Genomic DNA k-mer Spectra: Models and Modalities

Chor, Benny; Horn, David; Goldman, Nick; Levy, Yaron Y.; Massingham, Tim

doi:10.1007/978-3-642-12683-3_37

Cited by 43 publications

(54 citation statements)

References 18 publications

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“…Additionally, the comparative analysis of k-mer fold enrichment scores revealed a mixture of unimodal and multimodal spectra for the different genomic sub-regions analyzed. This contrasts sharply with the unimodal k-mer spectra observed for only the S.cerevisiae genome [3].…”

Section: Discussioncontrasting

confidence: 87%

“…The exceptions were the tetrapods, including all mammals, whose genomes and most genomic sub-regions exhibited multimodal k-mer spectra. They also underscored the suitability of low order (l<k/2) Markov models in recapitulating the multimodal spectra and heavy tails of such distributions, refuting the DPLN model [3]. The 8-mer genomic spectra reported for S.cerevisiae by Chor et al is typically unimodal with the mode at a relatively low k-mer copy number, at around 200.…”

Section: Discussionmentioning

confidence: 83%

“…Explorations of k-mer frequency distributions (or “k-mer spectra”) for genomic regions in different species have allowed us to take new perspectives on the complexity of genomes and to find associations between k-mer spectral modality and GC content, as well as those between CpG suppression and modality [3], [4]. These studies have reported unimodal genomic k-mer spectra for the vast majority of analyzed species, with the striking exception of tetrapod animal genomes where the k-mer distributions are typically multimodal [3].…”

Section: Introductionmentioning

confidence: 99%

“…These studies have reported unimodal genomic k-mer spectra for the vast majority of analyzed species, with the striking exception of tetrapod animal genomes where the k-mer distributions are typically multimodal [3].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of DNA Word Abundances in Four Yeast Genomes Using a Novel Statistical Background Model

et al. 2013

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Previous studies have shown that the identification and analysis of both abundant and rare k-mers or “DNA words of length k” in genomic sequences using suitable statistical background models can reveal biologically significant sequence elements. Other studies have investigated the uni/multimodal distribution of k-mer abundances or “k-mer spectra” in different DNA sequences. However, the existing background models are affected to varying extents by compositional bias. Moreover, the distribution of k-mer abundances in the context of related genomes has not been studied previously. Here, we present a novel statistical background model for calculating k-mer enrichment in DNA sequences based on the average of the frequencies of the two (k-1) mers for each k-mer. Comparison of our null model with the commonly used ones, including Markov models of different orders and the single mismatch model, shows that our method is more robust to compositional AT-rich bias and detects many additional, repeat-poor over-abundant k-mers that are biologically meaningful. Analysis of overrepresented genomic k-mers (4≤k≤16) from four yeast species using this model showed that the fraction of overrepresented DNA words falls linearly as k increases; however, a significant number of overabundant k-mers exists at higher values of k. Finally, comparative analysis of k-mer abundance scores across four yeast species revealed a mixture of unimodal and multimodal spectra for the various genomic sub-regions analyzed.

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Section: Discussioncontrasting

confidence: 87%

Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of DNA Word Abundances in Four Yeast Genomes Using a Novel Statistical Background Model

et al. 2013

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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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Sequence‐free Phylogenetics With Mass Spectrometry

Downard

2020

Mass Spectrometry Reviews

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An alternative, more rapid, sequence-free approach to build phylogenetic trees has been conceived and implemented. Molecular phylogenetics has continued to mostly focus on improvement in tree construction based on gene sequence alignments. Here protein-based phylogenies are constructed using numerical data sets ("phylonumerics") representing the masses of peptide segments recorded in a mass mapping experiment. This truly sequence-free method requires no gene sequences, nor their alignment, to build the trees affording a considerable time and cost-saving to conventional phylogenetics methods. The approach also calculates single point amino acid mutations from a comparison of mass pairs from different maps in the data set and displays these at branch nodes across the tree together with their frequency. Studies of the consecutive, and near-consecutive, ancestral and descendant mutations across interconnected branches of a mass tree allow putative adaptive, epistatic, and compensatory mutations to be identified in order to investigate mechanisms associated with evolutionary processes and pathways. A side-by-side comparison of this sequence-free approach and conventional gene sequence phylogenetics is discussed.

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Genomic DNA k-mer Spectra: Models and Modalities

Abstract: Background: The empirical frequencies of DNA k-mers in whole genome sequences provide an interesting perspective on genomic complexity, and the availability of large segments of genomic sequence from many organisms means that analysis of k-mers with non-trivial lengths is now possible.

Cited by 43 publications

References 18 publications

Comparative Analysis of DNA Word Abundances in Four Yeast Genomes Using a Novel Statistical Background Model

Comparative Analysis of DNA Word Abundances in Four Yeast Genomes Using a Novel Statistical Background Model

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

Sequence‐free Phylogenetics With Mass Spectrometry

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