Abstract
Single nucleotide polymorphisms (SNPs) are the most common type of genetic variations in humans. Understanding the functions of SNPs can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. The method to identify functional SNPs from a pool, containing both functional and neutral SNPs is challenging by experimental protocols. To explore possible relationships between genetic mutation and phenotypic variation, different computational algorithm tools like Sorting Intolerant from Tolerant, Polymorphism Phenotyping, UTRscan, FASTSNP, and PupaSuite were used for prioritization of high-risk SNPs in coding region (exonic nonsynonymous SNPs) and noncoding regions (intronic and exonic 5’ and 3’-untranslated region (UTR) SNPs). In this work, we have analyzed the SNPs that can alter the expression and function of transcriptional factor TP53 as a pipeline and for providing a guide to experimental work. We identified the possible mutations and proposed modeled structure for the mutant proteins and compared them with the native protein. These nsSNPs play a critical role in cancer association studies aiming to explain the disparity in cancer treatment responses as well as to improve the effectiveness of the cancer treatments. Our results endorse the study with in vivo experimental protocols.
Similar content being viewed by others
References
Becky MP, Anne EW (2005) The implications of structured 5’ untranslated regions on translation and disease. Cell Dev Biol 16:39–47
Brian Sprague L, Dietz AT, Closas MG, Newcomb AP, Ernstoff LT, Hampton JM, Chanock JS, Haines LJ, Egan MK (2007) Genetic variation in TP53 and risk of breast cancer in a population-based case-control study. Carcinogenesis 28(8):680–1686
Cargill M, Altshuler D, Ireland J, Sklar P, Ardlie K, Patil N, Shaw N, Lane CR, Lim EP, Kalyanaraman N, Nemesh J, Ziaugra L, Friedland L, Rolfe A, Warrington J, Lipshutz R, Daley GQ, Lander ES (1999) Characterization of single nucleotide polymorphisms in coding regions of human genes. Nat Genet 22:231–238
Cavallo A, Martin AC (2005) Mapping SNPs to protein sequence and structure data. Bioinformatics 8:1443–1450
Chen X, Sullivan PF (2003) Single nucleotide polymorphism genotyping: biochemistry, protocol, cost and throughput. Pharmacogenomics J 3:77–96
Conde L, Vaquerizas MJ, Santoyo J, Al-Shahrour F, Ruiz-Llorente S, Robledo M, Dopazo J (2004) PupaSNP Finder: a web tool for finding SNPs with putative effect at transcriptional level. Nucleic Acids Res 32:W242–W248
Conde L, Vaquerizas JM, Ferrer-Costa C, de la Cruz X, Orozco M, Dopazo J (2005) PupasView: a visual tool for selecting suitable SNPs, with putative pathological effect in genes, for genotyping purposes. Nucleic Acids Res 33:W501–W505
Conde L, Vaquerizas JM, Dopazo H, Arbiza L, Reumers J, Rousseau F, Schymkowitz J, Dopazo J (2006) PupaSuite: finding functional single nucleotide polymorphisms for large-scale genotyping purposes. Nucleic Acids Res 34:W621–W625
Delarue M, Dumas P (2004) On the use of low-frequency normal modes to enforce collective movements in refining macromolecular structural models. Proc Natl Acad Sci 101:6957–6962
Deventer VS (2000) Cytokine and cytokine receptor polymorphisms in infectious disease. Intensive Care Med 26:S98–S102
Goto Y, Yue L, Yokoi A, Nishimura R, Uehara T, Koizumi S, Saikawa Y (2001) A novel single-nucleotide polymorphism in the 3-untranslated region of the human dihydrofolate reductase gene with enhanced expression. Clin Cancer Res 7:1952–1956
Hollstein M, Sidransky D, Vogelstein B, Harris CC (1991) p53 mutations in human cancers. Science 253:49–53
Horvath MM, Wang X, Resnick MA, Bel DA (2007) Divergent evolution of human p53 binding sites: cell cycle versus apoptosis. PLoS Genet 3(7):e127
Krawczak M, Ball EV, Fenton I, Stenson PD, Abeysinghe S, Thomas N, Strachan T, Read AP, Cooper DN (2000) Human gene mutation database—a biomedical information and research resource. Hum Mutat 15:45–51
Kwock PY (2003) Single nucleotide polymorphisms methods and protocols (Methods in molecular biology), vol. 212. Humana Press, Totowa
Lindahl E, Azuara C, Koehl P, Delarue M (2006) NOMAD-Ref: visualization, deformation and refinement of macromolecular structures based on all-atom normal mode analysis. Nucleic Acids Res 34:W52–W56
Nowak R (1994) Mining treasures from ‘junk DNA’. Science 263:608–610
Pauline Ng C, Henikoff S (2001) Predicting deleterious amino acid substitutions. Genome Res 11:863–874
Pauline Ng C, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucl Acids Res 31:3812–3814
Pesole G, Liuni S (1999) Internet resources for the functional analysis of 5’ and 3’ untranslated regions of eukaryotic mRNA. TIG 15:378
Pesole G, Liuni S, Grillo G, Licciulli F, Mignone F, Gissi C, Saccone C (2002) UTRdb and UTRsite: specialized databases of sequences and functional elements of 5’ and 3’ untranslated regions of eukaryotic mRNAs. Nucleic Acids Res 30:335–340
Petitjean E, Mathe E, Kato S, Ishioka C, Tavtigian SV, Hainaut P, Olivier M (2007) Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database. Hum Mutat 28(6):622–629
Ramensky V, Pork P, Sunyaev S (2002) Human non-synonymous SNPs: server and survey. Nucleic Acids Res 30(17):3894–3900
Reumers J, Maurer-Stroh S, Schymkowitz J, Rousseau F (2006) SNPeffect v2.0: a new step in investigating the molecular phenotypic effects of human non-synonymous SNPs. Bioinformatics 22(17):2183–2185
Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K (2001) dbSNP: the NCBI database of genetic variation. Nucl Acids Res 29:308–311
Sonenberg N (1994) mRNA translation: influence of the 59 and 39 untranslated regions. Curr Opin Genet Dev 4:310–315
Soussi T, Asselain B, Hamroun D, Kato S, Ishioka C, Claustres M, Béroud C (2006) Meta-analysis of the p53 mutation database for mutant p53 biological activity reveals a methodologic bias in mutation detection. Clin Cancer Res 12:62–69
Strachan T, Read AP (1999) Human molecular genetics, 2nd edn. Bios Scientific, Oxford Ch. 18 Cancer Genet
Vogelstein B, Lane D, Levine AJ (2000) Surfing the p53 network. Nature 408:307–310
Yuan H, Chiou J, Tseng W, Liu C, Liu C, Lin Y, Wang H, Yao A, Chen Y, Hsu C (2006) FASTSNP: an always up-to-date and extendable service for SNP function analysis and prioritization. Nucleic Acids Res 34:W635–W641
Acknowledgments
The authors thank the management of Vellore Institute of Technology for providing the facilities to carry out this work. All the authors thank the Editor-in-Chief Prof. Rudi Appels for his invaluable suggestions during the preparation of this compact review.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
George Priya Doss, C., Sudandiradoss, C., Rajasekaran, R. et al. Applications of computational algorithm tools to identify functional SNPs. Funct Integr Genomics 8, 309–316 (2008). https://doi.org/10.1007/s10142-008-0086-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10142-008-0086-7