Why is it so important for bioinformatics to get alignments? Where are the problems and how can they be solved?
This manuscript gives a short overview about some of the methods to analyse sequences as well as the Needleman- Wunsch and the Smith-Waterman Algorithm.
You can get an Overview how to interpret a Dotplot. Also you can learn how to create global and local alignments.
Table of Contents
- Introduction
- Dotplot
- Dynamic Programming
- Global Alignment: Needleman-Wunsch Algorithm
- Local Alignment: Smith-Waterman Algorithm
Objectives and Key Themes
This manuscript provides a comprehensive overview of sequence alignment methods used in bioinformatics, focusing on the Needleman-Wunsch and Smith-Waterman algorithms. It delves into the importance of sequence alignment for understanding the functional, structural, and evolutionary characteristics of biomolecules.
- Sequence Alignment Techniques
- Global vs. Local Alignment Algorithms
- Needleman-Wunsch Algorithm
- Smith-Waterman Algorithm
- Applications of Alignment in Bioinformatics
Chapter Summaries
- Introduction: This chapter introduces the concept of sequence alignment and its significance in bioinformatics. It discusses the importance of understanding how sequences relate to each other and the challenges posed by comparing long sequences.
- Dotplot: This chapter explains the Dotplot method as a visual representation of sequence similarity. It describes the creation and interpretation of Dotplots, highlighting their utility in identifying various patterns like repeats, palindromes, and insertions/deletions.
- Dynamic Programming: This chapter explores the concept of dynamic programming for sequence alignment. It introduces global and local alignment algorithms, focusing on the Needleman-Wunsch and Smith-Waterman algorithms. The chapter outlines the principles of these algorithms and demonstrates their application in aligning sequences.
- Global Alignment: Needleman-Wunsch Algorithm: This section delves into the Needleman-Wunsch algorithm, a global alignment method. It provides a detailed explanation of the algorithm's formula and steps for calculating the optimal alignment between two complete sequences.
- Local Alignment: Smith-Waterman Algorithm: This section discusses the Smith-Waterman algorithm, a local alignment method. It describes the algorithm's formula and procedures for finding the optimal alignment of a specific region within two sequences.
Keywords
Sequence alignment, bioinformatics, Needleman-Wunsch algorithm, Smith-Waterman algorithm, dynamic programming, global alignment, local alignment, Dotplot, substitution matrices, PAM, BLOSUM, evolutionary analysis, functional analysis, structural analysis.
- Citation du texte
- Markus Hoffmann (Auteur), 2016, Pairwise Alignment. Global and Local, Munich, GRIN Verlag, https://www.grin.com/document/346638
