FungiExpresZ
FungiExpresZ enables analysis and visualization of high-throughput gene expression data to support comparative and functional studies in fungal biology and other organisms.
Key Features:
- Comprehensive Data Analysis Tools: Provides a suite of commonly used bioinformatics tools covering stages of gene expression analysis from preprocessing to visualization.
- Extensive RNA-seq Dataset Collection: Includes pre-processed public ribonucleic acid sequencing (RNA-seq) datasets for numerous fungal species, including human, plant, and insect pathogens.
- Integrated Data Analysis: Supports combined analysis of user-generated experimental data alongside publicly available RNA-seq datasets for comparative studies.
- Versatility Across Organisms: Tailored for fungal gene expression but applicable to gene expression data from any organism.
Scientific Applications:
- Fungal genomics and pathogenicity: Analysis of gene expression patterns to investigate genetic determinants of fungal biology and disease.
- Host–pathogen interaction studies: Comparative expression analyses to explore responses and interactions between fungi and their hosts.
- Virulence mechanism investigation: Identification and characterization of genes and pathways associated with fungal virulence.
- Antifungal resistance research: Examination of expression changes linked to resistance mechanisms against antifungal agents.
- Comparative and meta-analyses: Integration of user and public datasets to perform meta-analyses across experiments and species.
Methodology:
Implemented in R using the R-Shiny framework and incorporating pre-processed public RNA-seq datasets.
Topics
Details
- License:
- MIT
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- R
- Added:
- 3/19/2023
- Last Updated:
- 11/24/2024
Operations
Publications
Parsania C, Chen R, Sethiya P, Miao Z, Dong L, Wong KH. FungiExpresZ: an intuitive package for fungal gene expression data analysis, visualization and discovery. Briefings in Bioinformatics. 2023;24(2). doi:10.1093/bib/bbad051. PMID:36806894. PMCID:PMC10025439.
DOI: 10.1093/bib/bbad051
PMID: 36806894
PMCID: PMC10025439
Funding: - Science and Technology Development Fund: FDCT0033/2021/A1, FDCT0099/2022/A2, FDCT0106/2020/A
- University of Macau – Dr Stanley Ho Medical Development Foundation: SHMDF-OIRFS/2022/001
- Research Services and Knowledge Transfer Office: MYRG2022-00107-FHS