blast workshop maya schushan june 2009. workshop outline part 1: introduction and motivation how...

BLAST Workshop

Maya SchushanJune 2009

Workshop OUTLINE

Part 1:

• Introduction and motivation

• How does BLAST work?

Part 2:

• BLAST programs

• Sequence databases

• Work Steps

• Extract and analyze results

Why BLAST?Finding homologous

• Homology- similarity between sequences that result from a common ancestor.

• Sequences look alike probably have the same function and structure.

• Use a sequence as a search query in order to find homologous sequences in a data base.

• Save time! – exploit the knowledge you have about your homologues, and conclude about your query.

More then: 25% for proteins

70% for nucleotideswill be considered as homologous

Why BLAST?

Identify sequence motifs

Finding homologous

Why BLAST?

Find out which region are evolutionary conserved important for function and\or structure

Finding homologous

Why BLAST?

Finding homologous

Construct phylogenetic trees understand the evolution of the sequence’s family

Why BLAST?

Finding homologous

Inferring function for a novel sequence learning from previous data available for

homologous sequences

Why BLAST?

Finding homologous

Finding out if your protein sequence has a structure (or a close homologue has one….)

What Is An Alignment?

Before we can understand how BLAST works, we first

have to understand the principles of sequence

alignment….

How does BLAST work?

What Is An Alignment?• Comparing 2 (pairwise) or more (multiple) sequences.

• Searching for a series of identical or similar characters in the sequences.


VLSPADKTNVKAAWAKVGAHAAGHG||| | | |||| | ||||VLSEAEWQLVLHVWAKVEADVAGHG

T C A T G

C A T T G

T C A T G

C A T T G

T C A T G

C A T T Gor

?

A process of lining-up 2 or more sequences to achieve maximum level of identity, in order to find homologies.



S = ACTG S’ = AC_TG S’ = ACTG S’ = ACTGT = AGT T’ = A_GT_ T’ = AGT_ T’ = _AGT

Good: Identical characters- match.Bad: Different characters- mismatch; gap (InDel).

• Each pair of characters gets a value, depending on its identity.

•The similarity score of the alignment is the sum of pair values.



Example: Aligning Two Globins

Human Hemoglobin (HH):VLSPADKTNVKAAWGKVGAHAGYEG

Sperm Whale Myoglobin (SWM):VLSEGEWQLVLHVWAKVEADVAGHG

General Alignment Methodology




(HH) VLSPADKTNVKAAWGKVGAHAGYEG

(SWM) VLSEGEWQLVLHVWAKVEADVAGHG

No Gaps:• Percent identity: 36• Percent similarity: 40



(HH) VLSPADKTNVKAAWGKVGAH-AGYEG

(SWM) VLSEGEWQLVLHVWAKVEADVAGH-G


With Gaps:• Gaps: 2• Percent identity: 45.833 (instead of 36 without gaps)• Percent similarity: 54.167 (instead of 40 without gaps)



Alignment Scoring

1. Assume independent mutation model

2. Score at each position– Positive if the same/similar– Negative if different or gap

3. Score of an alignment is sum of position score



Scoring Matrix• A matrix n n : n=4 for DNA, n=20 for proteins

• Each entry matrix defines the score for observing the two letters in the alignment– Positive if likely to change– Negative otherwise

A G C T

A 1

G -5 1

C -5 -5 1

T -5 -5 -5 1



DNA scoring matrices

• Transitions – purine to purine or pyrmidine to pyrmidine(4 possibilities)

• Transversions – purine to pyrmidine or pyrmidine to purine (8 possibilities)

• By chance alone transversions should occur twice as often as transitions.

• De-facto transitions are more frequent than transversions.



FromTo

A G C T

A 2

G -4 2

C -6 -6 2

T -6 -6 -4 2

MatchTransitionTransversion

DNA scoring matrices



Proteins scoring matrices• Observation: some substitutions

are more frequent than others, e.g., chemically similar amino acids

• As for DNA, protein matrices define the probabilities of change between the different amino acids

• Popular matrices are based on empirical data: PAM & BLOSUM



PAM Matrices

• PAM matrices are based on sequences with 85% identity.

• The changes are “accepted” by natural selection

• 1 PAM unit:the probability of 1 point mutation per 100 residues.

• Multiplying PAM1 by itself gives higher PAMs matrices that are suitable for larger evolutionary distance.



BLOSUM Matrices• Based on BLOCKS database:

• Low BLUSOM numbers for distant sequences, High BLUSOM numbers for similar sequence

• BLOSUMn is based on sequences that shared at least n percent identity, generally: BLOSUM62 for general useBLOSUM80 for close relationsBLOSUM45 for distant relations



PAM100 = BLOSUM90 PAM120 = BLOSUM80 PAM160 = BLOSUM60 PAM200 = BLOSUM52 PAM250 = BLOSUM45

Distant sequences

Closer sequences

Proteins scoring matrices



How do we calculate gap scores

- Same substitution scores are applied on gapped and ungapped local alignments.

- Appropriate gap scores have been selected over the years by trial and error default gap scores

- If you wish to apply a different scoring matrix-No grantee that the gap scores will remain appropriate!!!!

- large penalty for opening and much smaller one for extending it are most effective


• The final score of the alignment is the sum of the positive scores and penalty scores:

+Number of Identities

+Number of Similarities

-Number of Gap insertions

-Number of Gap extensions

Alignment score

Scoring

Scoring Matrix

Gap penalties



BLAST(Basic Local Alignment Search Tool)

• Goal: A fast search for homologues in a huge database

• The underlying hypothesis: when two sequences are similar there are short ungapped regions of high similarity between them

• The heuristic:1. Discard irrelevant sequences2. Perform exact local alignment only with the

remaining sequences

Altschul, S.F.,Gish, W., Miller, W., Myers, E.W., and Lipman,D.J(1990) “basic local alignment search

tool” J. Mol. Biol. 215: 403-410


27

Searching a sequence database•Idea:In order to find homologous sequences to a sequence of interest, one should compute its pairwise alignment against all known sequences in a database, and detect the best scoring significant homologs

•Query sequence - the sequence with which we are searching

•Hit – a sequence found in the database, suspected as homologous



The parameters-

W : Word size – find W-mers in target/query2-3 for aa, 6-11 for nucleotides.

T : Threshold – focus on pairs scoring >Tusually 11-13

X : Drop-off – stop extending when loss >X

S : Score – the final score of segment pair


The algorithm:

1. Align a query sequence with the database.

2. Find “hits”: short word pairs of length W with an ungapped alignment score of at least T.

3. Extend alignments until score drops more than X below hitherto best scoreConsumes most of the processing time (>90%)

s

t

How do we discard irrelevantsequences quickly?

• Divide the database into words of length w (default: w = 3 for protein and w = 7 for DNA)

• Save the words in a look-up table that can be searched quickly

WTDFGYPAILKGGTAC

WTDTDFDFGFGYGYP …


BLAST: discarding sequences• When the user enters a query sequence, it is also

divided into words

• Search the database for consecutive neighboring words

• neighbor words are defined according to a scoring matrix (e.g., BLOSUM62 for proteins) with a certain cutoff level


GFB

GFC (20)

GPC (11)

WAC (5)

Query

Data

base

re

cord

Neighbor word Look for a seed: hits on the same diagonal which

can be connected

At least 2 hits on the same diagonal with

distance which is smaller than a predetermined

cutoff

This is the filtering stage – many unrelated hits are filtered, saving lots of

time!

A


Try to extend the alignment

• Stop extending when the score of the alignment drops X beneath the maximal score obtained so far

• Discard segments with score < S

ASKIOPLLWLAASFLHNEQAPALSDAN

JWQEOPLWPLAASOIHLFACNSIFYASScore=15

Score=17

Score=14


Two-Hit Gapped BLAST

The new gapped BLAST algorithm:1. Start with the two hit method-

(a) find two hits of score higher then T, within a distance A.(b) invoke an ungapped extension on the second hit.

2. If the HSP generated has an expected score:(a) Trigger a gapped extension(b) If the final score has a significant E-value –

report the gapped alignment.


The result – local alignment

• The result of BLAST will be a series of local alignments between the query and the different hits found



The scoring system• BLAST uses BLOSSOM62 as the scoring matrix to

perform the alignment (default).


E-value• To asses the bits score we calculate E-value:

E-value = The expected number of HSP’s with a score of at least S:

• For each score S there is a specific E-value.

Small E-value better score

sKMNeE


E-valueTheoretically, we could trust any result with an E-value ≤ 1

In practice – BLAST uses estimations.

•E-values of 10-4 and lower indicate a significant homology.

•E-values between 10-4 and 10-2 should be checked (similar domains, maybe non-homologous).

•E-values between 10-2 and 1 do not indicate a good homology


PSI-BLAST

Step 1:1. Set a standard protein-protein BLAST search

(BLOSUM62)

2. Build a position specific scoring matrix (PSSM) according to MSA of the alignment results with low E-value.

Step 2:1. Set a BLAST search using the PSSM to evaluate the

alignment. PSSM vs. DB instead of seq vs. DB 2. Update the PSSM according to the new result3. Go back to the beginning of step two or stop.


PSI-BLASTThe difference-

• The score for aligning a letter with a pattern position is given by the matrix itself!

• The matrix is of the length of the original seq. (L* 20)

• No theory for deriving gap costs Gap scores the same as the one in the 1st iteration

1 2 3 4 5 6 7 8 9

A .1 .3 .3 .2 .3 .1 .8 0 .3

D .3 .3 .6 .2 .4 .2 .2 .1 .3

L .6 .4 .1 .6 .3 .7 0 .9 .4


The power of PSI-BLAST:

1. A much sensitive scoring system .each position has its own pattern probabilities .

2. Different weight to conserved positions.

3. Important motifs are bounded

4. Lowers the level of random noise.

5. Finding distant relatives.


Lets sum up…- Blast is a fast way to find homologues

- No analytic theory that estimates the statistical significance of gapped alignments

- Gap scores have been selected by trial and error.applying different scoring matrix No grantee for gap scores

- PSI-BLAST finds weak homologues fast

blast workshop maya schushan june 2009. workshop outline part 1: introduction and motivation how...

Documents

resultswhy blast

sequences familywhy

t t gor

t t gt c

homologous sequences

multiple sequences

percent identity

gaps percent similarity