3.1 Pairwise sequence alignment (global and local alignment)

Pairwise sequence alignment is a crucial tool in computational biology. It compares two DNA, RNA, or protein sequences to find similarities that might reveal functional or evolutionary connections. This technique is essential for identifying homologs, tracing evolution, and predicting protein structures.

Global alignment matches entire sequences, while local alignment finds similar regions within sequences. Both use scoring systems to maximize matches and minimize gaps. These methods are key to understanding genetic relationships and uncovering shared biological features between organisms.

Pairwise Sequence Alignment Principles

Fundamentals of Pairwise Sequence Alignment

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• Pairwise sequence alignment compares two biological sequences (DNA, RNA, or protein) to identify regions of similarity that may indicate functional, structural, or evolutionary relationships between the sequences
• Helps in various applications such as identifying homologous sequences, inferring evolutionary relationships, predicting protein structure and function, and designing primers for PCR amplification
• Pairwise alignment algorithms find the best alignment between two sequences by maximizing the number of matches and minimizing the number of gaps and mismatches
• Use scoring schemes to assign positive scores for matches and negative scores for mismatches and gaps, which reflect the biological significance of these events (substitution matrices for proteins, match/mismatch scores for DNA)
• Determine the optimal alignment by finding the alignment with the highest overall score, considering the trade-off between maximizing matches and minimizing gaps and mismatches

Scoring Schemes and Alignment Optimization

• Scoring schemes quantify the quality of the alignment and reflect the biological likelihood of the observed sequence similarities
• Common DNA scoring schemes include match/mismatch scores (+1 for a match, -1 for a mismatch) and gap penalties (affine gap penalties with separate opening and extension costs)
• Protein sequence alignment uses substitution matrices (BLOSUM, PAM) that define scores for amino acid substitutions based on their observed frequencies in aligned protein families
• Higher alignment scores indicate better alignments and more significant sequence similarities
• Optimal alignment balances the trade-off between maximizing matches and minimizing gaps and mismatches to achieve the highest overall score

Global vs Local Alignment

Global Alignment

• Global alignment algorithms (Needleman-Wunsch) align the entire length of two sequences, from start to end
• Suitable when sequences are of similar length and expected to share similarity across their entire length (closely related sequences, orthologs, sequences from the same gene family)
• Identifies conserved regions and overall sequence similarity between the two sequences
• Useful for comparing sequences that are expected to have a high degree of similarity and few insertions, deletions, or rearrangements

Local Alignment

• Local alignment algorithms (Smith-Waterman) find the best alignment between subsequences of the two input sequences
• Identifies locally similar regions even if the overall sequences are divergent (distantly related homologs, sequences with domain rearrangements)
• Suitable for comparing sequences that may have diverged significantly over time or have undergone insertions, deletions, or rearrangements
• Detects shared motifs, functional domains, or conserved regions within otherwise dissimilar sequences
• Allows for the identification of biologically relevant subsequence similarities without the constraint of aligning the entire sequences

Dynamic Programming for Alignment

Dynamic Programming Principles

• Dynamic programming efficiently finds the optimal alignment by breaking down the problem into smaller subproblems and storing intermediate results to avoid redundant calculations
• Needleman-Wunsch algorithm for global alignment and Smith-Waterman algorithm for local alignment are based on dynamic programming principles
• Use a scoring matrix to assign scores for matches, mismatches, and gaps, and a traceback matrix to keep track of the optimal alignment path
• Fill the scoring matrix based on the recursive relationship between the scores of adjacent cells, considering match/mismatch scores and gap penalties

Alignment Process and Interpretation

• Traceback step follows the path of the highest scores from the bottom-right corner of the matrix to the top-left corner (global alignment) or from the maximum score cell to a cell with a score of zero (local alignment) to reconstruct the optimal alignment
• Interpret alignment results by examining aligned sequences, identifying conserved regions, gaps, and mismatches
• Assess the biological significance of the alignment based on the specific research question and prior knowledge
• Visual inspection of aligned sequences, along with consideration of the biological context, is crucial in interpreting the alignment results and their biological relevance

Alignment Quality and Significance

Statistical Measures

• Assess statistical significance of the alignment using measures such as E-value (Expectation value) or P-value
• E-value and P-value estimate the likelihood of observing an alignment with a given score by chance
• Lower E-values or P-values indicate higher statistical significance, suggesting that the observed alignment is unlikely to occur by chance and more likely represents a true biological relationship
• Sensitivity and specificity evaluate the performance of alignment algorithms in correctly identifying true positive and true negative alignments

Biological Relevance and Interpretation

• Alignment score provides a measure of the overall quality of the alignment, with higher scores indicating better alignments
• Interpret alignment results in the context of the specific biological question and prior knowledge
• Consider factors such as the evolutionary distance between the sequences, the presence of conserved domains or motifs, and the functional implications of the aligned regions
• Assess the biological significance of the aligned regions based on their conservation, functional importance, and potential impact on the structure or function of the sequences
• Integrate alignment results with other sources of information (structural data, experimental evidence, literature) to gain a comprehensive understanding of the biological relationship between the sequences