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E-grāmata: Introduction to Computational Biology: Maps, Sequences and Genomes

(University of Southern California, Los Angeles, USA)
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Introduction to Computational Biology: Maps, Sequences and GenomesPalielināt
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Biology is in the midst of a era yielding many significant discoveries and promising many more. Unique to this era is the exponential growth in the size of information-packed databases. Inspired by a pressing need to analyze that data, Introduction to Computational Biology explores a new area of expertise that emerged from this fertile field- the combination of biological and information sciences.

This introduction describes the mathematical structure of biological data, especially from sequences and chromosomes. After a brief survey of molecular biology, it studies restriction maps of DNA, rough landmark maps of the underlying sequences, and clones and clone maps. It examines problems associated with reading DNA sequences and comparing sequences to finding common patterns. The author then considers that statistics of pattern counts in sequences, RNA secondary structure, and the inference of evolutionary history of related sequences.

Introduction to Computational Biology exposes the reader to the fascinating structure of biological data and explains how to treat related combinatorial and statistical problems. Written to describe mathematical formulation and development, this book helps set the stage for even more, truly interdisciplinary work in biology.

Recenzijas

"I very much enjoyed the book, and was delighted to recommend itthe use of molecular biology to introduce and illustrate application of sophisticated mathematical theory was excellentas an illustration of the challenges and rewards of collaborative work, it is ideal." -Statistics: Monash University

Papildus informācija

Springer Book Archives
Preface xiii
Introduction
1(4)
Molecular Biology
3(1)
Mathematics, Statistics, and Computer Science
3(2)
Some Molecular Biology
5(24)
DNA and Proteins
6(1)
The Double Helix
6(1)
The Central Dogma
7(1)
The Genetic Code
8(4)
Transfer RNA and Protein Sequences
12(4)
Genes Are Not Simple
16(2)
Starting and Stopping
16(1)
Control of Gene Expression
16(1)
Split Genes
17(1)
Jumping Genes
18(1)
Biological Chemistry
18(11)
Restriction Maps
29(12)
Introduction
29(2)
Graphs
31(2)
Interval Graphs
33(5)
Measuring Fragment Sizes
38(3)
Multiple Maps
41(24)
Double Digest Problem
42(6)
Multiple Solutions in the Double Digest Problem
43(5)
Classifying Multiple Solutions
48(17)
Reflections
48(1)
Overlap Equivalence
48(3)
Overlap Size Equivalence
51(1)
More Graph Theory
52(1)
From One Path to Another
53(3)
Restriction Maps and the Border Block Graph
56(2)
Cassette Transformations of Restriction Maps
58(3)
An Example
61(4)
Algorithms for DDP
65(18)
Algorithms and Complexity
65(2)
DDP is NP-Complete
67(1)
Approaches to DDP
68(2)
Integer Programming
68(1)
Partition Problems
69(1)
TSP
70(1)
Simulated Annealing: TSP and DDP
70(9)
Simulated Annealing
70(5)
Traveling Salesman Problem
75(1)
DDP
76(2)
Circular Maps
78(1)
Mapping with Real Data
79(4)
Fitting Data to a Map
80(1)
Map Algorithms
81(2)
Cloning and Clone Libraries
83(18)
A Finite Number of Random Clones
85(1)
Libraries by Complete Digestion
85(2)
Libraries by Partial Digestion
87(11)
The Fraction of Clonable Bases
88(3)
Sampling, Approach 1
91(1)
Designing Partial Digest Libraries
92(6)
Genomes per Microgram
98(3)
Physical Genome Maps: Oceans, Islands and Anchors
101(34)
Mapping by Fingerprinting
102(17)
Oceans and Islands
102(8)
Divide and Conquer
110(1)
Two Pioneering Experiments
111(3)
Evaluating a Fingerprinting Scheme
114(5)
Mapping by Anchoring
119(8)
Oceans, Islands and Anchors
119(7)
Duality Between Clones and Anchors
126(1)
An Overview of Clone Overlap
127(2)
Putting It Together
129(6)
Sequence Assembly
135(26)
Shotgun Sequencing
135(13)
SSP in NP-complete
137(1)
Greedy is at most Four Times Optimal
138(5)
Assembly in Practice
143(2)
Sequence Accuracy
145(2)
Expected Progress
147(1)
Sequencing by Hybridization
148(8)
Other SBH Designs
154(2)
Shotgun Sequencing Revisited
156(5)
Databases and Rapid Sequence Analysis
161(22)
DNA and Protein Sequence Databases
162(5)
Description of the Entires in a Sequence Data File
163(1)
Sample Sequence Data File
164(2)
Statistical Summary
166(1)
A Tree Representation of a Sequence
167(1)
Hashing a Sequence
168(3)
A Hash Table
169(1)
Hashing in Linear Time
170(1)
Hashing and Chaining
170(1)
Repeats in a Sequence
171(1)
Sequence Comparison by Hashing
172(4)
Sequence Comparison with at most l Mismatches
176(4)
Sequence Comparison by Statistical Content
180(3)
Dynamic Programming Alignment of Two Sequences
183(50)
The Number of Alignments
186(4)
Shortest and Longest Paths in a Network
190(2)
Global Distance Alignment
192(6)
Indel Functions
194(3)
Position-Dependent Weights
197(1)
Global Similarity Alignment
198(3)
Fitting One Sequence into Another
201(1)
Local Alignment and Clumps
202(7)
Self-Comparison
206(1)
Tandem Repeats
207(2)
Linear Space Algorithms
209(3)
Tracebacks
212(3)
Inversions
215(4)
Map Alignment
219(4)
Parametric Sequence Comparisons
223(10)
One-Dimension Parameter Sets
225(3)
Into Two-Dimensions
228(5)
Multiple Sequence Alignment
233(20)
The Cystic Fibrosis Gene
233(3)
Dynamic Programming in r-Dimensions
236(2)
Reducing the Volume
237(1)
Weighted-Average Sequences
238(4)
Aligning Alignments
242(1)
Center of Gravity Sequences
242(1)
Profile Analysis
242(3)
Statistical Significance
244(1)
Alignment by Hidden Markov Models
245(3)
Consensus Word Analysis
248(5)
Analysis by Words
249(1)
Consensus Alignment
250(1)
More Complex Scoring
251(2)
Probability and Statistics for Sequence Alignment
253(52)
Global Alignment
254(9)
Alignment Given
254(1)
Alignment Unknown
255(1)
Linear Growth of Alignment Score
256(1)
The Azuma-Hoeffding Lemma
257(2)
Large Deviations from the Mean
259(2)
Large Deviations for Binomials
261(2)
Local Alignment
263(12)
Laws of Large Numbers
263(12)
Extreme Value Distributions
275(3)
The Chein-Stein Method
278(2)
Poisson Approximation and Long Matches
280(14)
Headruns
280(2)
Exact Matching Between Sequences
282(6)
Approximate Matching
288(6)
Sequence Alignment with Scores
294(11)
A Phase Transition
294(5)
Practical p-Values
299(6)
Probability and Statistics for Sequence Patterns
305(22)
A Central Limit Theorem
307(7)
Generalized Words
313(1)
Estimating Probabilities
313(1)
Nonoverlapping Pattern Counts
314(7)
Renewal Theory for One Pattern
314(4)
Li's Method and Multiple Patterns
318(3)
Poisson Approximation
321(2)
Site Distributions
323(4)
Intersite Distances
324(3)
RNA Secondary Structure
327(18)
Combinatorics
327(7)
Counting More Shapes
332(2)
Minimum Free-energy Structures
334(6)
Reduction of Computation Time for Hairpins
336(2)
Linear Destabilization Functions
338(1)
Multibranch Loops
339(1)
Consensus folding
340(5)
Trees and Sequences
345(32)
Trees
345(8)
Splits
347(4)
Metrics on Trees
351(2)
Distance
353(8)
Additive Trees
353(4)
Ultrametric Trees
357(2)
Nonadditive Distances
359(2)
Parsimony
361(6)
Maximum Likelihood Trees
367(10)
Continuous Time Markov Chains
367(2)
Estimating the Rate of Change
369(3)
Likelihood and Trees
372(5)
Sources and Perspectives
377(10)
Molecular Biology
377(1)
Physical Maps and Clone Libraries
377(2)
Sequence Assembly
379(1)
Sequence Comparisons
379(3)
Databases and Rapid Sequence Analysis
379(1)
Dynamic Programming for Two Sequences
380(2)
Multiple Sequence Alignment
382(1)
Probability and Statistics
382(2)
Sequence Alignment
382(1)
Sequence Patterns
383(1)
RNA Secondary Structure
384(1)
Trees and Sequences
385(2)
References 387(36)
Problem Solutions and Hints
401(20)
Mathematical Notation
421(2)
Algorithm Index 423(2)
Author Index 425(3)
Subject Index 428
Waterman, Michael S.
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