Basic local alignment search tool.

By Stephen F. Altschul, W. Gish, Webb Miller, Eugene W. Myers, David J. Lipman. 1990

A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.

Subfields: Algorithms & Complexity, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Algorithms & Complexity

In Computational Biology & Bioinformatics

Citations in the corpus

Uniclust databases of clustered and deeply annotated protein sequences and alignments (2016, methodology): “…E domain is the HHblits E -value of the domain match, s subali is the BLOSUM62 score of the pair-wise subalignment between the consensus sequence and the member sequence overlapping the database match, and the term including s subali is an E -value computed with the Karlin-Altschul statistic (17).”
Improvement of molecular-replacement models with Sculptor (2011, methodology): “Suitable models for these targets were selected from a BLAST search (Altschul et al., 1990).”
Hidden Markov model speed heuristic and iterative HMM search procedure (2010, methodology): “In comparison, the heuristic pairwise sequence comparison method WU-BLAST is greater than 200-fold faster [2,3].”
Evaluating Protein Transfer Learning with TAPE (2019, methodology): “The key technique for modeling sequence relationships in computational biology is alignment [13, 16, 21, 22].”
Learning protein sequence embeddings using information from structure (2019, methodology): “These methods are either direct sequence alignment tools [31, 32] or based on profile HMMs in which multiple sequence alignments are first built by iterative search against a large sequence database, the multiple sequence alignments are converted into profile HMMs, and then sequences are compared us”
DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, methodology): “The sequence alignment methods [11, 12] align the sequence of a target protein against the sequences of template proteins whose folds are known to generate alignment scores.”
UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches (2014, background): “Users can avoid compiling and comparing hundreds of BLAST (Altschul et al., 1990) result pages containing highly similar, if not identical, sequences.”
Accelerated Profile HMM Searches (2011, methodology): “The most widely used tool for sequence comparison and database search is BLAST [1–3].”
Clustering huge protein sequence sets in linear time (2017, methodology): “For each clustering run, we searched with BLAST [15] a random sample of 1 000 representative sequences against all representative sequences of the clustering.”
Predicting the functional impact of protein mutations: application to cancer genomics (2011, methodology): “In the current implementation, multiple sequence alignments were derived using the BLAST program (60) retrieving up to 700 homologous sequences at an E-value threshold of 0.”
High resolution comparative modeling with RosettaCM (2013, methodology): “Starting from alignments of the query sequence to templates of known structure, which may be generated using remote homologue detection methods such as PsiBlast(Altschul et al., 1997) or HHsearch(Remmert et al., 2012), or using expert knowledge, RosettaCM builds models in three stages as outlined in”
BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities (2006, background): “Search capabilities include queries by target name; ligand name; affinity range; chemical structure, substructure and similarity; and target sequence, via BLAST (14).”