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Yeast As a Chassis for Developing Functional Assays to Study Human P53
Published on: August 4, 2019
In silico analysis of p53 using the p53 knowledgebase: mutations, polymorphisms, microRNAs and pathways
Yuchen Yang1, Erwin Tantoso, Gek Huey Chua
1Institute of Molecular and Cell Biology, Proteos, Singapore.
In Silico Biology
|August 11, 2007
Summary
The p53 tumor suppressor gene is crucial for cancer prevention. This study created a p53 knowledgebase, revealing new insights into cancer mutations and gene regulation.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genetics
Background:
- The p53 gene is a critical tumor suppressor with extensive research.
- A comprehensive knowledgebase is needed to consolidate p53 information for researchers.
Purpose of the Study:
- To develop a p53 knowledgebase for experimental design and data analysis.
- To perform bioinformatics analyses for novel discoveries regarding p53.
Main Methods:
- Curated data on p53 mutations, transcription factors, targets, and SNPs.
- Bioinformatics analysis of curated data.
- Assessment of tagSNP selection strategies using HapMap and NIEHS data.
Main Results:
- Identified over-represented point missense mutations in cancers lacking functional studies.
- NIEHS data is superior for tagSNP selection in p53 association studies.
- Discovered potential microRNA regulation of p53 transcription factors.
- Differentiated p53 transcriptional controls in cell cycle and apoptosis pathways.
Conclusions:
- The p53 knowledgebase facilitates research and discovery.
- Bioinformatics analysis yields significant new findings on p53.
- Strategic SNP selection enhances future genetic association studies.
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