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FungiNetDB: a uniform (pre-)processing platform for fungal expression data
Sascha Schäuble1, Daniela Albrecht-Eckardt2, Gianni Panagiotou1,3
1Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute (Leibniz-HKI), 07745 Jena, Germany.
Abstract:
Fungi, and particularly fungal pathogens, are having an increasing impact on human health and economy. At the same time, modern high-throughput technologies offer insights deep into the molecular level and thus mechanisms, giving scientists new opportunities to identify fungal biomarkers and essential components for survival and virulence. This wealth of data, however, is most often only analyzed in the context of a specific scientific question, while many more projects may benefit from a multifaceted view on e.g. fungal gene expression under various conditions. The prime challenge is the limited access to readily pre-processed data and circumventing technological biases introduced by different sequencing platforms and software tools across different projects. We here present FungiNetDB, a web platform comprising 139 fungal pathogenicity datasets and statistical analysis of more than 2000 different pairwise gene expression comparisons. FungiNetDB thus resembles a most comprehensive fungal transcriptomics resource, which can be explored without any programming knowledge, allows highly customizable filtering and cross-project comparisons and download of all offered data tables and visualizations.
Insights
FungiNetDB offers a comprehensive resource for fungal transcriptomics, integrating 139 datasets. This platform aids researchers in analyzing fungal gene expression and identifying virulence factors without needing programming skills.
Area of Science:
- Mycology and Bioinformatics
- Genomics and Transcriptomics
- Computational Biology
Background:
- Fungal pathogens pose increasing threats to human health and economies.
- High-throughput technologies provide deep molecular insights into fungal mechanisms.
- Existing fungal gene expression data is often siloed and difficult to compare across studies.
Purpose of the Study:
- To develop a centralized, accessible platform for fungal transcriptomics data analysis.
- To facilitate cross-project comparisons and identification of fungal biomarkers.
- To overcome challenges related to data preprocessing and technological biases.
Main Methods:
- Compilation of 139 fungal pathogenicity datasets.
- Statistical analysis of over 2000 pairwise gene expression comparisons.
- Development of a user-friendly web platform (FungiNetDB) for data exploration.
Main Results:
- FungiNetDB provides a comprehensive fungal transcriptomics resource.
- The platform enables exploration without programming knowledge.
- Customizable filtering, cross-project comparisons, and data downloads are supported.
Conclusions:
- FungiNetDB democratizes access to fungal transcriptomics data.
- The resource supports multifaceted analysis of fungal gene expression.
- It aids in identifying essential fungal components for survival and virulence.
