Basics of sequence similarity and homology searches
September 28, 2023Sequence similarity and homology searches are crucial techniques in bioinformatics to study evolutionary relationships, infer function, and understand the structural aspects of biological sequences like DNA, RNA, and proteins. Here’s a basic overview of these concepts:
Table of Contents
Sequence Similarity
Sequence similarity refers to the degree by which biological sequences, like DNA, RNA, or protein sequences, are related. Similarity is determined by aligning two sequences and identifying the number of positions with matching elements (bases or amino acids).
Homology
Homology is a more specific concept. If two sequences are homologous, they are believed to share a common evolutionary ancestor. Homology can be inferred from high sequence similarity, but not all similar sequences are homologous. Homology can be categorized as:
- Orthology: Homologous sequences diverged after a speciation event.
- Paralogy: Homologous sequences diverged after a gene duplication event.
Homology Searches
Homology searches involve using algorithms to identify similar sequences within a database. There are several tools and algorithms available for this purpose:
1. BLAST (Basic Local Alignment Search Tool)
BLAST is a widely-used algorithm to compare an input sequence against a database of sequences. It identifies regions that align better than would be expected by chance, providing a list of matches ranked by similarity.
2. FASTA
FASTA is another algorithm similar to BLAST. It is used for searching sequence databases and identifying sequences with similarities to a query sequence.
3. Smith-Waterman Algorithm
This is a more accurate but computationally intensive method for local sequence alignment, suitable for comparing shorter sequences.
4. Needleman-Wunsch Algorithm
This algorithm is used for global sequence alignment and is suitable for comparing sequences of approximately equal length.
Steps in Homology Search
- Preparation of Query Sequence: The user prepares a sequence (DNA, RNA, or protein) that is queried against a database.
- Database Search: The sequence is compared to a database of sequences using algorithms like BLAST or FASTA to find similar sequences.
- Alignment: The algorithm aligns the query sequence against the found sequences to determine the degree of similarity.
- Scoring and Ranking: The matches are scored based on the number and quality of alignments, and the results are ranked accordingly.
- Evaluation of Results: The user evaluates the results to determine whether the found sequences are homologous and infers functional, structural, or evolutionary relationships.
Interpretation of Results
When interpreting results, it’s crucial to consider statistical measures like E-values and p-values. The E-value represents the number of hits one can “expect” by chance when searching a database of a particular size. Lower E-values indicate more significant matches.
Applications
- Functional Annotation: Predicting the function of newly discovered genes or proteins based on the function of homologous sequences.
- Evolutionary Studies: Understanding the evolutionary relationships between species.
- Structural Biology: Inferring structural features and domains of proteins.
- Drug Discovery: Identifying potential drug targets and designing drugs based on the structural and functional information of biological sequences.
Considerations
While sequence similarity and homology searches are powerful tools, they have limitations and should be used cautiously, considering factors like the quality of the sequence data, the appropriateness of the search algorithm, and the potential for false positives and negatives. It’s often essential to validate predictions experimentally.
Sequence similarity searching, also recognized as homology searching, is a critical method employed for searching sequence databases. It involves aligning a query sequence to sequences within a database, and it is foundational in the analysis of newly determined sequences. This approach is pivotal in identifying homologous sequences, which share a common ancestry and, typically, similar functions. The execution of sequence similarity searches is often one of the foremost and insightful steps taken to characterize newly discovered sequences, rendering it indispensable in bioinformatics.
BLAST is a widely acknowledged tool in this domain, utilizing heuristics to expedite local alignment searches, making it a reliable strategy for characterizing novel sequences. PSI-BLAST, a variant of BLAST, extends its functionality, allowing users to construct a position-specific scoring matrix, enhancing the detection of distant evolutionary relationships. Similarly, FASTA is another prevalent tool employing heuristics to facilitate fast local alignment searching.
For more rigorous, optimal local alignment, SSEARCH is employed as it doesn’t rely on heuristics and thus provides optimal alignments. The distinction between heuristic and optimal methods is crucial, with the former being faster but approximate and the latter being more accurate.
Homology, a concept inferred from sequence similarity, underlines the evolutionary relationships and common ancestry of sequences. A plethora of methods have been devised to discern sequences that manifest statistically significant similarity. The sequence order, especially the amino acid arrangement in proteins, is perceived to be the catalyst for protein folding. Consequently, sequence comparison emerges as the predominant strategy to discern homology between proteins or their domains, assisting in unraveling the structural and functional aspects of proteins.
Summary:
Sequence similarity and homology searches are fundamental in exploring sequence databases using alignment to a query sequence. It’s pivotal for detecting sequences that are homologous, meaning they share common ancestry and likely analogous functions. Tools like BLAST, PSI-BLAST, and FASTA are widely utilized, employing heuristic methods for quick local alignment searches, while SSEARCH provides optimal alignments. These methods are integral in understanding the evolutionary relationships, structural configurations, and functions, making them vital for diverse applications including evolutionary studies, structural biology, and drug discovery. They guide in discerning the nuances of protein folding and are indispensable in establishing the homology between proteins and protein domains, thereby facilitating a comprehensive understanding of biological sequences.
