Sequence Alignment Methods PDF
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This document provides an overview of sequence alignment methods, including global and local alignment, pairwise and multiple sequence alignment. It explores different techniques such as dot matrix, dynamic programming, and progressive methods. The methods are useful for comparing biological sequences, identifying conserved regions, and understanding evolutionary relationships. It also covers applications of sequence alignment.
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Sequence comparison is a crucial aspect of bioinformatics analysis that involves comparing newly determined biological sequences with previously known sequences stored in databases. **Sequence alignment is considered the most essential step in comparing biological sequences. **Sequence alignment a...
Sequence comparison is a crucial aspect of bioinformatics analysis that involves comparing newly determined biological sequences with previously known sequences stored in databases. **Sequence alignment is considered the most essential step in comparing biological sequences. **Sequence alignment arranges two or more nucleotide or amino acid sequences to identify regions of similarity between the sequences. These regions of similarity are helpful in understanding the functional, structural, and evolutionary relationships between the sequences. Two commonly used sequence alignment algorithms are global alignment and local alignment. Global alignment and local alignment are two fundamental concepts in sequence alignment that serve different purposes and address different alignment scenarios: 1. The Needleman-Wunsch algorithm is commonly used for global alignment. It assigns scores to different alignment possibilities and finds the alignment with the highest score. Global alignment is useful for comparing sequences that share a common evolutionary history, identifying conserved regions, and studying overall sequence similarity. Global and Local Alignment of two sequences 2. The Smith-Waterman algorithm is commonly used for local alignment. It allows for the identification of local similarities by considering negative scores as zero, thereby finding the alignment with the highest local similarity score. Local alignment is useful for identifying functional domains, detecting conserved motifs, and identifying regions of significance within larger sequences. In summary, global alignment aligns the entire length of sequences and is suitable for sequences with significant similarity throughout their lengths, while local alignment focuses on aligning specific regions of similarity within sequences and is suitable for sequences with localized similarity. The choice between global and local alignment depends on the specific research question, the expected characteristics of the sequences, and the desired insights from the alignment analysis. **Types of sequence alignment:** 1.Pair wise alignment 2\. Multiple sequence alignment **1.Pair wise alignment** - - - **Methods of pairwise alignment:** There are three main methods for generating pairwise alignments: a\) Dot Matrix method b\) Dynamic programming method c\) Word or k-tuple method a. **Dot Matrix method** - - - - -  **Example of comparing two sequences using dot plot** **b) Dynamic programming method** - - This method works in the following three steps. 1. 2. **3. Traceback to identify optimal alignment**: After filling the matrix, the algorithm performs a traceback to find the optimal alignment path. Starting from the bottom-right corner and moving towards the top-left corner, adjacent cells are examined in reverse order to determine the best path with the highest total score. The optimal alignment path is the one with the maximum score. **c)** Word or k-tuple method - - **2. Multiple sequence alignment** - Multiple Sequence Alignment involves aligning multiple (three or more) biological sequences to achieve optimal sequence matching. - Multiple sequence alignments are used to identify conserved sequence regions and to construct phylogenetic trees, which help us understand the functional and evolutionary relationships between different species or groups of organisms. **Methods of multiple sequence alignment:** Multiple sequence alignment can be performed using either exhaustive or heuristic approaches. **A)Exhaustive alignment:** - - - - ### B. Heuristic algorithm #### i. Progressive method - - - - - Progressive alignment procedure #### Progressive alignment procedure #### ii. Iterative Method - - - #### **Iterative alignment procedure** #### iii. Block-based method - - - **Applications of sequence alignment:** - - - - -
