aidan budd, embl heidelberg pairwise alignments and sequence similarity-based searching
TRANSCRIPT
Aidan Budd, EMBL Heidelberg
Pairwise Alignments and Sequence Similarity-Based
Searching
Aidan Budd, EMBL Heidelberg
Residues:Monomers within a polymer (polypeptide or polynucleotide) chain
residues
residues
"Anatomy" of a Sequence Alignment
WKKLGSNVG
WGKVKNVDsequences
Sequences:List of residues in a polymer chain... ...listed in the same order they occur within the polymer
Aidan Budd, EMBL Heidelberg
1:1 residue correspondences/relationships
sequences
residues
residues
"Anatomy" of a Sequence Alignment
WKKLGSNVG
WGKVKNVD
1:1 residue correspondences/relationshipsCorrespondences between
•a single residue in one sequence and •a single residue in another sequence
Aidan Budd, EMBL Heidelberg
Could perhaps say there is a "1:2" relationship between this residue and these residues
Residue has no equivalent in the top sequencei.e. no residue in the top sequence has a 1:1 relationship with this residue
1:1 residue correspondences/relationships
"Anatomy" of a Sequence Alignment
WKKLGSNVG
WGKVKNVD
However, alignments focus on 1:1 relationships
Aidan Budd, EMBL Heidelberg
Often represented using a grid/matrix:
W K K L G S N V GW G K V K N V D
Sequence Alignment Within a Grid
WKKLGSNVG
WGKVKNVD
One sequence per row
--
Gap characters (usually "-") indicate that the sequence contains no residues 'equivalent' to other residues in that column
-
Residues in the same column are 'equivalent'
Aidan Budd, EMBL Heidelberg
Multiple Sequence Alignment
W K K L G S N V GW G K V - - N V D- A K V - - - V D
Aidan Budd, EMBL Heidelberg
Pairwise Sequence Alignment
Raise your hands:•Who has ever seen a "pairwise alignment" before?•Who has ever used/encountered one in their research?
Pairwise sequence alignments are a crucial component of many bioinformatic analyses and tools
In particular they lie at the heart of the tools most commonly used to predict function of protein/DNA/RNA molecules i.e. to generate hypotheses for the function of key biological entities that can be tested by wet-lab experiments
Aidan Budd, EMBL Heidelberg
Building Sequence Alignments
Aidan Budd, EMBL Heidelberg
How They Used to Align Sequences...
Courtesy of Geoff Barton, Dundee
Aidan Budd, EMBL Heidelberg
Pairwise Alignments
How we can build them today "by hand" using JalView
Why it's useful to look at this, despite the many automatic methods
Sometimes we're right and the automatic methods are wrong and we can spot this
Useful to get you thinking about how we choose a good from a bad alingment, and to identify sequences that are easier of more difficult to align (as these are the same kinds of sequences that automatic tools find easy/difficult to align, so we know better what to expect from such tools
Aidan Budd, EMBL Heidelberg
Pairwise Alignments
How we can build them today "by hand" using JalViewhttp://tinyurl.com/protBioinf2011
During exercises, write down:
1.Features that describe a relatively "good" alignment, thinking in terms of•sizes of gaps•numbers of gaps•properties of residues in the same column (the same as each other? different?)1.Instructions on how to change a "bad" alignment into a better one2.Characteristics of sequences that are more difficult/take more time to align than othersDescribe what you've written to your neighbour - have you come up with the same answers?
Aidan Budd, EMBL Heidelberg
Pairwise Alignments
1.Features that describe a relatively "good" alignment:• small, few gaps• residues in the same column either the same amino acid, or
ones with similar physical/chemical property1.We build a "good" alignment by inserting as few, as short as
possible gaps into the alignment, so that as many columns as possible contain the same/similar residues
2.Sequences that are more difficult/take more time to align than others when:
• They are more divergent (e.g. when the best alignment contains fewer "identical" residues)
• They are longer• They have very different lengths• They are DNA rather than the corresponding coding
sequences• They contain "repeated" regionsPart of the reason why these situations cause problems is that, rather than there clearly being one best alignment, we find several that are "equally" good (or bad)
Aidan Budd, EMBL Heidelberg
1:1 Relationships Between Residues in the Same Column
Thinking in terms of (i) protein structure and (ii) evolution, what defines the relationship that you expect residues in the same column of a good/correct alignment to share with each other?
Think about this on your own for a minute.Perhaps there's just a single word you might want to use to describe this sought-after relationship?
Try and explain your answers to your neighbours
Try and write down a definition of this relationship
Aidan Budd, EMBL Heidelberg
Alternative Interpretations of MSAs (Evolutionary and
Structural)
Aidan Budd, EMBL Heidelberg
•Structurally equivalent/similar
•Evolutionary equivalent/related/homologous
Residues in the same column either:
Different applications assume different types of equivalence
Different types of similarity not necessarily equivalent
“Equivalence”/similarity of residues
Aidan Budd, EMBL Heidelberg
Structural Similarity
Bacterial toxins 1ji6 and 1i5p
Unaligned Structurally Aligned
Aidan Budd, EMBL Heidelberg
Structural Similarity
Chain1: 68 ELIGLQANIREFNQQVDNF 1111111111111111111Chain2: 70 ELQGLQNNFEDYVNALNSW
Residues with a similar structural context may lie almost on top of each other within a structural alignment. Clearly, the dark green and red side chains have more similar structural contexts than they do with the adjacent light-coloured side chains
Aidan Budd, EMBL Heidelberg
Chain 1: 16 KVGSLIGKR---ILSELWGIIFPSGST 111111111 11111111111 111Chain 2: 16 VVGVPFAGALTSFYQSFLNTIWP-SDA
Structural Similarity
Aidan Budd, EMBL Heidelberg
Structural equivalence
Some regions of the structures do not have structurally equivalent residues in the other structure
1i5p: DNFLNPTQN----PVPLSITSSVN 111111 111111111111ji6: NSWKKTPLSLRSKRSQDRIRELFS
Alignment gaps are a sure sign of such residues
Placing such residues in the same column as residues from other sequences is a misalignment - to be avoided!
Aidan Budd, EMBL Heidelberg
“Evolutionary Equivalence”
AGWYTI
AGWWTI
AGWYTI
AGWWTI
AAWYTI
AGWWTI
AAQQQWYTI
Mutation / Substituti
onY-W
Substitution G-A
QQQ Insertio
n
AGWYTI
AGWYTI
Two copies of
gene generate
d
AGWYTI AGWYTI
AGWYTI
AGWWTI
AGWYTI
AGWWTI
AAWYTI
AG---WWTI
AAQQQWYTI
Residues in the same alignment column should trace their history back to the same residue in the ancestral sequence with any changes due only to point substitutions
Aidan Budd, EMBL Heidelberg
Quiz - Evolutionary Interpretation of Alignments
Which alignment of the final sequences (X, Y or Z) only places residues in the same column if they are related by substitution events?
KGE--------PGIGL------PGKGIPG-----------DPAFGDPGRGIPGEVLGAQ-----------PG
ZKGEPG------IGL------PGKGIPG---------DPAFGDPGRGIPGEVLGAQ---------PG
YKGEPG---IGLPGKGIPGDPAFGDPGRGIPGEVLGAQPG
X
Aidan Budd, EMBL Heidelberg
Quiz - Evolutionary Interpretation of Alignments
KGE--------PGIGL------PGKGIPG-----------DPAFGDPGRGIPGEVLGAQ-----------PG
"True" alignment given history described aboveRGIPGEVLGAQPGKGIPGDPAFGDPG---KGEPGIGLPG
PRANK
Aidan Budd, EMBL Heidelberg
RGIPGEVLGAQPGKGIPGDPAFGDPG---KGEPGIGLPG
PRANKKGEPG---IGLPGKGIPGDPAFGDPGRGIPGEVLGAQPG
MAFFTK---GEPGIGLPGKGIPGDPAFGDPGRGIPGEVLGAQPG
CLUSTALX
Different automatic MSA software gives different results
All are different from the "true" alignment (assuming the scenario of transformation on the previous slide is true)...
... because that scenario is very unlikely under the models of evolutionary transformation incorporated within these tools
KGE--------PGIGL------PGKGIPG-----------DPAFGDPGRGIPGEVLGAQ-----------PG
ZKGEPG------IGL------PGKGIPG---------DPAFGDPGRGIPGEVLGAQ---------PG
YKGEPG---IGLPGKGIPGDPAFGDPGRGIPGEVLGAQPG
X
Quiz - Evolutionary Interpretation of Alignments
Aidan Budd, EMBL Heidelberg
Interpreting Alignments
• Special 1:1 relationship between residues in the same column
• Structural: very similar structural context
• Evolutionary: any difference between residues in the same column due to point substitution (not to any other kind of mutation e.g. deletion followed by insertion)
• Structural and Evolutionary equivalence need not necessarily be the same
• Not all residues have 1:1 equivalents in other sequences
Aidan Budd, EMBL Heidelberg
nef1/fyn1PDB:1efn
Non-Equivalence of Evolutionary and Structural Alignments
Demonstration 1:Structural equivalence without evolutionary equivalence
Structural alignment of SH3-interaction motifs from nef and ncf1
ncf1/ncf4PDB:1w70
aligned ncf1/nef1 SH3 interaction motifs
Aidan Budd, EMBL Heidelberg
Non-Equivalence of Evolutionary and Structural Alignments
Demonstration 2b:Sequences differ by ONE amino acid residue and have different folds
Proc Natl Acad Sci U S A. 2009 Dec 15;106(50):21149-54.A minimal sequence code for switching protein structure and function.Alexander PA, He Y, Chen Y, Orban J, Bryan PN.PMID: 19923431
GA95 GB95
Aidan Budd, EMBL Heidelberg
Quiz - Numbers of Insertions
(a) 2 (b) 1 (c) 0 (d) 3
The minimum number of insertion events required to account for the section of haemoglobin alignment shown above is?
Aidan Budd, EMBL Heidelberg
Quiz - Numbers of Insertions
If all sequences are the same length, we can explain their diversity without inferring ANY insertions or deletions
If and alignment contains sequences that are all either length x or y, then we can explain their diversity by inferring just one insertion or deletion
The minimum number of insertion events required to account for the section of haemoglobin alignment shown above is?
Aidan Budd, EMBL Heidelberg
Quiz - Numbers of Insertions
The minimum number of insertion events required to account for the section of haemoglobin alignment shown above is?We can ALWAYS explain observed sequence length
diversity with:•0 insertions (all length variation due to deletion)•0 deletions (all length variation due to insertion)•a combination of insertions and deletions
Perhaps we should instead focus on inferring the most likely scenario?(Although if this is not particularly relevant for our analysis, perhaps we should focus instead on something completely different!)
Aidan Budd, EMBL Heidelberg
Identifying Good Alignments
Aidan Budd, EMBL Heidelberg
Distinguishing Better from Worse Alignments
An "objective" way of choosing which of two alignments (between the same pair of sequences) is "better" (more likely to be correct) would help us identify "good" alignments
Scoring schemes/rules have been developed for this purpose
These aim to assign higher scores to alignments that are more similar to true alignments
NOTE - we are optimising against some "average"
protein - thse may be different for different proteins, howver it's still demonstrably useful
higher score
lower score
Aidan Budd, EMBL Heidelberg
Scoring Schemes for Aligned Residues
SeqA N A N
SeqB N - G
3.8 0.4
SeqA N A N
SeqB N G -
3.8 0.5 score = 3.8 + 0.5 = 4.3
score = 3.8 + 0.4 = 4.2
Commonly, scoring schemes (for columns containing no gaps) assign a score for each column...
...the score for the full alignment is then the sum of the individual column scores
Below we calculate, in this way, a score for two alignments of the same sequencesFor each column, the score assigned depends on which residues are found in the column
Aligned residue scores are taken from an amino acid substitution matrix - see next slide...
Aidan Budd, EMBL Heidelberg
Amino Acid Substitution Matrices
Gonnet PAM250
SeqA N A N
SeqB N - G
3.8 0.4
SeqA N A N
SeqB N G -
3.8 0.5 score = 3.8 + 0.5 = 4.3
score = 3.8 + 0.4 = 4.2
Matrix provides a score for alignment of each different pair of amino acids
Aidan Budd, EMBL Heidelberg
Amino Acid Substitution Matrices
Gonnet PAM250
Values in some cells in this matrix are positive
Although more cells contain negative values
Aidan Budd, EMBL Heidelberg
Amino Acid Substitution Matrices
Gonnet PAM250
Values are obtained analysing alignments between sequences that we are very confident:•have similar 3D structures•evolutionarily related by processes of:
•point substitution•insertion•deletion
Aidan Budd, EMBL Heidelberg
Amino Acid Substitution Matrices
One set of matrices (the BLOSUM series) are built by analysing many ungapped regions ("blocks") of alignments of fairly similar sequences
Block IPB000108A
ID P67PHOX; BLOCK
AC IPB000108A; distance from previous block=(7,386)
DE Neutrophil cytosol factor 2 signature
BL PR00499; width=18; seqs=27; 99.5%=1042; strength=1146
NCF2_BOVIN|O77775 ( 218) APLQPQAAEPPPRPKTPE 9
NCF2_HUMAN|P19878 ( 218) APLQPQAAEPPPRPKTPE 9
O70145|O70145_MOUSE ( 218) APLQPQSAEPPPRPKTPE 10
YKA7_CAEEL|P34258 ( 117) IPLKEAFTALPPRPAAPS 40
Q8NFC7 ( 218) APLQPQAAEPPPRPKTPE 9
Q9BV51 ( 218) APLQPQAAEPPPRPKTPE 9
Q95MN2|Q95MN2_RABIT ( 218) APLQPQAAEPPPRPKTPE 9
Q95L70|Q95L70_BISBI ( 218) APLQPQAAEPPPRPKTPE 9
Q9N0E9|Q9N0E9_TURTR ( 218) APLQPQAAEPPPRPKTPE 9
Q6GMC8|Q6GMC8_XENLA ( 219) APLQPQANNPPSRPKTPE 22
Q59F14|Q59F14_HUMAN ( 246) APLQPQVRQSDLLGAQAG 95
Q61QT5|Q61QT5_CAEBR ( 237) IPLKEAFSAPPPRPAAPS 37
Q5R5J0|Q5R5J0_PONPY ( 218) APLQPQAAEPPPRPKTPE 9
O08635|O08635_MOUSE ( 20) QIFKNQDPVLPPRPKPGH 20
Q3TC92|Q3TC92_MOUSE ( 232) APLQPQSAEPPPRPKTPE 10
Q3U5S4|Q3U5S4_MOUSE ( 218) APLQPQSAEPPPRPKTPE 10
Q6DFH8|Q6DFH8_XENLA ( 52) YVIKRQQPDLPPRPKPGH 43
Q499C5|Q499C5_XENTR ( 219) APLQPQASNPPPRPKTPE 13
Q60FB5|Q60FB5_ONCMY ( 218) APLQPQVEEVPTRPKVPE 25
Q32N10|Q32N10_HUMAN ( 387) QVFKNQDPVLPPRPKPGH 16
Q5HYK7|Q5HYK7_HUMAN ( 387) QVFKNQDPVLPPRPKPGH 16
Q5U3B8|Q5U3B8_HUMAN ( 20) QVFKNQDPVLPPRPKPGH 16
Q9UFC8|Q9UFC8_HUMAN ( 8) QVFKNQDPVLPPRPKPGH 16
Q4R8R2|Q4R8R2_MACFA ( 20) QVFKNQDPVLPPRPKPGH 16
O35146|O35146_MOUSE ( 10) KIVTPLDERPRGRPNDSG 100
Q1PCS1|Q1PCS1_MUSMM ( 218) APLQPQSAEPPPRPKTPE 10
Q1A3S2|Q1A3S2_9PERO ( 218) APLQPQVEDGPTRPKEPE 27
//
http://blocks.fhcrc.org/blocks-bin/getblock.pl#IPB000108A
Values are obtained analysing alignments between sequences that we are very confident:•have similar 3D structures•evolutionarily related by processes of:
•point substitution•insertion•deletion
Aidan Budd, EMBL Heidelberg
Amino Acid Substitution Matrices
Block IPB000108A
ID P67PHOX; BLOCK
AC IPB000108A; distance from previous block=(7,386)
DE Neutrophil cytosol factor 2 signature
BL PR00499; width=18; seqs=27; 99.5%=1042; strength=1146
NCF2_BOVIN|O77775 ( 218) APLQPQAAEPPPRPKTPE 9
NCF2_HUMAN|P19878 ( 218) APLQPQAAEPPPRPKTPE 9
O70145|O70145_MOUSE ( 218) APLQPQSAEPPPRPKTPE 10
YKA7_CAEEL|P34258 ( 117) IPLKEAFTALPPRPAAPS 40
Q8NFC7 ( 218) APLQPQAAEPPPRPKTPE 9
Q9BV51 ( 218) APLQPQAAEPPPRPKTPE 9
Q95MN2|Q95MN2_RABIT ( 218) APLQPQAAEPPPRPKTPE 9
Q95L70|Q95L70_BISBI ( 218) APLQPQAAEPPPRPKTPE 9
Q9N0E9|Q9N0E9_TURTR ( 218) APLQPQAAEPPPRPKTPE 9
Q6GMC8|Q6GMC8_XENLA ( 219) APLQPQANNPPSRPKTPE 22
Q59F14|Q59F14_HUMAN ( 246) APLQPQVRQSDLLGAQAG 95
Q61QT5|Q61QT5_CAEBR ( 237) IPLKEAFSAPPPRPAAPS 37
Q5R5J0|Q5R5J0_PONPY ( 218) APLQPQAAEPPPRPKTPE 9
O08635|O08635_MOUSE ( 20) QIFKNQDPVLPPRPKPGH 20
Q3TC92|Q3TC92_MOUSE ( 232) APLQPQSAEPPPRPKTPE 10
Q3U5S4|Q3U5S4_MOUSE ( 218) APLQPQSAEPPPRPKTPE 10
Q6DFH8|Q6DFH8_XENLA ( 52) YVIKRQQPDLPPRPKPGH 43
Q499C5|Q499C5_XENTR ( 219) APLQPQASNPPPRPKTPE 13
Q60FB5|Q60FB5_ONCMY ( 218) APLQPQVEEVPTRPKVPE 25
Q32N10|Q32N10_HUMAN ( 387) QVFKNQDPVLPPRPKPGH 16
Q5HYK7|Q5HYK7_HUMAN ( 387) QVFKNQDPVLPPRPKPGH 16
Q5U3B8|Q5U3B8_HUMAN ( 20) QVFKNQDPVLPPRPKPGH 16
Q9UFC8|Q9UFC8_HUMAN ( 8) QVFKNQDPVLPPRPKPGH 16
Q4R8R2|Q4R8R2_MACFA ( 20) QVFKNQDPVLPPRPKPGH 16
O35146|O35146_MOUSE ( 10) KIVTPLDERPRGRPNDSG 100
Q1PCS1|Q1PCS1_MUSMM ( 218) APLQPQSAEPPPRPKTPE 10
Q1A3S2|Q1A3S2_9PERO ( 218) APLQPQVEDGPTRPKEPE 27
//
http://blocks.fhcrc.org/blocks-bin/getblock.pl#IPB000108A
Key parameters estimated in analysis:
•frequency (qij) with which residues i and j are found in the same column (averaged over all analysed blocks)
•frequency with which each pair of residues are found in same column if all sequences randomised
•where pi is the frequency with which residue i is present in the complete dataset, this is: pi * pj
If two residues i and j are more often found in the same column in blocks compared to randomised sequences thenqij / pi*pj > 1 and log(qij / pi*pj) > 0
Aidan Budd, EMBL Heidelberg
Amino Acid Substitution Matrices
Block IPB000108A
ID P67PHOX; BLOCK
AC IPB000108A; distance from previous block=(7,386)
DE Neutrophil cytosol factor 2 signature
BL PR00499; width=18; seqs=27; 99.5%=1042; strength=1146
NCF2_BOVIN|O77775 ( 218) APLQPQAAEPPPRPKTPE 9
NCF2_HUMAN|P19878 ( 218) APLQPQAAEPPPRPKTPE 9
O70145|O70145_MOUSE ( 218) APLQPQSAEPPPRPKTPE 10
YKA7_CAEEL|P34258 ( 117) IPLKEAFTALPPRPAAPS 40
Q8NFC7 ( 218) APLQPQAAEPPPRPKTPE 9
Q9BV51 ( 218) APLQPQAAEPPPRPKTPE 9
Q95MN2|Q95MN2_RABIT ( 218) APLQPQAAEPPPRPKTPE 9
Q95L70|Q95L70_BISBI ( 218) APLQPQAAEPPPRPKTPE 9
Q9N0E9|Q9N0E9_TURTR ( 218) APLQPQAAEPPPRPKTPE 9
Q6GMC8|Q6GMC8_XENLA ( 219) APLQPQANNPPSRPKTPE 22
Q59F14|Q59F14_HUMAN ( 246) APLQPQVRQSDLLGAQAG 95
Q61QT5|Q61QT5_CAEBR ( 237) IPLKEAFSAPPPRPAAPS 37
Q5R5J0|Q5R5J0_PONPY ( 218) APLQPQAAEPPPRPKTPE 9
O08635|O08635_MOUSE ( 20) QIFKNQDPVLPPRPKPGH 20
Q3TC92|Q3TC92_MOUSE ( 232) APLQPQSAEPPPRPKTPE 10
Q3U5S4|Q3U5S4_MOUSE ( 218) APLQPQSAEPPPRPKTPE 10
Q6DFH8|Q6DFH8_XENLA ( 52) YVIKRQQPDLPPRPKPGH 43
Q499C5|Q499C5_XENTR ( 219) APLQPQASNPPPRPKTPE 13
Q60FB5|Q60FB5_ONCMY ( 218) APLQPQVEEVPTRPKVPE 25
Q32N10|Q32N10_HUMAN ( 387) QVFKNQDPVLPPRPKPGH 16
Q5HYK7|Q5HYK7_HUMAN ( 387) QVFKNQDPVLPPRPKPGH 16
Q5U3B8|Q5U3B8_HUMAN ( 20) QVFKNQDPVLPPRPKPGH 16
Q9UFC8|Q9UFC8_HUMAN ( 8) QVFKNQDPVLPPRPKPGH 16
Q4R8R2|Q4R8R2_MACFA ( 20) QVFKNQDPVLPPRPKPGH 16
O35146|O35146_MOUSE ( 10) KIVTPLDERPRGRPNDSG 100
Q1PCS1|Q1PCS1_MUSMM ( 218) APLQPQSAEPPPRPKTPE 10
Q1A3S2|Q1A3S2_9PERO ( 218) APLQPQVEDGPTRPKEPE 27
//
http://blocks.fhcrc.org/blocks-bin/getblock.pl#IPB000108A
Key parameters estimated in analysis:
•frequency (qij) with which residues i and j are found in the same column (averaged over all analysed blocks)
•frequency with which each pair of residues are found in same column if all sequences randomised
•where pi is the frequency with which residue i is present in the complete dataset, this is: pi * pj
qij / pi*pj < 1 and log(qij / pi*pj) < 0
If two residues i and j are less often found in the same column in blocks compared to randomised sequences then
Aidan Budd, EMBL Heidelberg
aa Substitution Matrix Values
What range of scores would you expect alignments that are more similar to alignments found in blocks, compared to alignments between random sequences, to have:
A. scores < 0
B. 0 < scores < 1
C. 0 > scores
Values in matrix cells are proportional to log(qij / pi*pj)
SeqA N A N
SeqB N G -
3.8 0.5score = 3.8 + 0.5 = 4.3
Aidan Budd, EMBL Heidelberg
Sequence Similarity Searching
Query sequence
Database of
sequences
...
Build optimal alignment between query sequence and each database sequence
Calculate score for each optimal alignment
For each alignment, calculate how many alignments between randomised (structurally dissimilar/evolutionarily unrelated) sequences you would expect with a score the same or greater than the score for this alignment
This number is the E-value
Aidan Budd, EMBL Heidelberg
Interpreting E-values
E-value of 0.001 means that you would expect this score to be found for an alignment between randomised sequences once in every 1000 similar searches
For each of values below, decide whether it it impossible that it be reported as the result of such a search:A.1B.0C.0.00001D.1000000000E.-1
Aidan Budd, EMBL Heidelberg
Interpreting E-values
You have carried out a BLAST search with a query sequence seqA against a database dbB, such that there are no sequences in dbB that are "related" to seqA
What is the most likely E-value for the highest-scoring alignment?
Aidan Budd, EMBL Heidelberg
Interpreting E-values
You have carried out a BLAST search with a query sequence seqA against a database dbB, such that there are no sequences in dbB that are "related" to seqA
You carry out several variants of this search, as described below. For each search, decide:•the most likely value of the E-value for the highest-scoring alignment•the most likely fraction of the score of highest-scoring alignment compared to that of the initial search (5 times larger? 5 times smaller etc.)
•delete the second half of seqA, search against dbB•query seqA against a database (dbC) which contains two copies of every sequence in dbB•query seqA against a database (dbD) from which every second sequence in dbB has been removed from