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Genomic Repeat Visualisation Using Suffix Arrays
Nava Whiteford
Department of ChemistryUniversity of Southampton
Repeat Visualisation Using Suffix Arrays
• The Analysis
• Artificial Sequences
• Genomic Sequences
• The Algorithm
• Larger Sequences
• Non-genomic sequences
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA1 2 3
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
AT Occurs 3 time(s)
TG Occurs 1 time(s)
GC Occurs 1 time(s)
CA Occurs 1 time(s)
TA Occurs 2 time(s)
The repeatscore plot
A sliding window is ran over the entire sequence to divide it into all substrings of a given length. (in this case 2).
ATGCATATA
AT TG GC CA AT TA AT TA
No. occurrences (r)
No. sequences the occur r times.
1 3
2 1
3 1
4 0
AT Occurs 3 time(s)
TG Occurs 1 time(s)
GC Occurs 1 time(s)
CA Occurs 1 time(s)
TA Occurs 2 time(s)
The repeat-score plot
Number of occurrences
Sub-string length 1
Sub-string length 2
Sub-string length 3
Sub-string length 4
Sub-string length 5
1 2 3 5 6 5
2 0 1 1 0 0
3 1 1 0 0 0
4 1 0 0 0 0
5 0 0 0 0 0
The repeat-score plot
The resulting matrix is then plotted as an image:
Repeatscore plots of Artificial Sequences
Small repeats
Reverse strand is also included
Random Sequences
DNA Sequences
• “The language of life”
• Composed of four different bases A, T, G and C
• Sequences range in size from 2000bp to 670 billion bp.
Small Genomic Sequences
Lambda Phage
Small Genomic Sequences
Lambda Phage Random Sequence
E.Coli
E.Coli
E.Coli
Sequences coding for rRNA
Known inter-genic repeat elements
E.Coli
Repeats in Genomic Sequences
A Linear time algorithm
• The plots shown would take hours to construct using traditional methods.
• The algorithms used would not scale linearly
• It is not feasible to create these plots on large sequences unless more advanced algorithms are used.
The suffix array
• banana$• anana$• nana$• ana$• na$• a$
•Original string: banana$
All suffixes
The suffix array
• banana$• anana$• nana$• ana$• na$• a$
•Original string: banana$
In sorted order
• a$• ana$• anana$• banana$• na$• nana$
All suffixes
Generating the repeatscore plot
a$ana$
anana$
banana$
na$
nana$
Generating the repeatscore plot
a$ana$
anana$
banana$
na$
nana$
Whole human genome
Whole human genome
Whole human genome
Human Chromosome 18
Arabidopsis thaliana chromosome 1, coding region
Fibonacci derived sequences
Gallus gallus chromosome 20
Application to other sequences
• Analysing writing styles
• Finding plagiarised text
• Any sequence that may contain motif based, language like structure.
Shakespeare
Text document containing the text “The quick brown fox jumped over the lazy dog” 16times.
“On the Economy of Machinery and Manufacturers” by Charles Babbage with artificial
repeat inserted 16times.
“On the Economy of Machinery and Manufacturers” by Charles Babbage with artificial
repeat inserted 16times.
Conclusion
• This new visualisation technique can highlight repeat structure in sequences.
• In genomic sequences this maybe useful in generating annotation.
• There are applications in other areas worth pursuing.
• Our next step is to allow the repeatscore plot to be easily interrogated by a user in order to better understand the repeat structure.
