Micro genome segment clustering method based on fuzzy k-mean

Abstract

The invention relates to a micro genome segment clustering method based on fuzzy k-mean, and belongs to the technical field of bioinformatics analysis. The micro genome segment clustering method based on the fuzzy k-mean aims at utilizing the self features of micro genome segments under the condition of no assembly of the micro genome segments, and then the micro genome segments are clustered, so the number of contained species and the abundance ratio of species are obtained. The method comprises the following steps of obtaining the micro genome segments, establishing the feature vectors, utilizing the fuzzy k-mean method to cluster, and calculating the number of the contained species and the abundance ratio of the species according to the clustering results. The method has the advantages of directness and convenience.

Description

technical field [0001] The invention belongs to the technical field of bioinformatics analysis. Background technique [0002] Traditional gene sequencing technology must first culture microorganisms in a laboratory environment before sequencing can be completed, and only a single species can be sequenced. However, only a very small number of microorganisms on the earth can be cultivated in an experimental environment, which means that the genetic data obtained by traditional gene sequencing technology is very incomplete and cannot describe the true appearance of the microbial world. In addition, there is almost no microbial community containing only a single species on the earth, and the interaction relationship between species is very complex. Therefore, it is obviously unreasonable to only sequence the genes of a single species and ignore the species that interact with it. [0003] With the development of gene sequencing technology, it is possible to obtain DNA sequences...

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Problems solved by technology

[0005] China Patent No. 201110439198.X, titled "Classification Method and Device Based on Metagenome 16S Hypervariable Region V3" discloses a classification method of metagenomic data, but this method uses 16S rDNA to classify metagenomic data. Data classification, 16SrDNA identification is a PCR-based identification method, which is prone to contamination like other PCR identification methods; and this method still assembles the overlapping relationship of DNA sequences before classification, which inevitably brings errors. And ultimately affect the accuracy of classification results

[0006] The master's thesis "Species Classification Method Based on k-mer Frequency Statistics" introduced the method of using k-mer frequency to classify species, but it only selected six DNA sequences of bacteria as data sets, and the length of each sequence was Above 1000bp, it cannot reflect the characteristics of large number of species and short sequences of metagenomic data

In addition, in the process of classification, only the Euclidean distance between feature vectors is used as the basis for species classification, which is easy to cause misclassification.

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