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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Updated: Jul 4, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

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Applications of computational algorithm tools to identify functional SNPs.

C George Priya Doss¹, C Sudandiradoss, R Rajasekaran

¹School of Biotechnology, Chemical and Biomedical Engineering, Bioinformatics Division, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.

Functional & Integrative Genomics

|June 20, 2008

Summary

This study uses computational tools to identify functional single nucleotide polymorphisms (SNPs) affecting the TP53 gene. These identified SNPs are crucial for understanding cancer genetics and improving treatment strategies.

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

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Last Updated: Jul 4, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

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Published on: December 9, 2015

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Single nucleotide polymorphisms (SNPs) are common genetic variations influencing human traits and diseases.
Identifying functional SNPs is challenging, hindering understanding of phenotype variation and complex diseases.
The TP53 gene is critical in cellular regulation and cancer development.

Purpose of the Study:

To computationally identify and prioritize high-risk SNPs impacting the TP53 gene's function and expression.
To provide a pipeline for analyzing SNPs in coding and noncoding regions.
To guide experimental validation of functional SNPs in cancer research.

Main Methods:

Utilized computational tools including SIFT, PolyPhen, UTRscan, FASTSNP, and PupaSuite.
Analyzed exonic nonsynonymous SNPs (nsSNPs) and SNPs in untranslated regions (UTRs).
Modeled mutant protein structures and compared them to the native TP53 protein.

Main Results:

Identified specific SNPs that potentially alter TP53 expression and protein function.
Generated modeled structures for mutant TP53 proteins, highlighting potential functional impacts.
Prioritized nsSNPs relevant to cancer association studies.

Conclusions:

Computational analysis effectively identifies functional SNPs in the TP53 gene.
These findings support further in vivo studies for cancer treatment response and effectiveness.
Understanding TP53 SNPs can elucidate cancer disparities and inform therapeutic strategies.