Heuristics for the Sorting by Length-Weighted Inversions Problem on Signed Permutations

Size: px
Start display at page:

Download "Heuristics for the Sorting by Length-Weighted Inversions Problem on Signed Permutations"

Transcription

1 Heuristics for the Sorting by Length-Weighted Inversions Problem on Signed Permutations AlCoB 2014 First International Conference on Algorithms for Computational Biology Thiago da Silva Arruda Institute of Computing University of Campinas Campinas, SP, Brazil Ulisses Dias Institute of Computing University of Campinas Campinas, SP, Brazil Zanoni Dias Institute of Computing University of Campinas Campinas, SP, Brazil Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

2 Outline Genome Rearrangement Field Length-Weighted Inversions Greedy Randomized Search Procedure Experimental Results Conclusions Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

3 Part I Genome Rearrangement Field Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

4 Genome Rearrangement Field Genome Rearrangements Genes are shared by genomes from different species. Given two contemporary genomes, gene order and orientation may differ Yesrsinia pestis Nepal Yesrsinia pestis Angola Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

5 Genome Rearrangement Field Genome Rearrangements Genomes undergo large scale mutations during the evolutionary process (move DNA-sequence from one place to the other). An inversion occurs when a chromosome breaks at two locations called breakpoints, and the DNA between the breakpoints is reversed. Pseudomonas putida KT Pseudomonas putida F1 Inversion 0... i-1 i... j j+1... n ρ(i,j) 0... i-1 -j... -i j+1... n Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

6 Genome Rearrangement Field Evolutionary Distance The genetic data available for many organisms allows accurate evolutionary inference. The traditional approach uses nucleotide (or amino acid) comparison to find the edit distance. When mutational events (like inversions) affect very large stretches of DNA sequence? Whole-Genome Distance Measure. Compute the minimum number of lage-scale events needed to transform one genome into the other (parsimony criterion). Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

7 Genome Rearrangement Field Evolutionary Distance Regarding the computation of parsimonious scenarios when only inversions are considered. A polynomial problem (Hannenhali and Pevzner, 1998). GRIMM is the most used tool for this job. Several studies have shown that inversions are shorter than expected under a neutral model. The sorting by inversion problem do not take into account the length of the reversed sequence. The sequence of operations that most likely happened during the evolution may not involve the movement of many long sequences. Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

8 Part II Length-Weighted Inversions Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

9 Length-Weighted Inversions Genome Representation We regard genomes as permutations: π = (π 1 π 2... π n ), for π i Z, 1 π i n and i j π i π j. Same Orientation Different Orientation Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

10 Length-Weighted Inversions Sorting by Inversions Problem Given the gene order of two contemporary genomes, the task of finding the minimum number of inversions which transform one genome into the other is called Sorting by Inversion Problem Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

11 Length-Weighted Inversions Sorting by Length-Weighted Inversions Problem cost = cost = 5 cost = 4 cost = 1 cost = 2 Total = 14 Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

12 Part III Greedy Randomized Search Procedure Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

13 Greedy Randomized Search Procedure Building Blocks Initial Solution Neighborhood Local Search Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

14 Greedy Randomized Search Procedure Building Blocks Initial Solution A solution is a sequence of permutations s =< s 0,s 1,...,s m > such that s k differs from s k 1 by one inversion, 1 k m, s 0 = π and s m = ι. s =< ( ),( ),( ),( ),( ) >. We use an optimal solution for the Sorting by Inversions Problem as Initial Solution. Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

15 Greedy Randomized Search Procedure Building Blocks Neighborhood Let s =< s 0,s 1,...,s m > be the current solution. Another solution s =< s 0,s 1,...,s m > is in the neighborhood of s, namely N f (s), if they differ by a frame that has no more than f elements. j - i +1 = f Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

16 Greedy Randomized Search Procedure Building Blocks Local Search Our method iteratively improves the current solution. Each step requires a local change. The local change will be restricted to a given frame choosen by random. If a less costly sequence is found, it is made the current solution. Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

17 Greedy Randomized Search Procedure Local Search - Definitions Let < s i,...,s j > be the frame where the change will be performed. We will call s i = α and s j = β. Breakpoint: pair of elements that are consecutive in α but not consecutive in β. α = ( ) β = ( ) Entropy: How far each element in α is from its position in beta. ent(α,β) = n p(α,i) p(β,i). i=1 Benefit: for any inversion ρ, the benefit δ is given by: δ(α,β,ρ) = ent(π,β) ent(π ρ,β) cost(ρ) Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

18 Greedy Randomized Search Procedure Local Search We start the construction of new frame that will starts with α and ends with β. We select inversions that decrease the number of breakpoints. When no inversion of the kind exists, we execute one step of the algorithm proposed by Bergeron, 2006 for the Sorting by Inversions Problem. Building a Frame α... β Benefit Inversion Inv 1 Inv 2... Inv 5 Inv 6 Inv 7... Five best scored permutations Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

19 Greedy Randomized Search Procedure Local Search We rank the selected inversions based on the benefit. The inversions ranked as high as fifth qualify to the second phase. We choose one inversion based on a random process called roulette wheel selection mechanism. Each inversion has a selection likelihood proportional to the square of its benefit. Building a Frame α... β Benefit Inversion Inv 1 Inv 2... Inv 5 Inv 6 Inv 7... Five best scored permutations Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

20 Part IV Experimental Results Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

21 Experimental Results Parameters Frame Size: < 14,12,10,8,6,4 > Iterations: 900 (150 for each frame size). Instances: we generated 1000 permutations whose sizes range from 10 to 100 in intervals of 5. Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

22 Experimental Results Number of Improved Initial Solutions 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Permutation size Percentage of Times our Heuristic Improved the Initial Solution Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

23 Experimental Results 15% Average Improvement 12% 9% 6% 3% 0% Permutation size Percentage of Improvement on the Initial Solution Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

24 Experimental Results Average Cost Swidan GRASP 0 GRIMM Permutation Size Comparative Analysis: Average Cost Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

25 Part V Conclusions Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

26 Conclusions We presented a new method for the length-weighted inversion problem on signed permutations. We considered the case where the weight function is simply the number of elements in the reversed segment. We were able to improve the initial solution in 94% of the cases. Our solutions cost 12% less than the initial solution, on average. We also show that our method provides solution that are less costly than a previous algorithm. Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

27 Acknowledgments Thiago da Silva Arruda, Ulisses Dias and Zanoni Dias AlCoB 2014 July 1-3, / 27

Worksheet - COMPARATIVE MAPPING 1

Worksheet - COMPARATIVE MAPPING 1 Worksheet - COMPARATIVE MAPPING 1 The arrangement of genes and other DNA markers is compared between species in Comparative genome mapping. As early as 1915, the geneticist J.B.S Haldane reported that

More information

Asexual Versus Sexual Reproduction in Genetic Algorithms 1

Asexual Versus Sexual Reproduction in Genetic Algorithms 1 Asexual Versus Sexual Reproduction in Genetic Algorithms Wendy Ann Deslauriers (wendyd@alumni.princeton.edu) Institute of Cognitive Science,Room 22, Dunton Tower Carleton University, 25 Colonel By Drive

More information

Pairwise Sequence Alignment

Pairwise Sequence Alignment Pairwise Sequence Alignment carolin.kosiol@vetmeduni.ac.at SS 2013 Outline Pairwise sequence alignment global - Needleman Wunsch Gotoh algorithm local - Smith Waterman algorithm BLAST - heuristics What

More information

Optimizing CPU Scheduling Problem using Genetic Algorithms

Optimizing CPU Scheduling Problem using Genetic Algorithms Optimizing CPU Scheduling Problem using Genetic Algorithms Anu Taneja Amit Kumar Computer Science Department Hindu College of Engineering, Sonepat (MDU) anutaneja16@gmail.com amitkumar.cs08@pec.edu.in

More information

Lecture 5 Mutation and Genetic Variation

Lecture 5 Mutation and Genetic Variation 1 Lecture 5 Mutation and Genetic Variation I. Review of DNA structure and function you should already know this. A. The Central Dogma DNA mrna Protein where the mistakes are made. 1. Some definitions based

More information

DNA Insertions and Deletions in the Human Genome. Philipp W. Messer

DNA Insertions and Deletions in the Human Genome. Philipp W. Messer DNA Insertions and Deletions in the Human Genome Philipp W. Messer Genetic Variation CGACAATAGCGCTCTTACTACGTGTATCG : : CGACAATGGCGCT---ACTACGTGCATCG 1. Nucleotide mutations 2. Genomic rearrangements 3.

More information

BASIC STATISTICAL METHODS FOR GENOMIC DATA ANALYSIS

BASIC STATISTICAL METHODS FOR GENOMIC DATA ANALYSIS BASIC STATISTICAL METHODS FOR GENOMIC DATA ANALYSIS SEEMA JAGGI Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-110 012 seema@iasri.res.in Genomics A genome is an organism s

More information

Hidden Markov Models

Hidden Markov Models 8.47 Introduction to omputational Molecular Biology Lecture 7: November 4, 2004 Scribe: Han-Pang hiu Lecturer: Ross Lippert Editor: Russ ox Hidden Markov Models The G island phenomenon The nucleotide frequencies

More information

Genetic Algorithm. Based on Darwinian Paradigm. Intrinsically a robust search and optimization mechanism. Conceptual Algorithm

Genetic Algorithm. Based on Darwinian Paradigm. Intrinsically a robust search and optimization mechanism. Conceptual Algorithm 24 Genetic Algorithm Based on Darwinian Paradigm Reproduction Competition Survive Selection Intrinsically a robust search and optimization mechanism Slide -47 - Conceptual Algorithm Slide -48 - 25 Genetic

More information

Regents Biology REGENTS REVIEW: PROTEIN SYNTHESIS

Regents Biology REGENTS REVIEW: PROTEIN SYNTHESIS Period Date REGENTS REVIEW: PROTEIN SYNTHESIS 1. The diagram at the right represents a portion of a type of organic molecule present in the cells of organisms. What will most likely happen if there is

More information

Biology Performance Level Descriptors

Biology Performance Level Descriptors Limited A student performing at the Limited Level demonstrates a minimal command of Ohio s Learning Standards for Biology. A student at this level has an emerging ability to describe genetic patterns of

More information

Bio-Informatics Lectures. A Short Introduction

Bio-Informatics Lectures. A Short Introduction Bio-Informatics Lectures A Short Introduction The History of Bioinformatics Sanger Sequencing PCR in presence of fluorescent, chain-terminating dideoxynucleotides Massively Parallel Sequencing Massively

More information

Genetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve

Genetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve Genetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve Outline Selection methods Replacement methods Variation operators Selection Methods

More information

Human-Mouse Synteny in Functional Genomics Experiment

Human-Mouse Synteny in Functional Genomics Experiment Human-Mouse Synteny in Functional Genomics Experiment Ksenia Krasheninnikova University of the Russian Academy of Sciences, JetBrains krasheninnikova@gmail.com September 18, 2012 Ksenia Krasheninnikova

More information

Master's projects at ITMO University. Daniil Chivilikhin PhD Student @ ITMO University

Master's projects at ITMO University. Daniil Chivilikhin PhD Student @ ITMO University Master's projects at ITMO University Daniil Chivilikhin PhD Student @ ITMO University General information Guidance from our lab's researchers Publishable results 2 Research areas Research at ITMO Evolutionary

More information

CCR Biology - Chapter 9 Practice Test - Summer 2012

CCR Biology - Chapter 9 Practice Test - Summer 2012 Name: Class: Date: CCR Biology - Chapter 9 Practice Test - Summer 2012 Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Genetic engineering is possible

More information

The sequence of bases on the mrna is a code that determines the sequence of amino acids in the polypeptide being synthesized:

The sequence of bases on the mrna is a code that determines the sequence of amino acids in the polypeptide being synthesized: Module 3F Protein Synthesis So far in this unit, we have examined: How genes are transmitted from one generation to the next Where genes are located What genes are made of How genes are replicated How

More information

RETRIEVING SEQUENCE INFORMATION. Nucleotide sequence databases. Database search. Sequence alignment and comparison

RETRIEVING SEQUENCE INFORMATION. Nucleotide sequence databases. Database search. Sequence alignment and comparison RETRIEVING SEQUENCE INFORMATION Nucleotide sequence databases Database search Sequence alignment and comparison Biological sequence databases Originally just a storage place for sequences. Currently the

More information

Modules 5: Behavior Genetics and Evolutionary Psychology

Modules 5: Behavior Genetics and Evolutionary Psychology Modules 5: Behavior Genetics and Evolutionary Psychology Source of similarities and differences Similarities with other people such as developing a languag, showing similar emotions, following similar

More information

GA as a Data Optimization Tool for Predictive Analytics

GA as a Data Optimization Tool for Predictive Analytics GA as a Data Optimization Tool for Predictive Analytics Chandra.J 1, Dr.Nachamai.M 2,Dr.Anitha.S.Pillai 3 1Assistant Professor, Department of computer Science, Christ University, Bangalore,India, chandra.j@christunivesity.in

More information

Assessment Schedule 2014 Biology: Demonstrate understanding of genetic variation and change (91157) Evidence Statement

Assessment Schedule 2014 Biology: Demonstrate understanding of genetic variation and change (91157) Evidence Statement NCEA Level 2 Biology (91157) 2014 page 1 of 5 Assessment Schedule 2014 Biology: Demonstrate understanding of genetic variation and change (91157) Evidence Statement NCEA Level 2 Biology (91157) 2014 page

More information

Genome Explorer For Comparative Genome Analysis

Genome Explorer For Comparative Genome Analysis Genome Explorer For Comparative Genome Analysis Jenn Conn 1, Jo L. Dicks 1 and Ian N. Roberts 2 Abstract Genome Explorer brings together the tools required to build and compare phylogenies from both sequence

More information

Biological Sciences Initiative. Human Genome

Biological Sciences Initiative. Human Genome Biological Sciences Initiative HHMI Human Genome Introduction In 2000, researchers from around the world published a draft sequence of the entire genome. 20 labs from 6 countries worked on the sequence.

More information

Alpha Cut based Novel Selection for Genetic Algorithm

Alpha Cut based Novel Selection for Genetic Algorithm Alpha Cut based Novel for Genetic Algorithm Rakesh Kumar Professor Girdhar Gopal Research Scholar Rajesh Kumar Assistant Professor ABSTRACT Genetic algorithm (GA) has several genetic operators that can

More information

Protein Protein Interaction Networks

Protein Protein Interaction Networks Functional Pattern Mining from Genome Scale Protein Protein Interaction Networks Young-Rae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University it My Definition of Bioinformatics

More information

6 Creating the Animation

6 Creating the Animation 6 Creating the Animation Now that the animation can be represented, stored, and played back, all that is left to do is understand how it is created. This is where we will use genetic algorithms, and this

More information

arxiv:1501.07546v1 [q-bio.gn] 29 Jan 2015

arxiv:1501.07546v1 [q-bio.gn] 29 Jan 2015 A Computational Method for the Rate Estimation of Evolutionary Transpositions Nikita Alexeev 1,2, Rustem Aidagulov 3, and Max A. Alekseyev 1, 1 Computational Biology Institute, George Washington University,

More information

SAM Teacher s Guide DNA to Proteins

SAM Teacher s Guide DNA to Proteins SAM Teacher s Guide DNA to Proteins Note: Answers to activity and homework questions are only included in the Teacher Guides available after registering for the SAM activities, and not in this sample version.

More information

1 Mutation and Genetic Change

1 Mutation and Genetic Change CHAPTER 14 1 Mutation and Genetic Change SECTION Genes in Action KEY IDEAS As you read this section, keep these questions in mind: What is the origin of genetic differences among organisms? What kinds

More information

Ancestral reconstruction and investigations of genomic recombination on Campanulids chloroplasts

Ancestral reconstruction and investigations of genomic recombination on Campanulids chloroplasts Ancestral reconstruction and investigations of genomic recombination on Campanulids chloroplasts Bashar Al-Nuaimi, Roxane Mallouhi, Bassam AlKindy, Christophe Guyeux, Michel Salomon, and Jean-Franc ois

More information

Chapter 8: Recombinant DNA 2002 by W. H. Freeman and Company Chapter 8: Recombinant DNA 2002 by W. H. Freeman and Company

Chapter 8: Recombinant DNA 2002 by W. H. Freeman and Company Chapter 8: Recombinant DNA 2002 by W. H. Freeman and Company Genetic engineering: humans Gene replacement therapy or gene therapy Many technical and ethical issues implications for gene pool for germ-line gene therapy what traits constitute disease rather than just

More information

Meiosis and Sexual Life Cycles

Meiosis and Sexual Life Cycles Meiosis and Sexual Life Cycles Chapter 13 1 Ojectives Distinguish between the following terms: somatic cell and gamete; autosome and sex chromosomes; haploid and diploid. List the phases of meiosis I and

More information

Bioinformatics Resources at a Glance

Bioinformatics Resources at a Glance Bioinformatics Resources at a Glance A Note about FASTA Format There are MANY free bioinformatics tools available online. Bioinformaticists have developed a standard format for nucleotide and protein sequences

More information

MUTATION, DNA REPAIR AND CANCER

MUTATION, DNA REPAIR AND CANCER MUTATION, DNA REPAIR AND CANCER 1 Mutation A heritable change in the genetic material Essential to the continuity of life Source of variation for natural selection New mutations are more likely to be harmful

More information

Ingenious Genes Curriculum Links for AQA AS (7401) and A-Level Biology (7402)

Ingenious Genes Curriculum Links for AQA AS (7401) and A-Level Biology (7402) Ingenious Genes Curriculum Links for AQA AS (7401) and A-Level Biology (7402) 3.1.1 Monomers and Polymers 3.1.4 Proteins 3.1.5 Nucleic acids are important information-carrying molecules 3.2.1 Cell structure

More information

Evolution at Two Levels in Humans and Chimpanzees

Evolution at Two Levels in Humans and Chimpanzees Evolution at Two Levels in Humans and Chimpanzees Mary-Claire King and A.C. Wilson What did we know prior to 1975? 1700 s: Linnaeus and others of that time considered Great Apes to be the closest relatives

More information

Project Ideas in Computer Science. Keld Helsgaun

Project Ideas in Computer Science. Keld Helsgaun Project Ideas in Computer Science Keld Helsgaun 1 Keld Helsgaun Research: Combinatorial optimization Heuristic search (artificial intelligence) Simulation Programming tools Teaching: Programming, algorithms

More information

What s the Point? --- Point, Frameshift, Inversion, & Deletion Mutations

What s the Point? --- Point, Frameshift, Inversion, & Deletion Mutations What s the Point? --- Point, Frameshift, Inversion, & Deletion Mutations http://members.cox.net/amgough/mutation_chromosome_translocation.gif Introduction: In biology, mutations are changes to the base

More information

Comparative Study: ACO and EC for TSP

Comparative Study: ACO and EC for TSP Comparative Study: ACO and EC for TSP Urszula Boryczka 1 and Rafa l Skinderowicz 1 and Damian Świstowski1 1 University of Silesia, Institute of Computer Science, Sosnowiec, Poland, e-mail: uboryczk@us.edu.pl

More information

Introduction To Genetic Algorithms

Introduction To Genetic Algorithms 1 Introduction To Genetic Algorithms Dr. Rajib Kumar Bhattacharjya Department of Civil Engineering IIT Guwahati Email: rkbc@iitg.ernet.in References 2 D. E. Goldberg, Genetic Algorithm In Search, Optimization

More information

Complexity in life, multicellular organisms and micrornas

Complexity in life, multicellular organisms and micrornas Complexity in life, multicellular organisms and micrornas Ohad Manor Abstract In this work I would like to discuss the question of defining complexity, and to focus specifically on the question of defining

More information

Nature of Genetic Material. Nature of Genetic Material

Nature of Genetic Material. Nature of Genetic Material Core Category Nature of Genetic Material Nature of Genetic Material Core Concepts in Genetics (in bold)/example Learning Objectives How is DNA organized? Describe the types of DNA regions that do not encode

More information

A very brief introduction to genetic algorithms

A very brief introduction to genetic algorithms A very brief introduction to genetic algorithms Radoslav Harman Design of experiments seminar FACULTY OF MATHEMATICS, PHYSICS AND INFORMATICS COMENIUS UNIVERSITY IN BRATISLAVA 25.2.2013 Optimization problems:

More information

When you install Mascot, it includes a copy of the Swiss-Prot protein database. However, it is almost certain that you and your colleagues will want

When you install Mascot, it includes a copy of the Swiss-Prot protein database. However, it is almost certain that you and your colleagues will want 1 When you install Mascot, it includes a copy of the Swiss-Prot protein database. However, it is almost certain that you and your colleagues will want to search other databases as well. There are very

More information

APPENDIX MODELING, SIMULATION AND APPLICATION OF BACTERIAL TRANSDUCTION IN GENETIC ALGORITHMS

APPENDIX MODELING, SIMULATION AND APPLICATION OF BACTERIAL TRANSDUCTION IN GENETIC ALGORITHMS Modeling, Simulation and Application of Bacterial Transduction in Genetic Algorithms. Appendix 1 APPENDIX MODELING, SIMULATION AND APPLICATION OF BACTERIAL TRANSDUCTION IN GENETIC ALGORITHMS Carlos Perales-Gravan,

More information

A Non-Linear Schema Theorem for Genetic Algorithms

A Non-Linear Schema Theorem for Genetic Algorithms A Non-Linear Schema Theorem for Genetic Algorithms William A Greene Computer Science Department University of New Orleans New Orleans, LA 70148 bill@csunoedu 504-280-6755 Abstract We generalize Holland

More information

CAP BIOINFORMATICS Su-Shing Chen CISE. 10/5/2005 Su-Shing Chen, CISE 1

CAP BIOINFORMATICS Su-Shing Chen CISE. 10/5/2005 Su-Shing Chen, CISE 1 CAP 5510-8 BIOINFORMATICS Su-Shing Chen CISE 10/5/2005 Su-Shing Chen, CISE 1 Genomic Mapping & Mapping Databases High resolution, genome-wide maps of DNA markers. Integrated maps, genome catalogs and comprehensive

More information

College of information technology Department of software

College of information technology Department of software University of Babylon Undergraduate: third class College of information technology Department of software Subj.: Application of AI lecture notes/2011-2012 ***************************************************************************

More information

Genetic Algorithm Performance with Different Selection Strategies in Solving TSP

Genetic Algorithm Performance with Different Selection Strategies in Solving TSP Proceedings of the World Congress on Engineering Vol II WCE, July 6-8,, London, U.K. Genetic Algorithm Performance with Different Selection Strategies in Solving TSP Noraini Mohd Razali, John Geraghty

More information

PREDA S4-classes. Francesco Ferrari October 13, 2015

PREDA S4-classes. Francesco Ferrari October 13, 2015 PREDA S4-classes Francesco Ferrari October 13, 2015 Abstract This document provides a description of custom S4 classes used to manage data structures for PREDA: an R package for Position RElated Data Analysis.

More information

9th Grade. 9th -12th Grade History - Social Science. 9th -12th Grade Sciences

9th Grade. 9th -12th Grade History - Social Science. 9th -12th Grade Sciences 9th Grade 9th -12th Grade History - Social Science Historical and Social Sciences Analysis Skills Chronological and Spatial Thinking 1. Students compare and contrast the present with the past, evaluating

More information

Algorithms in Computational Biology (236522) spring 2007 Lecture #1

Algorithms in Computational Biology (236522) spring 2007 Lecture #1 Algorithms in Computational Biology (236522) spring 2007 Lecture #1 Lecturer: Shlomo Moran, Taub 639, tel 4363 Office hours: Tuesday 11:00-12:00/by appointment TA: Ilan Gronau, Taub 700, tel 4894 Office

More information

MAKING AN EVOLUTIONARY TREE

MAKING AN EVOLUTIONARY TREE Student manual MAKING AN EVOLUTIONARY TREE THEORY The relationship between different species can be derived from different information sources. The connection between species may turn out by similarities

More information

Fact Sheet 1 AN INTRODUCTION TO DNA, GENES AND CHROMOSOMES

Fact Sheet 1 AN INTRODUCTION TO DNA, GENES AND CHROMOSOMES 10:23 AM11111 DNA contains the instructions for growth and development in humans and all living things. Our DNA is packaged into chromosomes that contain all of our genes. In summary DNA stands for (DeoxyriboNucleic

More information

Mutations & DNA Technology Worksheet

Mutations & DNA Technology Worksheet Mutations & DNA Technology Worksheet Name Section A: Mutations Mutations are changes in DNA. Somatic mutations occur in non-reproductive cells and won't be passed onto offspring. Mutations that occur in

More information

Assessment Schedule 2012 Science: Demonstrate understanding of biological ideas relating to genetic variation (90948)

Assessment Schedule 2012 Science: Demonstrate understanding of biological ideas relating to genetic variation (90948) NCEA Level 1 Science (90948) 2012 page 1 of 5 Assessment Schedule 2012 Science: Demonstrate understanding of biological ideas relating to genetic variation (90948) Assessment Criteria ONE (a) (b) DNA contains

More information

Integrating DNA Motif Discovery and Genome-Wide Expression Analysis. Erin M. Conlon

Integrating DNA Motif Discovery and Genome-Wide Expression Analysis. Erin M. Conlon Integrating DNA Motif Discovery and Genome-Wide Expression Analysis Department of Mathematics and Statistics University of Massachusetts Amherst Statistics in Functional Genomics Workshop Ascona, Switzerland

More information

Focusing on results not data comprehensive data analysis for targeted next generation sequencing

Focusing on results not data comprehensive data analysis for targeted next generation sequencing Focusing on results not data comprehensive data analysis for targeted next generation sequencing Daniel Swan, Jolyon Holdstock, Angela Matchan, Richard Stark, John Shovelton, Duarte Mohla and Simon Hughes

More information

Integer Programming: Algorithms - 3

Integer Programming: Algorithms - 3 Week 9 Integer Programming: Algorithms - 3 OPR 992 Applied Mathematical Programming OPR 992 - Applied Mathematical Programming - p. 1/12 Dantzig-Wolfe Reformulation Example Strength of the Linear Programming

More information

Volume 3, Issue 2, February 2015 International Journal of Advance Research in Computer Science and Management Studies

Volume 3, Issue 2, February 2015 International Journal of Advance Research in Computer Science and Management Studies Volume 3, Issue 2, February 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com

More information

Just the Facts: A Basic Introduction to the Science Underlying NCBI Resources

Just the Facts: A Basic Introduction to the Science Underlying NCBI Resources 1 of 8 11/7/2004 11:00 AM National Center for Biotechnology Information About NCBI NCBI at a Glance A Science Primer Human Genome Resources Model Organisms Guide Outreach and Education Databases and Tools

More information

Next Generation Sequencing: Technology, Mapping, and Analysis

Next Generation Sequencing: Technology, Mapping, and Analysis Next Generation Sequencing: Technology, Mapping, and Analysis Gary Benson Computer Science, Biology, Bioinformatics Boston University gbenson@bu.edu http://tandem.bu.edu/ The Human Genome Project took

More information

Genetomic Promototypes

Genetomic Promototypes Genetomic Promototypes Mirkó Palla and Dana Pe er Department of Mechanical Engineering Clarkson University Potsdam, New York and Department of Genetics Harvard Medical School 77 Avenue Louis Pasteur Boston,

More information

GenBank, Entrez, & FASTA

GenBank, Entrez, & FASTA GenBank, Entrez, & FASTA Nucleotide Sequence Databases First generation GenBank is a representative example started as sort of a museum to preserve knowledge of a sequence from first discovery great repositories,

More information

Human Mendelian Disorders. Genetic Technology. What is Genetics? Genes are DNA 9/3/2008. Multifactorial Disorders

Human Mendelian Disorders. Genetic Technology. What is Genetics? Genes are DNA 9/3/2008. Multifactorial Disorders Human genetics: Why? Human Genetics Introduction Determine genotypic basis of variant phenotypes to facilitate: Understanding biological basis of human genetic diversity Prenatal diagnosis Predictive testing

More information

NSilico Life Science Introductory Bioinformatics Course

NSilico Life Science Introductory Bioinformatics Course NSilico Life Science Introductory Bioinformatics Course INTRODUCTORY BIOINFORMATICS COURSE A public course delivered over three days on the fundamentals of bioinformatics and illustrated with lectures,

More information

Constructing nurse schedules at large hospitals

Constructing nurse schedules at large hospitals Intl. Trans. in Op. Res. 10 (2003) 245 265 INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH Constructing nurse schedules at large hospitals Tiago M. Dias a, Daniel F. Ferber b, Cid C. de Souza c and

More information

CSE8393 Introduction to Bioinformatics Lecture 3: More problems, Global Alignment. DNA sequencing

CSE8393 Introduction to Bioinformatics Lecture 3: More problems, Global Alignment. DNA sequencing SE8393 Introduction to Bioinformatics Lecture 3: More problems, Global lignment DN sequencing Recall that in biological experiments only relatively short segments of the DN can be investigated. To investigate

More information

Evolutionary SAT Solver (ESS)

Evolutionary SAT Solver (ESS) Ninth LACCEI Latin American and Caribbean Conference (LACCEI 2011), Engineering for a Smart Planet, Innovation, Information Technology and Computational Tools for Sustainable Development, August 3-5, 2011,

More information

Genetic Inversion Rearrangement: Are you a rotated mouse? BMCI 05

Genetic Inversion Rearrangement: Are you a rotated mouse? BMCI 05 Genetic Inversion Rearrangement: Are you a rotated mouse? BMCI 05 Kathy Erickson Great Barrington, MA spence417@aol.com Tom Fleetwood Wilmington, DE tfleetwood@charterschool.org Patrick Flynn Kansas City,

More information

Ch. 12: DNA and RNA 12.1 DNA Chromosomes and DNA Replication

Ch. 12: DNA and RNA 12.1 DNA Chromosomes and DNA Replication Ch. 12: DNA and RNA 12.1 DNA A. To understand genetics, biologists had to learn the chemical makeup of the gene Genes are made of DNA DNA stores and transmits the genetic information from one generation

More information

DEVELOPMENT OF A WEBBASED APPLICATION TO DETECT PALINDROMES IN DNA SEQUENCES

DEVELOPMENT OF A WEBBASED APPLICATION TO DETECT PALINDROMES IN DNA SEQUENCES DEVELOPMENT OF A WEBBASED APPLICATION TO DETECT PALINDROMES IN DNA SEQUENCES Fatma Eltayeb 1, Muna Elbahir 2, Sahar Mohamed 3, Mohamed Ahmed and 4 Nazar Zaki 5 1, 2, 3,4 Faculty of Mathematical Science,

More information

Lecture 19: Proteins, Primary Struture

Lecture 19: Proteins, Primary Struture CPS260/BGT204.1 Algorithms in Computational Biology November 04, 2003 Lecture 19: Proteins, Primary Struture Lecturer: Pankaj K. Agarwal Scribe: Qiuhua Liu 19.1 The Building Blocks of Protein [1] Proteins

More information

A response to charges of error in Biology by Miller & Levine

A response to charges of error in Biology by Miller & Levine A response to charges of error in Biology by Miller & Levine According to TEA, a citizen disputes two sentences on page 767 of our textbook, Biology, by Miller & Levine. These sentences are: SE 767, par.

More information

ISSN: 2319-5967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013

ISSN: 2319-5967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013 Transistor Level Fault Finding in VLSI Circuits using Genetic Algorithm Lalit A. Patel, Sarman K. Hadia CSPIT, CHARUSAT, Changa., CSPIT, CHARUSAT, Changa Abstract This paper presents, genetic based algorithm

More information

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO)

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) Overview Kyrre Glette kyrrehg@ifi INF3490 Swarm Intelligence Particle Swarm Optimization Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) 3 Swarms in nature Fish, birds,

More information

Comparative genomic hybridization Because arrays are more than just a tool for expression analysis

Comparative genomic hybridization Because arrays are more than just a tool for expression analysis Microarray Data Analysis Workshop MedVetNet Workshop, DTU 2008 Comparative genomic hybridization Because arrays are more than just a tool for expression analysis Carsten Friis ( with several slides from

More information

PROC. CAIRO INTERNATIONAL BIOMEDICAL ENGINEERING CONFERENCE 2006 1. E-mail: msm_eng@k-space.org

PROC. CAIRO INTERNATIONAL BIOMEDICAL ENGINEERING CONFERENCE 2006 1. E-mail: msm_eng@k-space.org BIOINFTool: Bioinformatics and sequence data analysis in molecular biology using Matlab Mai S. Mabrouk 1, Marwa Hamdy 2, Marwa Mamdouh 2, Marwa Aboelfotoh 2,Yasser M. Kadah 2 1 Biomedical Engineering Department,

More information

DnaSP, DNA polymorphism analyses by the coalescent and other methods.

DnaSP, DNA polymorphism analyses by the coalescent and other methods. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Author affiliation: Julio Rozas 1, *, Juan C. Sánchez-DelBarrio 2,3, Xavier Messeguer 2 and Ricardo Rozas 1 1 Departament de Genètica,

More information

Lecture 6: Single nucleotide polymorphisms (SNPs) and Restriction Fragment Length Polymorphisms (RFLPs)

Lecture 6: Single nucleotide polymorphisms (SNPs) and Restriction Fragment Length Polymorphisms (RFLPs) Lecture 6: Single nucleotide polymorphisms (SNPs) and Restriction Fragment Length Polymorphisms (RFLPs) Single nucleotide polymorphisms or SNPs (pronounced "snips") are DNA sequence variations that occur

More information

Modified Version of Roulette Selection for Evolution Algorithms - the Fan Selection

Modified Version of Roulette Selection for Evolution Algorithms - the Fan Selection Modified Version of Roulette Selection for Evolution Algorithms - the Fan Selection Adam S lowik, Micha l Bia lko Department of Electronic, Technical University of Koszalin, ul. Śniadeckich 2, 75-453 Koszalin,

More information

Hybrid Genetic Algorithm for DNA Sequencing with Errors

Hybrid Genetic Algorithm for DNA Sequencing with Errors Journal of Heuristics, 8: 495 502, 2002 c 2002 Kluwer Academic Publishers. Manufactured in The Netherlands. Hybrid Genetic Algorithm for DNA Sequencing with Errors JACEK B LAŻEWICZ AND MARTA KASPRZAK Institute

More information

A Review And Evaluations Of Shortest Path Algorithms

A Review And Evaluations Of Shortest Path Algorithms A Review And Evaluations Of Shortest Path Algorithms Kairanbay Magzhan, Hajar Mat Jani Abstract: Nowadays, in computer networks, the routing is based on the shortest path problem. This will help in minimizing

More information

GENETIC ALGORITHMS. Introduction to Genetic Algorithms

GENETIC ALGORITHMS. Introduction to Genetic Algorithms Introduction to genetic algorithms with Java applets Introduction to Genetic Algorithms GENETIC ALGORITHMS Main page Introduction Biological Background Search Space Genetic Algorithm GA Operators GA Example

More information

CPO Science and the NGSS

CPO Science and the NGSS CPO Science and the NGSS It is no coincidence that the performance expectations in the Next Generation Science Standards (NGSS) are all action-based. The NGSS champion the idea that science content cannot

More information

Genetic Algorithm for Solving Simple Mathematical Equality Problem

Genetic Algorithm for Solving Simple Mathematical Equality Problem Genetic Algorithm for Solving Simple Mathematical Equality Problem Denny Hermawanto Indonesian Institute of Sciences (LIPI), INDONESIA Mail: denny.hermawanto@gmail.com Abstract This paper explains genetic

More information

2015 NATIONAL SCIENCE OLYMPIAD AND NEXT GENERATION SCIENCE STANDARDS ALIGNMENT

2015 NATIONAL SCIENCE OLYMPIAD AND NEXT GENERATION SCIENCE STANDARDS ALIGNMENT 2015 NATIONAL SCIENCE OLYMPIAD AND NEXT GENERATION SCIENCE STANDARDS ALIGNMENT C (HIGH SCHOOL) DIVISION AIR TRAJECTORY Prior to the competition, teams will design, construct, and calibrate a single device

More information

New Modifications of Selection Operator in Genetic Algorithms for the Traveling Salesman Problem

New Modifications of Selection Operator in Genetic Algorithms for the Traveling Salesman Problem New Modifications of Selection Operator in Genetic Algorithms for the Traveling Salesman Problem Radovic, Marija; and Milutinovic, Veljko Abstract One of the algorithms used for solving Traveling Salesman

More information

Principles of Evolution - Origin of Species

Principles of Evolution - Origin of Species Theories of Organic Evolution X Multiple Centers of Creation (de Buffon) developed the concept of "centers of creation throughout the world organisms had arisen, which other species had evolved from X

More information

Management Science Letters

Management Science Letters Management Science Letters 4 (2014) 905 912 Contents lists available at GrowingScience Management Science Letters homepage: www.growingscience.com/msl Measuring customer loyalty using an extended RFM and

More information

Effect of Using Neural Networks in GA-Based School Timetabling

Effect of Using Neural Networks in GA-Based School Timetabling Effect of Using Neural Networks in GA-Based School Timetabling JANIS ZUTERS Department of Computer Science University of Latvia Raina bulv. 19, Riga, LV-1050 LATVIA janis.zuters@lu.lv Abstract: - The school

More information

Evolution (18%) 11 Items Sample Test Prep Questions

Evolution (18%) 11 Items Sample Test Prep Questions Evolution (18%) 11 Items Sample Test Prep Questions Grade 7 (Evolution) 3.a Students know both genetic variation and environmental factors are causes of evolution and diversity of organisms. (pg. 109 Science

More information

Name Class Date. KEY CONCEPT Mutations are changes in DNA that may or may not affect phenotype. frameshift mutation

Name Class Date. KEY CONCEPT Mutations are changes in DNA that may or may not affect phenotype. frameshift mutation Unit 7 Study Guide Section 8.7: Mutations KEY CONCEPT Mutations are changes in DNA that may or may not affect phenotype. VOCABULARY mutation point mutation frameshift mutation mutagen MAIN IDEA: Some mutations

More information

How to Build a Phylogenetic Tree

How to Build a Phylogenetic Tree How to Build a Phylogenetic Tree Phylogenetics tree is a structure in which species are arranged on branches that link them according to their relationship and/or evolutionary descent. A typical rooted

More information

A Framework for Genetic Algorithms in Games

A Framework for Genetic Algorithms in Games A Framework for Genetic Algorithms in Games Vinícius Godoy de Mendonça Cesar Tadeu Pozzer Roberto Tadeu Raiitz 1 Universidade Positivo, Departamento de Informática 2 Universidade Federal de Santa Maria,

More information

Evolutionary Detection of Rules for Text Categorization. Application to Spam Filtering

Evolutionary Detection of Rules for Text Categorization. Application to Spam Filtering Advances in Intelligent Systems and Technologies Proceedings ECIT2004 - Third European Conference on Intelligent Systems and Technologies Iasi, Romania, July 21-23, 2004 Evolutionary Detection of Rules

More information

Aim #29: NYS Biodiversity Lab Review

Aim #29: NYS Biodiversity Lab Review Name: Aim #29: NYS Biodiversity Lab Review Date: 1. Which chemicals are used to cut DNA into fragments for a gel electrophoresis procedure? A) enzymes B) molecular bases C) hormones D) ATP molecules 2.

More information

Statistical mechanics for real biological networks

Statistical mechanics for real biological networks Statistical mechanics for real biological networks William Bialek Joseph Henry Laboratories of Physics, and Lewis-Sigler Institute for Integrative Genomics Princeton University Initiative for the Theoretical

More information

Bob Jesberg. Boston, MA April 3, 2014

Bob Jesberg. Boston, MA April 3, 2014 DNA, Replication and Transcription Bob Jesberg NSTA Conference Boston, MA April 3, 2014 1 Workshop Agenda Looking at DNA and Forensics The DNA, Replication i and Transcription i Set DNA Ladder The Double

More information

Terms: The following terms are presented in this lesson (shown in bold italics and on PowerPoint Slides 2 and 3):

Terms: The following terms are presented in this lesson (shown in bold italics and on PowerPoint Slides 2 and 3): Unit B: Understanding Animal Reproduction Lesson 4: Understanding Genetics Student Learning Objectives: Instruction in this lesson should result in students achieving the following objectives: 1. Explain

More information