HantaNet: A New MicrobeTrace Application for Hantavirus Classification, Genomic Surveillance, Epidemiology and Outbreak Investigations

Abstract

Hantaviruses zoonotically infect humans worldwide with pathogenic consequences and are mainly spread by rodents that shed aerosolized virus particles in urine and feces. Bioinformatics methods for hantavirus diagnostics, genomic surveillance and epidemiology are currently lacking a comprehensive approach for data sharing, integration, visualization, analytics and reporting. With the possibility of hantavirus cases going undetected and spreading over international borders, a significant reporting delay can miss linked transmission events and impedes timely, targeted public health interventions. To overcome these challenges, we built HantaNet, a standalone visualization engine for hantavirus genomes that facilitates viral surveillance and classification for early outbreak detection and response. HantaNet is powered by MicrobeTrace, a browser-based multitool originally developed at the Centers for Disease Control and Prevention (CDC) to visualize HIV clusters and transmission networks. HantaNet integrates coding gene sequences and standardized metadata from hantavirus reference genomes into three separate gene modules for dashboard visualization of phylogenetic trees, viral strain clusters for classification, epidemiological networks and spatiotemporal analysis. We used 85 hantavirus reference datasets from GenBank to validate HantaNet as a classification and enhanced visualization tool, and as a public repository to download standardized sequence data and metadata for building analytic datasets. HantaNet is a model on how to deploy MicrobeTrace-specific tools to advance pathogen surveillance, epidemiology and public health globally.

Deciphering hepatocellular carcinoma pathogenesis and therapeutics: a study on anoikis, ceRNA regulatory network and traditional Chinese medicine

“…This type of cell death has been found to have significant prognostic and immunological implications in various cancers, such as lung adenocarcinoma (Diao et al, 2023), gastric cancer (Ye et al, Frontiers in Pharmacology frontiersin.org 2020), and breast cancer (Li et al, 2019). Recent advancements in bioinformatics have led to groundbreaking discoveries in terms of therapeutic targets and drugs (Zhang et al, 2020;Cintron et al, 2023). However, there is still a lack of comprehensive bioinformatics characterization of ARGs in HCC, particularly in relation to immune infiltration, functional analysis, and predicting patient outcomes.…”

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

“…Of the methods that we observed, the most used was the analysis of task accuracy, applied in 24 studies. Most evaluation methods were applied in works that developed an interactive tool [15,17,[19][20][21]23,25,26,62,63,74,80,85,86,90,91,[93][94][95][96][97]99,[103][104][105]107,109,111,[114][115][116]119,[121][122][123]125]. Out of those that used visualization techniques to communicate data with some sort of interactivity (7/114), only 1 [92] validated its techniques by obtaining users' feedback.…”

“…The studies that used a map-style representation (79/114) adopted different strategies: static geographic 2D or 3D maps [14,15,19,20,22,31,33,36,38,[42][43][44]50,75,81,83,88,93,102,104,107,109,117,118,123], dynamic 2D maps [20][21][22]25,61,65,80,85,89,90,94,97], or choropleth maps [17,23,24,27,30,32,34,35,37,40,46,48,49,52,…”

“…The studies that used a map-style representation (79/114) adopted different strategies: static geographic 2D or 3D maps [14,15,19,20,22,31,33,36,38 [113][114][115][116]119,121,127], along with the incidence of a disease [30,32,56,62,63,105,119,120,127]. It was also used to a lesser extent to visualize other measures, such as prevalence [35,64,76] and morbidity [111].…”

“…[14][15][16][17][18][19][20][21][22][23][24] N cases, deaths, incidence, outbreaks, recovered, case fatality rate (CFR), reproduction number, comorbidity, symptoms, hospital beds, spread, vaccinated, contacts, accumulative cases, infection rate, mortality rate, bed occupancy[25,26] N cases, contacts, outbreaks, hotspots, movements N cases, deaths, incidence, contacts, outbreaks, hotspots, recovered, accumulative cases, mortality, CFR, reproduction number, prevalence, morbidity, infection rate, LOS, tests, movements, spread, oc-…”