Literature DB >> 33633572

TOXPANEL: A Gene-Set Analysis Tool to Assess Liver and Kidney Injuries.

Patric Schyman1,2, Zhen Xu1,2, Valmik Desai1,2, Anders Wallqvist1.   

Abstract

Gene-set analysis is commonly used to identify trends in gene expression when cells, tissues, organs, or organisms are subjected to conditions that differ from those within the normal physiological range. However, tools for gene-set analysis to assess liver and kidney injury responses are less common. Furthermore, most websites for gene-set analysis lack the option for users to customize their gene-set database. Here, we present the ToxPanel website, which allows users to perform gene-set analysis to assess liver and kidney injuries using activation scores based on gene-expression fold-change values. The results are graphically presented to assess constituent injury phenotypes (histopathology), with interactive result tables that identify the main contributing genes to a given signal. In addition, ToxPanel offers the flexibility to analyze any set of custom genes based on gene fold-change values. ToxPanel is publically available online at https://toxpanel.bhsai.org. ToxPanel allows users to access our previously developed liver and kidney injury gene sets, which we have shown in previous work to yield robust results that correlate with the degree of injury. Users can also test and validate their customized gene sets using the ToxPanel website.
Copyright © 2021 Schyman, Xu, Desai and Wallqvist.

Entities:  

Keywords:  RNA-seq; hepatotoxicity; nephrotoxicity; predictive toxicology; systems toxicology; toxicogenomics

Year:  2021        PMID: 33633572      PMCID: PMC7900624          DOI: 10.3389/fphar.2021.601511

Source DB:  PubMed          Journal:  Front Pharmacol        ISSN: 1663-9812            Impact factor:   5.810


  34 in total

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Journal:  Arch Toxicol       Date:  2017-02-10       Impact factor: 5.153

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8.  Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses.

Authors:  Hao Wang; Ruifeng Liu; Patric Schyman; Anders Wallqvist
Journal:  Front Pharmacol       Date:  2019-02-05       Impact factor: 5.810

9.  Mining Public Toxicogenomic Data Reveals Insights and Challenges in Delineating Liver Steatosis Adverse Outcome Pathways.

Authors:  Mohamed Diwan M AbdulHameed; Venkat R Pannala; Anders Wallqvist
Journal:  Front Genet       Date:  2019-10-18       Impact factor: 4.599

10.  WikiPathways: connecting communities.

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Journal:  Nucleic Acids Res       Date:  2021-01-08       Impact factor: 16.971

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