Sequence Alignment and Search
Sequence alignment is a method of arranging sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
1. Types of Sequence Alignments:
- Pairwise Alignment: Aligns two sequences. This can be further categorized into:
- Global Alignment (Needleman-Wunsch algorithm): Tries to align every residue in every sequence. Useful when the sequences are of roughly the same length and are very similar.
- Local Alignment (Smith-Waterman algorithm): Identifies regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable for comparing sequences that are suspected to contain regions of similarity or motifs within their larger sequence context (e.g., functional domains within proteins).
- Multiple Sequence Alignment: Aligns three or more sequences. Tools like CLUSTAL and MUSCLE are used for this purpose. This kind of alignment helps in identifying conserved regions and evolutionary relationships among a set of sequences.
2. Sequence Search: The most famous algorithm for this purpose is BLAST (Basic Local Alignment Search Tool). It allows for rapid searching of nucleotide or protein databases based on an input sequence. It finds regions of local similarity between sequences and can provide insights into the evolutionary and functional significance of the sequences.
Phylogenetics is the study of evolutionary relationships among groups of organisms. Phylogenetic trees are graphical representations of these evolutionary relationships.
1. Constructing Phylogenetic Trees:
- Distance-based methods: These methods estimate the evolutionary distance between sequences and cluster them based on these distances. Popular methods include:
- Neighbor-Joining (NJ): A fast distance method especially useful for large datasets.
- UPGMA (Unweighted Pair Group Method with Arithmetic Mean): Assumes a molecular clock.
- Character-based methods: These methods use character state changes (like nucleotide or amino acid changes) to infer phylogenetic relationships.
- Maximum Parsimony (MP): Finds the tree that requires the fewest evolutionary changes.
- Maximum Likelihood (ML): Finds the tree that makes the observed sequences most likely, given a model of evolution.
- Bayesian methods: These use a probabilistic model to produce a posterior distribution of trees.
2. Tree Evaluation: Once a phylogenetic tree is constructed, it’s important to evaluate its reliability. Bootstrap resampling is a common method to assess the robustness of the inferred trees.
3. Molecular Clocks: Some methods assume a constant rate of evolution across all lineages (a “molecular clock”). This can be used to estimate divergence times between species, but it’s worth noting not all lineages evolve at the same rate.
In essence, both sequence alignment and phylogenetic analysis are foundational algorithms in bioinformatics. They provide insights into the functional and evolutionary relationships of genes and organisms, respectively. Understanding these algorithms and their assumptions is essential for correctly interpreting results and drawing meaningful biological conclusions.