序列中snp和索引的工具、资源和数据库综述。

Q4 Health Professions
Abhik Seal, Arun Gupta, M Mahalaxmi, Riju Aykkal, Tiratha Raj Singh, Vadivel Arunachalam
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引用次数: 16

摘要

单核苷酸多态性(SNP)是一种突变,其中DNA中的单个碱基与该位置的通常碱基不同。snp是遗传分析的首选标记,在定位与疾病相关的基因方面也很有用。snp是基因组中重要且经常发生的点突变,具有许多实际意义。在后基因组时代,计算机方法很容易研究在已知基因组或感兴趣的物种序列中发生的snp。有许多在线和独立的工具来分析snp。我们打算用算法背景、文件要求、操作系统特异性和物种特异性(如果有的话)等软件细节来指导读者使用植物和动物的snp检测工具。我们还列出了许多可用的数据库和资源,以描述广泛生物体中的snp。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tools, resources and databases for SNPs and indels in sequences: a review.

Single Nucleotide Polymorphism (SNP) is a mutation where, a single base in the DNA differs from the usual base at that position. SNPs are the marker of choice in genetic analysis and also useful in locating genes associated with diseases. SNPs are important and frequently occurring point mutations in genomes and have many practical implications. In silico methods are easy to study the SNPs that are occurring in known genomes or sequences of a species of interest during the post genomic era. There are many on-line and stand alone tools to analyse the SNPs. We intend to guide the reader with the software details such as algorithmic background, file requirements, operating system specificity and species specificity, if any, for the tools of SNPs detection in plants and animals. We also list many databases and resources available today to describe SNPs in wide range of organisms.

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来源期刊
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management
CiteScore
0.60
自引率
0.00%
发文量
26
期刊介绍: Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.
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