From DrugPedia: A Wikipedia for Drug discovery

Blasts stands for Basic Local Alignment Search Tool.Its Based on Karlin-Altschul statistics.

It compares novel sequence with that present in nucleotide & protein databases which are already characterized. It finds regions of sequence similarity which will yield functional & evolutionary clues. Regions of similarity can be:

local: where the region of similarity is based on small stretches in the sequence global: regions of similarity are present across the sequence.

Main idea of basic BLAST is that "Homologous sequences are likely to contain a short high scoring similarity region a hit. Each hit gives a seed that BLAST tries to extend on both sides".

Contents 1 How does blast works? 1.1 The Seeding step 1.1.1 Listing of words in a sequence 1.2 The Extension Step 1.2.1 When do we stop the extension? 1.2.2 Trimming back for getting hsp 1.3 Evaluation Step 1.4 References

[edit] How does blast works?

First BLAST optionally filters out low-complexity regions that are not useful for producing meaningful sequence alignments. Then, a three-step layers to sequentially refine the “good alignments”.

1. Seeding step
2. Extension step
3. Evaluation step

[edit] The Seeding step

BLAST assumes that significant alignments have words in common. A word refers to number of letters.For example, if 3 letters is a word, then the sequence PQGEFG has words PQG,QGE, GEF and EFG. Protein sequences have word length of 3 and 11 for DNA sequences.

[edit] Listing of words in a sequence

BLAST cares about only the high-scoring words. The scores are created by comparing the word in the list(eg. PQG) with all the 3-letter words (PQG,QGE, GEF and EFG). 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 15 and 12, respectively. For DNA words, a match is scored as +5 and a mismatch as -4. 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.

                                           Seq       Score                      Seq     Score       
                                            …..       ….                       .......  ........
     PQG        aligns with                PEG	       15                        PEG	   15
                                           PRG	       14       T=13             PRG	   14 
                                           PSG	       13                        PSG	   13
                                           PQA	       12                        

If T=13 gives us approximately 50 word hits and if a sequence is of length 250 amino acids, what is the approximate total number of words to search for

50 x 250 = 12,500 !!

As oppose to 20^3 x 250 = 2,000,000!! After getting the list of 50 words in the first sequence position, we can use these words to search the database. These 50 words can be pre-computed and this speeds up the overall search process!

[edit] The Extension Step

After rounding up all the word hits as seeds in the neighborhood region, both directions of a seed are extended to generate an alignment. …..???? PQL ??? ….

…. $$$ PQL $$$ ….

[edit] When do we stop the extension?

Let’s do an example:

Using match score = +1, mismatch score = -1, and “T” is our seed and extending to the right... Use a drop off score variable, X = 5

                    The quick  brown fox  jumps over the lazy dog
                    The quiet  brown cat  purrs when she sees him
                    123 45654  56789 876 5654…    score
                    000 00012  10000 123 4345…     drop off score

After terminating the extension, the alignment is trimmed back to the maximum score, 9. And the larger stretch of sequence is our High Scoring Pair.

[edit] Trimming back for getting hsp

Extension should be invoked only when two non-overlapping hits are found within distance A on the same diagonal.

[edit] Evaluation Step

We know that Blast is based on Karlin-Altschul statistics . The Karlin-Altschul equation is given by:

Error creating thumbnail: convert: unable to open image `/usr1/webserver/cgidocs/drugpedia/images/Blast6.png': No such file or directory @ blob.c/OpenBlob/2480. convert: unable to open file `/usr1/webserver/cgidocs/drugpedia/images/Blast6.png' @ png.c/ReadPNGImage/2889. convert: missing an image filename `/usr1/webserver/cgidocs/drugpedia/images/thumb/Blast6.png/100px-Blast6.png' @ convert.c/ConvertImageCommand/2800.

E = expected no. of alignments

k= minor constant

m= length of query

n= length of database

λ= Scaling factor

S= raw score

λS= Normalized score

mn= search space

Raw score (S): Sum of scores for each aligned position and scores for gaps S = sum(matches) - sum(mismatches) - sum(gap penalties) note: this score varies with the scoring matrix used and thus may not be meaningfully compared for different searches

Bit score (S’): Version of the raw score that is normalized by the scale of the scoring matrix ( ) and the scale of the search space size (K) S’ = (sum(S) – ln(K)) / ln(2) note: because it is normalized the bit score can be meaningfully compared across searches

E value: Number of alignments with score S’ or better that one would expect to find by chance in a search of a database of the same size E = mn2-S’ m = effective length of database n = effective length of query sequence note: E values may change if databases of different sizes are searched

Once we have all the HSP’s, we use a different cutoff “S” to rank the HSP’s. The raw scores of HSPs are sorted base on “S” The “S” alignment threshold can remove many random and low-scoring alignments. But if it's set too high, it may also remove real alignments. Once the HSPs are organized, they can be evaluated with a Final threshold based on the entire search space and corresponding to the value E set for the search. BLAST reports any alignment or group of alignments that meets the E requirement.

Sometimes, two or more HSP regions that can be made into a longer alignment will be found.

For example, HSP score #1: 65 and 40 and HSP score #2: 52 and 45

Poisson method: probability of multiple score is higher when the lower score of each set is higher. (45 is higher than 40) Sum-of-scores method: 65+40=105 is higher than 52+45=97.

original BLAST uses the Poisson method whereas gapped BLAST and the WU-BLAST use the sum-of scores method.

Smith-Waterman local alignments are shown for the query sequence with each of the matched sequences in the database. Those matches whose E value is lower then a threshold value are reported.

[edit] References

• D.W. Mount (2004). "Bioinformatics: Sequence and Genome Analysis."

• Ian Korf, Mark Yandell & Joseph Bedell."An essential guide to basic local alignment search tool"