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Yeast As a Chassis for Developing Functional Assays to Study Human P53
Published on: August 4, 2019
TP53-META, a meta-analysis tool for comparative transcriptomics of TP53 dependency: examples from target silencing
Ayse G Keskus1, Eren Kumak2, Merve Vural-Ozdeniz1
1Department Neuroscience, Bilkent University, 06800, Ankara, Turkey.
Background:
TP53 is the most frequently mutated transcription factor (TF) in sporadic cancers; and its targets exhibit dysregulation at the level of expression in both cancer and non-cancer pathologies. However, there is not yet a web-based tool that enables meta-analysis and visualization of TP53-related gene expression datasets, although several databases exist to access and annotate TP53 target information. To address this gap, we developed TP53-META, an interactive R Shiny-based web tool that allows users to upload and simultaneously analyze user's or integrated public RNA-seq datasets for effects of TP53 depletion and/or induction on the transcriptome.
Results:
TP53-META can be used to visualize significant expression clusters as well as TF-TF, pathway-pathway, disease-gene and treatment-gene networks to determine the extent to which selected treatment contrasts are associated with TP53 regulated transcriptional programs. We demonstrated the utility of TP53-META through two case studies. In the first, using an in-house RNA-seq data from MCF7 cells treated with siRNA against a target gene of interest, i.e., CHRNA5, alone or in combination with an siRNA against TP53, we identified transcriptomic changes induced by CHRNA5 depletion that were either associated with TP53 signaling or maintained independently of TP53 by comparing with selected public datasets in TP53-META. In the second, we demonstrated the user data upload functionality of TP53-META before meta-analysis and extracted commonly modulated TP53-related genes in liver fibrosis using public RNA-seq datasets. TP53-META is available at http://konulabapps.bilkent.edu.tr:3838/TP53-Meta1.5/ and the docker image is downloadable from https://github.com/erenkumak/TP53-Meta.
Conclusions:
By facilitating meta-analysis, clustering, and network-based visualizations, TP53-META enables researchers to efficiently integrate and explore TP53-related transcriptomic datasets from diverse sources, and help uncover robust expression patterns, and investigate context-specific TP53 functions.
Insights
TP53-META is a new web tool for analyzing TP53 gene expression data. It helps researchers explore TP53's role in various diseases by integrating and visualizing transcriptomic datasets.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- TP53 is a frequently mutated transcription factor (TF) in cancers.
- TP53 target genes show expression dysregulation in various pathologies.
- Existing databases lack tools for meta-analysis and visualization of TP53 gene expression data.
Purpose of the Study:
- To develop TP53-META, an R Shiny-based web tool for meta-analysis and visualization of TP53-related gene expression datasets.
- To enable simultaneous analysis of user-uploaded and public RNA-seq datasets.
- To investigate the effects of TP53 depletion or induction on the transcriptome.
Main Methods:
- Developed an interactive R Shiny web tool named TP53-META.
- Integrated functionality for uploading and analyzing user-generated RNA-seq data.
- Enabled meta-analysis and visualization of TP53-related transcriptomic datasets.
Main Results:
- TP53-META visualizes expression clusters and gene networks (TF-TF, pathway-pathway, disease-gene, treatment-gene).
- Case studies demonstrated identifying transcriptomic changes related to TP53 signaling and TP53-independent pathways.
- Successfully extracted commonly modulated TP53-related genes in liver fibrosis from public datasets.
Conclusions:
- TP53-META facilitates efficient integration and exploration of diverse TP53-related transcriptomic datasets.
- The tool aids in uncovering robust expression patterns and context-specific TP53 functions.
- Enables researchers to conduct meta-analysis, clustering, and network-based visualizations for TP53 research.
