DNA Pattern Matching Techniques

Combinatorial
Pattern Matching

Section 1:
Repeat Finding and
Hash Tables

Genomic Repeats
• Repeat: A sequence of DNA that occurs more than once in a
genome.
• Example: ATGGTCTAGGTCCTAGTGGTC

Genomic Repeats
• Repeat: A sequence of DNA that occurs more than once in a
genome.
• Example: ATGGTCTAGGTCCTAGTGGTC
• Why do we want to find repeats?
1. Understand more about evolution.
2. Many tumors are characterized by an explosion of repeats.
3. Genomic rearrangements are often associated with repeats.

Genomic Repeats
• Note: Often, a repeat will refer to a segment of DNA that
occurs very often with slight modifications.
• As we might imagine, this is a more difficult computational
problem.

Genomic Repeats
• Note: Often, a repeat will refer to a segment of DNA that
occurs very often with slight modifications.
• As we might imagine, this is a more difficult computational
problem.
• Example: Say our target segment is GGTC and we allow one
substitution:
• ATGGTCTAGGACCTAGTGTTC

Extending l-mer Repeats
• Long repeats are difficult to find, but short repeats are easy.
• Simple approach to finding long repeats:
1. Find exact repeats of short l-mers (l is usually 10 to 13).
2. Use l-mer repeats to potentially extend into longer,
maximal repeats.

• Example: We start with a repeat of length 4.
• GCTTACAGATTCAGTCTTACAGATGGT

• Extend both these 4-mers:

• Extend both these 4-mers:
• Maximal repeat: CTTACAGAT

Maximal Repeats: Issue
• In order 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.

Hashing
• Hashing is a very efficient way to store and retrieve data.
• What does hashing do?
• Say we are given a collection of data.
• For different entry, generate a unique integer and store the
entry in an array at this integer location.

Hashing: Definitions
• Records: data stored in a hash table.
• Keys: Integers identifying set of records.
• Hash Table: The array of keys used in hashing.
• Hash Function: Assigns each record to a key.
• Collision: Occurs when more than one record is mapped to the
same index in the hash table.

Hashing: Example
• Records: Animals
• Keys: Where each
animal eats.

Hashing DNA sequences
• Each l-mer can be translated into a binary string:
• A can be represented as 00
• T can be represented as 01
• G can be represented as 10
• C can be represented as 11
• Example: ATGCTA = 000110110100
• After assigning a unique integer to each l-mer, it is easy to
obtain all start locations of each l-mer in a genome.

Hashing: Maximal Repeats
• To find repeats in a genome:
1. For all l-mers in the genome, note the start position and the
sequence.
2. Generate a hash table index for each unique l-mer
sequence.
3. At each index of the hash table, store all genome start
locations of the l-mer that generated the index.
4. Anywhere there are collisions in the table, extend
corresponding l-mers to maximal repeats.
• How do we deal with collisions?

Hashing: Collisions
• In order to deal with
collisions:
• ―Chain‖ all start locations of
l-mers.
• This can be done via a data
structure called a linked list.

Section 2:
Exact Pattern Matching

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.

Pattern Matching Problem
• Goal: Find all occurrences of a pattern in a text.
• Input:
• Pattern p = p1…pn of length n
• Text t = t1…tm of length m
• Output: All positions 1< i < (m – n + 1) such that the n-letter
substring of text t starting at i matches the pattern p.
• Motivation: Searching database for a known pattern.

Exact Pattern Matching: A Brute Force Algorithm
PatternMatching(p,t)
1 n  length of pattern p
2 m  length of text t
3 for i  1 to (m – n + 1)
4 if ti…ti+n-1 = p
5 output i

• PatternMatching algorithm for:
• Pattern GCAT
• Text AGCCGCATCT
Exact Pattern Matching: An Example

• PatternMatching algorithm for:
• Pattern GCAT
• Text AGCCGCATCT
• AGCCGCATCT
• GCAT
Exact Pattern Matching: An Example

• PatternMatching runtime: O(nm)
• In the worst case, we have to check n characters at each of
the m letters of the text.
• Example: Text = AAAAAAAAAAAAAAAA, Pattern = AAAC
• This is rare; on average, the run time is more like O(m).
• Rarely will there be close to n comparisons at each step.
• Better solution: suffix trees…solve in O(m) time.
• First we need keyword trees.
Exact Pattern Matching: Running Time

Keyword Trees: Example
• Keyword tree:
• Apple

• Keyword tree:
• Apple
• Apropos

• Keyword tree:
• Apple
• Apropos
• Banana

• Keyword tree:
• Apple
• Apropos
• Banana
• Bandana

• Keyword tree:
• Apple
• Apropos
• Banana
• Bandana
• Orange

Keyword Trees: Properties
• Stores a set of keywords in a
rooted labeled tree.
• Each edge is 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.
• Furthermore, every path from
root to leaf gives a keyword.

Keyword Trees: Threading
• Thread ―appeal‖
• appeal

• Thread ―apple‖
• apple

Multiple Pattern Matching (MPM) 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 of m characters t = t1…tm
• Output: Positions 1 < i < m where the substring of text t
starting at i matches one of the patterns pj for 1 < j < k.
• Motivation: Searching database for known multiple patterns.

MPM: Naïve Approach
• We could always solve MPM as k ―Pattern Matching
Problems.‖
• Runtime: Using the PatternMatching algorithm k times gives
O(kmn):
• m = length of the text.
• n = average pattern length.

MPM: Keyword Tree Approach
• Alternatively we could use a keyword approach.
• Let N = total length of all patterns.
• We can build the keyword tree in O(N) time.
• Total Runtime:
• With naive threading: O(N + nm)
• Aho-Corasick algorithm: Improvement to O(N + m)

• To match patterns in a text using a
keyword tree:
• Build keyword tree of patterns
• ―Thread‖ the text through the
keyword tree.

• To match patterns in a text using a
keyword tree:
• Build keyword tree of patterns
• ―Thread‖ the text through the
keyword tree.
• Threading is ―complete‖ when we
reach a leaf in the keyword tree.
• When threading is ―complete,‖
we‘ve found a pattern in the text.

Suffix Trees = Collapsed Keyword Trees
• Suffix Tree: Similar to
keyword tree, except edges
that form paths are
collapsed.
• Each edge is labeled with
a substring of the text.
• All internal edges have at
least two outgoing edges.
• Leaves are labeled by the
index of the pattern.
Keyword Tree Suffix Tree

Suffix Trees = Collapsed Keyword Trees
ATCATG
TCATG
CATG
ATG
TG
G
Keyword Tree Suffix Tree

Suffix Tree of a Text
Keyword
Tree
Suffix
Tree
• The suffix trees of a text is constructed for all its suffixes.
• Example: Suffix tree of ATCATG:
ATCATG
TCATG
CATG
ATG
TG
G

Suffix Trees: Example
• Example: Suffix tree of TCATACATGGCATACATGG

Suffix Trees: Runtime
ATCATG
TCATG
CATG
ATG
TG
G
Keyword
Tree
Suffix
Tree
• Say the length of the text is m.
• Construction of the keyword tree takes O(m2) time.
• Constructing the suffix tree takes O(m) time.
• So our method takes O(m2 + m) = O(m2) time.
• However, there exists a method of calculating the suffix tree in
O(m) time without needing the keyword tree for the suffixes.

Pattern Matching with Suffix Trees
• To find any pattern in a text:
1. Build suffix tree for text of length m—O(m) time
2. Thread the pattern of length n through the suffix tree of the
text—O(n) time.
• Therefore the runtime of the Pattern Matching Problem is only
O(m + n)!

Pattern Matching with Suffix Trees
SuffixTreePatternMatching(p,t)
1. Build suffix tree for text t
2. Thread pattern p through suffix tree
3. if threading is complete
4. output positions of all p-matching leaves in the tree
5. else
6. output “Pattern does not appear in text”

Threading ATG through a Suffix Tree

Multiple Pattern Matching: Summary
• Keyword and suffix trees are used to find patterns in a text.
• Keyword trees:
• Build the keyword tree of patterns, and thread the text
through it.
• Suffix trees:
• Build the suffix tree of the text, and thread the patterns
through it.

Section 5:
Heuristic Similarity Search
Algorithms

Approximate vs. Exact Pattern Matching
• So far all we‘ve seen exact pattern matching algorithms.
• Usually, because of mutations, it makes sense to find
approximate pattern matches.
• Biologists often use fast heuristic approaches (rather than local
alignment) to find approximate matches.

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 (e.g. BLAST) 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.

Dot Matrices
• Dot matrices show
similarities between two
sequences.
• FASTA makes an implicit
dot matrix from short exact
matches, and tries to find
long approximate diagonals
(allowing for some
mismatches).

Dot Matrices: Example
• Identify diagonals above a
threshold length.
• Diagonals in the dot matrix
indicate exact substring
matching.

Diagonals in Dot Matrices
• Extend diagonals and try to
link them together, allowing
for minimal
mismatches/indels.
• Linking exact diagonals
reveals approximate
matches over longer
substrings.

Section 6:
Approximate String
Matching

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 allowable 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.,
HammingDistance(ti…ti+n-1, p) ≤ k.

Approximate Pattern Matching: Brute Force
ApproximatePatternMatching(p, t, k)
1. n  length of pattern p
2. m  length of text t
3. for i  1 to m – n + 1
4. dist  0
5. for j  1 to n
6. if ti+j-1 ≠ pj
7. dist  dist + 1
8. if dist < k
9. output i

Approximate Pattern Matching: Running Time
• The brute force algorithm runs in O(mn) time.
• Landau-Vishkin algorithm: Gives improvement to O(km).
• We will next generalize the ―Approximate Pattern Matching
Problem‖ into a ―Query Matching Problem.‖
• Rather than patterns, 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.

Section 7:
Query Matching and
Filtration

Query Matching Problem
• Goal: Find all substrings of a 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 query q starting at i approximately matches the n-
letter substring of text t starting at j, with at most k mismatches.

Approximate Pattern Matching vs Query Matching

Filtration in 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).

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.
• Filtration involves two processes:
1. Potential match detection: Find all exact matches of l-
tuples in query and text for some small l.
2. Potential match verification: Verify each potential match
by extending it to the left and right, until (k + 1)
mismatches are found or we have n – k matches.

Step 1: Match Detection
• If x1…xn and y1…yn match with at most k mismatches, they
must share an l-tuple that is perfectly matched, where we have
l = n/(k + 1).
• Here  j  denotes the largest integer q with q ≤ j
• Where does this formula come from?
• 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.

Step 1: Match Detection
1…l l +1…2l 2l +1…3l 3l +1…n
1 2 k k + 1
• 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.

Step 2: Match Verification
Query
Extend perfect
match of length l
until we find an
approximate
match of length n
with k
mismatches
Text
• For each l-match we find, try to extend the match further to see
if it is substantial.

Filtration: Changing l Changes Performance
l -tuple
length
Shorter perfect matches required
Performance decreases

n
2

n

n
3

n
4

n
5

n
6

k  5

k  4

k  3

k  2

k  1

k  0

Section 8:
Comparing a Sequence
Against a Database

Local alignment is too slow…











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

),(
),(
),(
0
max
1,1
1,
,1
,
jiji
jji
iji
ji
wvs
ws
vs
s



• 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.

Local alignment is too slow…











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),(
),(
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0
max
1,1
1,
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jiji
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wvs
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


• BLAST: Basic Local
Alignment Search Tool
• Created in 1990 by
Altschul, Gish, Miller,
Myers, & Lipman.
• Idea: Search sequence
databases for local
alignments to a query.

Features of 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‖), and only explores locally
around these ―hits.‖

Section 9:
BLAST
Algorithm/Statistics

BLAST Algorithm
• Keyword search of all words of length w from a query of
length n in database of length m with score above threshold.
• w = 11 for DNA queries
• w =3 for proteins
• Perform local alignment extension for each keyword.
• Extend results until longest match above a given threshold is
achieved.
• Runtime: O(nm)

BLAST Algorithm
Query: 22 VLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVLK 60
+++DN +G + IR L G+K I+ L+ E+ RG++K
Sbjct: 226 IIKDNGRGFSGKQIRNLNYGIGLKVIADLV-EKHRGIIK 263
Query: KRHRKVLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVLKIFLENVIRD
keyword
GVK 18
GAK 16
GIK 16
GGK 14
GLK 13
GNK 12
GRK 11
GEK 11
GDK 11
neighborhood
score threshold
(T = 13)
Neighborhood
words
High-scoring Pair (HSP)
extension

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

Original BLAST: Example
From lectures by Serafim Batzoglou (Stanford)
• w = 4
• Exact keyword
match of GGTC
A C G A A G T A A G G T C C A G T
GATCCTGGATTGCGA

• w = 4
• Exact keyword
match of GGTC
GATCCTGGATTGCGA

• w = 4
• Exact keyword
match of GGTC
• Extend diagonals
with mismatches
until score is under
50%.
GATCCTGGATTGCGA

GATCCTGGATTGCGA
• w = 4
• Exact keyword
match of GGTC
• Extend diagonals
with mismatches
until score is under
50%.
• Output result:
• GTAAGGTCC
• GTTAGGTCC

Gapped BLAST: Example
• Original BLAST exact
keyword search, THEN:
• Extend with gaps
around ends of exact
match until score <
threshold
• Output result
GTAAGGTCCAGT
GTTAGGTC-AGT
GATCCTGGATTGCGA

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)

Incarnations of BLAST
• 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.

Assessing Sequence Similarity
• We need to know how strong an alignment can be expected to
be 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.

BLAST: Segment Score
• BLAST uses scoring matrices (d) 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:

d xi, yi i1
l


BLAST: Locally Maximal 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.

BLAST: Statistics
• Threshold: Altschul-Dembo-Karlin statistics.
• Identifies smallest segment score that is unlikely to happen by
chance.
• # matches above q has mean E(q) = Kmne-lq; K is a constant, m
and n are the lengths of the two compared sequences.
• Parameter l is positive root of S x,y in A(pxpyed(x,y)) = 1, where px
and py are frequencies of amino acids x and y, and A is the
twenty letter amino acid alphabet

P-values
• The probability of finding b HSPs with a score ≥S is given by:
• For b = 0, that chance is:
• Thus the probability of finding at least one HSP with a score ≥
S is:

(e
 E
E
b
)
b!

e
 E
P  1  e
 E

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-44
gi|18858331|ref|NP_571096.1| ba2 globin; SI:dZ118J2.3 [Danio rer... 170 7e-44
gi|37606100|emb|CAE48992.1| SI:bY187G17.6 (novel beta globin) [D... 170 7e-44
gi|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+MGNPK
Sbjct: 1 MVEWTDAERTAILGLWGKLNIDEIGPQALSRCLIVYPWTQRYFATFGNLSSPAAIMGNPK 60
Query: 61 VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG 120
V AHG+ V+G + ++DN+K T+A LS +H +KLHVDP+NFRLL + + A FG
Sbjct: 61 VAAHGRTVMGGLERAIKNMDNVKNTYAALSVMHSEKLHVDPDNFRLLADCITVCAAMKFG 120
• BLAST of human betaglobin protein against zebra fish:

Sample BLAST оutput
Score E
Sequences producing significant alignments: (bits) Value
gi|19849266|gb|AF487523.1| Homo sapiens gamma A hemoglobin (HBG1... 289 1e-75
gi|183868|gb|M11427.1|HUMHBG3E Human gamma-globin mRNA, 3' end 289 1e-75
gi|44887617|gb|AY534688.1| Homo sapiens A-gamma globin (HBG1) ge... 280 1e-72
gi|31726|emb|V00512.1|HSGGL1 Human messenger RNA for gamma-globin 260 1e-66
gi|38683401|ref|NR_001589.1| Homo sapiens hemoglobin, beta pseud... 151 7e-34
gi|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
• BLAST of human betaglobin protein against zebra fish:

Section 10:
PatternHunter and BLAT

Timeline
• 1970: Needleman-Wunsch global alignment algorithm
• 1981: Smith-Waterman local alignment algorithm
• 1985: FASTA
• 1990: BLAST (basic local alignment search tool)
• 2000s: Faster algorithms evolve for genome/genome:
• Pattern Hunter
• BLAT

BLAT vs. BLAST
• BLAT (BLAST-Like Alignment Tool): Same idea as
BLAST—locate short sequence hits and extend.
• Differences between BLAT and BLAST:
• BLAST builds an index of the query sequence and then
scans linearly through the database.
• BLAT builds an index of the database and scans linearly
through the query sequence.
• Index is stored in RAM, resulting in faster searches.

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 – matching k-mer positions in
cDNA and in genome.

• Steps:
1. Break cDNA into 500 base chunks.
2. Use an index to find regions in genome similar to each
chunk of cDNA.
3. Construct alignment between genomic regions and cDNA
chunks.
4. Use dynamic programming to stitch together alignments
of chunks into the alignment of the whole.
BLAT: Fast cDNA Alignments

Indexing: An Example
Position of 3-mer in query, genome
• Here is an example with k = 3:
Genome: cacaattatcacgaccgc
3-mers (non-overlapping): cac aat tat cac gac cgc
Index: aat 3 gac 12
cac 0,9 tat 6
cgc 15
cDNA (query sequence): aattctcac
3-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

However…
• BLAT was designed to find sequences of 95% and greater
similarity of length > 40.
• Therefore we may miss more divergent or shorter sequence
alignments.

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 with 11 matches):
111010010100110111
• Each 0 represents a ―don‘t
care‖, so there can be a
match or a mismatch.

Spaced Seeds
• Example of a hit using a spaced seed:
• How does this result in better sensitivity?

Why is PH Better?
• BLAST: redundant hits
• This results in > 1 hit
and creates clusters of
redundant hits.
• Example:
• PatternHunter: very few
redundant hits.
• Example:

Why is PH Better?
GAGTACTCAACACCAACATTAGTGGGCAATGGAAAAT
|||||||||||||||||||||||||||||||||||||
GAATACTCAACAGCAACATCAATGGGCAGCAGAAAAT
• In this example, despite a clear similarity, 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.
• Example:
9 matches

Advantage of Gapped Seeds
11 positions
11 positions
10 positions

Why is PH Better?
1. Higher hit probability.
2. Lower expected number of random hits.

Use of Multiple Seeds
• Basic Search Algorithm:
1. Select a group of spaced seed models.
2. For each hit of each model, conduct extension to find a
homology.

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.

Resources
• http://tandem.bu.edu/classes/2004/papers/pathunter_grp
_prsnt.ppt
• http://www.jax.org/courses/archives/2004/gsa04_king_pr
esentation.pdf
• http://www.genomeblat.com/genomeblat/blatRapShow.p
ps

DNA Pattern Matching Techniques

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