Basic Local Alignment Search Tool (BLAST)

Introduction

The Basic Local Alignment Search Tool (BLAST) is a sequence comparison algorithm and program that finds regions of local similarity between sequences. BLAST compares nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches. BLAST can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families.

History and Development

BLAST was developed by Stephen Altschul, Warren Gish, Webb Miller, Eugene Myers, and David J. Lipman at the National Institutes of Health (NIH) and was published in the Journal of Molecular Biology in 1990. The algorithm represents an important innovation in the field of bioinformatics, enabling researchers to efficiently search large databases of biological sequences.

Prior to BLAST, biological sequence database searches were typically performed using exact or approximate matching methods that required significant computational resources. BLAST introduced a heuristic approach that dramatically improved search speed while maintaining sensitivity. This breakthrough made it practical to search rapidly growing sequence databases, such as GenBank, which were expanding exponentially with advances in DNA sequencing technology.

How BLAST Works

BLAST operates using a heuristic algorithm that finds regions of similarity without determining a complete alignment of all sequences. The process can be broken down into several steps:

• Query preprocessing: The input query sequence is analyzed to identify words (short sub-sequences) that are statistically significant.
• Word lookup: The algorithm scans the database for exact matches to these words, called "hits."
• Extension: For each hit, BLAST performs an extension in both directions along the sequences to find local alignments with scores above a threshold.
• Evaluation: The algorithm evaluates the statistical significance of each alignment using the normalized score and E-value.
• Reporting: BLAST reports the alignments that meet the specified significance criteria, along with scores and statistics.

The speed of BLAST comes from its use of the "seed and extend" approach. By first finding short exact matches (seeds) between sequences, BLAST can focus its computational efforts on regions likely to contain significant alignments rather than attempting to compare every possible alignment.

Types of BLAST Programs

Several variants of BLAST have been developed to handle different types of sequence comparisons:

• blastn: Searches a nucleotide query against a nucleotide database.
• blastp: Searches a protein query against a protein database.
• blastx: Translates a nucleotide query in all six reading frames and compares the resulting protein sequences against a protein database.
• tblastn: Searches a protein query against a nucleotide database whose sequences are translated in six frames.
• tblastx: Translates a nucleotide query in all six reading frames and searches these against a nucleotide database that is also translated in six frames.

Each variant is optimized for specific research scenarios. For example, blastx is particularly useful when analyzing newly sequenced DNA that may contain coding regions, as it can identify protein homologs even before gene prediction. The tblastn program is valuable for finding potential coding regions in unannotated genomic DNA by searching with a known protein sequence.

Interpreting BLAST Results

BLAST results present several key parameters that help researchers assess the significance of sequence matches:

• Score: A measure of alignment quality based on the scoring system (gaps, matches, mismatches).
• Bit Score: A normalized version of the raw score that allows comparison between different searches.
• E-value: The expected number of random hits with at least this score in the current database size. Lower E-values indicate more significant matches.
• Identity: The percentage of positions in the alignment with identical residues.
• Query and Subject coverage: The proportion of each sequence that participates in the alignment.

When interpreting BLAST results, the E-value is often the most critical metric. An E-value close to zero indicates that the match is unlikely to have occurred by chance, suggesting a genuine biological relationship between sequences. E-values below 10^-3 are generally considered statistically significant, though the threshold may vary depending on the size of the database and the research context.

Applications of BLAST

BLAST has become an indispensable tool in molecular biology and genomics with numerous applications:

• Gene identification: Researchers use BLAST to identify genes in newly sequenced genomes by finding homologs in other organisms.
• Functional annotation: BLAST can help predict the function of unknown proteins based on similarity to proteins of known function.
• Evolutionary studies: By comparing sequences across species, BLAST enables researchers to study evolutionary relationships and construct phylogenetic trees.
• Disease gene discovery: BLAST helps identify potential disease-causing mutations by comparing human genes to orthologs in model organisms.
• Metagenomics: BLAST enables the taxonomic classification of sequences from environmental samples.
• Variant analysis: Researchers use BLAST to assess the potential impact of genetic variants by comparing them to reference sequences.

Limitations and Alternatives

Despite its widespread utility, BLAST has several limitations:

• Sensitivity: BLAST may miss very distant relationships or highly divergent sequences due to its heuristic approach.
• Statistical accuracy: The statistical model used by BLAST may be inaccurate for very short sequences or certain search contexts.
• Complex queries: BLAST can't directly handle certain types of queries, such as motifs, patterns, or structural information.

Several alternatives and complements to BLAST have been developed to address these limitations:

• FASTA: An earlier algorithm with different heuristics that may be more sensitive for certain relationships.
• PSI-BLAST: An iterative version of BLAST that builds a position-specific scoring matrix to detect distant relationships.
• HMMER: Uses profile hidden Markov models for more sensitive detection of remote homologs.
• Smith-Waterman: The gold standard for sensitivity but computationally expensive for whole database searches.
• DIAMOND: A faster alternative to BLASTX for protein-alignment searches, offering similar sensitivity at dramatically reduced run times.

Future Directions

As sequence databases continue to expand exponentially, the development of increasingly efficient and sensitive algorithms remains a priority in bioinformatics. Current research directions include:

• Machine learning approaches: Incorporating machine learning to improve scoring systems and detect meaningful patterns.
• Cloud computing: Enhancing BLAST for distributed computing environments to handle larger datasets.
• Hardware acceleration: Implementing BLAST on graphics processing units (GPUs) and other specialized hardware.
• Integration with other data types: Combining sequence similarity with structural, functional, and expression data.
• Better handling of metagenomic data: Developing specialized versions of BLAST for the unique challenges presented by environmental sequencing.

Resources for Using BLAST

Numerous resources are available for researchers who wish to use BLAST:

• NCBI BLAST: The National Center for Biotechnology Information (NCBI) provides web interfaces and downloadable versions of BLAST.
• Ensembl BLAST: The European Molecular Biology Laboratory's Ensembl project offers BLAST servers for querying genomic data.
• BLAST+suite: The current command-line implementation of BLAST from the NCBI, replacing the older standalone BLAST.
• API access: Programmatic access to BLAST is available through the NCBI E-utilities and other interfaces.
• Tutorials and documentation: Extensive tutorials, guides, and documentation are available from NCBI and other sources.

BLAST continues to evolve with updates and new versions that improve performance, accuracy, and usability. As a fundamental tool in computational biology, BLAST has enabled countless discoveries and remains a cornerstone of biological research in the genomic era.