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An Insight to Application of Computational Statistics and computational Biology in Bioinformatics


An Insight to Application of Computational Statistics and Computational Biology in Bioinformatics
  • August 2012
  • In book: Applied Computational Biology and Statistics in Biotechnology and Bioinformatics
  • Edition: 1
  • Chapter: ch-36
  • Publisher: NIPA,New Delhi
  • Editors: akroy
  • Ajit kumar RoyAjit kumar Roy
    • Computation has become an essential tool in life science research. This is exemplified in genomics, where first microarrays and now massively parallel DNA sequencing have enabled a variety of genome-wide functional assays that require increasingly complex analysis tools. The problem of accessibility of computational tools has long been recognized. Without programming or informatics expertise, scientists needing to use computational approaches are impeded by problems ranging from tool installation; to determining which parameter values to use; to efficiently combining multiple tools together in an analysis chain. The severity of these problems is evidenced by the numerous solutions to address them. Knowledge on software and web-based interfaces for tools all improve the accessibility of computation. These approaches each have advantages, but do not offer a general solution that enables a computational tool to be easily included in an analysis chain and run by scientists without programming experience. However, making tools accessible does not necessarily address the crucial problem of reproducibility. For computational experiments, researchers have argued that computational results, such as analyses and methods, are of equal or even greater importance than text and figures as experimental outputs. In this chapter, it is attempted to highlight basic concept of new biology, tools, techniques of computational biology and computational statistics applied in biotechnology and bioinformatics. Further some of the common useful statistical aspects of microarray data analysis, including detection of differential expression methods are highlighted.
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