- Article -
| In Silico Biology 7, 0005 (2006); ©2006, Bioinformation Systems e.V. |
In silico analysis of p53 using the P53 Knowledgebase: mutations, polymorphisms, microRNAs and pathways
Yuchen Yang1, Erwin Tantoso2, Gek Huey Chua3, Zhen Xuan Yeo4, Felicia Soo Lee Ng2, Sum Thai Wong2, Cheuk Wang Chung2 and Kuo-Bin Li5*
1 Institute of Molecular and Cell Biology, 61 Biopolis Drive, Proteos, 138673, Singapore
2 Bioinformatics Institute, 30 Biopolis Street, Matrix, 138671, Singapore
3 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD7, #02-03, 8 Medical Drive, 117597, Singapore
4 Genomics Institute of Singapore, 60 Biopolis Street, 138672, Genome, Singapore
5 Bioinformatics Center, National Yang-Ming University, Taipei, 112, Taiwan
* Corresponding author
Email: kbli@ym.edu.tw
Edited by E. Wingender; received June 20, 2006; revised October 25 and November 13, 2006; accepted December 05, 2006; published December 14, 2006
Abstract
P53 is probably the most important tumor suppressor known. Over the years, information about this gene has increased dramatically. We have built a comprehensive knowledgebase of p53, which aims to facilitate wet-lab biologists to formulate their experiments and new-comers to learn whatever they need about the gene and bioinformaticians to make new discoveries through data analysis. Using the information curated, including mutation information, transcription factors, transcriptional targets, and single nucleotide polymorphisms, we have performed extensive bioinformatics analysis, and made several new discoveries about p53. We have identified point missense mutations that are over-represented in cancers, but lack of functional studies. By assessing the capability of six p53 transcriptional targets' tag SNPs selected from HapMap to capture SNPs obtained from National Institute of Environmental Health Sciences (NIEHS) Environmental Genome project and vice versa, we conclude that NIEHS data is a better source for tagSNP selections of these genes in future association studies. Analysis of microRNA regulation in the transcriptional network of the p53 gene reveals potentially important regulatory relationships between oncogenic microRNAs and transcription factors of p53. By mapping transcription factors of p53 to pathways involved in cell cycle and apoptosis, we have identified distinctive transcriptional controls of p53 in these two physiological states.
Keywords: p53, transcription factor, transcriptional target, somatic mutation, point missense mutation, mutation analysis, single nucleotide polymorphism, tagSNP, HapMap, NIEHS, microRNA, pathway analysis