I519 Introduction to Bioinformatics, Genome Comparison. Yuzhen Ye School of Informatics & Computing, IUB
|
|
- Lucinda Burns
- 5 years ago
- Views:
Transcription
1 I519 Introduction to Bioinformatics, 2011 Genome Comparison Yuzhen Ye School of Informatics & Computing, IUB
2 Whole genome comparison/alignment Build better phylogenies Identify polymorphism Detect gene-level events Compare different assemblies of a single genome
3 Whole genome comparison Aligning whole genomes is a fundamentally different problem than aligning short sequences. Need to consider the presence of large-scale evolutionary events Gene duplication & loss Horizontal gene transfer Repetitive sequences (repeats) Gene rearrangement and inversion Pairwise and multiple genome comparison Multiple genome alignment provides a basis for research into comparative genomics and the study of evolutionary dynamics.
4 Genome evolution Genome A Point Substitution Translocation Inversion Inversion and Translocation Insertion Repeat (Duplication)
5 Basic algorithms: use anchoring as a heuristic to speed alignment Assumption: highly similar subsequences can be found quickly and are likely to be part of the correct global alignment. These local alignments are used to anchor a global alignment (alignment anchor), reducing the number of possible global alignments considered during a subsequent O(n2) dynamic programming step. Select a single collinear set of alignment anchors Many tools have been developed
6 Rearrangement free or not Free of rearrangement Assume the input sequences are free from significant rearrangements of sequence elements, selecting a single collinear set of alignment anchors Pairwise: MUMmer, GLASS, AVID, and WABA align pairs of long sequences Multiple alignment: MAVID, MLAGAN, and MGA Consider rearrangement Shuffle-LAGAN (2003, first genome comparison method described that explicitly deals with genome rearrangements) MultiPipMaker (2003) Mauve (2004, multiple) Enredo and Pecan (2008) GR-Aligner (2009, pairwise)
7 MUMer method MUMer combines suffix trees, the longest increasing subsequence (LIS) and SW alignment Maximal Unique Match (MUM) Identification - Identify the longest strings in Genome 1 that have one identical match in Genome 2 Naïve method: O(N 2 ) Using suffix tree: O(N) Ordered MUM Selection - Identify the longest set of MUMs such that they occur in order in each of the genomes (using a variation of the well-known algorithm to find the LIS of a sequence of integers) Processing Non-matched Regions - Classify nonmatched regions as either insertions, SNPs or highly polymorphic regions
8 Suffix tree Suffix tree is data structure, which allows one to find, extremely efficiently, all distinct subsequences in a given sequence. There are efficient algorithms to construct suffix trees given by Weiner (1973) and McCreight (1976) (in linear time) For the task of comparing two DNA sequences, suffix trees allow one to quickly find all subsequences shared by the two inputs. The genome alignment is then built upon this information.
9 Suffix tree for finding MUMs Suffix Tree for sequence gaaccgacct An internal node is a repeated sequence in the original string Leaf is a unique suffix Every unique matching sequence is represented by an internal node with exactly two child nodes, such that the child nodes are leaf nodes from different genomes
10 ATCGTA# # 7 A# 6 TA# 5 GTA# 4 CGTA# 3 TCGTA# 2 ATCGTA# 1 ATCGAT$ $ 14 T$ 13 AT$ 12 GAT$ 11 CGAT$ 10 TCGAT$ 9 ATCGAT$ 8 ATCGTA# # 7 $ 14 A# 6 AT$ 12 ATCGAT$ 8 ATCGTA# 1 CGAT$ 10 CGTA# 3 GAT$ 11 GTA# 4 T$ 13 TA# 5 TCGAT$ 9 TCGTA# 2 A toy example T A 0 CG $ # TA# AT$ AT$ TA# A# CG T$ T AT$ 9 2 TA# AT$ 4 CG TA# 8 1 G
11 Suffix tree & suffix array for string matching Preprocess text T, not pattern P O(m) preprocess time (m: the length of the text) O(n+k) search time (n: the length of the pattern) k is number of occurrences of P in T Match pattern P against tree starting at root until Case 1, P is completely matched Every leaf below this match point is the starting location of P in T Case 2: No match is possible P does not occur in T
12 A toy example of string (pattern) matching T = xabxac suffixes ={xabxac, abxac, bxac, xac, ac, c} Pattern P 1 : xa Pattern P 2 : xb 1 b b b x x x x a a a a a c c c c c c
13 Suffix array Suffix array: a sorted list of the suffixes of a given string; the start positions are sorted in lexicographical (alphabetical) order Straightforward implementation: O(m 2 logm), reduced to O(mlogm) (utilizing partial sorts) m: the length of the text Suffix array enables binary search for any substring, e.g. CAD O(nlogm), reduced to O(n + logm) if use LCP (longest common prefix) n: the length of the pattern Suffix array is more compact than a suffix tree ABRACADABRA# 11 # 10 A# 7 ABRA# 0 ABRACADABRA# 3 ACADABRA# 5 ADABRA# 8 BRA# 1 BRACADABRA# 4 CADABRA# 6 DABRA# 9 RA# 2 RACADABRA# webglimpse.net/pubs/suffix.pdf
14 G1 Ordered MUM selection G2 A B C D... MUMs: Possible Selections <1,A>, <2,C>, <3,B>, <4,D> <1,A>, <2,C>, <4,D> <1,A>, <3,B>, <4,D> Then process non-matched regions (by dynamic programming algorithm) See more at
15 LIS algorithm B positions is given by the sequence 1, 3, 2, 4, 6, 7, 5 The LIS (longest increasing sequence) is: 1, 2, 4, 6, 7 LIS problem can be solved by a dynamic programming algorithm
16 Mauve Mauve is a system for efficiently constructing multiple genome alignments in the presence of large-scale evolutionary events Identifies conserved genomic regions, rearrangements and inversions in conserved regions, and the exact sequence breakpoints of such rearrangements across multiple genomes. Also performs traditional multiple alignment of conserved regions to identify nucleotide substitutions and indels, using the progressive dynamic programming approach of CLUSTAL W
17 Mauve's anchor selection algorithm Relax anchor selection method: do not assume that the genomes under study are collinear Identifie and align regions of local collinearity called locally collinear blocks (LCBs) Each LCB is a homologous region of sequence shared by two or more of the genomes under study Does not contain any rearrangements of homologous sequence (within LCB)
18 Mauve algorithm 1. Find local alignments (multi-mums), using seed-and-extend hashing method (time complexity O(G 2 n + Gn loggn), G is the number of genomes and n the average genome length) 2. Use the multi-mums to calculate a phylogenetic guide tree. 3. Select a subset of the multi-mums to use as anchors these anchors are partitioned into collinear groups called LCBs, using a greedy breakpoint elimination algorithm 4. Perform recursive anchoring to identify additional alignment anchors within and outside each LCB. 5. Perform a progressive alignment of each LCB using the guide tree.
19 Greedy breakpoint elimination in three genomes Darling A C et al. Genome Res. 2004;14: by Cold Spring Harbor Laboratory Press
20 An example of LCB identified among nine enterobacterial genomes Darling A C et al. Genome Res. 2004;14:
21 LCBs identified among concatenated chromosomes of the mouse, rat, and human genomes Darling A C et al. Genome Res. 2004;14:
22 Turnip vs cabbage: almost identical mtdna gene sequences In 1980s Jeffrey Palmer studied evolution of plant organelles by comparing mitochondrial genomes of the cabbage and turnip (using physical mapping) 99%-99.9% similarity between genes These surprisingly identical gene sequences differed in gene order This study helped pave the way to analyzing genome rearrangements in molecular evolution
23 Why we care about genome rearrangement Evolutionary and functional analysis Examples: Dynamics of Genome Rearrangement in Bacterial Populations, using comparison of eight Yersinia (pathogenic bacteria) genomes. PLoS Genet 4(7): e , 2008 Genome-wide DNA excision (Oxytricha trifallax destroys 95% of its germline genome during development, including the elimination of all transposon DNA, through an exaggerated process of genome rearrangement). Science, Vol no. 5929, pp , 2009
24 Transforming cabbage into turnip
25 Reversals and breakpoints , 2, 3, 4, 5, 6, 7, 8, 9, , 2, 3, -8, -7, -6, -5, -4, 9, The reversion introduced two breakpoints (disruptions in order).
26 Unknown ancestor ~ 75 million years ago Genome rearrangements Mouse (X chrom.) Human (X chrom.) What are the similarity blocks and how to find them? What is the architecture of the ancestral genome? What is the evolutionary scenario for transforming one genome into the other?
27 Comparative genomic architectures: mouse vs human genome Humans and mice have similar genomes, but their genes are ordered differently ~245 rearrangements Reversals Fusions Fissions Translocation
28 History of Chromosome X Rat Consortium, Nature, 2004
29 GRIMM Real genome architectures are represented by signed permutations Efficient algorithms to sort signed permutations have been developed GRIMM web server computes the reversal distances between signed permutations:
I519 Introduction to Bioinformatics, Genome Comparison. Yuzhen Ye School of Informatics & Computing, IUB
I519 Introduction to Bioinformatics, 2015 Genome Comparison Yuzhen Ye (yye@indiana.edu) School of Informatics & Computing, IUB Whole genome comparison/alignment Build better phylogenies Identify polymorphism
More informationBMI/CS 776 Lecture #20 Alignment of whole genomes. Colin Dewey (with slides adapted from those by Mark Craven)
BMI/CS 776 Lecture #20 Alignment of whole genomes Colin Dewey (with slides adapted from those by Mark Craven) 2007.03.29 1 Multiple whole genome alignment Input set of whole genome sequences genomes diverged
More information7 Multiple Genome Alignment
94 Bioinformatics I, WS /3, D. Huson, December 3, 0 7 Multiple Genome Alignment Assume we have a set of genomes G,..., G t that we want to align with each other. If they are short and very closely related,
More information17 Non-collinear alignment Motivation A B C A B C A B C A B C D A C. This exposition is based on:
17 Non-collinear alignment This exposition is based on: 1. Darling, A.E., Mau, B., Perna, N.T. (2010) progressivemauve: multiple genome alignment with gene gain, loss and rearrangement. PLoS One 5(6):e11147.
More informationMultiple Whole Genome Alignment
Multiple Whole Genome Alignment BMI/CS 776 www.biostat.wisc.edu/bmi776/ Spring 206 Anthony Gitter gitter@biostat.wisc.edu These slides, excluding third-party material, are licensed under CC BY-NC 4.0 by
More informationGenômica comparativa. João Carlos Setubal IQ-USP outubro /5/2012 J. C. Setubal
Genômica comparativa João Carlos Setubal IQ-USP outubro 2012 11/5/2012 J. C. Setubal 1 Comparative genomics There are currently (out/2012) 2,230 completed sequenced microbial genomes publicly available
More informationGreedy Algorithms. CS 498 SS Saurabh Sinha
Greedy Algorithms CS 498 SS Saurabh Sinha Chapter 5.5 A greedy approach to the motif finding problem Given t sequences of length n each, to find a profile matrix of length l. Enumerative approach O(l n
More informationGenome Rearrangements In Man and Mouse. Abhinav Tiwari Department of Bioengineering
Genome Rearrangements In Man and Mouse Abhinav Tiwari Department of Bioengineering Genome Rearrangement Scrambling of the order of the genome during evolution Operations on chromosomes Reversal Translocation
More informationRead Mapping. Burrows Wheeler Transform and Reference Based Assembly. Genomics: Lecture #5 WS 2014/2015
Mapping Burrows Wheeler and Reference Based Assembly Institut für Medizinische Genetik und Humangenetik Charité Universitätsmedizin Berlin Genomics: Lecture #5 WS 2014/2015 Today Burrows Wheeler FM index
More informationA PARSIMONY APPROACH TO ANALYSIS OF HUMAN SEGMENTAL DUPLICATIONS
A PARSIMONY APPROACH TO ANALYSIS OF HUMAN SEGMENTAL DUPLICATIONS CRYSTAL L. KAHN and BENJAMIN J. RAPHAEL Box 1910, Brown University Department of Computer Science & Center for Computational Molecular Biology
More informationPhylogenetic Tree Reconstruction
I519 Introduction to Bioinformatics, 2011 Phylogenetic Tree Reconstruction Yuzhen Ye (yye@indiana.edu) School of Informatics & Computing, IUB Evolution theory Speciation Evolution of new organisms is driven
More informationComparative genomics: Overview & Tools + MUMmer algorithm
Comparative genomics: Overview & Tools + MUMmer algorithm Urmila Kulkarni-Kale Bioinformatics Centre University of Pune, Pune 411 007. urmila@bioinfo.ernet.in Genome sequence: Fact file 1995: The first
More informationWhole Genome Alignments and Synteny Maps
Whole Genome Alignments and Synteny Maps IINTRODUCTION It was not until closely related organism genomes have been sequenced that people start to think about aligning genomes and chromosomes instead of
More informationTheoretical aspects of ERa, the fastest practical suffix tree construction algorithm
Theoretical aspects of ERa, the fastest practical suffix tree construction algorithm Matevž Jekovec University of Ljubljana Faculty of Computer and Information Science Oct 10, 2013 Text indexing problem
More informationWhole Genome Alignment. Adam Phillippy University of Maryland, Fall 2012
Whole Genome Alignment Adam Phillippy University of Maryland, Fall 2012 Motivation cancergenome.nih.gov Breast cancer karyotypes www.path.cam.ac.uk Goal of whole-genome alignment } For two genomes, A and
More informationComputational Genetics Winter 2013 Lecture 10. Eleazar Eskin University of California, Los Angeles
Computational Genetics Winter 2013 Lecture 10 Eleazar Eskin University of California, Los ngeles Pair End Sequencing Lecture 10. February 20th, 2013 (Slides from Ben Raphael) Chromosome Painting: Normal
More information9/30/11. Evolution theory. Phylogenetic Tree Reconstruction. Phylogenetic trees (binary trees) Phylogeny (phylogenetic tree)
I9 Introduction to Bioinformatics, 0 Phylogenetic ree Reconstruction Yuzhen Ye (yye@indiana.edu) School of Informatics & omputing, IUB Evolution theory Speciation Evolution of new organisms is driven by
More informationLinear-Space Alignment
Linear-Space Alignment Subsequences and Substrings Definition A string x is a substring of a string x, if x = ux v for some prefix string u and suffix string v (similarly, x = x i x j, for some 1 i j x
More informationAlgorithms for Bioinformatics
Adapted from slides by Alexandru Tomescu, Leena Salmela, Veli Mäkinen, Esa Pitkänen 582670 Algorithms for Bioinformatics Lecture 5: Combinatorial Algorithms and Genomic Rearrangements 1.10.2015 Background
More informationMultiple Alignment of Genomic Sequences
Ross Metzger June 4, 2004 Biochemistry 218 Multiple Alignment of Genomic Sequences Genomic sequence is currently available from ENTREZ for more than 40 eukaryotic and 157 prokaryotic organisms. As part
More informationAN EXACT SOLVER FOR THE DCJ MEDIAN PROBLEM
AN EXACT SOLVER FOR THE DCJ MEDIAN PROBLEM MENG ZHANG College of Computer Science and Technology, Jilin University, China Email: zhangmeng@jlueducn WILLIAM ARNDT AND JIJUN TANG Dept of Computer Science
More informationA Phylogenetic Network Construction due to Constrained Recombination
A Phylogenetic Network Construction due to Constrained Recombination Mohd. Abdul Hai Zahid Research Scholar Research Supervisors: Dr. R.C. Joshi Dr. Ankush Mittal Department of Electronics and Computer
More informationAnalysis of Gene Order Evolution beyond Single-Copy Genes
Analysis of Gene Order Evolution beyond Single-Copy Genes Nadia El-Mabrouk Département d Informatique et de Recherche Opérationnelle Université de Montréal mabrouk@iro.umontreal.ca David Sankoff Department
More informationChapter 26: Phylogeny and the Tree of Life Phylogenies Show Evolutionary Relationships
Chapter 26: Phylogeny and the Tree of Life You Must Know The taxonomic categories and how they indicate relatedness. How systematics is used to develop phylogenetic trees. How to construct a phylogenetic
More informationHandling Rearrangements in DNA Sequence Alignment
Handling Rearrangements in DNA Sequence Alignment Maneesh Bhand 12/5/10 1 Introduction Sequence alignment is one of the core problems of bioinformatics, with a broad range of applications such as genome
More informationMultiple Sequence Alignment. Sequences
Multiple Sequence Alignment Sequences > YOR020c mstllksaksivplmdrvlvqrikaqaktasglylpe knveklnqaevvavgpgftdangnkvvpqvkvgdqvl ipqfggstiklgnddevilfrdaeilakiakd > crassa mattvrsvksliplldrvlvqrvkaeaktasgiflpe
More informationApplications of genome alignment
Applications of genome alignment Comparing different genome assemblies Locating genome duplications and conserved segments Gene finding through comparative genomics Analyzing pathogenic bacteria against
More informationReversal Distance for Strings with Duplicates: Linear Time Approximation using Hitting Set
Reversal Distance for Strings with Duplicates: Linear Time Approximation using Hitting Set Petr Kolman Charles University in Prague Faculty of Mathematics and Physics Department of Applied Mathematics
More informationTree of Life iological Sequence nalysis Chapter http://tolweb.org/tree/ Phylogenetic Prediction ll organisms on Earth have a common ancestor. ll species are related. The relationship is called a phylogeny
More informationAlgorithms in Bioinformatics FOUR Pairwise Sequence Alignment. Pairwise Sequence Alignment. Convention: DNA Sequences 5. Sequence Alignment
Algorithms in Bioinformatics FOUR Sami Khuri Department of Computer Science San José State University Pairwise Sequence Alignment Homology Similarity Global string alignment Local string alignment Dot
More informationGeneral context Anchor-based method Evaluation Discussion. CoCoGen meeting. Accuracy of the anchor-based strategy for genome alignment.
CoCoGen meeting Accuracy of the anchor-based strategy for genome alignment Raluca Uricaru LIRMM, CNRS Université de Montpellier 2 3 octobre 2008 1 / 31 Summary 1 General context 2 Global alignment : anchor-based
More informationNetwork alignment and querying
Network biology minicourse (part 4) Algorithmic challenges in genomics Network alignment and querying Roded Sharan School of Computer Science, Tel Aviv University Multiple Species PPI Data Rapid growth
More informationComparative Genomics Background and Strategies. Nitya Sharma, Emily Rogers, Kanika Arora, Zhiming Zhao, Yun Gyeong Lee
Comparative Genomics Background and Strategies Nitya Sharma, Emily Rogers, Kanika Arora, Zhiming Zhao, Yun Gyeong Lee Introduction Why comparative genomes? h"p://www.ensembl.org/info/about/species.html
More informationGraphs, permutations and sets in genome rearrangement
ntroduction Graphs, permutations and sets in genome rearrangement 1 alabarre@ulb.ac.be Universite Libre de Bruxelles February 6, 2006 Computers in Scientic Discovery 1 Funded by the \Fonds pour la Formation
More informationFast String Kernels. Alexander J. Smola Machine Learning Group, RSISE The Australian National University Canberra, ACT 0200
Fast String Kernels Alexander J. Smola Machine Learning Group, RSISE The Australian National University Canberra, ACT 0200 Alex.Smola@anu.edu.au joint work with S.V.N. Vishwanathan Slides (soon) available
More informationMotif Extraction from Weighted Sequences
Motif Extraction from Weighted Sequences C. Iliopoulos 1, K. Perdikuri 2,3, E. Theodoridis 2,3,, A. Tsakalidis 2,3 and K. Tsichlas 1 1 Department of Computer Science, King s College London, London WC2R
More information3/1/17. Content. TWINSCAN model. Example. TWINSCAN algorithm. HMM for modeling aligned multiple sequences: phylo-hmm & multivariate HMM
I529: Machine Learning in Bioinformatics (Spring 2017) Content HMM for modeling aligned multiple sequences: phylo-hmm & multivariate HMM Yuzhen Ye School of Informatics and Computing Indiana University,
More informationSelf-Indexed Grammar-Based Compression
Fundamenta Informaticae XXI (2001) 1001 1025 1001 IOS Press Self-Indexed Grammar-Based Compression Francisco Claude David R. Cheriton School of Computer Science University of Waterloo fclaude@cs.uwaterloo.ca
More informationPhylogenetics without multiple sequence alignment
Phylogenetics without multiple sequence alignment Mark Ragan Institute for Molecular Bioscience and School of Information Technology & Electrical Engineering The University of Queensland, Brisbane, Australia
More informationHMM for modeling aligned multiple sequences: phylo-hmm & multivariate HMM
I529: Machine Learning in Bioinformatics (Spring 2017) HMM for modeling aligned multiple sequences: phylo-hmm & multivariate HMM Yuzhen Ye School of Informatics and Computing Indiana University, Bloomington
More informationSelf-Indexed Grammar-Based Compression
Fundamenta Informaticae XXI (2001) 1001 1025 1001 IOS Press Self-Indexed Grammar-Based Compression Francisco Claude David R. Cheriton School of Computer Science University of Waterloo fclaude@cs.uwaterloo.ca
More informationOn the complexity of unsigned translocation distance
Theoretical Computer Science 352 (2006) 322 328 Note On the complexity of unsigned translocation distance Daming Zhu a, Lusheng Wang b, a School of Computer Science and Technology, Shandong University,
More informationarxiv: v1 [q-bio.gn] 30 Oct 2009
arxiv:0910.5780v1 [q-bio.gn] 30 Oct 2009 Progressive Mauve: Multiple alignment of genomes with gene flux and rearrangement Aaron E. Darling 1,2,3 Bob Mau 4 Nicole T. Perna 5 Running title: Multiple genome
More informationThe combinatorics and algorithmics of genomic rearrangements have been the subject of much
JOURNAL OF COMPUTATIONAL BIOLOGY Volume 22, Number 5, 2015 # Mary Ann Liebert, Inc. Pp. 425 435 DOI: 10.1089/cmb.2014.0096 An Exact Algorithm to Compute the Double-Cutand-Join Distance for Genomes with
More informationPattern Matching (Exact Matching) Overview
CSI/BINF 5330 Pattern Matching (Exact Matching) Young-Rae Cho Associate Professor Department of Computer Science Baylor University Overview Pattern Matching Exhaustive Search DFA Algorithm KMP Algorithm
More informationSequence comparison by compression
Sequence comparison by compression Motivation similarity as a marker for homology. And homology is used to infer function. Sometimes, we are only interested in a numerical distance between two sequences.
More informationEvolution of Tandemly Arrayed Genes in Multiple Species
Evolution of Tandemly Arrayed Genes in Multiple Species Mathieu Lajoie 1, Denis Bertrand 1, and Nadia El-Mabrouk 1 DIRO - Université de Montréal - H3C 3J7 - Canada {bertrden,lajoimat,mabrouk}@iro.umontreal.ca
More informationWhole-Genome Alignments and Polytopes for Comparative Genomics
Whole-Genome Alignments and Polytopes for Comparative Genomics Colin Noel Dewey Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2006-104
More information3. SEQUENCE ANALYSIS BIOINFORMATICS COURSE MTAT
3. SEQUENCE ANALYSIS BIOINFORMATICS COURSE MTAT.03.239 25.09.2012 SEQUENCE ANALYSIS IS IMPORTANT FOR... Prediction of function Gene finding the process of identifying the regions of genomic DNA that encode
More informationarxiv: v1 [cs.ds] 15 Feb 2012
Linear-Space Substring Range Counting over Polylogarithmic Alphabets Travis Gagie 1 and Pawe l Gawrychowski 2 1 Aalto University, Finland travis.gagie@aalto.fi 2 Max Planck Institute, Germany gawry@cs.uni.wroc.pl
More informationAnalysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5R 3G4 Canada
Multiple Whole Genome Alignments Without a Reference Organism Inna Dubchak 1,2, Alexander Poliakov 1, Andrey Kislyuk 3, Michael Brudno 4* 1 Genome Sciences Division, Lawrence Berkeley National Laboratory,
More informationMultiple Sequence Alignment
Multiple Sequence Alignment Multiple Alignment versus Pairwise Alignment Up until now we have only tried to align two sequences. What about more than two? And what for? A faint similarity between two sequences
More informationChromosomal rearrangements in mammalian genomes : characterising the breakpoints. Claire Lemaitre
PhD defense Chromosomal rearrangements in mammalian genomes : characterising the breakpoints Claire Lemaitre Laboratoire de Biométrie et Biologie Évolutive Université Claude Bernard Lyon 1 6 novembre 2008
More informationOrganizing Life s Diversity
17 Organizing Life s Diversity section 2 Modern Classification Classification systems have changed over time as information has increased. What You ll Learn species concepts methods to reveal phylogeny
More informationRandomized Sorting Algorithms Quick sort can be converted to a randomized algorithm by picking the pivot element randomly. In this case we can show th
CSE 3500 Algorithms and Complexity Fall 2016 Lecture 10: September 29, 2016 Quick sort: Average Run Time In the last lecture we started analyzing the expected run time of quick sort. Let X = k 1, k 2,...,
More information10-810: Advanced Algorithms and Models for Computational Biology. microrna and Whole Genome Comparison
10-810: Advanced Algorithms and Models for Computational Biology microrna and Whole Genome Comparison Central Dogma: 90s Transcription factors DNA transcription mrna translation Proteins Central Dogma:
More informationPhylogenetic Networks, Trees, and Clusters
Phylogenetic Networks, Trees, and Clusters Luay Nakhleh 1 and Li-San Wang 2 1 Department of Computer Science Rice University Houston, TX 77005, USA nakhleh@cs.rice.edu 2 Department of Biology University
More informationMETHODS FOR DETERMINING PHYLOGENY. In Chapter 11, we discovered that classifying organisms into groups was, and still is, a difficult task.
Chapter 12 (Strikberger) Molecular Phylogenies and Evolution METHODS FOR DETERMINING PHYLOGENY In Chapter 11, we discovered that classifying organisms into groups was, and still is, a difficult task. Modern
More informationInferring positional homologs with common intervals of sequences
Outline Introduction Our approach Results Conclusion Inferring positional homologs with common intervals of sequences Guillaume Blin, Annie Chateau, Cedric Chauve, Yannick Gingras CGL - Université du Québec
More informationChapter 18 Active Reading Guide Genomes and Their Evolution
Name: AP Biology Mr. Croft Chapter 18 Active Reading Guide Genomes and Their Evolution Most AP Biology teachers think this chapter involves an advanced topic. The questions posed here will help you understand
More informationGenomes and Their Evolution
Chapter 21 Genomes and Their Evolution PowerPoint Lecture Presentations for Biology Eighth Edition Neil Campbell and Jane Reece Lectures by Chris Romero, updated by Erin Barley with contributions from
More informationGenomes Comparision via de Bruijn graphs
Genomes Comparision via de Bruijn graphs Student: Ilya Minkin Advisor: Son Pham St. Petersburg Academic University June 4, 2012 1 / 19 Synteny Blocks: Algorithmic challenge Suppose that we are given two
More informationarxiv: v1 [cs.db] 29 Sep 2015
Probabilistic Threshold Indexing for Uncertain Strings arxiv:509.08608v [cs.db] 29 Sep 205 Sharma Thankachan Georgia Institute of Technology Georgia, USA thanks@csc.lsu.edu ABSTRACT Strings form a fundamental
More informationLecture 8 Multiple Alignment and Phylogeny
Introduction to Bioinformatics for Medical Research Gideon Greenspan gdg@cs.technion.ac.il Lecture 8 Multiple Alignment and Phylogeny Multiple Alignment & Phylogeny Multiple Alignment Scoring Complexity
More informationChapter 0 Introduction. Fourth Academic Year/ Elective Course Electrical Engineering Department College of Engineering University of Salahaddin
Chapter 0 Introduction Fourth Academic Year/ Elective Course Electrical Engineering Department College of Engineering University of Salahaddin October 2014 Automata Theory 2 of 22 Automata theory deals
More informationMolecular phylogeny - Using molecular sequences to infer evolutionary relationships. Tore Samuelsson Feb 2016
Molecular phylogeny - Using molecular sequences to infer evolutionary relationships Tore Samuelsson Feb 2016 Molecular phylogeny is being used in the identification and characterization of new pathogens,
More informationCSE182-L7. Protein Sequence Analysis Patterns (regular expressions) Profiles HMM Gene Finding CSE182
CSE182-L7 Protein Sequence Analysis Patterns (regular expressions) Profiles HMM Gene Finding 10-07 CSE182 Bell Labs Honors Pattern matching 10-07 CSE182 Just the Facts Consider the set of all substrings
More informationAlgorithms Design & Analysis. String matching
Algorithms Design & Analysis String matching Greedy algorithm Recap 2 Today s topics KM algorithm Suffix tree Approximate string matching 3 String Matching roblem Given a text string T of length n and
More informationDefine M to be a binary n by m matrix such that:
The Shift-And Method Define M to be a binary n by m matrix such that: M(i,j) = iff the first i characters of P exactly match the i characters of T ending at character j. M(i,j) = iff P[.. i] T[j-i+.. j]
More informationModule 9: Tries and String Matching
Module 9: Tries and String Matching CS 240 - Data Structures and Data Management Sajed Haque Veronika Irvine Taylor Smith Based on lecture notes by many previous cs240 instructors David R. Cheriton School
More informationHumans have two copies of each chromosome. Inherited from mother and father. Genotyping technologies do not maintain the phase
Humans have two copies of each chromosome Inherited from mother and father. Genotyping technologies do not maintain the phase Genotyping technologies do not maintain the phase Recall that proximal SNPs
More informationAlignment Strategies for Large Scale Genome Alignments
Alignment Strategies for Large Scale Genome Alignments CSHL Computational Genomics 9 November 2003 Algorithms for Biological Sequence Comparison algorithm value scoring gap time calculated matrix penalty
More informationarxiv: v2 [cs.ds] 16 Mar 2015
Longest common substrings with k mismatches Tomas Flouri 1, Emanuele Giaquinta 2, Kassian Kobert 1, and Esko Ukkonen 3 arxiv:1409.1694v2 [cs.ds] 16 Mar 2015 1 Heidelberg Institute for Theoretical Studies,
More informationCREATING PHYLOGENETIC TREES FROM DNA SEQUENCES
INTRODUCTION CREATING PHYLOGENETIC TREES FROM DNA SEQUENCES This worksheet complements the Click and Learn developed in conjunction with the 2011 Holiday Lectures on Science, Bones, Stones, and Genes:
More informationBiological Networks: Comparison, Conservation, and Evolution via Relative Description Length By: Tamir Tuller & Benny Chor
Biological Networks:,, and via Relative Description Length By: Tamir Tuller & Benny Chor Presented by: Noga Grebla Content of the presentation Presenting the goals of the research Reviewing basic terms
More informationPhylogenetic Networks with Recombination
Phylogenetic Networks with Recombination October 17 2012 Recombination All DNA is recombinant DNA... [The] natural process of recombination and mutation have acted throughout evolution... Genetic exchange
More information1 Alphabets and Languages
1 Alphabets and Languages Look at handout 1 (inference rules for sets) and use the rules on some examples like {a} {{a}} {a} {a, b}, {a} {{a}}, {a} {{a}}, {a} {a, b}, a {{a}}, a {a, b}, a {{a}}, a {a,
More informationPattern Matching. a b a c a a b. a b a c a b. a b a c a b. Pattern Matching 1
Pattern Matching a b a c a a b 1 4 3 2 Pattern Matching 1 Outline and Reading Strings ( 9.1.1) Pattern matching algorithms Brute-force algorithm ( 9.1.2) Boyer-Moore algorithm ( 9.1.3) Knuth-Morris-Pratt
More informationEVOLUTIONARY DISTANCES
EVOLUTIONARY DISTANCES FROM STRINGS TO TREES Luca Bortolussi 1 1 Dipartimento di Matematica ed Informatica Università degli studi di Trieste luca@dmi.units.it Trieste, 14 th November 2007 OUTLINE 1 STRINGS:
More informationEvolutionary Tree Analysis. Overview
CSI/BINF 5330 Evolutionary Tree Analysis Young-Rae Cho Associate Professor Department of Computer Science Baylor University Overview Backgrounds Distance-Based Evolutionary Tree Reconstruction Character-Based
More informationBio 1B Lecture Outline (please print and bring along) Fall, 2007
Bio 1B Lecture Outline (please print and bring along) Fall, 2007 B.D. Mishler, Dept. of Integrative Biology 2-6810, bmishler@berkeley.edu Evolution lecture #5 -- Molecular genetics and molecular evolution
More informationMotivating the need for optimal sequence alignments...
1 Motivating the need for optimal sequence alignments... 2 3 Note that this actually combines two objectives of optimal sequence alignments: (i) use the score of the alignment o infer homology; (ii) use
More informationO 3 O 4 O 5. q 3. q 4. Transition
Hidden Markov Models Hidden Markov models (HMM) were developed in the early part of the 1970 s and at that time mostly applied in the area of computerized speech recognition. They are first described in
More informationComparison of Cost Functions in Sequence Alignment. Ryan Healey
Comparison of Cost Functions in Sequence Alignment Ryan Healey Use of Cost Functions Used to score and align sequences Mathematically model how sequences mutate and evolve. Evolution and mutation can be
More informationIntroduction to Bioinformatics. Shifra Ben-Dor Irit Orr
Introduction to Bioinformatics Shifra Ben-Dor Irit Orr Lecture Outline: Technical Course Items Introduction to Bioinformatics Introduction to Databases This week and next week What is bioinformatics? A
More informationBLAST: Basic Local Alignment Search Tool
.. CSC 448 Bioinformatics Algorithms Alexander Dekhtyar.. (Rapid) Local Sequence Alignment BLAST BLAST: Basic Local Alignment Search Tool BLAST is a family of rapid approximate local alignment algorithms[2].
More informationBioinformatics tools for phylogeny and visualization. Yanbin Yin
Bioinformatics tools for phylogeny and visualization Yanbin Yin 1 Homework assignment 5 1. Take the MAFFT alignment http://cys.bios.niu.edu/yyin/teach/pbb/purdue.cellwall.list.lignin.f a.aln as input and
More informationList of Code Challenges. About the Textbook Meet the Authors... xix Meet the Development Team... xx Acknowledgments... xxi
Contents List of Code Challenges xvii About the Textbook xix Meet the Authors................................... xix Meet the Development Team............................ xx Acknowledgments..................................
More informationThis is a survey designed for mathematical programming people who do not know molecular biology and
INFORMS Journal on Computing Vol. 16, No. 3, Summer 2004, pp. 211 231 issn 0899-1499 eissn 1526-5528 04 1603 0211 informs doi 10.1287/ijoc.1040.0073 2004 INFORMS Opportunities for Combinatorial Optimization
More information11/3/13. Indexing techniques. Short-read mapping software. Indexing a text (a genome, etc) Some terminologies. Hashing
I9 Introdution to Bioinformtis, 0 Indeing tehniques Yuzhen Ye (yye@indin.edu) Shool of Informtis & Computing, IUB Contents We hve seen indeing tehnique used in BLAST Applitions tht rely on n effiient indeing
More informationSUFFIX TREE. SYNONYMS Compact suffix trie
SUFFIX TREE Maxime Crochemore King s College London and Université Paris-Est, http://www.dcs.kcl.ac.uk/staff/mac/ Thierry Lecroq Université de Rouen, http://monge.univ-mlv.fr/~lecroq SYNONYMS Compact suffix
More informationA greedy, graph-based algorithm for the alignment of multiple homologous gene lists
A greedy, graph-based algorithm for the alignment of multiple homologous gene lists Jan Fostier, Sebastian Proost, Bart Dhoedt, Yvan Saeys, Piet Demeester, Yves Van de Peer, and Klaas Vandepoele Bioinformatics
More informationPerfect Sorting by Reversals and Deletions/Insertions
The Ninth International Symposium on Operations Research and Its Applications (ISORA 10) Chengdu-Jiuzhaigou, China, August 19 23, 2010 Copyright 2010 ORSC & APORC, pp. 512 518 Perfect Sorting by Reversals
More informationSUPPLEMENTARY INFORMATION
Supplementary information S1 (box). Supplementary Methods description. Prokaryotic Genome Database Archaeal and bacterial genome sequences were downloaded from the NCBI FTP site (ftp://ftp.ncbi.nlm.nih.gov/genomes/all/)
More informationEvolution at the nucleotide level: the problem of multiple whole-genome alignment
Human Molecular Genetics, 2006, Vol. 15, Review Issue 1 doi:10.1093/hmg/ddl056 R51 R56 Evolution at the nucleotide level: the problem of multiple whole-genome alignment Colin N. Dewey 1, * and Lior Pachter
More informationCladistics and Bioinformatics Questions 2013
AP Biology Name Cladistics and Bioinformatics Questions 2013 1. The following table shows the percentage similarity in sequences of nucleotides from a homologous gene derived from five different species
More informationA Methodological Framework for the Reconstruction of Contiguous Regions of Ancestral Genomes and Its Application to Mammalian Genomes
A Methodological Framework for the Reconstruction of Contiguous Regions of Ancestral Genomes and Its Application to Mammalian Genomes Cedric Chauve 1, Eric Tannier 2,3,4,5 * 1 Department of Mathematics,
More informationText Indexing: Lecture 6
Simon Gog gog@kit.edu - 0 Simon Gog: KIT The Research University in the Helmholtz Association www.kit.edu Reviewing the last two lectures We have seen two top-k document retrieval frameworks. Question
More informationCourse: Visual Analytics of largescale biological data. Kay Nieselt Center for Bioinformatics Tübingen University of Tübingen
Course: Visual Analytics of largescale biological data Kay Nieselt Center for Bioinformatics Tübingen University of Tübingen THE SUPERGENOME AND GENOMERING Overview A revolution in genomics Flood of genomes:
More informationReading for Lecture 13 Release v10
Reading for Lecture 13 Release v10 Christopher Lee November 15, 2011 Contents 1 Evolutionary Trees i 1.1 Evolution as a Markov Process...................................... ii 1.2 Rooted vs. Unrooted Trees........................................
More information