Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

Liu, Suran; You, Yujie; Zhao-qi, Tong; Zhang, Le

doi:10.3389/fgene.2021.761629

Cited by 3 publications

(4 citation statements)

References 59 publications

(82 reference statements)

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“…Table 1 ; Supplementary Figure S5 not only demonstrate that the average length of CGIs of Human position-defined CGI prediction method (23.7 ± 11.5bp) is statistically shorter, but also the average GC content (89.3% ± 7.5%) and O/E value (1.54 ± 0.27) of Human position-defined CGI prediction method are statistically greater than other prediction methods by statistical test ( Zhang et al, 2021b ; Zhang et al, 2021d ; Gao et al, 2021 ; Liu et al, 2021 ; Lai et al, 2022 ; Song et al, 2022 ).…”

Section: Resultsmentioning

confidence: 93%

PCGIMA: developing the web server for human position-defined CpG islands methylation analysis

Xiao,

et al. 2024

Front. Genet.

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Introduction: CpG island (CGI) methylation is one of the key epigenomic mechanisms for gene expression regulation and chromosomal integrity. However, classical CGI prediction methods are neither easy to locate those short and position-sensitive CGIs (CpG islets), nor investigate genetic and expression pattern for CGIs under different CpG position- and interval- sensitive parameters in a genome-wide perspective. Therefore, it is urgent for us to develop such a bioinformatic algorithm that not only can locate CpG islets, but also provide CGI methylation site annotation and functional analysis to investigate the regulatory mechanisms for CGI methylation.Methods: This study develops Human position-defined CGI prediction method to locate CpG islets using high performance computing, and then builds up a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate these functions into PCGIMA to provide relevant online computing and visualization service.Results: The main results include: (1) Human position-defined CGI prediction method is more efficient to predict position-defined CGIs with multiple consecutive (d) values and locate more potential short CGIs than previous CGI prediction methods. (2) Our annotation and analysis method not only can investigate the connections between position-defined CGI methylation and gene expression specificity from a genome-wide perspective, but also can analysis the potential association of position-defined CGIs with gene functions. (3) PCGIMA (http://www.combio-lezhang.online/pcgima/home.html) provides an easy-to-use analysis and visualization platform for human CGI prediction and methylation.Discussion: This study not only develops Human position-defined CGI prediction method to locate short and position-sensitive CGIs (CpG islets) using high performance computing to construct MR-CpGCluster algorithm, but also a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate them into PCGIMA for online computing and visualization.

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Section: Resultsmentioning

confidence: 93%

PCGIMA: developing the web server for human position-defined CpG islands methylation analysis

Xiao,

et al. 2024

Front. Genet.

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“…This difference in surface tension causes water to move from an area of low surface tension to an area of high surface tension owing to the thermal Marangoni flow [179]. This upward movement counteracts the draining of the bubble film, resulting in older and thicker clean bubbles [181]. The replenishing effect of evaporation is universal and can be caused by temperature variations between the main fluid and the top of the bubble cap owing to evaporative cooling, as well as gradients of chemical or biological compounds [182].…”

Section: Experimental Frameworkmentioning

confidence: 99%

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Koley

2024

Trends Sci

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The spread of infectious diseases such as COVID-19 depends on complex fluid dynamics interactions between pathogens and fluid phases, including individual droplets and multiphase clouds. Understanding these interactions is crucial for predicting and controlling disease spread. This applies to human and animal exhalations, such as coughs and sneezes, as well as bursting bubbles that create micron-sized droplets in various indoor and outdoor environments. By exploring case studies in this regard, this study examines the emerging field of fluid dynamics in disease transmission, focusing on multiphase flows, interfacial flows, turbulence, pathogens, human traffic, aerosol transmission, ventilation, and breathing microenvironments. These results indicate that increased ventilation rates and local ventilation methods can effectively reduce the concentration of SARS-CoV-2-laden aerosols in the immediate breathing spaces between individuals. In a displacement-ventilated room, both neutral and unstable conditions were more effective in removing breathed SARS-CoV-2-laden aerosols from the air, regardless of the presence of test subjects. However, stable conditions may increase the risk of infection in individuals living in confined spaces. Thus, the findings of this study are useful for providing practical guidance for managing the spread of airborne infections. HIGHLIGHTS Fluid dynamics affect the transmission of infectious diseases such as COVID-19 This study explored multiphase fluid flow, aerosol dispersion, and respiratory zones Increased ventilation and local methods reduce SARS-CoV-2 aerosol concentration Displacement ventilation eliminates SARS-CoV-2 aerosols under unstable conditions Cramped and damp living environments can increase the risk of transmission of infection GRAPHICAL ABSTRACT

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“…Figure 4 shows the comparison results. The test of significance [4,[31][32][33][34][35][36][37][38] between the nucleobase filtering algorithm with dynamic threshold and the other four filtering methods are implemented, respectively, by Method S1. Figure 4A,B indicates that the average base quality of SARS-CoV-2 positivesense reference strand processed by unfiltered, fastp, seqtk and sickle are mostly distributed between 18-26, while the average base quality processed by the nucleobase filtering algorithm with dynamic threshold are mostly distributed between 30-34.…”

Section: Nucleobase Filtering Algorithm With Dynamic Thresholdmentioning

confidence: 99%

“…(D) The overall average base quality distribution of SARS-CoV-2 negative-sense reference strand at each site. *** p ≤ 0.001.The test of significance[4,31,32,33,34,35,36,37,38] between the nucleobase filtering algorithm with dynamic threshold and the other four filtering methods are implemented, respectively, by Method S1. Figure4A,B indicates that the average base quality of SARS-CoV-2 positive-sense reference strand processed by unfiltered, fastp, seqtk and sickle are…”

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confidence: 99%

A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis

Xiao

et al. 2022

Biomolecules

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It is very important to compute the mutation spectra, and simulate the intra-host mutation processes by sequencing data, which is not only for the understanding of SARS-CoV-2 genetic mechanism, but also for epidemic prediction, vaccine, and drug design. However, the current intra-host mutation analysis algorithms are not only inaccurate, but also the simulation methods are unable to quickly and precisely predict new SARS-CoV-2 variants generated from the accumulation of mutations. Therefore, this study proposes a novel accurate strand-specific SARS-CoV-2 intra-host mutation spectra computation method, develops an efficient and fast SARS-CoV-2 intra-host mutation simulation method based on mutation spectra, and establishes an online analysis and visualization platform. Our main results include: (1) There is a significant variability in the SARS-CoV-2 intra-host mutation spectra across different lineages, with the major mutations from G- > A, G- > C, G- > U on the positive-sense strand and C- > U, C- > G, C- > A on the negative-sense strand; (2) our mutation simulation reveals the simulation sequence starts to deviate from the base content percentage of Alpha-CoV/Delta-CoV after approximately 620 mutation steps; (3) 2019-NCSS provides an easy-to-use and visualized online platform for SARS-Cov-2 online analysis and mutation simulation.

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Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

Cited by 3 publications

References 59 publications

PCGIMA: developing the web server for human position-defined CpG islands methylation analysis

PCGIMA: developing the web server for human position-defined CpG islands methylation analysis

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis

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