Computer applications sequence analysis

Hanke, J. Reich, J. G. (1996). Kohonen map as a visualization tool for the analysis of protein sequences multiple alignments, domains and segments of secondary structures. Comput Applic Biosci 6,447-54. 

Lapedes, A., Barnes, C Burks, C., Farber, R. Sirotkin, K. (1989). Application of neural networks and other machine learning algorithms to DNA sequence analysis. In Computers and DNA, SFI Studies in the Sciences of Complexity, vol. 7 (ed. Bell, G. I. Marr, T. G.), pp. 157-82. Addison-Wesley, Rosewood City, CA. 

Sequence similarity database searching and protein sequence analysis constitute one of the most important computational approaches to understanding protein structure and function. Although most computational methods used for nucleic acid sequence analysis are also applicable to protein sequence studies, how to capture the enriched features of amino acid alphabets (Chapter 6) poses a special challenge for protein analysis. 

Mass spectrometry (MS) has changed its appearance in the scientific world considerably during recent years. At the beginning of the 20 century first applications in physics were described. Gradually MS methods entered more and more into the fields of biology, biochemistry and biomedicine and became a major tool in life sciences. Mass spectrometers consist of a sequence of functional units for sample introduction, ion formation, mass separation, and detection. The data handling is carried out by computers. Currently, a variety of different mass spectrometric techniques are used for the analysis of biomolecules (Fig. 6). 

The held has matured from the management and analysis of sequence data, albeit still the most important areas, into other areas of biochemistry. This text is an attempt to capture that spirit by introducing computational biochemistry from the biochemists prospect. The material content deals primarily with the applications of computer technology to solve biochemical problems. The subject is relatively new and perhaps a brief description of the text may benefit the students. 

The input data for the shape analysis methods are provided by well-established quantum chemical or empirical computational methods for the calculation of electronic charge distributions, electrostatic potentials, fused spheres Van der Waals surfaces, or protein backbones. The subsequent topological shape analysis is equally applicable to any existing molecule or to molecules which have not yet been synthesized. This is precisely where the predictive power of such shape analysis lies based on a detailed shape analysis, a prediction can be made on the expected activity of all molecules in the sequence and these methods can select the most promising candidates from a sequence of thousands of possible molecules. The actual expensive and time-consuming synthetic work and various chemical and biochemical tests of... 

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