Sequence — Evolution — Function

Released on by Eugene V. Koonin
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  • Sequence — Evolution — Function Book Detail

  • Author : Eugene V. Koonin
  • Release Date : 2013-06-29
  • Publisher : Springer Science & Business Media
  • Genre : Science
  • Pages : 482
  • ISBN 13 : 1475737831
  • File Size : 67,67 MB
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Sequence — Evolution — Function by Eugene V. Koonin PDF Summary

Book Description: Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as functional genomics. The book provides the reader with an understanding of the principles and approaches of functional genomics and of the potential and limitations of computational and experimental approaches to genome analysis. Sequence - Evolution - Function should help bridge the "digital divide" between biologists and computer scientists, allowing biologists to better grasp the peculiarities of the emerging field of Genome Biology and to learn how to benefit from the enormous amount of sequence data available in the public databases. The book is non-technical with respect to the computer methods for genome analysis and discusses these methods from the user's viewpoint, without addressing mathematical and algorithmic details. Prior practical familiarity with the basic methods for sequence analysis is a major advantage, but a reader without such experience will be able to use the book as an introduction to these methods. This book is perfect for introductory level courses in computational methods for comparative and functional genomics.

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Sequence — Evolution — Function

Sequence — Evolution — Function

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Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as func

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Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as func

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Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the H