Genome Rearrangements Tseng Chiu Ting Sept. 24, 2004.

Slides:



Advertisements
Similar presentations
A Simpler 1.5-Approximation Algorithm for Sorting by Transpositions Tzvika Hartman Weizmann Institute.
Advertisements

Sorting by reversals Bogdan Pasaniuc Dept. of Computer Science & Engineering.
Chapter 8 Topics in Graph Theory
Simple Graph Warmup. Cycles in Simple Graphs A cycle in a simple graph is a sequence of vertices v 0, …, v n for some n>0, where v 0, ….v n-1 are distinct,
Walks, Paths and Circuits Walks, Paths and Circuits Sanjay Jain, Lecturer, School of Computing.
School of CSE, Georgia Tech
Greedy Algorithms CS 466 Saurabh Sinha. A greedy approach to the motif finding problem Given t sequences of length n each, to find a profile matrix of.
Greedy Algorithms CS 6030 by Savitha Parur Venkitachalam.
Gene an d genome duplication Nadia El-Mabrouk Université de Montréal Canada.
Asynchronous Pattern Matching - Metrics Amihood Amir CPM 2006.
Rearrangements and Duplications in Tumor Genomes.
The Breakpoint Graph The Breakpoint Graph Augment with 0 = n
Bioinformatics Chromosome rearrangements Chromosome and genome comparison versus gene comparison Permutations and breakpoint graphs Transforming Men into.
Introduction Sorting permutations with reversals in order to reconstruct evolutionary history of genome Reversal mutations occur often in chromosomes where.
Greedy Algorithms And Genome Rearrangements
Genome Rearrangements CIS 667 April 13, Genome Rearrangements We have seen how differences in genes at the sequence level can be used to infer evolutionary.
Sorting Signed Permutations By Reversals (The Hannenhalli – Pevzner Theory) Seminar in Bioinformatics – ©Shai Lubliner.
Introduction to Bioinformatics Algorithms Greedy Algorithms And Genome Rearrangements.
Of Mice and Men Learning from genome reversal findings Genome Rearrangements in Mammalian Evolution: Lessons From Human and Mouse Genomes and Transforming.
Genome Rearrangements CSCI : Computational Genomics Debra Goldberg
1 Michal Ozery-Flato and Ron Shamir 2 The Genomic Sorting Problem HOW?
Cleber V. G. Mira Analysis of Sorting by Transpositions based on Algebraic Formalism RECOMB 2004 João Meidanis.
Transforming Cabbage into Turnip: Polynomial Algorithm for Sorting Signed Permutations by Reversals Journal of the ACM, vol. 46, No. 1, Jan 1999, pp
5. Lecture WS 2003/04Bioinformatics III1 Genome Rearrangements Compare to other areas in bioinformatics we still know very little about the rearrangement.
Genome Rearrangement SORTING BY REVERSALS Ankur Jain Hoda Mokhtar CS290I – SPRING 2003.
1 Sorting by Transpositions Based on the First Increasing Substring Concept Advisor: Professor R.C.T. Lee Speaker: Ming-Chiang Chen.
1 Genome Rearrangements João Meidanis São Paulo, Brazil December, 2004.
Efficient Data Structures and a New Randomized Approach for Sorting Signed Permutations by Reversals Haim Kaplan and Elad Verbin.
7-1 Chapter 7 Genome Rearrangement. 7-2 Background In the late 1980‘s Jeffrey Palmer and colleagues discovered a remarkable and novel pattern of evolutionary.
Pancakes With A Problem Steven Rudich The chef at our place is sloppy, and when he prepares a stack of pancakes they come out all different sizes.
CompSci 102 Spring 2012 Prof. Rodger January 11, 2012.
Genome Rearrangements …and YOU!! Presented by: Kevin Gaittens.
1 A Simpler 1.5- Approximation Algorithm for Sorting by Transpositions Combinatorial Pattern Matching (CPM) 2003 Authors: T. Hartman & R. Shamir Speaker:
UNIVERSIDADE ESTADUAL DE CAMPINAS - UNICAMP INSTITUTO DE COMPUTAÇÃO Cleber V. G. Mira Analysis of Sorting by Transpositions based on Algebraic Formalism.
16. Lecture WS 2004/05Bioinformatics III1 V16 – genome rearrangement Important information – contained in the order in which genes occur on the genomes.
A Simpler 1.5-Approximation Algorithm for sorting by transposition Tzvika Hartman.
Genome Rearrangements Unoriented Blocks. Quick Review Looking at evolutionary change through reversals Find the shortest possible series of reversals.
On The Connections Between Sorting Permutations By Interchanges and Generalized Swap Matching Joint work of: Amihood Amir, Gary Benson, Avivit Levy, Ely.
Greedy Algorithms And Genome Rearrangements An Introduction to Bioinformatics Algorithms (Jones and Pevzner)
Genome Rearrangements [1] Ch Types of Rearrangements Reversal Translocation
Chap. 7 Genome Rearrangements Introduction to Computational Molecular Biology Chap ~
Sorting by Cuts, Joins and Whole Chromosome Duplications
Combinatorial Optimization Problems in Computational Biology Ion Mandoiu CSE Department.
Andrew’s Leap 2011 Pancakes With A Problem Steven Rudich.
Chap. 7 Genome Rearrangements Introduction to Computational Molecular Biology Chapter 7.1~7.2.4.
Greedy Algorithms CS 498 SS Saurabh Sinha. A greedy approach to the motif finding problem Given t sequences of length n each, to find a profile matrix.
Gene: A sequence of nucleotides coding for protein Gene Prediction Problem: Determine the beginning and end positions of genes in a genome Gene Prediction:
Fall 2015 COMP 2300 Discrete Structures for Computation Donghyun (David) Kim Department of Mathematics and Physics North Carolina Central University 1.
Genome Rearrangement By Ghada Badr Part I.
Introduction to Bioinformatics Algorithms Chapter 5 Greedy Algorithms and Genome Rearrangements By: Hasnaa Imad.
Genome Rearrangements. Turnip vs Cabbage: Look and Taste Different Although cabbages and turnips share a recent common ancestor, they look and taste different.
Genome Rearrangements. Turnip vs Cabbage: Look and Taste Different Although cabbages and turnips share a recent common ancestor, they look and taste different.
Outline Today’s topic: greedy algorithms
1 Genome Rearrangements (Lecture for CS498-CXZ Algorithms in Bioinformatics) Dec. 6, 2005 ChengXiang Zhai Department of Computer Science University of.
Tzvika Hartman Elad Verbin Bar Ilan University Tel Aviv University
Senior Project Board Implementation of the Solution to the Conjugacy Problem in Thompson’s Group F by Nabil Hossain Advisers: James Belk & Robert McGrail.
Lecture 4: Genome Rearrangements. End Sequence Profiling (ESP) C. Collins and S. Volik (UCSF Cancer Center) 1)Pieces of tumor genome: clones ( kb).
Lecture 2: Genome Rearrangements. Outline Cancer Sequencing Transforming Cabbage into Turnip Genome Rearrangements Sorting By Reversals Pancake Flipping.
Amihood Amir, Gary Benson, Avivit Levy, Ely Porat, Uzi Vishne
Tao Jiang Department of Computer Science
Greedy (Approximation) Algorithms and Genome Rearrangements
Lecture 3: Genome Rearrangements and Duplications
CSCI2950-C Lecture 4 Genome Rearrangements
Greedy Algorithms And Genome Rearrangements
A Unifying View of Genome Rearrangement
CSCI2950-C Lecture 6 Genome Rearrangements and Duplications
FanChang Hao, Melvin Zhang, and Hon Wai Leong Review for TCBB
Greedy Algorithms And Genome Rearrangements
Richard Anderson Lecture 5 Graph Theory
JAKUB KOVÁĆ, ROBERT WARREN, MARÍLIA D.V. BRAGA and JENS STOYE
Presentation transcript:

Genome Rearrangements Tseng Chiu Ting Sept. 24, 2004

Genome Rearrangements Distance by Fusion, Fission, and Transposition is Easy Joao Meidanis, Zanoni Dias Proceedings of SPIRE'2001

Operation  α(x)=y means x moves to y.  αβ(x)=α (β(x)) for all x Ex1: α =(2 3 4), β=( ), then αβ=(1 5 3)(2 6) Ex2: α =(7 3 2), β=( ), then αβ=( )

Operation  α =(x y), if x and y are in different cycle of β, then αβ is a fusion, else αβ is a fission. Ex1: α=(2 3), β=(1 5 3)(2 6), then αβ=( ) fusion Ex2: α=(2 3), β=( ), then αβ=(1 5 3) (2 6) fission

Results  Given two distinct permutations(genomes) π and σ, there is always a good event for π with respect to σ.  Given two permutations(genomes) π and σ, the distance between them is n-c(π, σ), c(π, σ) denotes the number of orbits of σπ -1.

Sorting by Transpositions SIAM J. Discrete Math, vol. 11, No. 2, pp , 1998 V. Bafna, P. V. Pevzner

Method

 Identity permutation (1 2 3 … n) has n cycles, all are odd cycle.  Algorithm TransSort( π ) 1. While G( π ) has a long cycle, perform a valid 2-move or a valid 0, 2, 2-move. 2. If G( π ) has only short cycles, perform a good 0-move followed by a valid 2- move

Result  Algorithm TransSort sorts permutation in no more than 0.75 (n + 1 – C odd ( π )) transpositions, thereby ensuring a performance guarantee of 1.5.

A Simpler 1.5-Approximation algorithm for Sorting by Transpositions T. Hartman, R. Shamir CPM2003, pp

Linear & Circular Perms A B A C t BADCDBCA t B C Linear transposition : Circular transposition : Circular transpositions can be represented by exchanging any 2 of the 3 segments. A transposition “cuts” the perm at 3 points.

The Algorithm  While G contains a 2-cycle, apply a 2-transposition [Christie99].  If G contains an oriented 3-cycle, apply a 2- transposition on it.  If G contains a pair of interleaving 3-cycles, apply a (0,2,2)-sequence.  If G contains a shattered unoriented 3-cycle, apply a (0,2,2)-sequence.  Repeat until perm is sorted.

3 - Cycles  2 possible configurations of 3-cycles: Non-oriented 3-cycleOriented 3-cycle

Interleaving Cycles  2 cycles interleave if their black edges appear alternatively along the circle.  Lemma : If G contains 2 interleaving 3- cycles, then  a (0,2,2)-sequence.

Shattered Cycles  Lemma : If G contains a shattered cycle, then  a (0,2,2)-sequence.  2 pairs of black edges intersect if they appear alternatively along the circle.  Cycle A is shattered by cycles B and C if every pair of black edges in A intersects with a pair in B or with a pair in C.

A Simpler and Faster 1.5- Approximation Algorithm for Sorting by Transpositions T. Hartman Jan 14,2004

Exact and Approximation Algorithms for Sorting by Reversals, with Application to Genome Rearrangement John Kececioglu, David Sankoff Algorithmica, vol. 13, pp , 1995

Method

Results  Lemma 1: Every permutation with a decreasing strip has a reversal that removes a breakpoint … … … 4 2 1…

Results  Every Reversal can decrease at most two breakpoints.  Opt(π) ≧ 0.5Φ(π) ≧ 0.5App(π)

Transforming Cabbage into Turnip: Polynomial Algorithm for Sorting Signed Permutations by Reversals 1. In Proc. 27 th Annual ACM symposium on the Throry of Computing, pp , J. ACM, Vol. 46, No. 1, pp. 1-27, 1999 S. Hannenhalli, P. A. Pevzner

Breakpoint graph

Reversal Distance is a fortress = if otherwise  b: breakpoint c: cycle h: hurdle O(n 2 )

Fortress  A permutation  is called a fortress if it has odd number of hurdles and all of these hurdles are superhurdles.

Hurdle and Superhurdle

Polynomial Algorithm 1. while π is not sorted 2. if π has a long cycle 3. select a safe ( g, b)-padding ρ of π 4. else if π has an oriented component 5. select a safe reversal ρ in this component 6. else if π has an even number of hurdles 7. select a safe reversal ρ merging two hurdles in π 8. else if π has at least one simple hurdle 9. select a safe reversal ρ cutting this hurdle in π 10. else if π is a fortress with more than three superhurdles 11. select a safe reversal r merging two (super)hurdles in π 12. else /* π is a 3-fortress */ 13. select an (un)safe reversal r merging two arbitrary (super)hurdles in p 14. π = ρ π 15. endwhile 16. mimic (genuine) sorting of π using the computed generalized sorting of π O(n 4 )

Simple Polynomial Algorithm while π is not sorted select a valid reversal ρ in π π = ρ π endwhile O(n 5 )

Fast Sorting by Reversal CPM 1996, pp P. Berman, S. Hannenhalli

Improvement  Finding connected component in O(nα(n)) time.  Finding safe reversal in O((nα(n)) time.  Implementation of Reversal_Sort in O(n 2 α(n)).

A Faster and Simpler Algorithm for Sorting Signed Permutation by Reversals SIAM Journal on Computing, Vol. 29, No. 3, 2000, pp H. Kaplan, R. Shamir and R. E. Tarjan