A novel k-word relative measure for sequence comparison

Tang, Jie; Hua, Keru; Chen, Mengye; Zhang, Ruiming; Xie, Xiao

doi:10.1016/j.compbiolchem.2014.10.007

Cited by 14 publications

(8 citation statements)

References 43 publications

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“…They only extracted the number and position distribution of k -mers. Tang et al [ 37 ] presented the normalized k -mers average relative distance to improve the method of Ding et al [ 36 ]. Nevertheless, in their methods, the determination of the k value requires empirical calculation, while we directly determine

.…”

Section: Discussionmentioning

confidence: 99%

Phylogenetic Analysis of HIV-1 Genomes Based on the Position-Weighted K-mers Method

Tang

et al. 2020

Entropy

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HIV-1 viruses, which are predominant in the family of HIV viruses, have strong pathogenicity and infectivity. They can evolve into many different variants in a very short time. In this study, we propose a new and effective alignment-free method for the phylogenetic analysis of HIV-1 viruses using complete genome sequences. Our method combines the position distribution information and the counts of the k-mers together. We also propose a metric to determine the optimal k value. We name our method the Position-Weighted k-mers (PWkmer) method. Validation and comparison with the Robinson–Foulds distance method and the modified bootstrap method on a benchmark dataset show that our method is reliable for the phylogenetic analysis of HIV-1 viruses. PWkmer can resolve within-group variations for different known subtypes of Group M of HIV-1 viruses. This method is simple and computationally fast for whole genome phylogenetic analysis.

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.…”

Section: Discussionmentioning

confidence: 99%

Phylogenetic Analysis of HIV-1 Genomes Based on the Position-Weighted K-mers Method

Tang

et al. 2020

Entropy

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“…Studies on constructing phylogenetic trees using inter-nucleotide distance have stimulated the development of inter-nucleotide distance-based techniques [41,45] . The algorithms have become more efficient because of the use of k-word distance, which count the distance between k-tuple, 2 k 9 [45,46] . Extensive studies on phylogenetic tree construction have used inter-amino-acid distance to measure the distance of amino acids in protein sequence [47] .…”

Section: Inter-nucleotide Distance Encodingmentioning

confidence: 99%

Survey on encoding schemes for genomic data representation and feature learning—from signal processing to machine learning

Yu,

Li,

2018

Big Data Min. Anal.

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Data-driven machine learning, especially deep learning technology, is becoming an important tool for handling big data issues in bioinformatics. In machine learning, DNA sequences are often converted to numerical values for data representation and feature learning in various applications. Similar conversion occurs in Genomic Signal Processing (GSP), where genome sequences are transformed into numerical sequences for signal extraction and recognition. This kind of conversion is also called encoding scheme. The diverse encoding schemes can greatly affect the performance of GSP applications and machine learning models. This paper aims to collect, analyze, discuss, and summarize the existing encoding schemes of genome sequence particularly in GSP as well as other genome analysis applications to provide a comprehensive reference for the genomic data representation and feature learning in machine learning.

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“…The chaos game representation transforms the distribution of characters in a DNA sequence into the distribution of nodes in a graph (Hoang et al 2016;Yin, 2017;Mendizabal-Ruiz et al, 2018). Many researchers considered extracting the position information of a k-mer (Huang and Wang, 2011;Ding et al, 2013;Tang et al, 2014). Ding et al (2013) used the average interval distance of normalized k-mers to capture evolutionary information for sequence comparison.…”

Section: Introductionmentioning

confidence: 99%

“…Ding et al (2013) used the average interval distance of normalized k-mers to capture evolutionary information for sequence comparison. Tang et al (2014) presented the average relative distance of normalized k-mers to improve the method of Ding et al (2013). Ma et al (2020) proposed the PWKmer method, which combines the k-mer counts and k-mer position distributions for phylogenetic analysis.…”

Section: Introductionmentioning

confidence: 99%

An Information-Entropy Position-Weighted K-Mer Relative Measure for Whole Genome Phylogeny Reconstruction

Tang

et al. 2021

Front. Genet.

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Alignment methods have faced disadvantages in sequence comparison and phylogeny reconstruction due to their high computational costs in handling time and space complexity. On the other hand, alignment-free methods incur low computational costs and have recently gained popularity in the field of bioinformatics. Here we propose a new alignment-free method for phylogenetic tree reconstruction based on whole genome sequences. A key component is a measure called information-entropy position-weighted k-mer relative measure (IEPWRMkmer), which combines the position-weighted measure of k-mers proposed by our group and the information entropy of frequency of k-mers. The Manhattan distance is used to calculate the pairwise distance between species. Finally, we use the Neighbor-Joining method to construct the phylogenetic tree. To evaluate the performance of this method, we perform phylogenetic analysis on two datasets used by other researchers. The results demonstrate that the IEPWRMkmer method is efficient and reliable. The source codes of our method are provided at https://github.com/ wuyaoqun37/IEPWRMkmer.

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A novel k-word relative measure for sequence comparison

Cited by 14 publications

References 43 publications

Phylogenetic Analysis of HIV-1 Genomes Based on the Position-Weighted K-mers Method

Phylogenetic Analysis of HIV-1 Genomes Based on the Position-Weighted K-mers Method

Survey on encoding schemes for genomic data representation and feature learning—from signal processing to machine learning

An Information-Entropy Position-Weighted K-Mer Relative Measure for Whole Genome Phylogeny Reconstruction

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