Computational Genome Analysis: An Introduction by Richard C. Deonier, Simon Tavaré, Michael S. Waterman

By Richard C. Deonier, Simon Tavaré, Michael S. Waterman

This booklet provides the rules of key difficulties in computational molecular biology and bioinformatics. It makes a speciality of computational and statistical ideas utilized to genomes, and introduces the math and facts which are an important for figuring out those purposes. The ebook includes a loose obtain of the R software program data package deal and the textual content offers nice crossover fabric that's fascinating and obtainable to scholars in biology, arithmetic, facts and laptop technological know-how. greater than a hundred illustrations and diagrams toughen techniques and current key effects from the first literature. routines are given on the finish of chapters.

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Extra resources for Computational Genome Analysis: An Introduction

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Affibody AB, Sweden). ” We may think of the larger question as being composed of three components: – – – Characterizing the genome; Identifying patterns of gene expression and protein abundance under different conditions; Discovering mechanisms for controlling gene expression and the biochemical reactions in the cell. We expand on these topics in the introductions to subsequent chapters, but here we provide an overview. Computational biologists should be concerned about experimental details because computational approaches must be tailored to the structure of the experimental data.

Ln = ln for a particular simulation. In the next sections, we outline some basic probabilistic and statistical language that allows us to analyze such sequences. 1 Probability Distributions Suppose that on a single step our machine produces an output X that takes exactly one of the J possible values in a set X = {x1 , x2 , . . , xJ }. 3 Introduction to Probability 41 sequence example, we have J = 4 and X = {A, C, G, T}. We do not typically know with certainty which value in X will be produced by our machine, so we call X a discrete random variable.

For double-stranded DNA (Fig. 8), the two strands are antiparallel, which means that the two polynucleotide chains have opposite orientations or polarities. Base-pairing rules are usually observed: A base pairs with T and G base pairs with C (Fig. 9). Two strands whose sequences allow them to base pair are said to be complementary. A duplex DNA molecule can thus be represented by a string of letters drawn from {A, C, G, T}, with the left-to-right orientation of the string corresponding to the 5 to 3 polarity.

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