ArticleBriefings in bioinformatics2019
A survey and evaluations of histogram-based statistics in alignment-free sequence comparison.
Article in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- A novel dynamic part-wise direct and complementary template matching approach for DNA sequence similarity identification.Molecular genetics and genomics : MGG · 2026Article
- CREATE: a novel attention-based framework for efficient classification of transposable elements.Briefings in bioinformatics · 2025Article
- Starship giant transposable elements cluster by host taxonomy using k-mer-based phylogenetics.G3 (Bethesda, Md.) · 2025Article
- An alignment-free method for phylogeny estimation using maximum likelihood.BMC bioinformatics · 2025Article
- K-mer-based Approaches to Bridging Pangenomics and Population Genetics.Molecular biology and evolution · 2025Review
- SHARK enables sensitive detection of evolutionary homologs and functional analogs in unalignable and disordered sequences.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- CGRWDL: alignment-free phylogeny reconstruction method for viruses based on chaos game representation weighted by dynamical language model.Frontiers in microbiology · 2024Article
- Evaluation of metric and representation learning approaches: Effects of representations driven by relative distance on the performance.2023 Intelligent Methods, Systems, and Applications · 2023Article
- Seedability: optimizing alignment parameters for sensitive sequence comparison.Bioinformatics advances · 2023Article
- Interpreting alignment-free sequence comparison: what makes a score a good score?NAR genomics and bioinformatics · 2022Article
- MeShClust v3.0: high-quality clustering of DNA sequences using the mean shift algorithm and alignment-free identity scores.BMC genomics · 2022Article
- Phylogenies from unaligned proteomes using sequence environments of amino acid residues.Scientific reports · 2022Article
- Using networks to analyze and visualize the distribution of overlapping genes in virus genomes.PLoS pathogens · 2022Article
- Determination of k-mer density in a DNA sequence and subsequent cluster formation algorithm based on the application of electronic filter.Scientific reports · 2021Article
- Article
- Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy.PloS one · 2021Article
- Positional Correlation Natural Vector: A Novel Method for Genome Comparison.International journal of molecular sciences · 2020Article
- Benchmarking of alignment-free sequence comparison methods.Genome biology · 2019Article
- LtrDetector: A tool-suite for detecting long terminal repeat retrotransposons de-novo.BMC genomics · 2019Article
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3 authors.
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Abstract
motivationSince the dawn of the bioinformatics field, sequence alignment scores have been the main method for comparing sequences. However, alignment algorithms are quadratic, requiring long execution time. As alternatives, scientists have developed tens of alignment-free statistics for measuring the similarity between two sequences.
resultsWe surveyed tens of alignment-free k-mer statistics. Additionally, we evaluated 33 statistics and multiplicative combinations between the statistics and/or their squares. These statistics are calculated on two k-mer histograms representing two sequences. Our evaluations using global alignment scores revealed that the majority of the statistics are sensitive and capable of finding similar sequences to a query sequence. Therefore, any of these statistics can filter out dissimilar sequences quickly. Further, we observed that multiplicative combinations of the statistics are highly correlated with the identity score. Furthermore, combinations involving sequence length difference or Earth Mover's distance, which takes the length difference into account, are always among the highest correlated paired statistics with identity scores. Similarly, paired statistics including length difference or Earth Mover's distance are among the best performers in finding the K-closest sequences. Interestingly, similar performance can be obtained using histograms of shorter words, resulting in reducing the memory requirement and increasing the speed remarkably. Moreover, we found that simple single statistics are sufficient for processing next-generation sequencing reads and for applications relying on local alignment. Finally, we measured the time requirement of each statistic. The survey and the evaluations will help scientists with identifying efficient alternatives to the costly alignment algorithm, saving thousands of computational hours. AVAILABILITY: The source code of the benchmarking tool is available as Supplementary Materials.
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