Algorithms in Bioinformatics - Theory and Implementation

ALGORITHMS IN BIOINFORMATICS
 
Explore a comprehensive and insightful treatment of the practical application of bioinformatic algorithms in a variety of fields
 
Algorithms in Bioinformatics: Theory and Implementation delivers a fulsome treatment of some of the main algorithms used to explain biological functions and relationships. It introduces readers to the art of algorithms in a practical manner which is linked with biological theory and interpretation. The book covers many key areas of bioinformatics, including global and local sequence alignment, forced alignment, detection of motifs, Sequence logos, Markov chains or information entropy. Other novel approaches are also described, such as Self-Sequence alignment, Objective Digital Stains (ODSs) or Spectral Forecast and the Discrete Probability Detector (DPD) algorithm.
 
The text incorporates graphical illustrations to highlight and emphasize the technical details of computational algorithms found within, to further the reader's understanding and retention of the material. Throughout, the book is written in an accessible and practical manner, showing how algorithms can be implemented and used in JavaScript on Internet Browsers. The author has included more than 120 open-source implementations of the material, as well as 33 ready-to-use presentations. The book contains original material that has been class-tested by the author and numerous cases are examined in a biological and medical context. Readers will also benefit from the inclusion of:
* A thorough introduction to biological evolution, including the emergence of life, classifications and some known theories and molecular mechanisms
* A detailed presentation of new methods, such as Self-sequence alignment, Objective Digital Stains and Spectral Forecast
* A treatment of sequence alignment, including local sequence alignment, global sequence alignment and forced sequence alignment with full implementations
* Discussions of position-specific weight matrices, including the count, weight, relative frequencies, and log-likelihoods matrices
* A detailed presentation of the methods related to Markov Chains as well as a description of their implementation in Bioinformatics and adjacent fields
* An examination of information and entropy, including sequence logos and explanations related to their meaning
* An exploration of the current state of bioinformatics, including what is known and what issues are usually avoided in the field
* A chapter on philosophical transactions that allows the reader a broader view of the prediction process
* Native computer implementations in the context of the field of Bioinformatics
* Extensive worked examples with detailed case studies that point out the meaning of different results
 
Perfect for professionals and researchers in biology, medicine, engineering, and information technology, as well as upper level undergraduate students in these fields, Algorithms in Bioinformatics: Theory and Implementation will also earn a place in the libraries of software engineers who wish to understand how to implement bioinformatic algorithms in their products.

List of contents

Preface xv
 
About the Companion Website xvii
 
1 The Tree of Life (I) 1
 
1.1 Introduction 1
 
1.2 Emergence of Life 1
 
1.2.1 Timeline Disagreements 3
 
1.3 Classifications and Mechanisms 4
 
1.4 Chromatin Structure 5
 
1.5 Molecular Mechanisms 9
 
1.5.1 Precursor Messenger RNA 9
 
1.5.2 Precursor Messenger RNA to Messenger RNA 10
 
1.5.3 Classes of Introns 10
 
1.5.4 Messenger RNA 10
 
1.5.5 mRNA to Proteins 11
 
1.5.6 Transfer RNA 12
 
1.5.7 Small RNA 12
 
1.5.8 The Transcriptome 13
 
1.5.9 Gene Networks and Information Processing 13
 
1.5.10 Eukaryotic vs. Prokaryotic Regulation 14
 
1.5.11 What Is Life? 14
 
1.6 Known Species 14
 
1.7 Approaches for Compartmentalization 15
 
1.7.1 Two Main Approaches for Organism Formation 16
 
1.7.2 Size and Metabolism 16
 
1.8 Sizes in Eukaryotes 16
 
1.8.1 Sizes in Unicellular Eukaryotes 17
 
1.8.2 Sizes in Multicellular Eukaryotes 17
 
1.9 Sizes in Prokaryotes 17
 
1.10 Virus Sizes 18
 
1.10.1 Viruses vs. the Spark of Metabolism 20
 
1.11 The Diffusion Coefficient 20
 
1.12 The Origins of Eukaryotic Cells 21
 
1.12.1 Endosymbiosis Theory 21
 
1.12.2 DNA and Organelles 22
 
1.12.3 Membrane-bound Organelles with DNA 23
 
1.12.4 Membrane-bound Organelles Without DNA 23
 
1.12.5 Control and Division of Organelles 24
 
1.12.6 The Horizontal Gene Transfer 24
 
1.12.7 On the Mechanisms of Horizontal Gene Transfer 25
 
1.13 Origins of Eukaryotic Multicellularity 26
 
1.13.1 Colonies Inside an Early Unicellular Common Ancestor 26
 
1.13.2 Colonies of Early Unicellular Common Ancestors 26
 
1.13.3 Colonies of Inseparable Early Unicellular Common Ancestors
 
1.13.4 Chimerism and Mosaicism 28
 
1.14 Conclusions 29
 
2 Tree of Life: Genomes (II) 31
 
2.1 Introduction 31
 
2.2 Rules of Engagement 31
 
2.3 Genome Sizes in the Tree of Life 32
 
2.3.1 Alternative Methods 33
 
2.3.2 The Weaving of Scales 33
 
2.3.3 Computations on the Average Genome Size 36
 
2.3.4 Observations on Data 38
 
2.4 Organellar Genomes 40
 
2.4.1 Chloroplasts 40
 
2.4.2 Apicoplasts 40
 
2.4.3 Chromatophores 42
 
2.4.4 Cyanelles 42
 
2.4.5 Kinetoplasts 42
 
2.4.6 Mitochondria 43
 
2.5 Plasmids 43
 
2.6 Virus Genomes 44
 
2.7 Viroids and Their Implications 46
 
2.8 Genes vs. Proteins in the Tree of Life 47
 
2.9 Conclusions 49
 
3 Sequence Alignment (I) 51
 
3.1 Introduction 51
 
3.2 Style and Visualization 51
 
3.3 Initialization of the Score Matrix 54
 
3.4 Calculation of Scores 57
 
3.4.1 Initialization of the Score Matrix for Global Alignment 57
 
3.4.2 Initialization of the Score Matrix for Local Alignment 62
 
3.4.3 Optimization of the Initialization Steps 65
 
3.4.4 Curiosities 66
 
3.5 Traceback 71
 
3.6 Global Alignment 75
 
3.7 Local Alignment 79
 
3.8 Alignment Layout 84
 
3.9 Local Sequence Alignment - The Final Version 87
 
3.10 Complementarity 91
 
3.11 Conclusions 97
 
4 Forced Alignment (II) 99
 
4.1 Introduction 99
 
4.2 Global and Local Sequence Alignment 100
 
4.2.1 Short Notes 100
 
4.2.2 Understanding the Technology 101
 
4.2.3 Main Objectives 102
 
4.3 Experiments a

About the author

Paul A. Gagniuc, PhD, is an associated Professor of Bioinformatics and a Professor of Programming Languages at University Politehnica of Bucharest in Romania. He obtained his doctorate in Genetics at the University of Bucharest. Dr. Gagniuc is also an Academic Editor at PLoS ONE and a pro-active reviewer for several well-known scientific journals. He has published numerous high-profile scientific articles and is the recipient of several awards for exceptional scientific results.

Summary

ALGORITHMS IN BIOINFORMATICS

Explore a comprehensive and insightful treatment of the practical application of bioinformatic algorithms in a variety of fields

Algorithms in Bioinformatics: Theory and Implementation delivers a fulsome treatment of some of the main algorithms used to explain biological functions and relationships. It introduces readers to the art of algorithms in a practical manner which is linked with biological theory and interpretation. The book covers many key areas of bioinformatics, including global and local sequence alignment, forced alignment, detection of motifs, Sequence logos, Markov chains or information entropy. Other novel approaches are also described, such as Self-Sequence alignment, Objective Digital Stains (ODSs) or Spectral Forecast and the Discrete Probability Detector (DPD) algorithm.

The text incorporates graphical illustrations to highlight and emphasize the technical details of computational algorithms found within, to further the reader's understanding and retention of the material. Throughout, the book is written in an accessible and practical manner, showing how algorithms can be implemented and used in JavaScript on Internet Browsers. The author has included more than 120 open-source implementations of the material, as well as 33 ready-to-use presentations. The book contains original material that has been class-tested by the author and numerous cases are examined in a biological and medical context. Readers will also benefit from the inclusion of:
* A thorough introduction to biological evolution, including the emergence of life, classifications and some known theories and molecular mechanisms
* A detailed presentation of new methods, such as Self-sequence alignment, Objective Digital Stains and Spectral Forecast
* A treatment of sequence alignment, including local sequence alignment, global sequence alignment and forced sequence alignment with full implementations
* Discussions of position-specific weight matrices, including the count, weight, relative frequencies, and log-likelihoods matrices
* A detailed presentation of the methods related to Markov Chains as well as a description of their implementation in Bioinformatics and adjacent fields
* An examination of information and entropy, including sequence logos and explanations related to their meaning
* An exploration of the current state of bioinformatics, including what is known and what issues are usually avoided in the field
* A chapter on philosophical transactions that allows the reader a broader view of the prediction process
* Native computer implementations in the context of the field of Bioinformatics
* Extensive worked examples with detailed case studies that point out the meaning of different results

Perfect for professionals and researchers in biology, medicine, engineering, and information technology, as well as upper level undergraduate students in these fields, Algorithms in Bioinformatics: Theory and Implementation will also earn a place in the libraries of software engineers who wish to understand how to implement bioinformatic algorithms in their products.

Product details

• Authors: PA Gagniuc, Paul A Gagniuc, Paul A. Gagniuc
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 30.06.2021
• EAN: 9781119697961
• ISBN: 978-1-119-69796-1
• No. of pages: 528
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Statistik, Informatik, Datenanalyse, Bioinformatik, Statistics, Life Sciences, Biowissenschaften, computer science, data analysis, Algorithms & data structures, Bioinformatics & Computational Biology, Algorithmen u. Datenstrukturen