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A novel clustering method via nucleotide-based Fourier power spectrum analysis.

ABSTRACT: A novel clustering method is proposed to classify genes or genomes. This method uses a natural representation of genomic data by binary indicator sequences of each nucleotide (adenine (A), cytosine (C), guanine (G), and thymine (T)). Afterwards, the discrete Fourier transform is applied to these indicator sequences to calculate spectra of the nucleotides. Mathematical moments are calculated for each of these spectra to create a multidimensional vector in a Euclidean space for each gene or genome sequence. Thus, each gene or genome sequence is realized as a geometric point in the Euclidean space. Finally, pairwise Euclidean distances between these points (i.e. genome sequences) are calculated to cluster the gene or genome sequences. This method is applied to three sets of data. The first is 34 strains of coronavirus genomic data, the second is 118 of the known strains of Human rhinovirus (HRV), and the third is 30 bacteria genomes. The distance matrices are computed based on the three sets, showing the distances from each point to the others. We used the complete linkage clustering algorithm to build phylogenetic trees to indicate how the distances among these sequence correspond to the evolutionary relationship among these sequences. This genome representation provides a powerful and efficient method to classify genomes and is much faster than the widely acknowledged multiple sequence alignment method.

SUBMITTER: Zhao B

PROVIDER: S-EPMC7094093 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Microglial cells are tissue-resident macrophages of the central nervous system. They are extremely dynamic, sensitive to their microenvironment and present a characteristic complex and heterogeneous morphology and distribution within the brain tissue. Many experimental clues highlight a strong link between their morphology and their function in response to aggression. However, due to their complex "dendritic-like" aspect that constitutes the major pool of murine microglial cells and their dense network, precise and powerful morphological studies are not easy to realize and complicate correlation with molecular or clinical parameters. METHODS:Using the knock-in mouse model CX3CR1(GFP/+), we developed a 3D automated confocal tissue imaging system coupled with morphological modelling of many thousands of microglial cells revealing precise and quantitative assessment of major cell features: cell density, cell body area, cytoplasm area and number of primary, secondary and tertiary processes. We determined two morphological criteria that are the complexity index (CI) and the covered environment area (CEA) allowing an innovative approach lying in (i) an accurate and objective study of morphological changes in healthy or pathological condition, (ii) an in situ mapping of the microglial distribution in different neuroanatomical regions and (iii) a study of the clustering of numerous cells, allowing us to discriminate different sub-populations. RESULTS:Our results on more than 20,000 cells by condition confirm at baseline a regional heterogeneity of the microglial distribution and phenotype that persists after induction of neuroinflammation by systemic injection of lipopolysaccharide (LPS). Using clustering analysis, we highlight that, at resting state, microglial cells are distributed in four microglial sub-populations defined by their CI and CEA with a regional pattern and a specific behaviour after challenge. CONCLUSIONS:Our results counteract the classical view of a homogenous regional resting state of the microglial cells within the brain. Microglial cells are distributed in different defined sub-populations that present specific behaviour after pathological challenge, allowing postulating for a cellular and functional specialization. Moreover, this new experimental approach will provide a support not only to neuropathological diagnosis but also to study microglial function in various disease models while reducing the number of animals needed to approach the international ethical statements.

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A novel clustering method via nucleotide-based Fourier power spectrum analysis.

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