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Bioinformatics Tools for Discovery and Functional Analysis of Single Nucleotide Polymorphisms

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Advance in Structural Bioinformatics

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 827))

Abstract

With the high speed DNA sequencing of genome, databases of genome data continue to grow, and the understanding of genetic variation between individuals grows as well. Single nucleotide polymorphisms (SNPs), a main type of genetic variation, are increasingly important resource for understanding the structure and function of the human genome and become a valuable resource for investigating the genetic basis of disease. During the past years, in addition to experimental approaches to characterize specific variants, intense bioinformatics techniques were applied to understand effects of these genetic changes. In the genetics studies, one intends to understand the molecular basis of disease, and computational methods are becoming increasingly important for SNPs selection, prediction and understanding the downstream effects of genetic variation. The review provides systematic information on the available resources and methods for SNPs discovery and analysis. We also report some new results on DNA sequence-based prediction of SNPs in human cytochrome P450, which serves as an example of computational methods to predict and discovery SNPs. Additionally, annotation and prediction of functional SNPs, as well as a comprehensive list of existing tools and online recourses, are reviewed and described.

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  1. State Key Laboratory of Microbial Metabolism, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China

    Li Li & Dongqing Wei

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    Dongqing Wei

  2. Shanghai Jiao Tong University, Shanghai, China

    Qin Xu

  3. Shanghai Jiao Tong University, Shanghai, China

    Tangzhen Zhao

  4. Shanghai Jiao Tong University, Shanghai, China

    Hao Dai

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Li, L., Wei, D. (2015). Bioinformatics Tools for Discovery and Functional Analysis of Single Nucleotide Polymorphisms. In: Wei, D., Xu, Q., Zhao, T., Dai, H. (eds) Advance in Structural Bioinformatics. Advances in Experimental Medicine and Biology, vol 827. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9245-5_17

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