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Why Align Sequences? DNA sequences (4 letters in alphabet) – GTAAACTGGTACT… Amino acid (protein) sequences (20 letters) – SSHLDKLMNEFF… Align them so we can search databases – To help predict structure/function of new genes In particular, look for homologues (evolutionary relatives)

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Example matches 1. gattcagacctagct (no indels) gtcagatcct 2. gattcaga-cctagct (with indels) g-t-cagatcct 3. gattcagacctagc-t g-t-----cagatcct Need to come up with algorithms producing: – Ways of scoring alignments – Ways to search for high scoring alignments Concentrate first on alignments without indels

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Hamming Distances Suppose we have – Query sequence Q and database sequence D Hamming distance: – Number of places where Q and D are different (distance) Example (stars mark differences) – SSHLDKLMNEFF – * ** * – HSHLKLLMKEFFHDMN – Scores 4 for Hamming distance (sometimes worry about ends) Simple alignment algorithm: slide Q along D – Remember where the Hamming distance was minimised

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Scoring Schemes (Amino Acids) Hamming distance doesn’t take into account – Likelihood of one amino acid changing to another – Some amino acid substitutions are disastrous So they don’t survive evolution – Some substitutions barely change anything Because the two amino acids are chemically quite similar Scoring schemes address this problem – Give scores to the chances of each substitution 2 possibilities: – Use empirical evidence Of actual substitutions in known homologues (families) – Use theory from chemistry (hydrophobicity, etc.)

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The Scoring Scheme Give two sequences we need a number to associate with each possible alignment (i.e. the alignment score = goodness of alignment). The scoring scheme is a set of rules which assigns the alignment score to any given alignment of two sequences. The scoring scheme is residue based: it consists of residue substitution scores (i.e. score for each possible residue alignment), plus penalties for gaps. The alignment score is the sum of substitution scores and gap penalties.

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BLOSUM62 Scheme Blocks Amino Acid Substitution Matrices Empirical method – Based on roughly 2000 amino acid patterns (blocks) – Found in more than 500 families of related proteins Calculate the Log-odds scores for each pair (R 1, R 2 ) – Let O = observed frequency R 1 R 2 – Let E = expected frequency R 1 R 2 – I.e., Score = round(2 * log 2 (O/E)) To calculate the score for an alignment of two sequences – Add up the pairwise scores for residues We’ve calculated log odds

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BLOSUM62 Substitution Matrix Zero: by chance – + more than chance – - less than chance Arranged by – Sidegroups – So, high scoring in the end boxes Example – M,I,L,V – Interchangeable

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Example Calculation Query = S S H L D K L M R Dbase = H S H L K L L M G Score = Total score = = 21 Write Blosum(Query,Dbase) = 21 – Not standard to do this

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BLAST Algorithm Basic Local Alignment Search Tool Fast alignment technique(s) – Similar to FASTA algorithms (not used much now) – There are more accurate ones, but they’re slower – BLAST makes a big use of lookup tables Idea: statistically significant alignments (hits) – Will have regions of at least 3 letters same Or at least high scoring with respect to BLOSUM matrix Based on small local alignments CCNDHRKMTCSPNDNNRK TTNDHRMTACSPDNNNKH CCNDHRKMTCSPNDNNRK YTNHHMMTTYSLDNNNKK more likely than

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BLAST Overview Given a query sequence Q Seven main stages 1. Remove (filter) low complexity regions from Q 2. Harvest k-tuples (triples) from Q 3. Expand each triple into ~50 high scoring words 4. Seed a set of possible alignments 5. Generate high scoring pairs (HSPs) from the seeds 6. Test significance of matches from HSPs 7. Report the alignments found from the HSPs

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BLAST Algorithm Part 1 Removing Low-complexity Segments Imagine matching – HHHHHHHHKMAY and HHHHHHHHURHD – The KMAY and URHD are the interesting parts – But this pair score highly using BLOSUM It’s a good idea to remove the HHHHHHHs – From the query sequence (low complexity) SEG program does this kind of thing – Comes with most BLAST implementations – Often doesn’t do much, and it can be turned off

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Removing Low-complexity Segments Given a segment of length L – With each amino acid occurring n 1 n 2 … n 20 times Use the following measure for “compositional complexity”: To use this measure – Slide a “window” of ~12 residues along Query Sequence Q – Use a threshold to determine low complexity windows – Use a minimise routine to replace the segment With an optimal minimised segment (or just an X) Will do an example calculation in tutorial

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BLAST Algorithm Part 2 Harvesting k-tuples Collect all the k-tuples of elements in Q – k set to 3 for residues and 11 for DNA (can vary) – Triples are called ‘words’. Call this set W S T S L S T S D K L M R STS TSL SLS LST

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BLAST Algorithm Part 3 Finding High Scoring Triples Given a word w from W – Find all other words w’ of same length (3), which: Appear in some database sequence Blosum(w,w’) > a threshold T Choose T to limit number to around 50 – Call these the high scoring triples (words) for w Example: letting w=PQG, set T to be 13 – Suppose that PQG, PEG, PSG, PQA are found in database – Blosum(PQG,PQG) = 18, Blosum(PQG,PEG) = 15 – Blosum(PQG,PSG) = 13, Blosum(PQG,PQA) = 12 – Hence, PQG and PEG only are kept

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Finding High Scoring Triples For each w in W, find all the high scoring words – Organise these sets of words Remembering all the places where w was found in Q Each high scoring triple is going to be a seed – In order to generate possible alignment(s) One seed can generate more than one alignment End of the first half of the algorithm – Going to find alignments now

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BLAST Algorithm Part 4 Seeding Possible Alignments Look at first triple V in query sequence Q – Actually from Q (not from W - which has omissions) – Retrieve the set of ~50 high scoring words Call this set H V – Retrieve the list of places in Q where V occurs Call this set P V For every pair (word, pos) – Where word is from H V and pos is from P V Find all the database sequences D – Which have an exact match with word at position pos’ Store an alignment between Q and D – With V matched at pos in Q and pos’ in D Repeat this for the second triple in Q, and so on

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Seeding Possible Alignments Example Suppose Q = QQGPHUIQEGQQG Suppose V = QQG, H V = {QQG, QEG} – Then P V = {1, 11} Suppose we are looking in the database at: – D = PKLMMQQGKQEG Then the alignments seeded are: QQGPHUIQEGQQG word=QQG QQGPHUIQEGQQG word=QQG PKLMMQQGKQEG pos=1 PKLMMQQGKQEG pos=11 QQGPHUIQEGQQG word=QEG QQGPHUIQEGQQG word=QEG PKLMMQQGKQEG pos=1 PKLMMQQGKQEG pos=11

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BLAST Algorithm Part 5 Generating High Scoring Pairs (HSPs) For each alignment A – Where sequences Q and D are matched – Original region matching was M Extend M to the left – Until the Blosum score begins to decrease Extend M to the right – Until the Blosum score begins to decrease Larger stretch of sequence now matches – May have higher score than the original triple – Call these high scoring pairs Throw away any alignments for which the score S of the extended region M is lower than some cutoff score

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Extending Alignment Regions Example QQGPHUIQEGQQGKEEDPP Blosum(QQG,QQG) = 16 PKLMMQQGKQEGM QQGPHUIQEGQQGKEEDPP Blosum(QQGK,QQGK) = 21 PKLMMQQGKQEGM QQGPHUIQEGQQGKEEDPP Blosum(QQGKE,QQGKQ) = 23 PKLMMQQGKQEGM QQGPHUIQEGQQGKEEDPP Blosum(QQGKEE,QQGKQE) = 28 PKLMMQQGKQEGM QQGPHUIQEGQQGKEEDPP Blosum(QQGKEED,QQGKQEG) = 27 PKLMMQQGKQEGM So, the extension to the right stops here HSP (before left extension) is QQGKEE, scoring 28

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BLAST Algorithm Part 6 Checking Statistical Significance Reason we extended alignment regions – Give a more accurate picture of the probability of that BLOSUM score occurring by chance Question: is a HSP significant? Suppose we have a HSP such that – It scores S for a region of length L in sequences Q & D Then the probability of two random sequences Q’ and D’ scoring S in a region of length L is calculated – Where Q’ is same length as Q and D’ is same length as D This probability needs to be low for significance

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BLAST Algorithm Part 7 Reporting the Alignments For each statistically significant HSP – The alignment is reported If a sequence D has two HSPs with Query Q – Two different alignments are reported Later versions of BLAST – Try and unify the two alignments

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NCBI BLAST Server (protein-protein)

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Real Example MRPQAPGSLVDPNEDELRMAPWYWGRISREEAKSILHGKPDGSFLVRDAL SMKGEYTLTLMKDGCEKLIKICHMDRKYGFIETDLFNSVVEMINYYKENS LSMYNKTLDITLSNPIVRAREDEESQPHGDLCLLSNEFIRTCQLLQNLEQ NLENKRNSFNAIREELQEKKLHQSVFGNTEKIFRNQIKLNESFMKAPADA PSTEAGGAGDGANAAASAAANANARRSLQEHKQTLLNLLDALQAKGQVLN HYMENKKKEELLLERQINALKPELQILQLRKDKYIERLKGFNLKDDDLKM ILQMGFDKWQQLYETVSNQPHSNEALWLLKDAKRRNAEEMLKGAPSGTFL IRARDAGHYALSIACKNIVQHCLIYETSTGFGFAAPYNIYATLKSLVEHY ANNSLEEHNDTLTTTLRWPVLYWKNNPLQVQMIQLQEEMDLEYEQAATLR PPPMMGSSAPIPTSRSREHDVVDGTGSLEAEAAPASISPSNFSTSQ A gene taken from a fruit fly (Drosophila Melanogaster) – We’ll alter this a little – And see if the NCBI BLAST server can find it for us

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Database Searching Overview Database of sequences Comparison algorithm Query sequence Q List of similar protein sequences Infer homologues and similar structures

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True/False Positives and Negatives True Positive – A hit returned from the database search Which does match in reality with the query sequence False Positive – A hit returned from database search Which doesn’t match in reality with the query sequence True Negative – A sequence not returned from database search Which doesn’t match in reality with the query sequence False Negative – A sequence not returned from database search Which does match in reality with the query sequence

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Accuracy of database searching - an ideal search result Score Output ProgramCorrect answer High (good)A YESYES B YESYES C YESYES D YESYES E NONO F NONO GNONO Low (poor) HNONO A,B,C,DAll correctly assigned and true positives E,F,G,HAll correctly assigned and true negatives Cut off score

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Accuracy of database searching - a typical search result Score Output ProgramCorrect answer High (good)A YESYES B YESYES C YESYES D YESNO E NONO F NOYES GNONO Low (poor) HNONO A,B,CCorrectly assigned and true positives E,G,HCorrectly assigned and true negatives DIncorrectly assigned and false positive FIncorrectly assigned and false negative Cut off score

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Accuracy of database searching - a typical search result Score Output High (good)A B C D E F G Low (poor) H How much confidence do we have that this match at a particular score (S) is not due to chance ? S

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Sensitivity and Selectivity Given that you know: – The false positives and false negatives Ntp = number of true positives Nfp = number of false positives Ntn = number of true negatives Nfn = number of false negatives Sensitivity = Ntp / (Ntp + Nfn) – Proportion of the true answers the search found Selectivity = Ntp / (Ntp + Nfp) – Proportion of the answers the search found which were correct

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Sensitivity and Selectivity In David W. Mount’s book: “Sensitivity refers to the ability of the method to find most of the members of the protein family represented by the query sequence.” “Selectivity refers to the ability of the method not to find known members of other families as false positives.”

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Reliability of a Match at Score S P(x S) – is the probability of a score x greater than or equal to the observed score S occurring by chance E(x S) – is the expected number of chance occurrences of scores greater than or equal to S E-value – is the expected number of matches that are errors if you searched and took all matches scoring up to and including S – Estimated number of false positives found using S as the cut off

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From the NCBI BLAST FAQ Pages The Expect value (E) is a parameter that describes the number of hits one can "expect" to see just by chance when searching a database of a particular size. It decreases exponentially with the Score (S) that is assigned to a match between two sequences. Essentially, the E value describes the random background noise that exists for matches between sequences. For example, an E value of 1 assigned to a hit can be interpreted as meaning that in a database of the current size one might expect to see 1 match with a similar score simply by chance. This means that the lower the E-value, or the closer it is to "0" the more "significant" the match is. However, keep in mind that searches with short sequences, can be virtually identical and have relatively high E-Value. This is because the calculation of the E-value also takes into account the length of the Query sequence. This is because shorter sequences have a high probability of occurring in the database purely by chance.

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Using P and E Values Most search programs return one or both values For matches < 20 residues – We must still be very cautious in suggesting true homology – Also, we CANNOT infer short matches will have similar structures We can be confident if P or E < – However, as they are estimated values, these are often wrong You will need experience of the current version of the program Note that P is a probability, so 0 1 – For low values (<10 -3 ) P and E are virtually the same

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Calculating P and E Values in General Each algorithm/server seems to have its own method – Theory for gapped alignments is still very much under debate – Theory for non-gapped alignments is solved, but flexible Values consider both – the size of the database searched – and the score of the match Should also consider the length of the match – as short matches are easier to find Calculations often involve “random sequences” – Generate randomly with letters in proportion – Mix up substrings of existing protein sequences

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Calculating P and E values in BLAST Remember that each alignment – Has a HSP at its heart Suppose we have an alignment of Q and D – Q is of length m and D is of length n – And they have a HSP scoring S with BLOSUM62 Question we’re interested in: – Given two random sequences, also of length m and n How many HSPs of score S or greater can we expect to find – i.e., is our HSP special, or would we expect one?

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