Local Alignment and BLAST

Three key questions

• Query?

• Purpose?

• Database?

BLAST – the way it used to look

>gi|77630012|ref|ZP_00792598.1| COG0442: Prolyl-tRNA synthetase [Yersinia pseudotuberculosis

IP 31758]

Length=572

Score = 1013 bits (2619), Expect = 0.0, Method: Composition-based stats.

Identities = 498/572 (87%), Positives = 537/572 (93%), Gaps = 0/572 (0%)

Query 1 MRTSQYMLSTLKETPADAEVISHQLMLRAGMIRKLASGLYTWLPTGLRVLRKVENIVREE 60

MRTSQY+LST KETPADAEVISHQLMLRAGMIRKLASGLYTWLPTG+RVL+KVENIVREE

Sbjct 1 MRTSQYLLSTQKETPADAEVISHQLMLRAGMIRKLASGLYTWLPTGVRVLKKVENIVREE 60

Query 61 MNNAGAIEVSMPVVQPADLWVESGRWDQYGPELLRFVDRGERPFVLGPTHEEVITDLIRN 120

MNNAGAIEVSMPVVQPADLW ESGRW+QYGPELLRFVDRGERPFVLGPTHEEVITDLIR

Sbjct 61 MNNAGAIEVSMPVVQPADLWQESGRWEQYGPELLRFVDRGERPFVLGPTHEEVITDLIRG 120

Query 121 EVSSYKQLPLNFFQIQTKFRDEVRPRFGVMRSREFLMKDAYSFHTSQESLQATYDTMYAA 180

E++SYKQLPLNFFQIQTKFRDEVRPRFGVMR+REFLMKDAYSFHT+QESLQ TYD MY A

Sbjct 121 EINSYKQLPLNFFQIQTKFRDEVRPRFGVMRAREFLMKDAYSFHTTQESLQETYDAMYTA 180

………………………….

Query 481 MNMHKSFRVKEVAEDIYQQLRAKGIEVLLDDRKERPGVMFADMELIGVPHTIVIGDRNLD 540

MNMHKSFRVKE+AE++Y LR+ GI+V+LDDRKERPGVMFADMELIGVPH IVIGDRNLD

Sbjct 481 MNMHKSFRVKELAEELYTTLRSHGIDVILDDRKERPGVMFADMELIGVPHNIVIGDRNLD 540

Query 541 SEEIEYKNRRVGEKQMIKTSEIIDFLLANIIR 572

SEE+EYKNRRVGEKQMIKTSEI++FLL+ I R

Sbjct 541 SEEVEYKNRRVGEKQMIKTSEIVEFLLSQIKR 572

Global Alignment vs. Local Alignment

• Global Methods find the best alignment of both sequences in their entirety

• Local Methods find the best alignable subsections of both sequences

Sequence Similarity Searches using BLAST

BLAST: Basic Local Alignment Search Tool

Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. J Mol Biol. 1990 Oct 5;215(3):403-10.

Statistical basis:

Karlin, S., and Altschul, S. F. (1990) ``Method for assessing the statistical significance of molecular sequence features by using general scoring schemes,'' Proceedings of the National Academy of Science, USA 87, 2264-2268.

BLAST = Basic Local Alignment Search Tool

BLASTN DNA sequence vs. DNA sequence db

BLASTP protein sequence vs. protein sequence db

BLASTX DNA sequence translated in 6 reading frames vs. protein sequence db

tBLASTX DNA sequence translated in 6 reading frames vs. DNA sequence db translated in 6 frames

Comparing a Genome to Other Genes and Genomes

PSI-BLAST Iterative Search

BLAST = Basic Local Alignment Search Tool

1. Find a potential match in the database by finding a little seed (or seeds) of a match

2. Extend that seed and score the resulting alignment based on co-occurance of amino acids (nucleotides) in “known” alignments

3. Determine whether the possible alignment looks better than you might expect by chance alone.

4. Decide whether the match tells you anything about biology.

Comparing a Genome to Other Genes and Genomes

1. Find a potential match in the database by finding a little seed (or seeds) of a match

db

query

Your query is small relative to the universe of known sequences

2. Extend the seed and score the resulting alignment based on co-occurance of amino acids (nucleotides) in “known” alignments

0 2 4 6 8 10 12 14 16 18 20

E.coli ABC transport

V F Q N E L L P WR N V Q D N V A F G V F Q N E L L P WR N V Q D N V A F G

NNYYAALLLLPPWWMMTTAAYYEENNVVYYLLAAVVDD

Alignment Methods – Dynamic Programming

• Needleman-Wunsch (global) and Smith-Waterman (local) use dynamic programming

• Guaranteed to find an optimal alignment given a particular scoring function

• Computationally intensive

Dynamic Programming

One possible simple scoring scheme:

•Si,j = 1 if the residue at position i of sequence #1 is

the same as the residue at position j of sequence #2 (match score); otherwise •Si,j = 0 (mismatch score) •w = 0 (gap penalty)

Dynamic Programming

Three steps: 1) Initialize

Mi,j = MAXIMUM [Mi-1, j-1 + Si,j (match/mismatch in the diagonal), Mi,j-1 + w (gap in sequence #1), Mi-1,j + w (gap in sequence #2)]

2) Fill Matrix

Dynamic Programming

3) Traceback

G A A T T C A G T T AG G A - T C - G - - A

Score = 1+0+1+0+1+1+0+1+0+0+1 = 6

How does BLASTP score an alignment?

Substitution Matrix based on co-occurrence in related proteins

BLOSUM = BLOcks Substitution Matrix

Identify gap-free protein alignments in the BLOCKS database.

BLOSUM# corresponds to % identity for inclusion

Count co-occurrence of Amino Acids in alignment

Calculate log-odds ratio:Log (observed frequency/expected frequency)

62 means that contributions from proteins more than 62% identical are weighted to sum to one.

Other matrices are available for comparisons of more or less divergent proteins.

How does BLASTP score an alignment?

Substitution Matrix based on co-occurance in related proteins

How does BLASTP score an alignment?

Walk through the alignment and add up the score

Query: AFGECDA AF C+ASbjct: AFAFCEA

4+6+0+(-3)+9+2+4 = 22

Normalize bit score

Statistics of BLAST when no gaps are allowed

• The number of matches (E) expected to occur with a score as good as S just by random chance, when you search a sequence the size of your query against a database as large as the one you chose (m and n), tends to follow an Extreme Values Distribution (K and lambda).

• Simulation is used to estimate K and lambda for gapped BLAST

How good is your BLAST hit?

• The number of matches (E) expected to occur with a score as good as S just by random chance

>gi|77630012|ref|ZP_00792598.1| COG0442: Prolyl-tRNA synthetase [Yersinia pseudotuberculosis

IP 31758]

Length=572

Score = 1013 bits (2619), Expect = 0.0, Method: Composition-based stats.

Identities = 498/572 (87%), Positives = 537/572 (93%), Gaps = 0/572 (0%)

Query 1 MRTSQYMLSTLKETPADAEVISHQLMLRAGMIRKLASGLYTWLPTGLRVLRKVENIVREE 60

MRTSQY+LST KETPADAEVISHQLMLRAGMIRKLASGLYTWLPTG+RVL+KVENIVREE

Sbjct 1 MRTSQYLLSTQKETPADAEVISHQLMLRAGMIRKLASGLYTWLPTGVRVLKKVENIVREE 60

Search one protein against a given database and most of the E values are zero

Search one protein against a given database and most of the E values are zero

Search the protein encoded by the gene next to it in the genome against the same database and all the E values are much higher.

Search the same protein against two different databases and the E value is different for the same hit.

So,what’s a good match?

E-values…

0.0 is a perfect scoreReally good matches have really small E-values, like e-107

Matches can still be real with moderate E-values like e-05

Sometimes matches with higher E-values are still real.

You should also have some expectation of what level of match is typical for the type of comparison.

For example: if you are querying with the E. coli O157:H7 proteins against a database of E. coli K-12 proteins, and most orthologous proteins have matches on the order of e-107, then a match that scores e-05 is probably not an orthologous pair.

Other characteristics of a good match:

Amount of the sequences in the alignment

A match that includes >90% of both sequences is great

Divergent matches include blocks of higher identity

Conserved motifs can be indicative of conserved function

All equally good matches are to proteins with the same function

***The function of at least one of the best hits was experimentally determined.***