Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Cambridge: Cambridge University Press 1998.Īltschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Available from: ĭurbin R, Eddy SR, Krogh A, Mitchison G. An Overview of Multiple Sequence Alignment Systems. Mukhopadhyay CS, Choudhary RK, Iquebal MA. A systematic approach to dynamic programming in bioinformatics. The early introduction of dynamic programming into computational biology. Singapore: World Scientific Publishing Co Pte Ltd 1984. Dynamic Programming Algorithms for Biological Sequence and Structure Comparison. Comparative analysis of the quality of a global algorithm and a local algorithm for alignment of two sequences. Polyanovsky VO, Roytberg MA, Tumanyan VG. An improved algorithm for matching biological sequences. Global and local sequence alignment with a bounded number of gaps. Analysis of protein sequence/structure similarity relationships. Hark Gan H, Perlow RA, Roy S, Ko J, Wu M, Huang J, et al. Cambridge: Cambridge University Press 2006. Orthologs, paralogs, and evolutionary genomics. California: University of California Press 2009. Sequence Alignment: Methods, Models, Concepts, and Strategies. Sequence alignment: Concepts and history. Alignment uncertainty and genomic analysis. A benchmark study of sequence alignment methods for protein clustering. Identification of common molecular subsequences. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Encyclopedia of Bioinformatics and Computational Biology, Academic Press. In: Ranganathan S, Gribskov M, Nakai K, Schönbach C, editors. Prjibelski AD, Korobeynikov AI, Lapidus AL. In the near future, information present in this chapter will be useful for retriving information biological sequence. Thus, there is an urgent requirement to explore the effect of alignment bias on broad comparative genomics accuracy. However, our perception of alignment biases remains primitive. Thus, sequence alignment serves as an essential requirement for the most of the biological research ranging from genomics to proteomics. Sequence analysis helps us to detect evolutionary relationship as well as scan motifs by taking into consideration of various events, such as mutations, insertions, deletions, and reordering under some circumstances. For performing these alignment, various algorithms like dynamic programming, heuristic algorithms, or probabilistic methods have been developed. Information obtained revealed that alignment can be either global and local or pairwise sequence alignment and multiple sequence alignment. Thus, in this chapter, author attempted to understand the basics of sequence analysis and how researchers implement various computational tools to achieve them. ![]() The sequence analysis is one of the most effective and commonly applied methods (explicity or implicitly) in biological research.
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