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A Review on Principles of Bioinformatics and Its Methods Based on Sequence Techniques

Sona Singh

Abstract


As it is evident, the genetic material is stored in DNA sequences and transcript in RNA and protein sequences, it makes sense to evaluate two or more biological sequences to look for resemblance and disparity that can be used to infer the relatedness of the sequences. These sequences are compared to generate information of their evolution with the help of latest technique of computational biology. Sequence alignment provides a way to arrange the sequences of genetic material to identify regions of similarity that may be an effect of functional, structural or evolutionary relationships between the sequences .This paper is focused on the perceptive of sequence based techniques and construction of algorithms that deal with problems of biological relevance that is widely known as computational biology or bioinformatics. It emphasize on utilizing the competence of computer to gain knowledge from biological data. The majority of issues in computational biology is linked to molecular and evolutionary biology and its focus is on analysis and comparison of the genetic material of organisms. One integral component in forming the outline of computational biology is DNA, RNA and protein. These elements are responsible for storing and utilizing the genetic material in an organism and can be described as strings over finite alphabets.

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References


A.J. Gibbs and G.A. McIntyre, The diagram a method for comparing sequences. Its use with amino acid and nuceotide sequences. Eur J Biochem. 1970; 16: 1–11p.

A.L. Delcher, S. Kasif, R.D. Fleischmann, J. Peterson, O. White and S.L. Salzberg, Alignment of whole genomes. Nucleic Acids Res. 1999; 27: 2369–76p. http://mummer.sourceforge.net/

C.B. Do, M.S.P. Mahabhashyam, M. Brudno and S. Batzoglou, ProbCons: Probabilistic consistency-based multiple sequence alignment. Genome Res. 2005; 15: 330–40p. http://packages.debian.org/squeeze/probcons

C. Notredame, D.G. Higgins and J. Heringa, T-Coffee: A novel method for multiple sequence alignments. J Mol Biol. 2000; 302: 205–17p.

D.G. George, W.C. Barker and L.T. Hunt, The protein identification resource (PIR). Nucleic Acids Res. 1986; 14: 11–6p.

H.S. Bilofsky, C. Burks, J.W. Fickett, W.B. Goad, F.I. Lewitter, W.P. Rindone, C.D. Swindell and C.S. Tung, The GenBank™ genetic sequence data bank. Nucleic Acids Res. 1986; 14: 1–4p.

J.D. Thompson, T.J. Gibson, F. Plewniak, F. Jeanmougin, and D.G. Higgins, TheClustalX windows interface: Flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res. 1997; 24: 4876–82p. http://www.clustal.org/.

J.D. Thompson, D.G. Higgins and T.J. Gibson, CLUSTALW: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994; 22: 4673–80p.

J.R. Cole, B. Chai, R.J. Farris, Q. Wang,A.S. Kulam-Syed-Mohideen, D.M. McGarrell, A.M. Bandela, E. Cardenas, G.M. Garrity, and J.M. Tiedje, The ribosomal database project (RDP-II):Introducing myRDP space and quality controlled public data. Nucleic Acids Res. 2007; 35: 169–72p.

http://rdp.cme.msu.edu/.

K. Katoh, K. Kuma, H. Toh and T. Miyata, MAFFT version 5: Improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 2005; 33: 511–8p. http://mafft.cbrc.jp/alignment/software/index.html.

K. Katoh, K. Misawa, K. Kuma and T. Miyata, MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002; 30: 3059–66p.

L. Wang and T. Jiang. On the complexity of multiple sequence alignment. J Comput Biol. 1994; 1: 337–48p.

M.O. Dayhoff, R.V. Eck, M.A. Chang and M.R Sochard, in Dayhoff, M.O. (ed.), Atlas of Protein Sequence and Structure, Vol. 1. National Biomedical Research Foundation, Silver Spring, MD, 1965.

M.S. Waterman, Sequence alignments, in Waterman, M.S. (ed.), Mathematical Methods for DNA Sequences. Boca Raton, FL: CRC Press; 1989, 56p.

N. M. Luscombe, D. Greenbaum and M Gerestein, What is bioinformatics? A proposed definition and overview of the field. Methods Inf Med. 40(4), 346-358.

R.C. Edgar, MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004; 32: 1792–7p. http://www.drive5.com/muscle/.

R.C. Edgar and S.Batzoglou, Multiple sequence alignment. Curr Opin Struct Biol. 2006; 16: 368–73p.

R.F. Smith and T.F. Smith, Pattern-induced multi-sequence alignment (PIMA) algorithm employing secondary structure-dependent gap penalties for comparative protein modeling. Protein Eng. 1992; 5: 35–41p. http://genamics.com/software/downloads/pima- 1.40.tar.gz.

http://www.clustal.org/.

S B. Needleman and C. D. Wunsch"A general method applicable to the search for similarities in the amino acid sequence of two proteins". J Mol Biol. 48(3): 443–53p (1970). doi:10.1016/0022-2836(70)90057-4.

S. Rozen, and H. Skaletsky, Primer3 on the WWW for general users and for biologist programmers, in Krawetz, S. and Misener, S. (eds.), Bioinformatics Methods and Protocols: Methods in Molecular Biology. Totowa, NJ: Humana Press; 2000; 365–386p.

http://primer3.sourceforge.net/.

S.K. Gupta, J.D. Kececioglu and A. A. Schaffer, Improving the practical space and time efficiency of the shortest-paths approach to sum-of-pairs multiple sequence alignment. J Comput Biol. 1995; 2: 459–72p. http://www.ncbi.nlm.nih.gov/CBBresearch/Schaffer/msa.html.

S.M. Thompson, Computing Handbook. 3rd Edn. Vol. (1)30, 2014, 1–27p.




DOI: https://doi.org/10.37628/ijcbb.v2i2.134

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