Residential College | false |
Status | 已發表Published |
FungiExpresZ: an intuitive package for fungal gene expression data analysis, visualization and discovery | |
Parsania, Chirag1; Chen, Ruiwen1; Sethiya, Pooja1; Miao, Zhengqiang1; Dong, Liguo1; Wong, Koon Ho1,2,3 | |
2023-03-19 | |
Source Publication | Briefings in Bioinformatics |
ISSN | 1467-5463 |
Volume | 24Issue:2Pages:1–12 |
Abstract | Bioinformatics analysis and visualization of high-throughput gene expression data require extensive computer programming skills, posing a bottleneck for many wet-lab scientists. In this work, we present an intuitive user-friendly platform for gene expression data analysis and visualization called FungiExpresZ. FungiExpresZ aims to help wet-lab scientists with little to no knowledge of computer programming to become self-reliant in bioinformatics analysis and generating publication-ready figures. The platform contains many commonly used data analysis tools and an extensive collection of pre-processed public ribonucleic acid sequencing (RNA-seq) datasets of many fungal species, including important human, plant and insect pathogens. Users may analyse their data alone or in combination with public RNA-seq data for an integrated analysis. The FungiExpresZ platform helps wet-lab scientists to overcome their limitations in genomics data analysis and can be applied to analyse data of any organism. FungiExpresZ is available as an online web-based tool (https://cparsania.shinyapps.io/FungiExpresZ/) and an offline R-Shiny package (https://github.com/cparsania/FungiExpresZ). |
Keyword | Fungiexpresz Bioinformatics Tool Rna-seq Database Data Visualization Data Analysis Fungi |
DOI | 10.1093/bib/bbad051 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
WOS Subject | Biochemical Research Methods ; Mathematical & Computational Biology |
WOS ID | WOS:000936333900001 |
Scopus ID | 2-s2.0-85150666597 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau Faculty of Health Sciences Institute of Translational Medicine |
Corresponding Author | Wong, Koon Ho |
Affiliation | 1.Faculty of Health Sciences, University of Macau, Macau SAR of China, China 2.Institute of Translational Medicine, University of Macau, Macau SAR of China, China 3.Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Macau, China |
First Author Affilication | Faculty of Health Sciences |
Corresponding Author Affilication | Faculty of Health Sciences; University of Macau |
Recommended Citation GB/T 7714 | Parsania, Chirag,Chen, Ruiwen,Sethiya, Pooja,et al. FungiExpresZ: an intuitive package for fungal gene expression data analysis, visualization and discovery[J]. Briefings in Bioinformatics, 2023, 24(2), 1–12. |
APA | Parsania, Chirag., Chen, Ruiwen., Sethiya, Pooja., Miao, Zhengqiang., Dong, Liguo., & Wong, Koon Ho (2023). FungiExpresZ: an intuitive package for fungal gene expression data analysis, visualization and discovery. Briefings in Bioinformatics, 24(2), 1–12. |
MLA | Parsania, Chirag,et al."FungiExpresZ: an intuitive package for fungal gene expression data analysis, visualization and discovery".Briefings in Bioinformatics 24.2(2023):1–12. |
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