- 1. Introduction to R, PERL, Python and C/C++ for Bioinformatics
- 1.1 Introduction
- 1.2 Why Do so Many Programming Languages Exist?
- 1.3 Lisp, C, C++ and Java
- 1.4 PERL, Python and R
- 2. Biological Problem
- 2.1 Distinguishing SNPs from Sequencing Errors
- 2.2 Analysis from Both Strands using Asymmetric Primar Pairs
- 2.3 Paper - Detection of ultra-rare mutations by next-generation sequencing
- 2.4 Prelimaniry Processing Steps
- 2.5 Translating Proposed Solution into Procedure
- 3. Introduction to R
- 3.1 Why R?
- 3.2 Where does R fit into the big picture of bioinformatics?
- 3.3 Installation
- 3.4 Opening the First R Window
- 3.5 Learning R
- 3.6 Familiarity with R Language
- 3.7 Comparison of data analysis packages
- 3.8 Batch Processing in R
- 4. Introduction to PERL
- 4.1 Why PERL?
- 4.2 Installation
- 4.3 First PERL Program
- 4.4 Familiarity with PERL Language
- 5. Introduction to Python
- 5.1 Why Python?
- 5.2 Installation
- 5.3 First python Program
- 5.4 Familiarity with python Language
- 6. Introduction to C
- 6.1 Why C?
- 6.2 Installation
- 6.3 Writing the First C Program
- 6.4 Familiarity with C Language
- 6.5 From C to C++
- 7. Computer Architecture, Data Structures, Algorithms and Functions
- 7.1 Computer Architecture
- 7.2 Data Structure
- 7.3 Algorithms
- 7.4 Functions and Classes
- 8. Solving Biological Problem of Section 2
- 8.1 R
- 8.2 PERL
- 8.3 Python
- 8.4 C/C++
- 9. For Better Performance
- 9.1 Introduction
- 9.2 Mental Picture of Hardware and Data Flow
- 9.3 Multicore and Shared Memory
- 9.4 Parallel Programming - Technical Terms
- 9.5 Threading in C
- 9.6 PERL Multi-core
- 9.7 Python Multi-core
- 9.8 R Multi-core
- 9.9 Where to Go from Here?
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