3.3 Multiple sequence alignment

Multiple sequence alignment is a crucial tool in computational biology. It compares three or more biological sequences, revealing similarities and differences. This technique helps uncover evolutionary relationships, conserved regions, and functional domains across multiple organisms.

MSA algorithms optimize alignment quality using various scoring schemes. Applications include identifying orthologous genes, detecting motifs, and studying gene family evolution. Progressive and iterative methods are used to create and refine alignments, with tools available for visualization and analysis.

Multiple Sequence Alignment Principles

Fundamentals of Multiple Sequence Alignment

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• Multiple sequence alignment (MSA) aligns three or more biological sequences to identify regions of similarity and difference
• MSA studies evolutionary relationships, identifies conserved regions, predicts functional domains, and infers phylogenetic trees in comparative genomics
• Input for MSA is a set of homologous sequences assumed to have evolved from a common ancestral sequence through substitutions, insertions, and deletions
• Output of MSA is an alignment matrix with each row representing a sequence and each column representing a position, with gaps introduced to maximize overall similarity

Applications and Optimization of Multiple Sequence Alignment

• MSA algorithms optimize an objective function that measures alignment quality (sum-of-pairs score or consistency with a phylogenetic tree)
• Applications of MSA include identifying orthologous and paralogous genes, detecting functional domains and motifs, reconstructing ancestral sequences, and studying the evolution of gene families and species (BRCA1 gene family, globin superfamily)

Implementing Alignment Algorithms

Progressive Alignment Algorithms

• Progressive alignment algorithms (ClustalW, T-Coffee) build MSA incrementally by aligning most similar sequences first and adding more distant sequences to the growing alignment
• Progressive alignment algorithms use a guide tree, constructed using pairwise sequence similarities, to determine the order of sequence alignment
• Main steps in progressive alignment: compute pairwise similarities, construct a guide tree, align most similar sequences, and progressively add remaining sequences following the guide tree

Iterative Alignment Algorithms

• Iterative alignment algorithms (MUSCLE, MAFFT) refine the initial alignment obtained by progressive methods through multiple rounds of realignment and scoring
• Iterative alignment algorithms improve alignment quality by correcting errors from the progressive alignment stage and considering information from all sequences simultaneously
• Main steps in iterative alignment: perform initial progressive alignment, divide sequences into two groups, realign groups separately, and repeat until convergence or maximum iterations reached
• Interpreting MSA results involves analyzing the alignment matrix to identify conserved regions, variable regions, insertions, deletions, and potential errors or ambiguities
• Visualization tools (JalView, SeaView) display and manipulate the alignment, color-code residues based on properties, and highlight conserved and variable regions

Evaluating Alignment Quality

Scoring Schemes for Multiple Sequence Alignment

• Scoring schemes assign numerical values to each pair of aligned residues and gap penalties to quantify alignment quality and guide optimization
• Common scoring schemes for MSA: sum-of-pairs score (SP-score) sums pairwise scores for all aligned residue pairs, weighted sum-of-pairs score (WSP-score) assigns weights to sequences based on evolutionary relationships
• Gap penalties discourage excessive gaps in the alignment and can be constant, affine (opening and extension penalties), or profile-based (position-specific)

Consistency Measures for Multiple Sequence Alignment

• Consistency measures assess alignment reliability by comparing it to a reference alignment or measuring agreement between different alignment methods or parameters
• Sum-of-pairs consistency (SPC) score computes the fraction of aligned residue pairs in the reference alignment also present in the evaluated alignment
• Total column score (TC-score) measures the fraction of columns in the reference alignment perfectly reproduced in the evaluated alignment
• Head-or-tail score (HoT-score) assesses alignment consistency in the presence of sequence fragments or partially overlapping sequences
• Bootstrapping and statistical significance tests estimate the robustness of the alignment and confidence in the inferred evolutionary relationships

Identifying Conserved Regions

Detecting Conserved Regions and Motifs

• Conserved regions are sections of the alignment where sequences show high similarity, indicating potential functional or structural importance
• Conserved regions can be identified by calculating percentage identity or similarity for each alignment column and applying a threshold to highlight highly conserved positions
• Motifs are short, conserved patterns of residues often associated with specific biological functions (DNA-binding, catalytic activity, protein-protein interactions)
• Motif discovery algorithms (MEME, GLAM2) can be applied to MSA to identify overrepresented patterns and assign them to known motif databases (PROSITE, Pfam)

Identifying Functional Domains and Conservation Scores

• Functional domains are conserved regions that fold independently and carry out specific biological functions (enzymatic activity, signal transduction, ligand binding)
• Functional domains can be identified by comparing MSA to domain databases (InterPro, SMART) containing curated alignments and hidden Markov models (HMMs) for known domain families
• Conservation scores (Jensen-Shannon divergence (JSD), AL2CO score) can be calculated for each alignment position to quantify conservation degree and identify functionally important sites
• Comparative analysis of conserved regions, motifs, and functional domains across species or gene families provides insights into the evolution of protein function and adaptation to different ecological niches (vertebrate hemoglobin family, serine protease family)