...

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.

Motivation: Word-based or 'alignment-free' methods for phylogeny reconstruction are much faster than traditional approaches, but they are generally less accurate. Most of these methods calculate pairwise distances for a set of input sequences, for example from word frequencies, from so-called spaced-word matches or from the average length of common substrings. Results: In this paper, we propose the first word-based approach to tree reconstruction 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 1 arXiv:1803.09222v2 [q-bio.PE] 28 Apr 2018 then calculates a quartet tree and, finally, the program Quartet Max-Cut is used to infer a super tree topology for the full set of input taxa from the calculated quartet trees. Experimental results show that trees calculated with our approach are of high quality. Availability: The source code of the program is available at https://github.com/tdencker/multi-SpaM