7.2 BLAST Algorithm and Its Variants
5 min read•july 30, 2024
is a powerful tool for finding similar sequences in large databases. It uses clever shortcuts to quickly identify potential matches, then extends and scores them. This approach balances speed and accuracy, making BLAST essential for modern genomics research.
BLAST comes in various flavors, each tailored for specific types of sequences and comparisons. From nucleotide-to-nucleotide searches to protein translations, these variants help researchers tackle diverse biological questions and uncover evolutionary relationships across species.
BLAST Algorithm Fundamentals
Core Principles and Steps
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- BLAST (Basic Search Tool) employs a heuristic approach for rapid sequence similarity searching in large databases
- Operates by identifying short, exact matches (words) between query and database sequences, then extending them to form longer alignments
- Utilizes a substitution matrix () to score alignments and calculate statistical significance of matches
- Implements various parameters to control sensitivity and specificity
- determines the length of initial exact matches
- influence the allowance for insertions or deletions
- set the cutoff for reporting significant matches
- Presents results as (HSPs) between query and database sequences, including statistical measures of significance
Algorithm Workflow
- preprocessing prepares the input for efficient searching
- Word list generation creates a catalog of short subsequences from the query
- Word matching identifies exact matches between query words and database sequences
- Extension of word matches expands initial hits to form longer alignments
- Alignment scoring and significance assessment evaluate the quality of matches
- Achieves efficiency through indexed databases and optimized search strategies
- Allows for rapid comparisons against large sequence repositories (, )
- Enables quick identification of potential homologs or related sequences
BLAST Program Variations
Nucleotide-based BLAST Programs
- performs nucleotide-nucleotide comparisons
- Suitable for finding similar DNA or RNA sequences across species (orthologous genes)
- Used in identifying conserved non-coding regions (regulatory elements)
- translates nucleotide query into six reading frames for protein database comparison
- Useful for identifying potential coding regions in DNA sequences
- Helps in gene prediction and annotation of newly sequenced genomes
- translates both query and database sequences in all six frames
- Used for sensitive detection of distant relationships between nucleotide sequences
- Valuable in comparative genomics studies of distantly related organisms
Protein-based BLAST Programs
- designed for protein-protein comparisons
- Used to identify homologous proteins or protein domains across different organisms
- Crucial in of newly discovered proteins
- compares protein query against translated nucleotide database
- Helpful in identifying unannotated genes or pseudogenes in genomic sequences
- Useful for finding protein-coding genes in newly sequenced genomes
- (Position-Specific Iterative BLAST) uses position-specific scoring matrices
- Detects distant evolutionary relationships between proteins
- Improves sensitivity in finding remote homologs through iterative searches
- (Pattern-Hit Initiated BLAST) combines pattern matching with local alignment
- Finds sequences containing both a specific pattern and overall sequence similarity
- Useful in identifying proteins with specific motifs or functional domains
Applying BLAST for Sequence Similarity
Search Strategy and Parameter Optimization
- Formulate appropriate BLAST search by selecting correct program and database
- Consider nature of query sequence (DNA, RNA, protein) and research question
- Choose suitable database (nucleotide, protein, genome-specific) for comparison
- Set parameters to optimize search sensitivity and specificity
- Adjust threshold to control stringency of reported matches
- Modify word size to balance between speed and sensitivity
- Fine-tune gap penalties to accommodate insertions/deletions in alignments
Result Interpretation and Analysis
- Interpret BLAST output components
- Analyze graphical overview for distribution of hits along query sequence
- Examine individual alignments for extent and quality of sequence matches
- Evaluate statistical measures (E-values, bit scores, percent identities)
- Assess biological significance of BLAST hits
- Consider and coverage of query/subject sequences
- Examine conservation of functional domains or motifs
- Analyze phylogenetic patterns to distinguish orthologs from paralogs
- Infer potential functions of unknown sequences
- Compare to well-characterized homologs identified in search results
- Look for conserved functional motifs or domain architectures
Applications in Comparative Genomics
- Identify conserved regions across different species
- Locate syntenic blocks in genome comparisons
- Detect evolutionary conserved elements (enhancers, silencers)
- Analyze gene families and their evolution
- Trace expansion or contraction of gene families across lineages
- Identify species-specific gene duplications or losses
- Detect horizontally transferred genetic elements
- Identify genes with unexpected phylogenetic distributions
- Analyze compositional biases indicative of recent transfer events
BLAST Advantages vs Limitations
Strengths of BLAST
- Superior speed compared to exhaustive alignment methods
- Enables rapid searching of large genomic databases (GenBank, RefSeq)
- Facilitates high-throughput sequence analysis in genomics research
- Balances sensitivity and speed through heuristic approach
- Allows for efficient detection of biologically significant similarities
- Supports large-scale comparative genomics studies
- Local alignment approach advantageous for specific analyses
- Identifies conserved domains or motifs within larger sequences
- Detects partial matches in gene fusion events or multi-domain proteins
- Robust statistical framework provides meaningful interpretation
- E-values allow for assessment of alignment significance
- Bit scores enable comparison of alignments across different searches
Limitations and Considerations
- May occasionally miss biologically significant alignments due to heuristic nature
- Very distant homologs might be overlooked in standard searches
- Requires careful parameter tuning for optimal performance
- Less sensitive than profile-based methods for detecting distant relationships
- Methods like HMMER often outperform BLAST for remote homology detection
- PSI-BLAST partially addresses this limitation through iterative searches
- Performance affected by sequence composition
- Low-complexity regions can lead to spurious matches
- Repetitive elements may skew alignment statistics
- Not designed for multiple sequence alignment or phylogenetic analysis
- Requires additional tools for comprehensive evolutionary studies
- Limits direct application in certain comparative genomics analyses
- Effectiveness depends on database quality and completeness
- Results may vary across different organisms due to uneven sequencing efforts
- Regular database updates crucial for accessing most current sequence information
Key Terms to Review (30)
Alignment length: Alignment length refers to the number of characters (nucleotides or amino acids) that are matched between two sequences in a sequence alignment. This concept is crucial in bioinformatics for evaluating the similarity between biological sequences and helps to assess the quality of the alignment produced by algorithms like BLAST.
BLAST: BLAST, or Basic Local Alignment Search Tool, is a widely used bioinformatics algorithm designed to find regions of local similarity between sequences. It allows researchers to compare a query sequence against a database of sequences, helping to identify potential homologs and infer functional and evolutionary relationships.
Blast+: BLAST+ is an improved version of the Basic Local Alignment Search Tool, which is a widely used algorithm in bioinformatics for comparing nucleotide or protein sequences against databases to identify similarities. This version enhances the original BLAST by utilizing multithreading capabilities and a more efficient indexing system, making it faster and more accurate in sequence alignment tasks.
Blastn: blastn is a variant of the BLAST algorithm specifically designed for comparing nucleotide sequences. It is used to identify regions of similarity between nucleotide sequences, helping researchers find homologous sequences across different organisms and databases. This tool plays a crucial role in genomics and molecular biology, aiding in the understanding of gene function, evolution, and relationships among species.
Blastp: blastp is a variant of the BLAST algorithm specifically designed for comparing an amino acid query sequence against a protein sequence database. This tool is essential in bioinformatics for identifying similarities between protein sequences, which can provide insights into protein function, evolutionary relationships, and structural characteristics. By using a heuristic approach, blastp significantly speeds up the search process, allowing researchers to find relevant matches quickly.
Blastx: blastx is a bioinformatics tool that compares a nucleotide query sequence, translated in all six reading frames, against a protein database. This allows researchers to identify potential protein-coding regions in DNA sequences by finding similar protein sequences, making it a powerful resource for functional annotation and gene discovery.
Blosum62: blosum62 is a substitution matrix used in bioinformatics to score alignments between sequences, particularly proteins. It quantifies the likelihood of one amino acid being substituted for another during evolution, based on observed substitutions in protein sequences. This matrix is critical for sequence alignment algorithms, as it influences the accuracy and reliability of the alignment results, particularly in tools that analyze evolutionary relationships.
Database search: A database search refers to the process of querying a structured set of data to retrieve relevant information or sequences. In the context of bioinformatics, it is crucial for identifying sequences, proteins, or other biological data by comparing them against existing databases. This is particularly important for tasks like sequence alignment and functional annotation in molecular biology.
E-value: The e-value, or expectation value, is a statistical measure used in bioinformatics to indicate the number of hits one can expect to see by chance when searching a database. It helps assess the significance of sequence alignments and is crucial for evaluating results in sequence database searches, as it accounts for the size of the database and the scoring system used in alignments.
E-value thresholds: E-value thresholds refer to the expected number of false positives that can be encountered when searching for similarities in biological sequences using algorithms like BLAST. These thresholds help researchers determine the statistical significance of matches between sequences, influencing the confidence with which they can interpret results and make biological inferences.
Functional annotation: Functional annotation refers to the process of assigning biological information to gene sequences, such as identifying the function of genes, proteins, or other elements within a genome. This process helps researchers understand the roles of different genes and proteins in biological pathways and cellular processes, making it crucial for interpreting genomic data and facilitating further studies in molecular biology.
Gap penalties: Gap penalties are numerical values subtracted from a sequence alignment score when gaps are introduced in the alignment to account for insertions or deletions. These penalties are essential for creating optimal alignments by balancing the trade-off between having a high-quality alignment and the cost of introducing gaps, which can significantly affect the scoring in various alignment methods.
GenBank: GenBank is a comprehensive public database that stores nucleotide sequences and their associated information, providing a vital resource for molecular biology research. It serves as a key repository for genetic data, facilitating access to sequence information for various organisms and supporting multiple applications such as sequence alignment, gene prediction, and annotation.
Gene identification: Gene identification is the process of discovering and characterizing genes within a genomic sequence, which is crucial for understanding their functions and roles in various biological processes. This process often involves computational tools and algorithms to analyze DNA sequences, predict gene locations, and annotate their functions based on similarities to known genes. The efficiency of gene identification has dramatically improved with the development of algorithms like BLAST, enabling researchers to quickly compare sequences against extensive databases to find potential gene matches.
Global alignment: Global alignment refers to the process of aligning two sequences by matching every character in both sequences from start to finish. This method aims to find the optimal alignment that accounts for all characters, which is especially useful when comparing sequences that are similar in length and have a high degree of similarity.
Hash table: A hash table is a data structure that implements an associative array, allowing for efficient data retrieval through the use of a hash function. The hash function transforms a key into an index in an array, where the corresponding value is stored, enabling quick access to data. This structure plays a crucial role in various algorithms, including those that require fast lookups, such as sequence alignment and searching in databases.
High-scoring segment pairs: High-scoring segment pairs (HSPs) are regions of similarity between two biological sequences that are identified during sequence alignment, often indicative of functional, structural, or evolutionary relationships. These pairs are crucial in algorithms like BLAST, which finds significant alignments between query and database sequences, helping researchers to uncover important biological insights based on the degree of similarity.
Local Alignment: Local alignment refers to a method in bioinformatics used to identify the most similar regions between two sequences, allowing for gaps and mismatches. This approach is particularly useful when the sequences being compared may have only a portion of their length that is similar, making it ideal for finding conserved domains or motifs.
NCBI BLAST: NCBI BLAST (Basic Local Alignment Search Tool) is a bioinformatics program that compares nucleotide or protein sequences to sequence databases and identifies regions of similarity. It helps researchers find homologous sequences, infer functional and evolutionary relationships, and guide experimental studies by analyzing large biological datasets quickly and efficiently.
Percentage identity: Percentage identity is a measure used to quantify the degree of similarity between two sequences, typically in the context of biological sequences such as DNA, RNA, or proteins. It is calculated as the number of identical matches divided by the total length of the alignment, expressed as a percentage. This metric is crucial for assessing the accuracy and significance of sequence alignments in various computational biology applications, particularly when using algorithms like BLAST.
Phi-blast: Phi-blast is a variant of the Basic Local Alignment Search Tool (BLAST) algorithm specifically designed for protein sequence searches. It enhances the search process by incorporating both nucleotide sequences and their corresponding translated protein sequences, making it particularly useful in identifying homologous proteins across different organisms. This method allows researchers to leverage the information from both nucleotide and protein databases to improve sensitivity and accuracy in sequence alignment.
Psi-blast: Psi-BLAST (Position-specific Iterated BLAST) is an advanced variation of the original BLAST algorithm used for searching protein and DNA sequences against databases. It enhances the sensitivity of sequence alignment by using position-specific scoring matrices, which consider the frequency of amino acids at each position, allowing for more accurate identification of homologous sequences across evolutionary distances.
Query sequence: A query sequence is a specific DNA, RNA, or protein sequence that researchers input into a bioinformatics tool to find matches or similar sequences within a database. It serves as the starting point for various sequence analysis methods, especially in tools like BLAST, where it helps identify potential homologs or functionally related sequences by comparing it against a large collection of known sequences.
RaptorX: RaptorX is a web-based tool designed for predicting protein structure based on amino acid sequences. It uses advanced algorithms to generate models of protein 3D structures, focusing on accuracy and efficiency. By applying machine learning techniques and incorporating structural templates, RaptorX significantly enhances the ability to analyze protein functions and interactions, making it an important resource in computational biology.
Score matrix: A score matrix is a grid used to assign numerical values that quantify the similarity or difference between sequences, such as DNA, RNA, or protein sequences. In the context of alignment algorithms like BLAST, the score matrix is crucial for determining how closely related sequences are based on their nucleotide or amino acid composition, influencing the identification of homologous regions.
Suffix tree: A suffix tree is a compressed trie data structure that represents the suffixes of a given string. This powerful tool allows for efficient substring searching, pattern matching, and bioinformatics applications, making it integral in the context of sequence alignment and searching in large biological databases.
Tblastn: tblastn is a variant of the BLAST (Basic Local Alignment Search Tool) algorithm that allows for the comparison of a protein query sequence against a nucleotide database, translating the nucleotide sequences in all six reading frames. This tool is particularly useful for identifying potential coding regions within a genomic DNA sequence that may correspond to the protein of interest.
Tblastx: tblastx is a variant of the BLAST (Basic Local Alignment Search Tool) algorithm used for comparing nucleotide sequences to nucleotide databases, translating both the query and database sequences into protein sequences in all six reading frames. This method is particularly useful for identifying homologous genes and understanding evolutionary relationships when working with poorly characterized sequences or when the protein translations are unknown.
UniProt: UniProt is a comprehensive protein sequence and functional information database that provides detailed annotations for proteins from various organisms. It plays a crucial role in bioinformatics by offering a centralized resource for protein sequences, their functions, structures, and interactions, facilitating various computational analyses in molecular biology.
Word size: Word size refers to the number of bits processed by a computer's CPU in a single operation, often directly impacting the performance of algorithms used in bioinformatics searches. In the context of sequence alignment and search algorithms, word size can significantly affect sensitivity and speed, as it determines how sequences are indexed and matched against each other. The choice of word size is crucial in optimizing search efficiency while maintaining the ability to detect relevant biological similarities.