Download Clustering Method for Repeat Analysis in DNA sequences

Clustering Method for Repeat
Analysis in DNA sequences
GNANA SUNDAR RAJENDIRAN
JOYESH MISHRA
RISHI MISHRA
FALL 2008 BIOINFORMATICS
Abstract
 Implement a Proposed Clustering Technique by
Volfovsky et al. for Repeat Analysis
 Distribute Merging Repeats into Classes/Clusters
based on Similarity Measures
 Why analyze repeats?
Algorithm Description
 Selection using RepeatMatch
 Steps
1.
2.
3.
4.
Preprocessing
Merging and Repeat Map Generation
Classification
BLAST searches & further merging of
Clusters
 Results
RepeatMatch
 Uses Suffix Tree algorithm to determine all the exact
repeats in a given sequence
 It is a part of the MUMmer package used for the
rapid alignment of very large DNA and amino acid
sequences.
Example…
 Forward & Reverse Complement Repeats
Definitions
 Exact Repeat: Subsequence that occurs in DNA at
least twice.

Exact repeat is represented by pair of co-ordinates (A1,A2)
delimiting its location in the genome sequence and by the
repeat length.
 Maximal Repeat: Repeat that can not be extended in
either direction without incurring a mismatch
 Initial Repeat Set: Set of repeats chosen initially,
from which the repeat classes will be constructed
Definitions
Preprocessing
 Output from RepeatMatch is used to partition the
original genome sequence
 Each partition point has a reference to the pair coordinates (A1, A2) and repeat length l
 For each repeat starting at co-ordinates A1 and A2,
with length l, this list will include both (A1, A2, l) and
(A2, A1,l)
Merging & Repeat Map Generation
 This procedure works by repeatedly working
together two exact repeats that either overlap or that
occur within a limited distance(a gap) of each other.
 Significant subsequences of the new merging repeats
appear at least twice in the genome sequence.


Merging with Gap
Merging with Overlap
Merging with Gap
 Given 2 partition points:
p1 = (A1, A2, lA) and p2 = (B1, B2, lB) [A1 < B1]
 Compute the distance between the non-overlapping
repeats as
d(p1,p2) = max(0, B1 - A1 - lA + 1)
 Given a maximum Gap size G > 0
Sequences corresponding to p1 and p2 are merged if
d(p1, p2) < G
Merging with Overlap
 Merges sequences which are partially identical
 Overlap of 2 sequences is denoted as:
o(p1, p2) = max(o, A1 + lA – B1 + 1) for A1<B1
 Given a minimum overlap proportion op, where 0 <= op
<= 1, repeat points (A1, A2, lA) and (B1, B2, lB) are merged
if at least one of the four repeats has overlap satisfying
o(p1, p2) > op min(lA, lB)
 op : Interpreted as a fraction of the shorter of the 2
repeats. E.g. if op = 0.75, the two overlapped sequences
are merged if the length of their overlap is at least 75% of
the length of the shorter sequence.
Output: Merging Repeat
 The new sequence is defined as merging repeat with
starting position M = A1 and with length lM = max(A1
+ lA, B1 + lB) – A1
 A data structure stores with each merging repeat its
start co-ordinate, the length(nM), and a list of
references to itself and to other repeats.
Classification
 If a merging repeat has at least one reference in
common with one another, then they belong to the
same class
 If a merging repeat has references that belong to
multiple distinct classes, then those classes are
combined into one.
 If a merging repeat contains no reference to an
existing class, then the merging repeat forms a new
class.
BLAST searches and further merging
 To merge similar but non-exact repeats
 Search all merging repeats against all others
 A local BLAST database is created with all repeat sequences
using formatdb
 Classes are merged if any of their underlying
sequences have a BLAST E-value less than a UserSpecified Threshold when compared to any
sequence in another class
 If a class appears in multiple similarity pairs, all
these similar classes are merged with the original
class
Results
Results (contd.)
Results (contd.)
Results (contd.)
Results (contd.)
Results (contd.)
Results (contd.)
What’s Next?
 The results collected help to cluster similar repeats
and generate a database.
 This DB, in future, can be used to find Similar
Repeats faster.
 Also, if on future classification, we find common
traits among repeats, they can be analyzed only for
that cluster alone.
Resources
 Softwares Used:
 RepeatMatch (MUMmer 3.0)
 BLAST (formatdb, blastall)
 MS Excel 2007
 Programmming Language Used:
C++, UNIX Shell Script
 Source for Project & Presentation

www.cise.ufl.edu/~jmishra/bioinformatics.html
References
 Volfovsky N., Haas Brian J. and Salzberg Steven L. A
clustering method for repeat analysis in DNA
sequences, 2001
 Mummer software : http://mummer.sourceforge.net/
 http://www.ncbi.nlm.nih.gov/staff/tao/URLAPI/

blastall, formatdb
 www.ncbi.nlm.nih.gov/BLAST/
References
 http://www.tigr.org
 http://www.animalgenome.org/blast/docs/blast_faq.
html
 Of course, Wikipedia …
Data obtained from:
ftp://ftp.ncbi.nih.gov/genomes/Bacteria/
Thank You