Phenotype Prediction of Pathogenic Nonsynonymous Single Nucleotide Polymorphisms in Insulin with Bioinformatics Tools

Background: CDKN2A, encoding two important tumor suppressor proteins p16 and p14, is a tumor suppressor gene. Mutations in this gene and subsequently the defect in p16 and p14 proteins lead to the downregulation of RB1/p53 and cancer malignancy. To identify the structural and functional effects of mutations, various powerful bioinformatics tools are available. The aim of this study is the identification of high-risk non-synonymous single nucleotide variants in the CDKN2A gene via bioinformatics tools. Materials and Methods: Among the identified polymorphisms in this gene, 353 missense variants are retrieved from the national center for biotechnology information/single nucleotide polymorphism database (NCBI/dbSNP). Then, the pathogenicity of missense variants are considered using different bioinformatics tools. The stability of these mutant proteins, conservation of amino acids and the secondary and tertiary structural changes are analyzed by bioinformatics tools. After the identification of high-risk mutations, the changes in the hydrophobicity of high-risk amino acid substitutions are considered. Results: Deleterious single nucleotide polymorphisms (SNPs) were screened step by step using the bioinformatics tools. The results obtained from the set of bioinformatics tools identify high-risk mutations in CDKN2A gene. Conclusion: 18 high-risk mutations including L16R/Q, G23D/R/S, L32P, N42K, G55D, G67D/R, P81R, H83R, G89D/S, A102E, G101R, G122R, and V126D were identified. According to the previous experimental studies, the association of L16R, G23D/R/S, L32P, G67R, H83R, G89D, G101R, and V126D amino acid substitutions with various cancers has been confirmed.

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Next-generation Sequence-analysis Toolkit (NeST): A standardized bioinformatics framework for analyzing Single Nucleotide Polymorphisms in next-generation sequencing data

10.1101/323535 ◽

2018 ◽

Author(s):

Shashidhar Ravishankar ◽

Sarah E. Schmedes ◽

Dhruviben S. Patel ◽

Mateusz Plucinski ◽

Venkatachalam Udhayakumar ◽

...

Keyword(s):

Next Generation Sequencing ◽

Single Nucleotide Polymorphisms ◽

Variant Calling ◽

Next Generation Sequencing Data ◽

Nucleotide Polymorphisms ◽

Next Generation ◽

Sequencing Data ◽

Single Nucleotide ◽

Bioinformatics Tools ◽

Generation Sequencing

AbstractRapid advancements in next-generation sequencing (NGS) technologies have led to the development of numerous bioinformatics tools and pipelines. As these tools vary in their output function and complexity and some are not well-standardized, it is harder to choose a suitable pipeline to identify variants in NGS data. Here, we present NeST (NGS-analysis Toolkit), a modular consensus-based variant calling framework. NeST uses a combination of variant callers to overcome potential biases of an individual method used alone. NeST consists of four modules, that integrate open-source bioinformatics tools, a custom Variant Calling Format (VCF) parser and a summarization utility, that generate high-quality consensus variant calls. NeST was validated using targeted-amplicon deep sequencing data from 245 Plasmodium falciparum isolates to identify single-nucleotide polymorphisms conferring drug resistance. The results were verified using Sanger sequencing data for the same dataset in a supporting publication [28]. NeST offers a user-friendly pipeline for variant calling with standardized outputs and minimal computational demands for easy deployment for use with various organisms and applications.

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