Applications of computational algorithm tools to identify functional SNPs

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 functio...

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Vydáno v:Functional & integrative genomics Ročník 8; číslo 4; s. 309 - 316
Hlavní autoři: George Priya Doss, C, Sudandiradoss, C, Rajasekaran, R, Choudhury, Parikshit, Sinha, Priyanka, Hota, Pragnya, Batra, Udit Prakash, Rao, Sethumadhavan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.11.2008
Springer-Verlag
Springer
Springer Nature B.V
Témata:
3' untranslated regions
Algorithms
Animal Genetics and Genomics
Biochemistry
Bioinformatics
Biological and medical sciences
Biomedical and Life Sciences
Cell Biology
chemistry
Fundamental and applied biological sciences. Psychology
General aspects
Genetic research
genetics
Genotype
Genotype & phenotype
human genetics
Humans
in vivo studies
Life Sciences
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Microbial Genetics and Genomics
Modeled structure
Molecular Sequence Data
Phenotype
phenotypic variation
Plant Genetics and Genomics
Polymorphism
Polymorphism, Single Nucleotide
Protein Conformation
Review
single nucleotide polymorphism
SNPs
Software
TP53 gene
transcription factors
Tumor Suppressor Protein p53
Tumor Suppressor Protein p53 - chemistry
Tumor Suppressor Protein p53 - genetics
Modeled structure
SNPs
TP53 gene
TP53 Gene
Identification
Models
Single nucleotide polymorphism
Structure
Algorithm
Application
ISSN:1438-793X, 1438-7948
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Shrnutí: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.
Bibliografie:http://dx.doi.org/10.1007/s10142-008-0086-7
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ISSN:1438-793X
1438-7948
DOI:10.1007/s10142-008-0086-7