To run the software, BLAST requires a query sequence to search for, and a sequence to search against (also called the target sequence) or a sequence database containing multiple such sequences. BLAST will find sub-sequences in the database which are similar to subsequences in the query. In typical usage, the query sequence is much smaller than the database, e.g., the query may be one thousand nucleotides while the database is several billion nucleotides. The main idea of BLAST is that there are often High-scoring Segment Pairs (HSP) contained in a statistically significant alignment. BLAST searches for high scoring
sequence alignments between the query sequence and the existing sequences in the database using a heuristic approach that approximates the
Smith-Waterman algorithm. However, the exhaustive Smith-Waterman approach is too slow for searching large genomic databases such as
GenBank. Therefore, the BLAST algorithm uses a
heuristic approach that is less accurate than the Smith-Waterman algorithm but over 50 times faster. The speed and relatively good accuracy of BLAST are among the technical innovations of the BLAST programs. Key steps of the algorithm include filtering low-complexity regions, identifying high-scoring word matches, and statistically evaluating alignments. An overview of the BLAST algorithm (a protein to protein search) is as follows: •
List the possible matching words. • : This step is one of the main differences between BLAST and FASTA. FASTA cares about all of the common words in the database and query sequences that are listed in step 2; however, BLAST only cares about the high-scoring words. The scores are created by comparing the word in the list in step 2 with all the 3-letter words. By using the scoring matrix (
substitution matrix) to score the comparison of each residue pair, there are 20^3 possible match scores for a 3-letter word. For example, the score obtained by comparing PQG with PEG and PQA is respectively 15 and 12 with the
BLOSUM62 weighting scheme. For DNA words, a match is scored as +5 and a mismatch as -4, or as +2 and -3. After that, a neighborhood word score threshold
T is used to reduce the number of possible matching words. The words whose scores are greater than the threshold
T will remain in the possible matching words list, while those with lower scores will be discarded. For example, PEG is kept, but PQA is abandoned when T is 13. •
Organize the remaining high-scoring words into an efficient search tree. • : This allows the program to rapidly compare the high-scoring words to the database sequences. •
Repeat step 3 to 4 for each k
-letter word in the query sequence. •
Scan the database sequences for exact matches with the remaining high-scoring words. • : The BLAST program scans the database sequences for the remaining high-scoring word, such as PEG, of each position. If an exact match is found, this match is used to seed a possible un-gapped alignment between the query and database sequences. •
Extend the exact matches to high-scoring segment pair (HSP). • The original version of BLAST stretches a longer alignment between the query and the database sequence in the left and right directions, from the position where the exact match occurred. The extension does not stop until the accumulated total score of the HSP begins to decrease. A simplified example is presented in figure 2.File:extension process.jpg|frame|Fig. 2 The process to extend the exact match. Adapted from Biological Sequence Analysis I, Current Topics in Genome Analysis . • To save more time, a newer version of BLAST, called BLAST2 or gapped BLAST, has been developed. BLAST2 adopts a lower neighborhood word score threshold to maintain the same level of sensitivity for detecting sequence similarity. Therefore, the list of possible matching words list in step 3 becomes longer. Next, the exact matched regions, within distance A from each other on the same diagonal in figure 3, will be joined as a longer new region. Finally, the new regions are then extended by the same method as in the original version of BLAST, and the HSPs' (High-scoring segment pair) scores of the extended regions are then created by using a substitution matrix as before. •
List all of the HSPs in the database whose score is high enough to be considered. • : We list the HSPs whose scores are greater than the empirically determined cutoff score
S. By examining the distribution of the alignment scores modeled by comparing random sequences, a cutoff score
S can be determined such that its value is large enough to guarantee the significance of the remaining HSPs. •
Evaluate the significance of the HSP score. • : BLAST next assesses the statistical significance of each HSP score by exploiting the Gumbel extreme value distribution (EVD). (It is proved that the distribution of Smith-Waterman local alignment scores between two random sequences follows the Gumbel EVD. For local alignments containing gaps it is not proved.). In accordance with the Gumbel EVD, the probability
p of observing a score
S equal to or greater than x is given by the equation • ::p\left( S\ge x \right) =1-\exp \left( -e^{-\lambda \left( x-\mu \right) } \right) • : where • :: \mu = \frac{ \log \left( Km'n' \right) }{\lambda }\; • : The statistical parameters \lambda and \mathrm{K} are estimated by fitting the distribution of the un-gapped local alignment scores, of the query sequence and a lot of shuffled versions (Global or local shuffling) of a database sequence, to the Gumbel extreme value distribution. Note that \lambda and \mathrm{K} depend upon the substitution matrix, gap penalties, and sequence composition (the letter frequencies). m' and n' are the effective lengths of the query and database sequences, respectively. The original sequence length is shortened to the effective length to compensate for the edge effect (an alignment start near the end of one of the query or database sequence is likely not to have enough sequence to build an optimal alignment). They can be calculated as • :: m'\approx m-\frac{ \ln Kmn }{H}\; • :: n'\approx n-\frac{ \ln Kmn }{H}\; • : where \mathrm{H} is the average expected score per aligned pair of residues in an alignment of two random sequences. Altschul and Gish gave the typical values, \lambda = 0.318, \mathrm{K} = 0.13, and \mathrm{H} = 0.40, for un-gapped local alignment using BLOSUM62 as the substitution matrix. Using the typical values for assessing the significance is called the lookup table method; it is not accurate. The expect score
E of a database match is the number of times that an unrelated database sequence would obtain a score
S higher than
x by chance. The expectation
E obtained in a search for a database of
D sequences is given by • ::E\approx 1-e^{-p\left( s>x \right) D} • : Furthermore, when p, E could be approximated by the Poisson distribution as • :: E\approx pD • : This expectation or expect value "E" (often called an
E score or
E-value or
e-value) assessing the significance of the HSP score for un-gapped local alignment is reported in the BLAST results. The calculation shown here is modified if individual HSPs are combined, such as when producing gapped alignments (described below), due to the variation of the statistical parameters. •
Make two or more HSP regions into a longer alignment. • : Sometimes, we find two or more HSP regions in one database sequence that can be made into a longer alignment. This provides additional evidence of the relation between the query and database sequence. There are two methods, the Poisson method and the sum-of-scores method, to compare the significance of the newly combined HSP regions. Suppose that there are two combined HSP regions with the pairs of scores (65, 40) and (52, 45), respectively. The Poisson method gives more significance to the set with the maximal lower score (45>40). However, the sum-of-scores method prefers the first set, because 65+40 (105) is greater than 52+45(97). The original BLAST uses the Poisson method; gapped BLAST and the WU-BLAST uses the sum-of scores method. •
Show the gapped Smith-Waterman local alignments of the query and each of the matched database sequences. • The original BLAST only generates un-gapped alignments including the initially found HSPs individually, even when there is more than one HSP found in one database sequence. • BLAST2 produces a single alignment with gaps that can include all of the initially found HSP regions. Note that the computation of the score and its corresponding
E-value involves use of adequate gap penalties. •
Report every match whose expect score is lower than a threshold parameter E
. Types of BLAST ;BLASTn (Nucleotide BLAST): BLASTn offers nucleotide to nucelotide searches. This is useful when trying to identify evolutionary relationships between organisms. ;tBLASTn: tBLASTn used to carry out protein to translated DNA searches. This is useful when looking for similar protein-coding regions in DNA sequences that haven't been fully annotated, like ESTs (short, single-read cDNA sequences) and HTGs (draft genome sequences). Since these sequences don't have known protein translations, we can only search for them using tBLASTn. MPIblast makes use of a database segmentation technique to parallelize the computation process. This allows for significant performance improvements when conducting BLAST searches across a set of nodes in a cluster. In some scenarios a superlinear speedup is achievable. This makes MPIblast suitable for the extensive genomic datasets that are typically used in bioinformatics. BLAST generally runs at a speed of
O(n), where n is the size of the database. The time to complete the search increases linearly as the size of the database increases. MPIblast utilizes
parallel processing to speed up the search. The ideal speed for any
parallel computation is a complexity of O(n/p), with n being the size of the database and p being the number of processors. This would indicate that the job is evenly distributed among the p number of processors. This is visualized in the included graph. The superlinear speedup that can sometimes occur with MPIblast can have a complexity better than O(n/p). This occurs because the cache memory can be used to decrease the run time. == Alternatives to BLAST ==