scholarly journals Visualizations of Multiple Probability Measures for SARS-CoV-2 Genomes

2021 ◽  
Author(s):  
Tan Yao ◽  
Jeffrey Zheng
Keyword(s):  
Open Source ◽  
Metagenomic Analysis ◽  
Probability Measures ◽  
Analysis System ◽  
Visual Results

Abstract SARS-CoV-2 genomes are collected from various open source genomic banks. A set of SARS-CoV-2 genomes are selected for visualization under both the A3 and C1 modules of the metagenomic analysis system MAS. Multiple probability measures are mapped as relevant 1D histograms, and it is convenient to observe distinct differences among various distributions to organize similar patterns into relevant groups. Sample genomes were processed, and their visual results were illustrated.

scholarly journals Visualizations of Multiple Probability Measures for SARS-CoV-2 Genomes

2020 ◽  
Author(s):  
Tan Yao ◽  
Jeffrey Zheng
Keyword(s):  
Open Source ◽  
Metagenomic Analysis ◽  
Probability Measures ◽  
Analysis System ◽  
Visual Results

Abstract SARS-CoV-2 genomes are collected from various open source genomic banks. A set of SARS-CoV-2 genomes are selected for visualization under both the A3 and C1 modules of the metagenomic analysis system MAS. Multiple probability measures are mapped as relevant 1D histograms, and it is convenient to observe distinct differences among various distributions to organize similar patterns into relevant groups. Sample genomes were processed, and their visual results were illustrated.

scholarly journals Visualizations of Combinatorial Entropy Index on Whole SARS-CoV-2 Genomes

2021 ◽  
Author(s):  
Jeffrey Zheng ◽  
Yang Zhou
Keyword(s):  
Information Entropy ◽  
Clustering Analysis ◽  
Probability Measures ◽  
Distribution Characteristics ◽  
Entropy Index ◽  
Theory Of Information ◽  
Visualization Technology ◽  
Analysis System ◽  
Index Maps ◽  
Visual Results

Abstract In this paper, a set of SARS-CoV-2 genomes from four countries are selected for visualizations under the C1 modules of the metagenomic analysis system MAS. Based on the variant construction and the theory of information entropy, the module makes statistics on the number of bases in SARS-CoV-2 sequences to calculate the base probability measures in segments to generate the combinatorial entropy index data from the base probability measures. Under visualization technology, the combinatorial entropy index is projected on 2D clustering genomic index maps and 1D histogram maps to provide projection results. The visual results provide intuitive and easy properties to analyze complicated clustering among genomes to support clustering analysis of SARS-CoV-2 genomes in batches, showing the distribution characteristics of SARS-CoV-2 genomes in different countries or regions conveniently.

scholarly journals Visualizations of Combinatorial Entropy Index on Whole SARS-CoV-2 Genomes

2020 ◽  
Author(s):  
Jeffrey Zheng ◽  
Yang Zhou
Keyword(s):  
Information Entropy ◽  
Clustering Analysis ◽  
Probability Measures ◽  
Distribution Characteristics ◽  
Entropy Index ◽  
Theory Of Information ◽  
Visualization Technology ◽  
Analysis System ◽  
Index Maps ◽  
Visual Results

Abstract In this paper, a set of SARS-CoV-2 genomes from four countries are selected for visualizations under the C1 modules of the metagenomic analysis system MAS. Based on the variant construction and the theory of information entropy, the module makes statistics on the number of bases in SARS-CoV-2 sequences to calculate the base probability measures in segments to generate the combinatorial entropy index data from the base probability measures. Under visualization technology, the combinatorial entropy index is projected on 2D clustering genomic index maps and 1D histogram maps to provide projection results. The visual results provide intuitive and easy properties to analyze complicated clustering among genomes to support clustering analysis of SARS-CoV-2 genomes in batches, showing the distribution characteristics of SARS-CoV-2 genomes in different countries or regions conveniently.

scholarly journals Open Digital Shoreline Analysis System: ODSAS v1.0

2021 ◽  
Vol 10 (1) ◽  
pp. 26
Author(s):  
Alejandro Gómez-Pazo ◽  
Andres Payo ◽  
María Victoria Paz-Delgado ◽  
Miguel A. Delgadillo-Calzadilla
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Baseline Method ◽  
Free Open Source Software ◽  
Digital Shoreline Analysis System ◽  
Analysis System ◽  
Study Sites ◽  
Single Baseline ◽  
The One ◽  
Free Open Source

In this study, we propose a new baseline and transect method, the open-source digital shoreline analysis system (ODSAS), which is specifically designed to deal with very irregular coastlines. We have compared the ODSAS results with those obtained using the digital shoreline analysis system (DSAS). Like DSAS, our proposed method uses a single baseline parallel to the shoreline and offers the user different smoothing and spacing options to generate the transects. Our method differs from DSAS in the way that the transects’ starting points and orientation are delineated by combining raster and vector objects. ODSAS uses SAGA GIS and R, which are both free open-source software programs. In this paper, we delineate the ODSAS workflow, apply it to ten study sites along the very irregular Galician coastline (NW Iberian Peninsula), and compare it with the one obtained using DSAS. We show how ODSAS produces similar values of coastline changes in terms of the most common indicators at the aggregated level (i.e., using all transects), but the values differ when compared at the transect-by-transect level. We argue herein that explicitly requesting the user to define a minimum resolution is important to reduce the subjectivity of the transect and baseline method.

scholarly journals End Point Rate Tool for QGIS (EPR4Q): Validation using DSAS and AMBUR

2021 ◽  
Author(s):  
Lucas Terres de Lima ◽  
Sandra Fernández-Fernández ◽  
Jean Marcel de Almeida Espinoza ◽  
Miguel da Guia Albuquerque ◽  
Cristina de Almeida Bernardes
Keyword(s):  
Open Source ◽  
Pearson Correlation ◽  
Shoreline Change ◽  
End Point ◽  
Free Extension ◽  
Analysis System ◽  
End Point Rate ◽  
User Friendly ◽  
Free Open Source ◽  
Scientific Engineering

This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS Graphical Modeler to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

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