Methods for Repeat Detection In Nucleotide Sequences
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Methods for Repeat Detection In Nucleotide Sequences
Gary BensonComputer Science, Biology, Bioinformatics
Boston [email protected]
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Outline
• Classes of Repeats in DNA• Tandem Repeats• Techniques for finding repetitive sequence• Tandem Repeats Database
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Why Look at Repeats in DNA?
• Repeats make up the largest portion of DNA.– coding sequence (~5% of human DNA) – repetitive sequence (>50% of human DNA)
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Classes of Repeats in DNA
• Interspersed repeats:o Retrotransposons
• Sines:• Lines:• LTRs
o Transposons
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Classes of Repeats in DNA
• Inverted repeats• Tandem repeats
o Satellite repeatsomicrosatellitesominisatelliteso VNTR (variable number of tandem repeats)
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Tandem Repeats
A tandem repeat (TR) is any pattern of nucleotides that has been duplicated so that it appears several times in a row.
For example, the sequence fragment below contains a tandem repeat of the trinucleotide cgt:
tcgctggtcata cgt cgt cgt cgt cgt tacaaacgtcttccgt
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Approximate Tandem Repeats
More typically, the tandem copies are only approximate due to mutations. Here is an alignment of copies from a human TR from Chromosome 5.
Shown are
and
a consensus pattern
23.7 copies
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Why are tandem repeats interesting?
• They are associated with human disease:Fragile-X mental retardation Myotonic dystrophy Huntington’s diseaseFriedreich’s ataxiaEpilepsy DiabetesOvarian cancer
• They are often polymorphic, making them valuable genomic markers.
• They are involved in gene regulation and often may contain transcription factor binding sites.
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
LOCUS RATIGCA 4461 bp DNA ROD 18-APR-1994
DEFINITION Rat Ig germline epsilon H-chain gene C-region, 3' end.
2881 cgccccaagt aggcttcatc atgctctttg gtttagcaat agcccaaagc aagctatgca 2941 tccatctcag gcccagaggg atgaggagac cagaatcaag acatacccac gcccatccca
3001 cgcccaacca ccaaccacca gcacatcagg ttcacacacc tgagaccagt ggctcccatc
3061 acacacacac acacacacac acacacacac acacacacac acacacaagc ccgtacacat
3121 ccaccatatc cagagacaag tgtctgagtc tgagatacct ctgaggatca ccaatggcag
3181 agtcggccag cacctcagcc tccaggccaa tccttatact ttggcccact gcaggccatg
3241 agagatggag gaggtggagg cctgagctgt ggaaaaccag agacaggaag atggtctgta
3301 ctccaggcca atccttatac tttggcccac tgcaggccat gagagatgga ggaggtggag
3361 gcctgagctg tggaaaacca gagacaggaa gatggtctgt atggagagag tagtaaacca
3421 gattataggg agactgaggc aggagtagag ctcctacaag gccagtagtc taccttagag
3481 tcctataagt ctgggctggg agtccatgtg tcctgacttg ctcctcagat atcacaacca
3541 agattcctgg agccagagtg tgcatgcagg ccctagaaga aatgtggagc ttagagccct
3601 tcctggaggg ccctgggcac tctgaacaaa aggcaattct gtaggctgta tagaggcatc
3661 ctgtcagata cacacacaca tgcacacaca tacacacaca gagacacaga cacacacaca
3721 tgcccacaca catgcataca cacatgcaca cacatacaca cacagagaca cagacacaca
3781 catgcccaca cacatgcata cacacatgca tgcacacaca cacacacaca tacacataca
3841 cacacacaca cacaccccgc aggtagcctt catcatgctg tctagcgata gccctgctga
3901 gggtgggaga tactgggtca tggtgggcac cggagtagaa agagggaatg agcagtcagg
3961 gtcaggggaa aaggacatct gcctccaggg ctgaacagag acttggagca gtcccagagc
4021 aagtgggatg gggagctctg ccactccagt ttcaccagga ctgcctgaga ccagtgaggg
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
LOCUS RATIGCA 4461 bp DNA ROD 18-APR-1994
DEFINITION Rat Ig germline epsilon H-chain gene C-region, 3' end.
2881 cgccccaagt aggcttcatc atgctctttg gtttagcaat agcccaaagc aagctatgca 2941 tccatctcag gcccagaggg atgaggagac cagaatcaag acatacccac gcccatccca
3001 cgcccaacca ccaaccacca gcacatcagg ttcacacacc tgagaccagt ggctcccatc
3061 acacacacac acacacacac acacacacac acacacacac acacacaagc ccgtacacat
3121 ccaccatatc cagagacaag tgtctgagtc tgagatacct ctgaggatca ccaatggcag
3181 agtcggccag cacctcagcc tccaggccaa tccttatact ttggcccact gcaggccatg
3241 agagatggag gaggtggagg cctgagctgt ggaaaaccag agacaggaag atggtctgta
3301 ctccaggcca atccttatac tttggcccac tgcaggccat gagagatgga ggaggtggag
3361 gcctgagctg tggaaaacca gagacaggaa gatggtctgt atggagagag tagtaaacca
3421 gattataggg agactgaggc aggagtagag ctcctacaag gccagtagtc taccttagag
3481 tcctataagt ctgggctggg agtccatgtg tcctgacttg ctcctcagat atcacaacca
3541 agattcctgg agccagagtg tgcatgcagg ccctagaaga aatgtggagc ttagagccct
3601 tcctggaggg ccctgggcac tctgaacaaa aggcaattct gtaggctgta tagaggcatc
3661 ctgtcagata cacacacaca tgcacacaca tacacacaca gagacacaga cacacacaca
3721 tgcccacaca catgcataca cacatgcaca cacatacaca cacagagaca cagacacaca
3781 catgcccaca cacatgcata cacacatgca tgcacacaca cacacacaca tacacataca
3841 cacacacaca cacaccccgc aggtagcctt catcatgctg tctagcgata gccctgctga
3901 gggtgggaga tactgggtca tggtgggcac cggagtagaa agagggaatg agcagtcagg
3961 gtcaggggaa aaggacatct gcctccaggg ctgaacagag acttggagca gtcccagagc
4021 aagtgggatg gggagctctg ccactccagt ttcaccagga ctgcctgaga ccagtgaggg
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Tandem Repeats Finder
An online sequence analysis tool.
OR
A program to download and run locally.
Data from TRF is listed as “simple repeats” at the UCSC genome browser website.
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Similarity Models
Match/mismatch model – Sequences differ only by mismatches:
AAAGCTTCGGAGTGCCCGA
AATGCATCGGGGTGCCTGA
Sequence 1:
Sequence 2:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Similarity Models
Match/mismatch model – Sequences differ only by mismatches:
AAAGCTTCGGAGTGCCCGA
AATGCATCGGGGTGCCTGA
1101101111011111011
Alignments of similar sequences can be represented by bit strings (zeros and ones).
Sequence 1:
Sequence 2:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Similarity Models
Match/mismatch model – Sequences differ only by mismatches:
One model parameter required:
p = probability of matching letters in a column = probability of a 1 in the bit string
Sometimes known as a Bernoulli (coin toss) model.
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Similarity Models
Match/mismatch/indel model – adds indels to sequence differences:
AAAGCTTCGG-AGT--GCCCGA
AA-GCATCGGGAGTTAGCCTGA
Sequence 1:
Sequence 2:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Similarity Models
Match/mismatch/Indel model – adds indels to sequence differences:
AAAGCTTCGG-AGT--GCCCGA
AA-GCATCGGGAGTTAGCCTGA
1121101111211122111011
Alignments of sequences can be represented by strings of numbers in [0,1,2].
Sequence 1:
Sequence 2:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Similarity Models
Match/mismatch/indel model – adds indels to sequence differences:
At least two model parameters required:
p = probability of matching letters in a column = probability of a 1 in the numerical string r = probability of an insertion or deletion
= probability of a 2 or 3 in the numerical string
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Detecting Similar Sequences
Methods for similarity detection involve some form of scanning the input sequences, usually, with a window of fixed size. Information about the contents of the window is stored. This is called indexing.
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
A C G T T G C A G T T G A C T G A C G
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Indexing
The index is a list of all possible window contents together with a list, for each content, of where it occurs:
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…,TTG,…TTT
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Building an Index
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
A C G T T G C A G T T G A C T G A C G
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
First sequence:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Building an Index
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
A C G T T G C A G T T G A C T G A C G
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Building an Index
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
A C G T T G C A G T T G A C T G A C G
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Building an Index
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
A C G T T G C A G T T G A C T G A C G
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Building an Index
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
A C G T T G C A G T T G A C T G A C G
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2 3
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Building an Index
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
A C G T T G C A G T T G A C T G A C G
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2 4 3
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Building an Index
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
A C G T T G C A G T T G A C T G A C G
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2 4 3
8
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Building an Index
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
A C G T T G C A G T T G A C T G A C G
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2 4 3
8 9
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Scanning a new sequence
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2 4 3
8 9
T G C A G T T G . . .
Second sequence:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Scanning a new sequence
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2 4 3
8 9
T G C A G T T G . . .
Second sequence:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Scanning a new sequence
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2 4 3
8 9
T G C A G T T G . . .
Second sequence:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Scanning a new sequence
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2 4 3
8 9
T G C A G T T G . . .
Second sequence:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Scanning a new sequence
AAA, AAC,….,ACG,…,CAG,…,CGT,…GAC,…,GTT,…,TGC,…TTG,…TTT
0 1 2 4 3
8 9
T G C A G T T G . . .
Second sequence:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Test subject sequence at matching locations
Indexed sequence: 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 A C G T T G C A G T T G A C T G A C GSubject seq: T G C A G T T G . . .
Indexed sequence: 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 A C G T T G C A G T T G A C T G A C GSubject seq: T G C A G T T G . . .
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Interaction between the similarity model and the index
Once a model is chosen and the index is built, two questions arise:
1. Is it possible to find a match using the window size chosen?
2. How many character matches are likely to be detected with the window size chosen?
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Q1: Is it possible to find a match?
This is known as the waiting time problem. Waiting Time: How many consecutive positions must
be examined until a run of k ones occurs.
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Q1: Is it possible to find a match?
This is known as the waiting time problem. Waiting Time: How many consecutive positions must
be examined until a run of k ones occurs.
Specific sequence example:AAAGCTTCGGAGTGCCCGA
AATGCATCGGGGTGCCTGA
1101101111011111011
Sequence 1:
Sequence 2:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Waiting Time Specific ExampleAAAGCTTCGGAGTGCCCGA
AATGCATCGGGGTGCCTGA
1101101111011111011
k waiting time1 12 23 94 105 166 -
Sequence 1:
Sequence 2:
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Q1: Is it possible to find a match?
Waiting Time: Given a Bernoulli sequence with generating probability p and length n, what is the probability that a run of k ones occurs?
Randomly generated Bernoulli sequence using p: k
1110101111011011010
n
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Waiting Time Formulas
These calculate the probability of a first occurrence of a run of k ones at every sequence length from 1 to n.
for n ≥ 3, k = 3F(111:n) =
P(1)3 – F(111: n – 1) · P(1) – F(111: n – 2) · P(1)2 – ∑ k = 3 to n – 3 F(111: k) · P(1)3
where:F(111:n) is the probability of a first occurrence of 3 ones in a row at position
n,P(1) is the model probability of a match.
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Waiting Time Formulas
Predictions: If k = 3, p = .5, n = 121. In what position [0..12] is it most likely to get a first
occurrence of 3 ones in a row?2. By what position will there be a cumulative probability of 30%
to see a first occurrence of 3 ones in a row? 3. What is the likely cumulative probability of getting 3 ones in a
row anywhere up to position 12?
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Coin Flip ExperimentGoal: To estimate the probability of finding three heads in a row
at least once when tossing a coin n times.The Experiment: Each student tosses a coin until the first
occurrence of 3 heads in a row. This is one trial. Record the flip where the 3rd head is found. If twelve tosses do not produce three heads in a row, stop and begin a new trial. Repeat.
EXAMPLE: Note that the first occurrence of three heads in a row is marked in bold. 1 2 3 4 5 6 7 8 9 10 11 12
Trial 1: H T H H T H H HTrial 2: T T H H H Trial 3: H T T H H T H H H Trial 3: H T T H H T H T H H T T
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Waiting Time Formulas
Calculated probabilities:
Probabilities of first occurrence of patterns in coin toss sequences
P(1) P(0) P(1) P(1)^2 P(1)^30.5 0.5 0.5 0.25 0.125
HHHP(111) Position 1 2 3 4 5 6 7 8 9 10 11 12
Probability 0.000000 0.000000 0.125000 0.062500 0.062500 0.062500 0.054688 0.050781 0.046875 0.042969 0.039551 0.036377
Cumulative 0.000000 0.000000 0.125000 0.187500 0.250000 0.312500 0.367188 0.417969 0.464844 0.507813 0.547363 0.583740
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Q2: How many character matches will be detected?
This is known as the coverage problem. Coverage: Given a Bernoulli sequence with generating
probability p and length n, what is the probability distribution for number of ones contained in runs of k or more ones?
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Q2: How many character matches will be detected?
Specific sequence example: Let k = 3, n = 19
AAAGCTTCGGAGTGCCCGA
AATGCATCGGGGTGCCTGA
1101101111011111011
Sequence 1:
Sequence 2:
n
k
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Q2: How many character matches will be detected?
Specific sequence example: Let k = 3, n = 19
AAAGCTTCGGAGTGCCCGA
AATGCATCGGGGTGCCTGA
1101101111011111011
Sequence 1:
Sequence 2:
n
Total character matches detected is 9.
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Data Structure – modified Aho Corasick Tree
Seed is 1*1**1.
Tree represents all patternsobtained by replacing each * by either 0 or 1.
Fail links in AC tree go to longest match between a stringsuffix and a prefix of a pattern.
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Recurrence Formula
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Q2: How many character matches will be detected?
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Finding Tandem Repeats:Basic Assumption
We assume that two, mutated, adjacent copies of a pattern will contain runs of exact matches.
d d
T A T A C G T C T C C A C G G A
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
We assume that two, mutated, adjacent copies of a pattern will contain runs of exact matches.
d d
T A T A C G T C T C C A C G G A
We identify the runs with seeds.
Finding Tandem Repeats:Basic Assumption
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
We assume that two, mutated, adjacent copies of a pattern will contain runs of exact matches.
d d
T A T A C G T C T C C A C G G A
Finding Tandem Repeats:Basic Assumption
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The TRF Algorithm Outline
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Criteria for Recognition• Are there enough matches at a common distance?• Are there enough matches if nearby distances are
included (random walk)?• Do the matches start close enough to the left end?
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Part 2The Tandem Repeats Database
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
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Tools
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Selecting a Data Set
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Viewing a Data Set
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TRF Characteristics Table
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Filter for large patterns with many copies
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
More information about a single repeat
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Single repeat view
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Filter for Gene Overlap or Proximity
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Filters for Triplets in Genes
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Triplets in Genes
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Changing Visible Columns
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Changing Visible Columns
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Link for Annotations
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Annotations
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Information link to the Source Database
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Following the Information link to the UCSC Browser
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
The TRDB Browser link
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
The TRDB Browser
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Distributions for a Data Set
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Pattern size distribution Human chr. I: size 1 - 60
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Pattern size distribution Human chr. I: sizes 60 - 120
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Pattern size distribution Drosophila chr. 2R: size 1 - 60
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Clustering repeats
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Clustering repeats
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Human Chr. 15 Family
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Human Chr. 1 Family
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Data Download
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Data Download
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Using TRF on your own data
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Uploading a Sequence
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EMBO Global Exchange Lecture Course on Bioinformatics and Comparative Genome Analysis 13 Dec – 18 Dec 2010
Running TRF on a Sequence