Python Programming for Biology: Bioinformatics and Beyond

Document Outline

• Half title page
• Title page
• Copyright page
• Contents
• Preface
• Acknowledgements
• 1 Prologue
  • Python programming for biology
• 2 A beginners’ guide
  • Programming principles
  • Basic data types
  • Program flow
• 3 Python basics
  • Introducing the fundamentals
  • Simple data types
  • Collection data types
  • Importing modules
• 4 Program control and logic
  • Controlling command execution
  • Conditional execution
  • Loops
  • Error exceptions
  • Further considerations
• 5 Functions
  • Function basics
  • Input arguments
  • Variable scope
  • Further considerations
• 6 Files
  • Computer files
  • Reading files
  • File reading examples
  • Writing files
  • Further considerations
• 7 Object orientation
  • Creating classes
  • Further details
• 8 Object data modelling
  • Data models
  • Implementing a data model
  • Refined implementation
• 9 Mathematics
  • Using Python for mathematics
  • Linear algebra
  • NumPy package
  • Linear algebra examples
• 10 Coding tips
  • Improving Python code
  • A compendium of tips
• 11 Biological sequences
  • Bio-molecules for non-biologists
  • Using biological sequences in computing
  • Simple sub-sequence properties
  • Obtaining sequences with BioPython
• 12 Pairwise sequence alignments
  • Sequence alignment
  • Calculating an alignment score
  • Optimising pairwise alignment
  • Quick database searches
• 13 Multiple-sequence alignments
  • Multiple alignments
  • Alignment consensus and profiles
  • Generating simple multiple alignments in Python
  • Interfacing multiple-alignment programs
• 14 Sequence variation and evolution
  • A basic introduction to sequence variation
  • Similarity measures
  • Phylogenetic trees
• 15 Macromolecular structures
  • An introduction to 3D structures of bio-molecules
  • Using Python for macromolecular structures
  • Coordinate superimposition
  • External macromolecular structure modules
• 16 Array data
  • Multiplexed experiments
  • Reading array data
  • The ‘Microarray’ class
  • Array analysis
• 17 High-throughput sequence analyses
  • High-throughput sequencing
  • Mapping sequences to a genome
  • Using the HTSeq library
• 18 Images
  • Biological images
  • Basic image operations
  • Adjustments and filters
  • Feature detection
• 19 Signal processing
  • Signals
  • Fast Fourier transform
  • Peaks
• 20 Databases
  • A brief introduction to relational databases
  • Basic SQL
  • Designing a molecular structure database
• 21 Probability
  • The basics of probability theory
  • Restriction enzyme example
  • Random variables
  • Markov chains
• 22 Statistics
  • Statistical analyses
  • Simple statistical parameters
  • Statistical tests
  • Correlation and covariance
• 23 Clustering and discrimination
  • Separating and grouping data
  • Clustering methods
  • Data discrimination
• 24 Machine learning
  • A guide to machine learning
  • k-nearest neighbours
  • Self-organising maps
  • Feed-forward artificial neural networks
  • Support vector machines
• 25 Hard problems
  • Solving hard problems
  • The Monte Carlo method
  • Simulated annealing
• 26 Graphical interfaces
  • An introduction to graphical user interfaces
  • Python GUI examples
• 27 Improving speed
  • Running things faster
  • Parallelisation
  • Writing faster modules
• Appendices
• Appendix 1 Simplified language reference
• Appendix 2 Selected standard type methods and operations
• Appendix 3 Standard module highlights
• Appendix 4 String formatting
• Appendix 5 Regular expressions
• Appendix 6 Further statistics
• Glossary
• Index