www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms
Combinatorial Pattern Matching
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Outline• Hash Tables• Repeat Finding• Exact Pattern Matching• Keyword Trees• Suffix Trees• Heuristic Similarity Search Algorithms• Approximate String Matching• Filtration• Comparing a Sequence Against a Database• Algorithm behind BLAST• Statistics behind BLAST• PatternHunter and BLAT
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Genomic Repeats
• Example of repeats:• ATGGTCTAGGTCCTAGTGGTC
• Motivation to find them:• Genomic rearrangements are often
associated with repeats• Trace evolutionary secrets• Many tumors are characterized by an
explosion of repeats
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Genomic Repeats
• The problem is often more difficult: • ATGGTCTAGGACCTAGTGTTC
• Motivation to find them:• Genomic rearrangements are often
associated with repeats• Trace evolutionary secrets• Many tumors are characterized by an
explosion of repeats
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l-mer Repeats• Long repeats are difficult to find• Short repeats are easy to find (e.g., hashing)
• Simple approach to finding long repeats:
• Find exact repeats of short l-mers (l is usually 10 to 13)
• Use l-mer repeats to potentially extend into longer, maximal repeats
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l-mer Repeats (cont’d)
• There are typically many locations where an l-mer is repeated:
GCTTACAGATTCAGTCTTACAGATGGT
• The 4-mer TTAC starts at locations 3 and 17
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Extending l-mer Repeats
GCTTACAGATTCAGTCTTACAGATGGT
• Extend these 4-mer matches:
GCTTACAGATTCAGTCTTACAGATGGT
• Maximal repeat: TTACAGAT
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Maximal Repeats
• To find maximal repeats in this way, we need ALL start locations of all l-mers in the genome
• Hashing lets us find repeats quickly in this manner
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Hashing: What is it?
• What does hashing do?
• For different data, generate a unique integer
• Store data in an array at the unique integer index generated from the data
• Hashing is a very efficient way to store and retrieve data
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Hashing: Definitions
• Hash table: array used in hashing
• Records: data stored in a hash table
• Keys: identifies sets of records
• Hash function: uses a key to generate an index to insert at in hash table
• Collision: when more than one record is mapped to the same index in the hash table
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Hashing: Example
• Where do the animals eat?
• Records: each animal
• Keys: where each animal eats
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Hashing DNA sequences
• Each l-mer can be translated into a binary string (A, T, C, G can be represented as 00, 01, 10, 11)
• After assigning a unique integer per l-mer it is easy to get all start locations of each l-mer in a genome
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Hashing: Maximal Repeats
• To find repeats in a genome:• For all l-mers in the genome, note the start
position and the sequence• Generate a hash table index for each
unique l-mer sequence• In each index of the hash table, store all
genome start locations of the l-mer which generated that index
• Extend l-mer repeats to maximal repeats
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Hashing: Collisions
• Dealing with collisions:• “Chain” all start
locations of l-mers (linked list)
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Hashing: Summary
• When finding genomic repeats from l-mers:• Generate a hash table index for each l-mer
sequence• In each index, store all genome start
locations of the l-mer which generated that index
• Extend l-mer repeats to maximal repeats
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Pattern Matching
• What if, instead of finding repeats in a genome, we want to find all sequences in a database that contain a given pattern?
• This leads us to a different problem, the Pattern Matching Problem
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Pattern Matching Problem• Goal: Find all occurrences of a pattern in a text
• Input: Pattern p = p1…pn and text t = t1…tm
• Output: All positions 1< i < (m – n + 1) such that the n-letter substring of t starting at i matches p
• Motivation: Searching database for a known pattern
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Exact Pattern Matching: A Brute-Force Algorithm
PatternMatching(p,t)1 n length of pattern p2 m length of text t3 for i 1 to (m – n + 1)4 if ti…ti+n-1 = p
5 output i
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Exact Pattern Matching: An Example• PatternMatching
algorithm for:
• Pattern GCAT
• Text CGCATC
GCATCGCATC
GCATCGCATC
CGCATCGCAT
CGCATC
CGCATCGCAT
GCAT
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Exact Pattern Matching: Running Time
• PatternMatching runtime: O(nm)
• Probability-wise, it’s more like O(m)
• Rarely will there be close to n comparisons in line 4
• Better solution: suffix trees
• Can solve problem in O(m) time
• Conceptually related to keyword trees
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Keyword Trees: Example
• Keyword tree:• Apple
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Keyword Trees: Example (cont’d)• Keyword tree:
• Apple• Apropos
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Keyword Trees: Example (cont’d)• Keyword tree:
• Apple• Apropos• Banana
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Keyword Trees: Example (cont’d)• Keyword tree:
• Apple• Apropos• Banana• Bandana
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Keyword Trees: Example (cont’d)• Keyword tree:
• Apple• Apropos• Banana• Bandana• Orange
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Keyword Trees: Properties
• Stores a set of keywords in a rooted labeled tree
• Each edge labeled with a letter from an alphabet
• Any two edges coming out of the same vertex have distinct labels
• Every keyword stored can be spelled on a path from root to some leaf
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Keyword Trees: Threading (cont’d)• Thread “appeal”
• appeal
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Keyword Trees: Threading (cont’d)• Thread “appeal”
• appeal
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Keyword Trees: Threading (cont’d)• Thread “appeal”
• appeal
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Keyword Trees: Threading (cont’d)• Thread “appeal”
• appeal
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Keyword Trees: Threading (cont’d)• Thread “apple”
• apple
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Keyword Trees: Threading (cont’d)• Thread “apple”
• apple
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Keyword Trees: Threading (cont’d)• Thread “apple”
• apple
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Keyword Trees: Threading (cont’d)• Thread “apple”
• apple
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Keyword Trees: Threading (cont’d)• Thread “apple”
• apple
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Multiple Pattern Matching Problem
• Goal: Given a set of patterns and a text, find all occurrences of any of patterns in text
• Input: k patterns p1,…,pk, and text t = t1…tm
• Output: Positions 1 < i < m where substring of t starting at i matches pj for 1 < j < k
• Motivation: Searching database for known multiple patterns
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Multiple Pattern Matching: Straightforward Approach• Can solve as k “Pattern Matching Problems”
• Runtime:
O(kmn)
using the PatternMatching algorithm k times• m - length of the text• n - average length of the pattern
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Multiple Pattern Matching: Keyword Tree Approach• Or, we could use keyword trees:
• Build keyword tree in O(N) time; N is total length of all patterns
• With naive threading: O(N + nm)• Aho-Corasick algorithm: O(N + m)
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Keyword Trees: Threading
• To match patterns in a text using a keyword tree:• Build keyword
tree of patterns• “Thread” the
text through the keyword tree
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Keyword Trees: Threading (cont’d)• Threading is
“complete” when we reach a leaf in the keyword tree
• When threading is “complete,” we’ve found a pattern in the text
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Suffix Trees=Collapsed Keyword Trees• Similar to keyword trees,
except edges that form paths are collapsed
• Each edge is labeled with a substring of a text
• All internal edges have at least two outgoing edges
• Leaves labeled by the index of the pattern.
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Suffix Tree of a Text • Suffix trees of a text is constructed for all its suffixes
ATCATG TCATG CATG ATG TG G
Keyword Tree
Suffix Tree
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Suffix Tree of a Text • Suffix trees of a text is constructed for all its suffixes
ATCATG TCATG CATG ATG TG G
Keyword Tree
Suffix Tree
How much time does it take?
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Suffix Tree of a Text • Suffix trees of a text is constructed for all its suffixes
ATCATG TCATG CATG ATG TG G
quadratic Keyword Tree
Suffix Tree
Time is linear in the total size of all suffixes, i.e., it is quadratic in the length of the text
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Suffix Trees: Advantages• Suffix trees of a text is constructed for all its suffixes • Suffix trees build faster than keyword trees
ATCATG TCATG CATG ATG TG G
quadratic Keyword Tree
Suffix Tree
linear (Weiner suffix tree algorithm)
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Use of Suffix Trees
• Suffix trees hold all suffixes of a text• i.e., ATCGC: ATCGC, TCGC, CGC, GC, C• Builds in O(m) time for text of length m
• To find any pattern of length n in a text:• Build suffix tree for text• Thread the pattern through the suffix tree• Can find pattern in text in O(n) time!
• O(n + m) time for “Pattern Matching Problem”• Build suffix tree and lookup pattern
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Pattern Matching with Suffix TreesSuffixTreePatternMatching(p,t)1 Build suffix tree for text t2 Thread pattern p through suffix tree3 if threading is complete4 output positions of all p-matching leaves in the
tree5 else6 output “Pattern does not appear in text”
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Suffix Trees: Example
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Multiple Pattern Matching: Summary
• Keyword and suffix trees are used to find patterns in a text
• Keyword trees:• Build keyword tree of patterns, and thread text
through it• Suffix trees:
• Build suffix tree of text, and thread patterns through it
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Approximate vs. Exact Pattern Matching• So far all we’ve seen exact pattern matching
algorithms• Usually, because of mutations, it makes
much more biological sense to find approximate pattern matches
• Biologists often use fast heuristic approaches (rather than local alignment) to find approximate matches
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Heuristic Similarity Searches
• Genomes are huge: Smith-Waterman quadratic alignment algorithms are too slow
• Alignment of two sequences usually has short identical or highly similar fragments
• Many heuristic methods (i.e., FASTA) are based on the same idea of filtration• Find short exact matches, and use them as
seeds for potential match extension• “Filter” out positions with no extendable
matches
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Dot Matrices
• Dot matrices show similarities between two sequences
• FASTA makes an implicit dot matrix from short exact matches, and tries to find long diagonals (allowing for some mismatches)
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Dot Matrices (cont’d)
• Identify diagonals above a threshold length
• Diagonals in the dot matrix indicate exact substring matching
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Diagonals in Dot Matrices
• Extend diagonals and try to link them together, allowing for minimal mismatches/indels
• Linking diagonals reveals approximate matches over longer substrings
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Approximate Pattern Matching Problem
• Goal: Find all approximate occurrences of a pattern in a text
• Input: A pattern p = p1…pn, text t = t1…tm, and k, the maximum number of mismatches
• Output: All positions 1 < i < (m – n + 1) such that ti…ti+n-1 and p1…pn have at most k mismatches (i.e., Hamming distance between ti…ti+n-1 and p < k)
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Approximate Pattern Matching: A Brute-Force Algorithm
ApproximatePatternMatching(p, t, k)1 n length of pattern p2 m length of text t3 for i 1 to m – n + 14 dist 05 for j 1 to n6 if ti+j-1 != pj
7 dist dist + 18 if dist < k9 output i
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Approximate Pattern Matching: Running Time
• That algorithm runs in O(nm).• Landau-Vishkin algorithm: O(kn)• We can generalize the “Approximate Pattern
Matching Problem” into a “Query Matching Problem”:• We want to match substrings in a query to
substrings in a text with at most k mismatches• Motivation: we want to see similarities to
some gene, but we may not know which parts of the gene to look for
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Query Matching Problem• Goal: Find all substrings of the query that
approximately match the text• Input: Query q = q1…qw, text t = t1…tm, n (length of matching substrings), k (maximum number of mismatches)• Output: All pairs of positions (i, j) such that the n-letter substring of q starting at i
approximately matches the n-letter substring of t starting at j, with at most k mismatches
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Approximate Pattern Matching vs Query Matching
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Query Matching: Main Idea
• Approximately matching strings share some perfectly matching substrings.
• Instead of searching for approximately matching strings (difficult) search for perfectly matching substrings (easy).
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Filtration in Query Matching
• We want all n-matches between a query and a text with up to k mismatches
• “Filter” out positions we know do not match between text and query
• Potential match detection: find all matches of l-tuples in query and text for some small l
• Potential match verification: Verify each potential match by extending it to the left and right, until (k + 1) mismatches are found
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Filtration: Match Detection
• If x1…xn and y1…yn match with at most k mismatches, they must share an l-tuple that is perfectly matched, with l = n/(k + 1)
• Break string of length n into k+1 parts, each each of length n/(k + 1)• k mismatches can affect at most k of these
k+1 parts• At least one of these k+1 parts is perfectly
matched
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Filtration: Match Detection (cont’d)• Suppose k = 3. We would then have l=n/(k+1)=n/4:
• There are at most k mismatches in n, so at the very least there must be one out of the k+1 l –tuples without a mismatch
1…l l +1…2l 2l +1…3l 3l +1…n
1 2 k k + 1
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Filtration: Match Verification
• For each l -match we find, try to extend the match further to see if it is substantial
query
Extend perfect match of length luntil we find an approximate match of length n with k mismatchestext
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Filtration: Example
k = 0 k = 1 k = 2 k = 3 k = 4 k = 5
l -tuplelength n n/2 n/3 n/4 n/5 n/6
Shorter perfect matches required
Performance decreases
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local alignment is to slow…
• Quadratic local alignment is too slow while looking for similarities between long strings (e.g. the entire GenBank database)
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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local alignment is to slow…
• Quadratic local alignment is too slow while looking for similarities between long strings (e.g. the entire GenBank database)
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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local alignment is to slow…
• Quadratic local alignment is too slow while looking for similarities between long strings (e.g. the entire GenBank database)
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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local alignment is to slow…
• Quadratic local alignment is too slow while looking for similarities between long strings (e.g. the entire GenBank database)
• Guaranteed to find the optimal local alignment
• Sets the standard for sensitivity
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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local alignment is to slow…
• Quadratic local alignment is too slow while looking for similarities between long strings (e.g. the entire GenBank database)
• Basic Local Alignment Search Tool• Altschul, S., Gish, W., Miller, W.,
Myers, E. & Lipman, D.J.
Journal of Mol. Biol., 1990• Search sequence databases for
local alignments to a query
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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
BLAST• Great improvement in speed, with a modest
decrease in sensitivity• Minimizes search space instead of exploring entire
search space between two sequences• Finds short exact matches (“seeds”), only explores
locally around these “hits”
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What Similarity Reveals
• BLASTing a new gene
• Evolutionary relationship
• Similarity between protein function
• BLASTing a genome
• Potential genes
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BLAST algorithm
• Keyword search of all words of length w from the query of length n in database of length m with score above threshold• w = 11 for DNA queries, w =3 for proteins
• Local alignment extension for each found keyword• Extend result until longest match above
threshold is achieved• Running time O(nm)
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BLAST algorithm (cont’d)
Query: 22 VLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVLK 60 +++DN +G + IR L G+K I+ L+ E+ RG++KSbjct: 226 IIKDNGRGFSGKQIRNLNYGIGLKVIADLV-EKHRGIIK 263
Query: KRHRKVLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVLKIFLENVIRD
keyword
GVK 18GAK 16GIK 16GGK 14GLK 13GNK 12GRK 11GEK 11GDK 11
neighborhoodscore threshold
(T = 13)
Neighborhoodwords
High-scoring Pair (HSP)
extension
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Original BLAST
• Dictionary• All words of length w
• Alignment• Ungapped extensions until score falls
below some statistical threshold• Output
• All local alignments with score > threshold
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Original BLAST: ExampleA C G A A G T A A G G T C C A G T
C
T
G
A
T
C C
T
G
G
A
T
T
G C
G
A• w = 4
• Exact keyword match of GGTC
• Extend diagonals with mismatches until score is under 50%
• Output resultGTAAGGTCCGTTAGGTCCFrom lectures by Serafim Batzoglou
(Stanford)
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Gapped BLAST : Example• Original BLAST
exact keyword search, THEN:
• Extend with gaps around ends of exact match until score < threshold
• Output resultGTAAGGTCCAG
TGTTAGGTC-AGT
A C G A A G T A A G G T C C A G T
C
T
G
A
T
C C
T
G
G
A
T
T
G C
G
A
From lectures by Serafim Batzoglou (Stanford)
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Incarnations of BLAST
• blastn: Nucleotide-nucleotide
• blastp: Protein-protein
• blastx: Translated query vs. protein database
• tblastn: Protein query vs. translated database
• tblastx: Translated query vs. translated
database (6 frames each)
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Incarnations of BLAST (cont’d)
• PSI-BLAST• Find members of a protein family or build a
custom position-specific score matrix• Megablast:
• Search longer sequences with fewer differences
• WU-BLAST: (Wash U BLAST)• Optimized, added features
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Assessing sequence similarity• Need to know how strong an alignment can be
expected from chance alone• “Chance” relates to comparison of sequences that are
generated randomly based upon a certain sequence model
• Sequence models may take into account: • G+C content• Poly-A tails• “Junk” DNA • Codon bias• Etc.
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BLAST: Segment Score
• BLAST uses scoring matrices () to improve on efficiency of match detection• Some proteins may have very different
amino acid sequences, but are still similar
• For any two l-mers x1…xl and y1…yl :
• Segment pair: pair of l-mers, one from each sequence
• Segment score: li=1 (xi, yi)
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BLAST: Locally Maximal Segment Pairs• A segment pair is maximal if it has the best
score over all segment pairs• A segment pair is locally maximal if its score
can’t be improved by extending or shortening• Statistically significant locally maximal
segment pairs are of biological interest• BLAST finds all locally maximal segment
pairs with scores above some threshold• A significantly high threshold will filter out
some statistically insignificant matches
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BLAST: Statistics
• Threshold: Altschul-Dembo-Karlin statistics• Identifies smallest segment score that is
unlikely to happen by chance• # matches above has mean E() = Kmne-;
K is a constant, m and n are the lengths of the two compared sequences• Parameter is positive root of:
x,y in A(pxpye(x,y)) = 1, where px and py are frequenceies of amino acids x and y, and A is the twenty letter amino acid alphabet
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P-values
• The probability of finding b HSPs with a score ≥S is given by:• (e-EEb)/b!
• For b = 0, that chance is:• e-E
• Thus the probability of finding at least one HSP with a score ≥S is:• P = 1 – e-E
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Sample BLAST output Score E
Sequences producing significant alignments: (bits) Value
gi|18858329|ref|NP_571095.1| ba1 globin [Danio rerio] >gi|147757... 171 3e-44gi|18858331|ref|NP_571096.1| ba2 globin; SI:dZ118J2.3 [Danio rer... 170 7e-44gi|37606100|emb|CAE48992.1| SI:bY187G17.6 (novel beta globin) [D... 170 7e-44gi|31419195|gb|AAH53176.1| Ba1 protein [Danio rerio] 168 3e-43
ALIGNMENTS>gi|18858329|ref|NP_571095.1| ba1 globin [Danio rerio]Length = 148
Score = 171 bits (434), Expect = 3e-44 Identities = 76/148 (51%), Positives = 106/148 (71%), Gaps = 1/148 (0%)
Query: 1 MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK 60 MV T E++A+ LWGK+N+DE+G +AL R L+VYPWTQR+F +FG+LS+P A+MGNPKSbjct: 1 MVEWTDAERTAILGLWGKLNIDEIGPQALSRCLIVYPWTQRYFATFGNLSSPAAIMGNPK 60
Query: 61 VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG 120 V AHG+ V+G + ++DN+K T+A LS +H +KLHVDP+NFRLL + + A FGSbjct: 61 VAAHGRTVMGGLERAIKNMDNVKNTYAALSVMHSEKLHVDPDNFRLLADCITVCAAMKFG 120
Query: 121 KE-FTPPVQAAYQKVVAGVANALAHKYH 147 + F VQ A+QK +A V +AL +YHSbjct: 121 QAGFNADVQEAWQKFLAVVVSALCRQYH 148
• Blast of human beta globin protein against zebra fish
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Sample BLAST output (cont’d)
Score ESequences producing significant alignments: (bits) Value
gi|19849266|gb|AF487523.1| Homo sapiens gamma A hemoglobin (HBG1... 289 1e-75gi|183868|gb|M11427.1|HUMHBG3E Human gamma-globin mRNA, 3' end 289 1e-75gi|44887617|gb|AY534688.1| Homo sapiens A-gamma globin (HBG1) ge... 280 1e-72gi|31726|emb|V00512.1|HSGGL1 Human messenger RNA for gamma-globin 260 1e-66gi|38683401|ref|NR_001589.1| Homo sapiens hemoglobin, beta pseud... 151 7e-34gi|18462073|gb|AF339400.1| Homo sapiens haplotype PB26 beta-glob... 149 3e-33
ALIGNMENTS>gi|28380636|ref|NG_000007.3| Homo sapiens beta globin region (HBB@) on chromosome 11 Length = 81706 Score = 149 bits (75), Expect = 3e-33 Identities = 183/219 (83%) Strand = Plus / Plus Query: 267 ttgggagatgccacaaagcacctggatgatctcaagggcacctttgcccagctgagtgaa 326 || ||| | || | || | |||||| ||||| ||||||||||| |||||||| Sbjct: 54409 ttcggaaaagctgttatgctcacggatgacctcaaaggcacctttgctacactgagtgac 54468
Query: 327 ctgcactgtgacaagctgcatgtggatcctgagaacttc 365 ||||||||| |||||||||| ||||| ||||||||||||Sbjct: 54469 ctgcactgtaacaagctgcacgtggaccctgagaacttc 54507
• Blast of human beta globin DNA against human DNA
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Timeline • 1970: Needleman-Wunsch global alignment
algorithm• 1981: Smith-Waterman local alignment algorithm• 1985: FASTA• 1990: BLAST (basic local alignment search tool)• 2000s: BLAST has become too slow in “genome vs.
genome” comparisons - new faster algorithms evolve!
• Pattern Hunter• BLAT
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PatternHunter: faster and even more sensitive• BLAST: matches short
consecutive sequences (consecutive seed)
• Length = k• Example (k = 11):
11111111111
Each 1 represents a “match”
• PatternHunter: matches short non-consecutive sequences (spaced seed)
• Increases sensitivity by locating homologies that would otherwise be missed
• Example (a spaced seed of length 18 w/ 11 “matches”):
111010010100110111
Each 0 represents a “don’t care”, so there can be a match or a mismatch
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Spaced seeds
Example of a hit using a spaced seed:
How does this result in better sensitivity?
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Why is PH better?
• BLAST: redundant hits
PatternHunter
This results in > 1 hit and creates clusters of redundant hits
This results in very few redundant hits
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Why is PH better?
BLAST may also miss a hitGAGTACTCAACACCAACATTAGTGGGCAATGGAAAAT
|| ||||||||| |||||| | |||||| ||||||
GAATACTCAACAGCAACATCAATGGGCAGCAGAAAAT
In this example, despite a clear homology, there is no sequence of continuous matches longer than length 9. BLAST uses a length 11 and because of this, BLAST does not recognize this as a hit!
Resolving this would require reducing the seed length to 9, which would have a damaging effect on speed
9 9 matchesmatches
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Advantage of Gapped Seeds
11 positions
11 positions
10 positions
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Why is PH better?
• Higher hit probability• Lower expected number of random hits
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Use of Multiple Seeds
Basic Searching Algorithm
1. Select a group of spaced seed models
2. For each hit of each model, conduct extension to find a homology.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Another method: BLAT
• BLAT (BLAST-Like Alignment Tool)• Same idea as BLAST - locate short sequence
hits and extend
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
BLAT vs. BLAST: Differences
• BLAT builds an index of the database and scans linearly through the query sequence, whereas BLAST builds an index of the query sequence and then scans linearly through the database
• Index is stored in RAM which is memory intensive, but results in faster searches
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
BLAT: Fast cDNA Alignments
Steps:1. Break cDNA into 500 base chunks.2. Use an index to find regions in genome similar to
each chunk of cDNA.3. Do a detailed alignment between genomic regions
and cDNA chunk.4. Use dynamic programming to stitch together
detailed alignments of chunks into detailed alignment of whole.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
BLAT: Indexing
• An index is built that contains the positions of each k-mer in the genome
• Each k-mer in the query sequence is compared to each k-mer in the index
• A list of ‘hits’ is generated - positions in cDNA and in genome that match for k bases
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Indexing: An ExampleHere is an example with k = 3:
Genome: cacaattatcacgaccgc3-mers (non-overlapping): cac aat tat cac gac cgcIndex: aat 3 gac 12 cac 0,9 tat 6 cgc 15
cDNA (query sequence): aattctcac3-mers (overlapping): aat att ttc tct ctc tca cac 0 1 2 3 4 5 6
Hits: aat 0,3 cac 6,0 cac 6,9 clump: cacAATtatCACgaccgc
Multiple instances map to single index
Position of 3-mer in query, genome
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
However…
• BLAT was designed to find sequences of 95% and greater similarity of length >40; may miss more divergent or shorter sequence alignments
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
PatternHunter and BLAT vs. BLAST• PatternHunter is 5-100 times faster than
Blastn, depending on data size, at the same sensitivity
• BLAT is several times faster than BLAST, but best results are limited to closely related sequences
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Resources• tandem.bu.edu/classes/ 2004/papers/pathunter_grp_prsnt.ppt• http://www.jax.org/courses/archives/2004/gsa04_king_presentation.pdf• http://www.genomeblat.com/genomeblat/blatRapShow.pps
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