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BLAST - Wikipedia. In bioinformatics, BLAST for Basic Local Alignment Search Tool is an algorithm for comparing primary biological sequence information, such as the amino- acid sequences of proteins or the nucleotides of DNA sequences. A BLAST search enables a researcher to compare a query sequence with a library or database of sequences, and identify library sequences that resemble the query sequence above a certain threshold. Different types of BLASTs are available according to the query sequences.

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For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence. The BLAST algorithm and program were designed by Stephen Altschul, Warren Gish, Webb Miller, Eugene Myers, and David J.

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Lipman at the National Institutes of Health and was published in the Journal of Molecular Biology in 1. The heuristic algorithm it uses is much faster than other approaches, such as calculating an optimal alignment. This emphasis on speed is vital to making the algorithm practical on the huge genome databases currently available, although subsequent algorithms can be even faster. Before BLAST, FASTA was developed by David J. Lipman and William R.

Pearson in 1. 98. The optimality of Smith- Waterman . This could be further realized by understanding the algorithm of BLAST introduced below.

Examples of other questions that researchers use BLAST to answer are: Which bacterialspecies have a protein that is related in lineage to a certain protein with known amino- acid sequence. What other genes encode proteins that exhibit structures or motifs such as ones that have just been determined. BLAST is also often used as part of other algorithms that require approximate sequence matching. The BLAST algorithm and the computer program that implements it were developed by Stephen Altschul, Warren Gish, and David Lipman at the U. S. National Center for Biotechnology Information (NCBI), Webb Miller at the Pennsylvania State University, and Gene Myers at the University of Arizona. It is available on the web on the NCBI website.

Alternative implementations include AB- BLAST (formerly known as WU- BLAST), FSA- BLAST (last updated in 2. Scala. BLAST. These formats include HTML, plain text, and XML formatting.

For NCBI's web- page, the default format for output is HTML. When performing a BLAST on NCBI, the results are given in a graphical format showing the hits found, a table showing sequence identifiers for the hits with scoring related data, as well as alignments for the sequence of interest and the hits received with corresponding BLAST scores for these. The easiest to read and most informative of these is probably the table. If one is attempting to search for a proprietary sequence or simply one that is unavailable in databases available to the general public through sources such as NCBI, there is a BLAST program available for download to any computer, at no cost. This can be found at BLAST+ executables. There are also commercial programs available for purchase. Databases can be found from the NCBI site, as well as from Index of BLAST databases (FTP).

Process. This process of finding similar sequences is called seeding. It is after this first match that BLAST begins to make local alignments. While attempting to find similarity in sequences, sets of common letters, known as words, are very important. For example, suppose that the sequence contains the following stretch of letters, GLKFA. If a BLAST was being conducted under normal conditions, the word size would be 3 letters.

In this case, using the given stretch of letters, the searched words would be GLK, LKF, KFA. The heuristic algorithm of BLAST locates all common three- letter words between the sequence of interest and the hit sequence or sequences from the database. This result will then be used to build an alignment.

After making words for the sequence of interest, the rest of the words are also assembled. These words must satisfy a requirement of having a score of at least the threshold T, when compared by using a scoring matrix.

One commonly used scoring matrix for BLAST searches is BLOSUM6. Once both words and neighborhood words are assembled and compiled, they are compared to the sequences in the database in order to find matches. The threshold score T determines whether or not a particular word will be included in the alignment. Once seeding has been conducted, the alignment which is only 3 residues long, is extended in both directions by the algorithm used by BLAST. Each extension impacts the score of the alignment by either increasing or decreasing it. If this score is higher than a pre- determined T, the alignment will be included in the results given by BLAST.

However, if this score is lower than this pre- determined T, the alignment will cease to extend, preventing the areas of poor alignment from being included in the BLAST results. Note that increasing the T score limits the amount of space available to search, decreasing the number of neighborhood words, while at the same time speeding up the process of BLAST. Algorithm. BLAST will find sub- sequences in the database which are similar to sub sequences in the query. In typical usage, the query sequence is much smaller than the database, e. 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 Gen. Bank. Therefore, the BLAST algorithm uses a heuristic approach that is less accurate than the Smith- Waterman algorithm but over 5. These regions might give high scores that confuse the program to find the actual significant sequences in the database, so they should be filtered out. The regions will be marked with an X (protein sequences) or N (nucleic acid sequences) and then be ignored by the BLAST program. To filter out the low- complexity regions, the SEG program is used for protein sequences and the program DUST is used for DNA sequences.

On the other hand, the program XNU. The method is illustrated in figure 1.

The method to establish the k- letter query word list. Adapted from Biological Sequence Analysis I, Current Topics in Genome Analysis . 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 2.

For example, the score obtained by comparing PQG with PEG and PQA is respectively 1. BLOSUM6. 2 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 1. 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.

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