ArticleBMC evolutionary biology2018
A new efficient method for analyzing fungi species using correlations between nucleotides.
Article in BMC evolutionary biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Energy entropy vector: a novel approach for efficient microbial genomic sequence analysis and classification.Briefings in bioinformatics · 2025Article
- A new alignment-free method: K-mer Subsequence Natural Vector (K-mer SNV) for classification of fungi.BMC bioinformatics · 2025Article
- Identification of HIV Rapid Mutations Using Differences in Nucleotide Distribution over Time.Genes · 2022Article
- An efficient numerical representation of genome sequence: natural vector with covariance component.PeerJ · 2022Article
- Screening of Phosphate-Solubilizing Fungi From Air and Soil in Yunnan, China: Four Novel Species inFrontiers in microbiology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn recent years, DNA barcoding has become an important tool for biologists to identify species and understand their natural biodiversity. The complexity of barcode data makes it difficult to analyze quickly and effectively. Manual classification of this data cannot keep up to the rate of increase of available data.
resultsIn this study, we propose a new method for DNA barcode classification based on the distribution of nucleotides within the sequence. By adding the covariance of nucleotides to the original natural vector, this augmented 18-dimensional natural vector makes good use of the available information in the DNA sequence. The accurate classification results we obtained demonstrate that this new 18-dimensional natural vector method, together with the random forest classifier algorthm, can serve as a computationally efficient identification tool for DNA barcodes. We performed phylogenetic analysis on the genus Megacollybia to validate our method. We also studied how effective our method was in determining the genetic distance within and between species in our barcoding dataset.
conclusionsThe classification performs well on the fungi barcode dataset with high and robust accuracy. The reasonable phylogenetic trees we obtained further validate our methods. This method is alignment-free and does not depend on any model assumption, and it will become a powerful tool for classification and evolutionary analysis.
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