1. Biology 4900
Biocomputing

2. Chapter 4
BLAST

3. BLAST
BLAST allows user to search a sequence (the query) against millions of sequences in the NCBI database (the target).
Global alignments (e.g., Needleman-Wunsch) would be time consuming and computationally intensive for this amount of data.
BLAST is designed for local alignment, not global alignment.
Allows for faster searches, can match subsets of proteins (e.g., domains).
C-terminal domain of CaM (from 3cln.pdb)

4. Other BLAST Programs
Blastx: Compares nucleotide query sequence translated in all reading frames (3 possible proteins for each DNA strand) against a protein sequence DB.
Tblastn: Compares protein query sequence against a nucleotide sequence DB.
Tblastx: Compares the 6-frame translations of a nucleotide query sequence against the 6-frame translations of a nucleotide sequence database.
5’ CAT CAA
5’ ATC AAC
5’ TCA ACT
5’ CATCAACTACAACTCCAAAGACACCCTTACACATCAACAAACCTACCCAC 3’
3’ GTAGTTGATGTTGAGGTTTCTGTGGGAATGTGTAGTTGTTTGGATGGGTG 5’
5’ GTG GGT
5’ TGG GTA
5’ GGG TAG
Pevsner, Bioinformatics and Functional Genomics, 2009

5. Choose the BLAST program
Program Input Database 1
blastn DNA DNA
blastp protein protein 6
blastx DNA protein
tblastn protein DNA 36
tblastx DNA DNA

6. For sequence…FSGTWYA… A list of words (w=3) is: FSG SGT GTW TWY WYA
BLAST (Altschul 1990)
Blast uses a pre-indexed database of ‘words’ for all proteins in the database (Similar to FASTA).
A word is defined as a short sequence of letters.
For Blastp, the default word (W) size is 3 letters.
For Blastn, the default word (W) size is 11 letters.
For MegaBLAST (nucleotide), the default word (W) size is 28 letters.
When you run a query, BLAST breaks your query sequence into a series of words, and generates neighborhood words, as in the following example:
For sequence…FSGTWYA…
A list of words (w=3) is:
FSG SGT GTW TWY WYA
YSG TGT ATW SWY WFA
FTG SVT GSW TWF WYS
Words
Neighborhood Words

7. Why use BLAST?
BLAST searching is fundamental to understanding the relatedness of any favorite query sequence to other known proteins or DNA sequences.
Applications include
identifying orthologs and paralogs
discovering new genes or proteins
discovering variants of genes or proteins
investigating expressed sequence tags (ESTs)
exploring protein structure and function

8. Four steps to becoming a Master BLASTer
(1) Choose the sequence (query)
(2) Select the BLAST program
(3) Choose the database to search
(4) Choose optional parameters (may leave as default params the first time)
Then click “BLAST”

10. Step 1: Choose your sequence
Sequence can be input in FASTA format as text or by file upload, or as accession number

11. Example of the FASTA format for a BLAST query
Note link here

12. Step 2: Choose the BLAST program
Blastn and blastp are the main programs you will want to use

13. Step 3: choose the database to search
nr = non-redundant (most general database)
dbest = database of expressed sequence tags
dbsts = database of sequence tag sites
gss = genomic survey sequences
protein databases
nucleotide databases

14. Step 4a: Select optional search parameters
organism
Entrez!
algorithm

15. Step 4a: optional blastp search parameters
Expect
Word size
Right. So, what are these?
Scoring matrix
Filter, mask

16. Step 4a: optional blastn search parameters
Expect
Word size
Match/mismatch scores
Filter, mask

17. Algorithm Parameters: Expect
This setting specifies the statistical significance threshold for reporting matches against database sequences.
The default value (10) means that 10 such matches are expected to be found merely by chance, according to the stochastic model of Karlin and Altschul (1990).
If the statistical significance ascribed to a match is greater than the EXPECT threshold, the match will not be reported.
Lower EXPECT thresholds (e.g., set expect to 6) are more stringent, leading to fewer chance matches being reported.

18. Algorithm Parameters: Word Size
BLAST is a heuristic algorithm (makes approximations) that works by finding word-matches between the query and database sequences. This process finds "hot-spots" that BLAST can then potentiallyextend into full-blown alignments.
For nucleotide-nucleotide searches (i.e., "blastn") an exact match of the entire word is required before an extension is initiated, so that one normally regulates the sensitivity and speed of the search by increasing or decreasing the word-size.
For other BLAST searches non-exact word matches are taken into account based upon the similarity between words. The amount of similarity can be varied so one normally uses just the word-sizes 2 and 3 for these searches.
KENFDKARFSGTWYAMAKKDPEG 50 RBP (query)
MKGLDIQKVAGTWYSLAMAASD. 44 lactoglobulin (hit)
extend
extend
Hit!

19. Algorithm Parameters: Filters
The Low-complexity filter option masks part of query sequence that may represent very common, non-complex subsets of sequence.
May not be very useful.
The Species-repeats repeats for: filter option is designed to ignore species-specific genomic repeats in very long sequences.

20. Algorithm Parameters: Masks
The Mask for lookup table only option masks only for purposes of constructing the lookup table used by BLAST so that no hits are found based upon low-complexity sequence or repeats (if repeat filter is checked).
The BLAST extensions are performed without masking and so they can be extended through low-complexity sequence.
The Mask lower case letters option lets you cut and paste a FASTA sequence in upper case characters and denote areas you would like filtered with lower case. This allows you to customize what is filtered from the sequence during the comparison to the BLAST databases.
These parts of sequence in LC letters masked, or ignored
Ex. agvgpADEEWGYilmaagDDEEE

21. Algorithm Parameters: Match/Mismatch Scores
Many nucleotide searches use a simple scoring system that consists of a "reward" for a match and a "penalty" for a mismatch.
The (absolute) reward/penalty ratio should be increased as one looks at more divergent sequences.
A ratio of 0.33 (1/-3) is appropriate for sequences that are about 99% conserved
A ratio of 0.5 (1/-2) is best for sequences that are 95% conserved
A ratio of about one (1/-1) is best for sequences that are 75% conserved
States DJ, Gish W, and Altschul SF (1991)

22. Algorithm Parameters: Matrices
A key element in evaluating the quality of a pairwise sequence alignment is the "substitution matrix", which assigns a score for aligning any possible pair of residues.
Some matrices are good for comparing sequences that diverge very little, while other matrices are good for comparing sequences that diverge a lot.
The BLOSUM-62 matrix is among the best for detecting most weak protein similarities.
The BLOSUM-45 matrix may be better for particularly long and weak alignments.
The older PAM matrices may be better for short alignments, as these need to have a higher percentage of matching residues to exceed background noise (be detectable beyond random chance).

23. Calculate the score in BLOSUM-62 for a gap with 7 residues…
Matrices and Gap Costs
The raw score of an alignment is the sum of the scores for aligning pairs of residues and the scores for gaps.
Gapped BLAST and PSI-BLAST use "affine gap costs" which charge the score -a for the existence of a gap, and the score -b for each residue in the gap. Thus a gap of k residues receives a total score of -(a+bk); specifically, a gap of length 1 receives the score -(a+b).
Your total raw score for the alignment is reduced when you introduce gaps into the query sequence.
Calculate the score in BLOSUM-62 for a gap with 7 residues…

24. BLAST (Altschul 1990)
Neighborhood words are similar to constructed words from query, with one or more mismatched symbols.
These are given scores based on the matrix that you are using (for BLAST, the default matrix is BLOSUM62).
Neighborhood words that score above a user-defined threshold are also searched.
Word
Letter score
Total score
GTW 6,5,11 22
GSW 6,1,11 18
ATW 0,5,11 16
NTW 0,5,11 16
GTY 6,5,2 13
ANT 1,0,
Neighborhood word hit > threshold (T)
(T=11)
Neighborhood word hit < threshold (T)

25. extend extend Hit! BLAST (Altschul 1990)
Blast then searches the entire database for the search words and neighborhood words.
Once a match is found, BLAST then extends the search in both directions of the sequence, scoring each subsequent match, until the score drops below some cutoff value.
KENFDKARFSGTWYAMAKKDPEG 50 RBP (query)
MKGLDIQKVAGTWYSLAMAASD. 44 lactoglobulin (hit)
extend
extend
Hit!

26. BLAST (1997)
In a 1997 refinement of BLAST, two independent hits are required.
The hits must occur in close proximity to each other.
With this modification, only 1/7 as many extensions occur, greatly speeding the time required for a search.

27. Changing BLAST Input Parameters
Increasing W or T will increase speed, but will result in loss of sensitivity (i.e., you will miss some matches)
The expect value(E-value) can be changed in order to limit the number of hits to the most significant ones.
Lower E-value = better hit.
E-value is dependent on length of query sequence and size of database.
Example: an alignment obtaining an E-value of 0.05 means that there is a 5 in 100 chance of occurring by chance alone.

28. BLAST Output from DB Search
Graphic Summary includes conserved domains, when applicable.

29. BLAST Output from DB Search
Graphic Summary includes distribution of blast hits.
Color coded by bit Score.
Higher score related to higher sequence identity.

30. BLAST search output: tabular output
High scores
low E values

31. BLAST search output: alignment output

32. Blast Output include evolutionary tree view
Run 3cln to observe tree view options

33. Pairwise Alignment with Dot Plots
3CLN
1EXR
>lcl| CLN:A|PDBID|CHAIN|SEQUENCE
Length=148
Score = 268 bits (684), Expect = 3e-97, Method: Compositional matrix adjust.
Identities = 130/148 (88%), Positives = 143/148 (97%), Gaps = 0/148 (0%)
Query AEQLTEEQIAEFKEAFALFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGN 60
A+QLTEEQIAEFKEAF+LFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGN
Sbjct ADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGN 60
Query GTIDFPEFLSLMARKMKEQDSEEELIEAFKVFDRDGNGLISAAELRHVMTNLGEKLTDDE 120
GTIDFPEFL++MARKMK+ DSEEE+ EAF+VFD+DGNG ISAAELRHVMTNLGEKLTD+E
Sbjct GTIDFPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEE 120
Query VDEMIREADIDGDGHINYEEFVRMMVSK 148
VDEMIREA+IDGDG +NYEEFV+MM +K
Sbjct VDEMIREANIDGDGQVNYEEFVQMMTAK 148

34. Pairwise Alignment with Dot Plots
1RTP
3CLN
Score = 30.0 bits (66), Expect = 1e-06, Method: Compositional matrix adjust.
Identities = 14/51 (27%), Positives = 26/51 (51%), Gaps = 3/51 (6%)
Query TIDFPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNL 112
+ D +F M+ K K D F + DKD +G+I EL ++
Sbjct SFDHKKFFQMVGLKKKSAD---DVKKVFHILDKDKSGFIEEDELGSILKGF 70
Score = 25.8 bits (55), Expect = 3e-05, Method: Compositional matrix adjust.
Identities = 11/40 (28%), Positives = 21/40 (53%), Gaps = 0/40 (0%)
Query 4 LTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNP 43
L K+ F + DKD G I ELG
Sbjct 35 LKKKSADDVKKVFHILDKDKSGFIEEDELGSILKGFSSDA 74
3CLN
1RTP

35. Statistics of Local Alignments
For local pairwise alignments, best approach to determining statistical significance is to estimate an expect value (E value).
The expect value E is the number of alignments with scores greater than or equal to score S (your score) that are expected to occur by chance in a database search.
A score with an associated E value of 10-3 means that this particular score may occur 1 time out of 1000 alignments by chance.
An E value is related to a probability value p.
The key equation describing an E value is:
E = Kmn e-lS
Pevsner, Bioinformatics and Functional Genomics, 2009

36. E = Kmn e-lS
This equation is derived from a description of the extreme value distribution
S = the score
E = the expect value = the number of high-scoring segment pairs (HSPs) expected to occur with a score of at least S
m, n = the length of two sequences
l, K = Karlin Altschul statistics

37. Some properties of the equation E = Kmn e-lS
The value of E decreases exponentially with increasing S
(higher S values correspond to better alignments). Very
high scores correspond to very low E values.
The E value for aligning a pair of random sequences must
be negative! Otherwise, long random alignments would
acquire great scores
Parameter K describes the search space (database).
For E=1, one match with a similar score is expected to
occur by chance. For a very much larger or smaller
database, you would expect E to vary accordingly

38. From raw scores to bit scores
There are two kinds of scores:
raw scores (calculated from a substitution matrix) and
bit scores (normalized scores)
Bit scores are comparable between different searches
because they are normalized to account for the use
of different scoring matrices and different database sizes
S’ = bit score = (lS - lnK) / ln2
The E value corresponding to a given bit score is:
E = mn 2 -S’
Bit scores allow you to compare results between different
database searches, even using different scoring matrices.

39. How to interpret BLAST: E values and p values
The expect value E is the number of alignments
with scores greater than or equal to score S
that are expected to occur by chance in a
database search. A p value is a different way of
representing the significance of an alignment.
p = 1 - e-E

40. How to interpret BLAST: E values and p values
Very small E values are very similar to p values.
E values of about 1 to 10 are far easier to interpret
than corresponding p values.
E p
(about 0.1)
(about 0.05)
(about 0.001)