The number of spaced-word matches between two<i>DNA</i>sequences as a function of the underlying pattern weight

Röhling, Sophie; Dencker, Thomas; Morgenstern, Burkhard

doi:10.1101/527515

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…The tool can estimate distances between samples with high accuracy from low-coverage and mixed-coverage genome skims with no prior knowledge of the coverage or the sequencing error. Slope-SpaM [83] estimates the phylogenetic distance between two DNA sequences by calculating the number Nk of k-mer matches for a range of values of k. The distance between the sequences can then be accurately estimated from the slope of a certain function that depends on Nk. Instead of exact word matches, the program can also use SpaMs w.r.t.…”

Section: Multi-spammentioning

confidence: 99%

Benchmarking of alignment-free sequence comparison methods

Zieleziński

Girgis

Bernard

et al. 2019

Preprint

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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

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Section: Multi-spammentioning

confidence: 99%

Benchmarking of alignment-free sequence comparison methods

Zieleziński

Girgis

Bernard

et al. 2019

Preprint

Self Cite

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“…For example, if a match-pattern “ 11 0 11 ” is used, then “ CT G AC ” versus “ CT T AC ” constitutes a match. Spaced k -mers have been shown to improve mapping sensitivity, the accuracy of phylogenies, and the performance of sequence classification [5,7,13,15]. Analogously, substring-based methods can be relaxed to allow for some mismatches [6].…”

Section: Introductionmentioning

confidence: 99%

Fast Alignment-Free Similarity Estimation By Tensor Sketching

Joudaki

Rätsch

Kahles

2020

Preprint

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The sharp increase in next-generation sequencing technologies' capacity has created a demand for algorithms capable of quickly searching a large corpus of biological sequences. The complexity of biological variability and the magnitude of existing data sets have impeded finding algorithms with guaranteed accuracy that efficiently run in practice. Our main contribution is the Tensor Sketch method that efficiently and accurately estimates edit distances. In our experiments, Tensor Sketch had 0.88 Spearman's rank correlation with the exact edit distance, almost doubling the 0.466 correlation of the closest competitor while running 8.8 times faster. Finally, all sketches can be updated dynamically if the input is a sequence stream, making it appealing for large-scale applications where data cannot fit into memory. Conceptually, our approach has three steps: 1) represent sequences as tensors over their sub-sequences, 2) apply tensor sketching that preserves tensor inner products, 3) implicitly compute the sketch. The sub-sequences, which are not necessarily contiguous pieces of the sequence, allow us to outperform k-mer-based methods, such as min-hash sketching over a set of k-mers. Typically, the number of sub-sequences grows exponentially with the sub-sequence length, introducing both memory and time overheads. We directly address this problem in steps 2 and 3 of our method. While the sketching of rank-1 or super-symmetric tensors is known to admit efficient sketching, the sub-sequence tensor does not satisfy either of these properties. Hence, we propose a new sketching scheme that completely avoids the need for constructing the ambient space. Our tensor-sketching technique's main advantages are three-fold: 1) Tensor Sketch has higher accuracy than any of the other assessed sketching methods used in practice. 2) All sketches can be computed in a streaming fashion, leading to significant time and memory savings when there is overlap between input sequences. 3) It is straightforward to extend tensor sketching to different settings leading to efficient methods for related sequence analysis tasks. We view tensor sketching as a framework to tackle a wide range of relevant bioinformatics problems, and we are confident that it can bring significant improvements for applications based on edit distance estimation.

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“…In recent years, a large number of alignment-free approaches to phylogeny reconstruction have been developed and applied, since these methods are much faster than traditional, alignment-based phylogenetic methods, see [51,39,3,25] for recent review papers and [50] for a systematic evaluation of alignment-free software tools. Most alignment-free approaches are based on k-mer statistics [21,44,7,48,17], but there are also approaches based on the length of common substrings [47,8,27,37,32,46], on word or spaced-word matches [38,33,35,34,1,41] or on so-called micro-alignments [49,20,29,28]. As has been mentioned by various authors, an additional advantage of many alignment-free methods is that they can be applied not only to complete genome sequences, but also to unassembled reads.…”

Section: Introductionmentioning

confidence: 99%

Read-SpaM: assembly-free and alignment-free comparison of bacterial genomes with low sequencing coverage

Lau

Leimeister

Dörrer

et al. 2019

Preprint

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In many fields of biomedical research, it is important to estimate phylogenetic distances between taxa based on low-coverage sequencing reads. Major applications are, for example, phylogeny reconstruction, species identification from small sequencing samples, or bacterial strain typing in medical diagnostics. Herein, we adapt our previously developed software program Filtered Spaced-Word Matches (FSWM) for alignment-free phylogeny reconstruction to work on unassembled reads; we call this implementation Read-SpaM. Test runs on simulated reads from bacterial genomes show that our approach can estimate phylogenetic distances with high accuracy, even for large evolutionary distances and for very low sequencing coverage.

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The number of spaced-word matches between twoDNAsequences as a function of the underlying pattern weight

Cited by 6 publications

References 51 publications

Benchmarking of alignment-free sequence comparison methods

Benchmarking of alignment-free sequence comparison methods

Fast Alignment-Free Similarity Estimation By Tensor Sketching

Read-SpaM: assembly-free and alignment-free comparison of bacterial genomes with low sequencing coverage

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