ArticleComputational and structural biotechnology journal2019
Large-Scale Genome Comparison Based on Cumulative Fourier Power and Phase Spectra: Central Moment and Covariance Vector.
Article in Computational and structural biotechnology journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Comparative study of encoded and alignment-based methods for virus taxonomy classification.Scientific reports · 2023Article
- New proposal of viral genome representation applied in the classification of SARS-CoV-2 with deep learning.BMC bioinformatics · 2023Article
- FFP: joint Fast Fourier transform and fractal dimension in amino acid property-aware phylogenetic analysis.BMC bioinformatics · 2022Article
- Identification of HIV Rapid Mutations Using Differences in Nucleotide Distribution over Time.Genes · 2022Article
- Article
- Full Chromosomal Relationships Between Populations and the Origin of Humans.Frontiers in genetics · 2021Article
- Patient-specific seizure detection method using nonlinear mode decomposition for long-term EEG signals.Medical & biological engineering & computing · 2020Article
- Article
- A novel numerical representation for proteins: Three-dimensional Chaos Game Representation and its Extended Natural Vector.Computational and structural biotechnology journal · 2020Article
- Alignment-free genomic sequence comparison using FCGR and signal processing.BMC bioinformatics · 2019Article
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Authors and funding
4 authors.
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Abstract
Genome comparison is a vital research area of bioinformatics. For large-scale genome comparisons, the Multiple Sequence Alignment (MSA) methods have been impractical to use due to its algorithmic complexity. In this study, we propose a novel alignment-free method based on the one-to-one correspondence between a DNA sequence and its complete central moment vector of the cumulative Fourier power and phase spectra. In addition, the covariance between the four nucleotides in the power and phase spectra is included. We use the cumulative Fourier power and phase spectra to define a 28-dimensional vector for each DNA sequence. Euclidean distances between the vectors can measure the dissimilarity between DNA sequences. We perform testing with datasets of different sizes and types including simulated DNA sequences, exon-intron and complete genomes. The results show that our method is more accurate and efficient for performing hierarchical clustering than other alignment-free methods and MSA methods.
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