NCBI BLAST

NCBI BLAST
Name	BLAST
Developer	National Center for Biotechnology Information
Released	1990s
Programming language	C, C++
Operating system	Unix-like, Windows, macOS
Genre	Bioinformatics
License	Public domain / mixed

NCBI BLAST BLAST is a sequence comparison tool for identifying local alignments between biological sequences, developed by the National Center for Biotechnology Information and used in genomics, proteomics, and molecular biology. It integrates algorithms and databases to map query sequences against repositories such as GenBank, RefSeq, and UniProt, and has become integral to projects from the Human Genome Project to modern metagenomics consortia. Researchers at institutions like the National Institutes of Health, Cold Spring Harbor Laboratory, and European Molecular Biology Laboratory rely on BLAST outputs alongside tools from EMBL-EBI, DDBJ, and UCSC Genome Browser for annotation and discovery.

Introduction

BLAST performs rapid local alignments by comparing nucleotide or amino acid queries to large sequence collections including GenBank, RefSeq, EMBL, UniProt, Swiss-Prot, and PDB entries curated by institutions such as NCBI, EBI, and RCSB. Users across research centers like Harvard, Stanford, MIT, Princeton, and Oxford employ BLAST via web services, command-line clients, and APIs integrated with Galaxy, Bioconductor, and InterProScan pipelines. BLAST results inform downstream analyses in projects like the Human Genome Project, 1000 Genomes Project, ENCODE, and GTEx, and are cross-referenced with annotations from FlyBase, WormBase, and TAIR.

History and Development

BLAST emerged from algorithmic advances in the late 1980s and early 1990s building on foundational work by researchers at NIH, inspired by methods published in journals such as Nature and Science and by algorithms from institutions including Bell Labs and Los Alamos National Laboratory. Development intersected with large-scale efforts such as the Human Genome Project, the HapMap Project, and collaborations involving the Wellcome Sanger Institute and Broad Institute. Key implementations and updates were documented in proceedings from Cold Spring Harbor meetings and presented at conferences like ISMB and RECOMB, while software distribution involved partners such as NCBI, EMBL-EBI, DDBJ, and academic research groups at Yale and Cambridge.

Methods and Algorithms

BLAST uses heuristic seed-and-extend strategies derived from algorithmic principles that contrast with full dynamic programming methods pioneered by mathematicians and computer scientists at institutions like MIT, Princeton, and Stanford. It employs word-size seeding, substitution matrices such as BLOSUM and PAM developed by groups including the Columbia University and University of Michigan teams, and statistical models for E-value calculation informed by work from statisticians at Carnegie Mellon and University of Chicago. Variants optimize for nucleotide or protein similarity and incorporate techniques from applied mathematics, information theory, and computational biology labs at Johns Hopkins, University of California, and ETH Zurich. Heuristic filtering and low-complexity masking follow protocols influenced by work at Rockefeller University and Scripps Research.

Versions and Implementations

Multiple BLAST implementations exist across platforms and institutions: the original NCBI implementation used in web services and stand-alone packages, alternative implementations in Biopython and EMBOSS maintained by groups at University College London and the European Bioinformatics Institute, and accelerated versions leveraging GPUs and HPC clusters developed at Lawrence Berkeley National Laboratory, Argonne National Laboratory, and NVIDIA research collaborations. Cloud deployments integrate BLAST with AWS, Google Cloud, and Microsoft Azure infrastructures used by research groups at UC San Diego, University of Washington, and KAUST. Community tools and wrappers from Bioconductor, Galaxy Project, and Nextflow link BLAST to pipelines at institutions such as Memorial Sloan Kettering, Dana-Farber, and Fred Hutchinson.

Applications and Use Cases

BLAST underpins tasks in clinical genomics at hospitals such as Mayo Clinic, Cleveland Clinic, and Massachusetts General Hospital, informs pathogen surveillance by CDC and WHO teams, supports biodiversity studies at Smithsonian and Royal Botanical Gardens, and enables evolutionary analyses by laboratories at Max Planck Institute and Smithsonian Tropical Research Institute. It is used in forensic contexts by agencies like INTERPOL and national forensic labs, agricultural research at USDA and Rothamsted Research, drug discovery at Pfizer and Novartis, and synthetic biology projects at BioBricks Foundation and iGEM teams. Environmental and metagenomic studies by groups at JGI, Scripps Institution of Oceanography, and Woods Hole Oceanographic Institution also depend on BLAST for taxonomic assignment and functional annotation.

Limitations and Performance Considerations

BLAST's heuristic nature trades sensitivity for speed, leading researchers at academic centers such as Caltech, Imperial College London, and ETH Zurich to choose alternatives like Smith–Waterman implementations from EMBOSS or DIAMOND for particular use cases. Large-scale analyses at CERN-like compute scales and national supercomputing centers require optimized indexing, sharding, and parallelization strategies employed by teams at Oak Ridge National Laboratory and Los Alamos National Laboratory. Careful parameter selection, database curation with entries from UniProt and RefSeq, and integration with annotation resources like Pfam, InterPro, and CATH mitigate false positives and annotation errors noted in publications from journals like PNAS and Genome Research.

Category:Bioinformatics