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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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Multicore and Shared Memory
Parallel programming
Multiprocessor
Multicore
Threading
Shared Memory