Fourth Annual PRIMES Conference. MAY 18, 2014 Loop Extruding Enzymes in Interphase: Dynamic Folding of Chromatin Domains

Size: px
Start display at page:

Download "Fourth Annual PRIMES Conference. MAY 18, 2014 Loop Extruding Enzymes in Interphase: Dynamic Folding of Chromatin Domains"

Transcription

1 Fourth Annual PRIMES Conference. MAY 18, 2014 Loop Extruding Enzymes in Interphase: Dynamic Folding of Chromatin Domains Carolyn Lu Professor Leonid Mirny Maxim Imakaev, Geoffrey Fudenberg

2 Characterizing Chromosomal Contacts: Contact maps and scalings contacts Contact maps many contacts Contact probability scalings: (contact probability vs. separation distance) distance few contacts

3 6.5 Mb 55.5 Mb TADs: Topologically Associated Domains few contacts many contacts Regions of increased interaction ~1.3Mb Decreased interaction across boundaries 62 Mb Region of Chromosome Mb 62 Mb

4 contact frequency TAD Scalings distance in kb Within TAD slope in log-log of contact probability over distance in kb is -0.5 Between TADs slope goes from around -0.5 at closer distances to -1.0 at further

5 What mechanisms can define TADs and TAD boundaries consistent with experimental data?

6 Methods: Polymer simulations Experimental Simulation? contact map? scaling???

7 Previously explored models: thick-thin RNA stiff-flexible

8 SMCs as loop-extruding proteins Alipour, Elnaz, and John F. Marko. "Self-organization of domain structures by DNA-loopextruding enzymes." Nucleic acids research (2012): Anton Goloborodko & Leonid Mirny (Unpublished)

9 Basic kinetic model of SMC Loop Extrusion SMC binds DNA SMC extrudes DNA SMC releases DNA

10 SMC loop extrusion + boundary TAD boundaries? TAD boundaries have been shown to be highly bound by a number of key epigenetic regulators. Hypothesis: Boundaries which halt SMC loop extrusion or release SMC loops can in turn create TAD boundaries boundary

11 Complications of the kinetic model 1. What happens when a loop meets a boundary? 2. What happens when a loop meets another loop?

12 Kinetic SMC model: Loop-boundary behavior Boundary Stall OR Release

13 Kinetic SMC model: loop-loop behavior Stall OR Cross

14 Four variations on the basic kinetic model of SMC loop extrusion Since details of SMC loop extrusion are unknown, we tested a number of possible variations: - Boundary stalling and loop-loop stalling (stalling/stalling) - Long boundary stalling and loop-loop stalling (long-stalling/stalling) - Boundary release and loop-loop crossing (release/crossing) - Boundary release and loop-loop stalling (release/stalling)

15 time SMC extrusion kinetic Model 1: Boundary stalling and loop-loop stalling monomer boundary

16 SMC extrusion kinetic model 1: Contact Map (stall/stall) Simulated TADs Experimental TADs

17 Stall-stall: Scalings contact frequency

18 time SMC extrusion kinetic Model 2: Long boundaries and loop-loop stalling monomer Point boundary stall/stall

19 Long boundary: Contact maps Loop density 20 Loop density 5 Experimental TADs

20 Long boundary: Scalings Loop density 20 Loop density 5

21 time SMC extrusion Model 3: Boundary release and loop crossing monomer Point boundary stall/stall

22 Release/cross: Contact map Simulated TADs Experimental TADs

23 contact frequency Release/cross: Scalings monomer distance

24 time SMC extrusion Model 4: Boundary release and loop stalling monomer Point boundary stall/stall Model 3: release/cross

25 Release-stall: Contact map Simulated TADs Experimental TADs

26 contact frequency Release-stall: Scalings monomer distance

27 contact frequency experimental Comparison of models monomer distance

28 Conclusions and Discussion Our models of SMC loop extrusion can reproduce some features of TAD contact maps but do not completely reproduce TAD scalings Details of SMC behavior strongly affect simulated contact maps and scalings o Release at boundaries / stalling between loops best reproduced experimental data Further biological experiments are necessary to determine detailed mechanisms of SMC action & TAD formation

29 Thanks to PRIMES alum/mit student Boryana Doyle, awesome mentors Geoffrey Fudenberg and Maxim Imakaev, Prof. Leonid Mirny, and MIT PRIMES.

Biology 644: Bioinformatics

Biology 644: Bioinformatics A statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states in the training data. First used in speech and handwriting recognition In

More information

ChIP-seq Analysis Practical

ChIP-seq Analysis Practical ChIP-seq Analysis Practical Vladimir Teif (vteif@essex.ac.uk) An updated version of this document will be available at http://generegulation.info/index.php/teaching In this practical we will learn how

More information

Structured prediction using the network perceptron

Structured prediction using the network perceptron Structured prediction using the network perceptron Ta-tsen Soong Joint work with Stuart Andrews and Prof. Tony Jebara Motivation A lot of network-structured data Social networks Citation networks Biological

More information

Dynamic Programming User Manual v1.0 Anton E. Weisstein, Truman State University Aug. 19, 2014

Dynamic Programming User Manual v1.0 Anton E. Weisstein, Truman State University Aug. 19, 2014 Dynamic Programming User Manual v1.0 Anton E. Weisstein, Truman State University Aug. 19, 2014 Dynamic programming is a group of mathematical methods used to sequentially split a complicated problem into

More information

The PDB and experimental data

The PDB and experimental data The PDB and experimental data John Westbrook Rutgers, The State University of New Jersey www.wwpdb.org Workshop on Metadata for raw data from X-ray diffraction and other structural techniques Overview

More information

Biological Image Information I: A Description of the Modalities

Biological Image Information I: A Description of the Modalities 2.771J BEH.453J HST.958J Spring 2005 Lecture 21 April 2005 Biological Image I: A Description of the Modalities The Modalities Direct photography SEM and TEM Cryo-EM Two-photon microscopy Confocal microscopy

More information

Protecting Private Data in the Cloud: A Path Oblivious RAM Protocol

Protecting Private Data in the Cloud: A Path Oblivious RAM Protocol Protecting Private Data in the Cloud: A Path Oblivious RAM Protocol Nathan Wolfe and Ethan Zou Mentors: Ling Ren and Xiangyao Yu Fourth Annual MIT PRIMES Conference May 18, 2014 Outline 1. Background 2.

More information

Introduction to GE Microarray data analysis Practical Course MolBio 2012

Introduction to GE Microarray data analysis Practical Course MolBio 2012 Introduction to GE Microarray data analysis Practical Course MolBio 2012 Claudia Pommerenke Nov-2012 Transkriptomanalyselabor TAL Microarray and Deep Sequencing Core Facility Göttingen University Medical

More information

Supplementary Information. Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts

Supplementary Information. Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts Supplementary Information Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts Supplementary Figures Ferhat Ay, Timothy L. Bailey, William Stafford Noble 1 Spline fitting

More information

Persistence of activity in random Boolean networks

Persistence of activity in random Boolean networks Persistence of activity in random Boolean networks Shirshendu Chatterjee Courant Institute, NYU Joint work with Rick Durrett October 2012 S. Chatterjee (NYU) Random Boolean Networks 1 / 11 The process

More information

SIMULATION OF ARTIFICIAL SYSTEMS BEHAVIOR IN PARAMETRIC EIGHT-DIMENSIONAL SPACE

SIMULATION OF ARTIFICIAL SYSTEMS BEHAVIOR IN PARAMETRIC EIGHT-DIMENSIONAL SPACE 78 Proceedings of the 4 th International Conference on Informatics and Information Technology SIMULATION OF ARTIFICIAL SYSTEMS BEHAVIOR IN PARAMETRIC EIGHT-DIMENSIONAL SPACE D. Ulbikiene, J. Ulbikas, K.

More information

Felsenfeld, G. & Groudine, M. (2003), Nature 421:

Felsenfeld, G. & Groudine, M. (2003), Nature 421: Felsenfeld, G. & Groudine, M. (2003), Nature 421: 448-453 Subunit structure of eukaryotic chromosomes Alberts et al., 4th ed., Fig. 4 24 (2002) Structure of the nucleosome, the fundamental subunit of eukaryotic

More information

Theory of Computation. Prof. Kamala Krithivasan. Department of Computer Science and Engineering. Indian Institute of Technology, Madras

Theory of Computation. Prof. Kamala Krithivasan. Department of Computer Science and Engineering. Indian Institute of Technology, Madras Theory of Computation Prof. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras Lecture No. # 42 Membrane Computing Today, we shall consider a new paradigm

More information

Acceleration of the Smith-Waterman algorithm for DNA sequence alignment using an FPGA platform

Acceleration of the Smith-Waterman algorithm for DNA sequence alignment using an FPGA platform Acceleration of the Smith-Waterman algorithm for DNA sequence alignment using an FPGA platform Barry Strengholt Matthijs Brobbel Delft University of Technology Faculty of Electrical Engineering, Mathematics

More information

Genome Browsers - The UCSC Genome Browser

Genome Browsers - The UCSC Genome Browser Genome Browsers - The UCSC Genome Browser Background The UCSC Genome Browser is a well-curated site that provides users with a view of gene or sequence information in genomic context for a specific species,

More information

Browser Exercises - I. Alignments and Comparative genomics

Browser Exercises - I. Alignments and Comparative genomics Browser Exercises - I Alignments and Comparative genomics 1. Navigating to the Genome Browser (GBrowse) Note: For this exercise use http://www.tritrypdb.org a. Navigate to the Genome Browser (GBrowse)

More information

Searching for Handedness in Large Data

Searching for Handedness in Large Data Searching for Handedness in Large Data Summer project Mark Jenei, UCL [4] Supervisors: Prof. Ian Robinson, Chritopher Lynch Summer of 2015 1 Abstract Many biological systems have a well-defined chirality

More information

ChromHMM: automating chromatin-state discovery and characterization

ChromHMM: automating chromatin-state discovery and characterization Nature Methods ChromHMM: automating chromatin-state discovery and characterization Jason Ernst & Manolis Kellis Supplementary Figure 1 Supplementary Figure 2 Supplementary Figure 3 Supplementary Figure

More information

Accelerating InDel Detection on Modern Multi-Core SIMD CPU Architecture

Accelerating InDel Detection on Modern Multi-Core SIMD CPU Architecture Accelerating InDel Detection on Modern Multi-Core SIMD CPU Architecture Da Zhang Collaborators: Hao Wang, Kaixi Hou, Jing Zhang Advisor: Wu-chun Feng Evolution of Genome Sequencing1 In 20032: 1 human genome

More information

LER Degradation vs. EUV Resist Thickness Report for Technical Working Group

LER Degradation vs. EUV Resist Thickness Report for Technical Working Group LER Degradation vs. EUV Resist Thickness Report for Technical Working Group Brian Cardineau, 1 William Earley, 1 Tomohisa Fujisawa, 2 Ken Maruyama, 3 Makato Shimizu, 2 Shalini Sharma, 3 Karen Petrillo,

More information

Genome Browsers Guide

Genome Browsers Guide Genome Browsers Guide Take a Class This guide supports the Galter Library class called Genome Browsers. See our Classes schedule for the next available offering. If this class is not on our upcoming schedule,

More information

Evolutionary origins of modularity

Evolutionary origins of modularity Evolutionary origins of modularity Jeff Clune, Jean-Baptiste Mouret and Hod Lipson Proceedings of the Royal Society B 2013 Presented by Raghav Partha Evolvability Evolvability capacity to rapidly adapt

More information

Computational Genomics and Molecular Biology, Fall

Computational Genomics and Molecular Biology, Fall Computational Genomics and Molecular Biology, Fall 2015 1 Sequence Alignment Dannie Durand Pairwise Sequence Alignment The goal of pairwise sequence alignment is to establish a correspondence between the

More information

K Nearest Neighbor Wrap Up K- Means Clustering. Slides adapted from Prof. Carpuat

K Nearest Neighbor Wrap Up K- Means Clustering. Slides adapted from Prof. Carpuat K Nearest Neighbor Wrap Up K- Means Clustering Slides adapted from Prof. Carpuat K Nearest Neighbor classification Classification is based on Test instance with Training Data K: number of neighbors that

More information

Statistics 202: Data Mining. c Jonathan Taylor. Clustering Based in part on slides from textbook, slides of Susan Holmes.

Statistics 202: Data Mining. c Jonathan Taylor. Clustering Based in part on slides from textbook, slides of Susan Holmes. Clustering Based in part on slides from textbook, slides of Susan Holmes December 2, 2012 1 / 1 Clustering Clustering Goal: Finding groups of objects such that the objects in a group will be similar (or

More information

FASTA. Besides that, FASTA package provides SSEARCH, an implementation of the optimal Smith- Waterman algorithm.

FASTA. Besides that, FASTA package provides SSEARCH, an implementation of the optimal Smith- Waterman algorithm. FASTA INTRODUCTION Definition (by David J. Lipman and William R. Pearson in 1985) - Compares a sequence of protein to another sequence or database of a protein, or a sequence of DNA to another sequence

More information

GPU Multiple Sequence Alignment Fourier-Space Cross-Correlation Alignment

GPU Multiple Sequence Alignment Fourier-Space Cross-Correlation Alignment GPU Multiple Sequence Alignment Fourier-Space Cross-Correlation Alignment Final Project Writeup EN.600.639 Prof. Ben Langmead Matthias Alexander Lee May 3, 2013 Abstract The aim of this project is to explore

More information

S800 Spectrawave Visible Diode Array Spectrophotometer

S800 Spectrawave Visible Diode Array Spectrophotometer 6 SCANNING VISIBLE INSTRUMENT FOR EDUCATION OPTIMISED FOR THE TEACHING LABORATORY S800 Spectrawave Visible Diode Array Spectrophotometer ABSORBANCE, % TRANSMISSION, CONCENTRATION AND KINETICS LARGE, EASY

More information

Preliminary Syllabus. Genomics. Introduction & Genome Assembly Sequence Comparison Gene Modeling Gene Function Identification

Preliminary Syllabus. Genomics. Introduction & Genome Assembly Sequence Comparison Gene Modeling Gene Function Identification Preliminary Syllabus Sep 30 Oct 2 Oct 7 Oct 9 Oct 14 Oct 16 Oct 21 Oct 25 Oct 28 Nov 4 Nov 8 Introduction & Genome Assembly Sequence Comparison Gene Modeling Gene Function Identification OCTOBER BREAK

More information

Feature Selection. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani

Feature Selection. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani Feature Selection CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Dimensionality reduction Feature selection vs. feature extraction Filter univariate

More information

Lecture 5. Functional Analysis with Blast2GO Enriched functions. Kegg Pathway Analysis Functional Similarities B2G-Far. FatiGO Babelomics.

Lecture 5. Functional Analysis with Blast2GO Enriched functions. Kegg Pathway Analysis Functional Similarities B2G-Far. FatiGO Babelomics. Lecture 5 Functional Analysis with Blast2GO Enriched functions FatiGO Babelomics FatiScan Kegg Pathway Analysis Functional Similarities B2G-Far 1 Fisher's Exact Test One Gene List (A) The other list (B)

More information

COMPARATIVE STUDY OF IMAGE EDGE DETECTION ALGORITHMS

COMPARATIVE STUDY OF IMAGE EDGE DETECTION ALGORITHMS COMPARATIVE STUDY OF IMAGE EDGE DETECTION ALGORITHMS Shubham Saini 1, Bhavesh Kasliwal 2, Shraey Bhatia 3 1 Student, School of Computing Science and Engineering, Vellore Institute of Technology, India,

More information

On the Complexity of the Policy Improvement Algorithm. for Markov Decision Processes

On the Complexity of the Policy Improvement Algorithm. for Markov Decision Processes On the Complexity of the Policy Improvement Algorithm for Markov Decision Processes Mary Melekopoglou Anne Condon Computer Sciences Department University of Wisconsin - Madison 0 West Dayton Street Madison,

More information

Blast2GO Teaching Exercises SOLUTIONS

Blast2GO Teaching Exercises SOLUTIONS Blast2GO Teaching Exerces SOLUTIONS Ana Conesa and Stefan Götz 2012 BioBam Bioinformatics S.L. Valencia, Spain Contents 1 Annotate 10 sequences with Blast2GO 2 2 Perform a complete annotation with Blast2GO

More information

Motion I. Goals and Introduction

Motion I. Goals and Introduction Motion I Goals and Introduction As you have probably already seen in lecture or homework, it is important to develop a strong understanding of how to model an object s motion for success in this course.

More information

An Introduction to SolidWorks Flow Simulation 2010

An Introduction to SolidWorks Flow Simulation 2010 An Introduction to SolidWorks Flow Simulation 2010 John E. Matsson, Ph.D. SDC PUBLICATIONS www.sdcpublications.com Schroff Development Corporation Chapter 2 Flat Plate Boundary Layer Objectives Creating

More information

Multiresolution Motif Discovery in Time Series

Multiresolution Motif Discovery in Time Series Tenth SIAM International Conference on Data Mining Columbus, Ohio, USA Multiresolution Motif Discovery in Time Series NUNO CASTRO PAULO AZEVEDO Department of Informatics University of Minho Portugal April

More information

Supplementary document to Programming 2D/3D shape-shifting with hobbyist 3D printers

Supplementary document to Programming 2D/3D shape-shifting with hobbyist 3D printers Electronic Supplementary Material (ESI) for Materials Horizons. This journal is The Royal Society of Chemistry 2017 Supplementary document to Programming 2D/3D shape-shifting with hobbyist 3D printers

More information

Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks

Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks Liu and Wang BMC Bioinformatics, 19(Suppl 17):496 https://doi.org/10.1186/s12859-018-2464-z RESEARCH Open Access Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks

More information

Identifying and Understanding Differential Transcriptor Binding

Identifying and Understanding Differential Transcriptor Binding Identifying and Understanding Differential Transcriptor Binding 15-899: Computational Genomics David Koes Yong Lu Motivation Under different conditions, a transcription factor binds to different genes

More information

Abstract. Die Geometry. Introduction. Mesh Partitioning Technique for Coextrusion Simulation

Abstract. Die Geometry. Introduction. Mesh Partitioning Technique for Coextrusion Simulation OPTIMIZATION OF A PROFILE COEXTRUSION DIE USING A THREE-DIMENSIONAL FLOW SIMULATION SOFTWARE Kim Ryckebosh 1 and Mahesh Gupta 2, 3 1. Deceuninck nv, BE-8830 Hooglede-Gits, Belgium 2. Michigan Technological

More information

Wire Bonding Solutions for Complex Memory Packages. John Foley Kulicke & Soffa Industries

Wire Bonding Solutions for Complex Memory Packages. John Foley Kulicke & Soffa Industries Wire Bonding Solutions for Complex Memory Packages John Foley Kulicke & Soffa Industries Growth in the Memory Market Segment DRAM Unit Shipment Forecast (M units) By Density 2014 2015 2016 2017 2018 2019

More information

USING AN EXTENDED SUFFIX TREE TO SPEED-UP SEQUENCE ALIGNMENT

USING AN EXTENDED SUFFIX TREE TO SPEED-UP SEQUENCE ALIGNMENT IADIS International Conference Applied Computing 2006 USING AN EXTENDED SUFFIX TREE TO SPEED-UP SEQUENCE ALIGNMENT Divya R. Singh Software Engineer Microsoft Corporation, Redmond, WA 98052, USA Abdullah

More information

The Price of Anarchy on Complex Networks

The Price of Anarchy on Complex Networks The Price of Anarchy on Complex Networks NetSci. Conference May 22, 2006 HyeJin Youn, Hawoong Jeong Complex Systems & Statistical Physics Lab. (Dept. of Physics, KAIST, Korea) Importance of networks &

More information

Distributed Fine-Grained Node Localization in Ad-Hoc Networks. A Scalable Location Service for Geographic Ad Hoc Routing. Presented by An Nguyen

Distributed Fine-Grained Node Localization in Ad-Hoc Networks. A Scalable Location Service for Geographic Ad Hoc Routing. Presented by An Nguyen Distributed Fine-Grained Node Localization in Ad-Hoc Networks A Scalable Location Service for Geographic Ad Hoc Routing Presented by An Nguyen Distributed Fine-Grained Node Localization in Ad-Hoc Networks

More information

HIDDEN MARKOV MODELS AND SEQUENCE ALIGNMENT

HIDDEN MARKOV MODELS AND SEQUENCE ALIGNMENT HIDDEN MARKOV MODELS AND SEQUENCE ALIGNMENT - Swarbhanu Chatterjee. Hidden Markov models are a sophisticated and flexible statistical tool for the study of protein models. Using HMMs to analyze proteins

More information

Easy visualization of the read coverage using the CoverageView package

Easy visualization of the read coverage using the CoverageView package Easy visualization of the read coverage using the CoverageView package Ernesto Lowy European Bioinformatics Institute EMBL June 13, 2018 > options(width=40) > library(coverageview) 1 Introduction This

More information

SolidWorks Flow Simulation 2014

SolidWorks Flow Simulation 2014 An Introduction to SolidWorks Flow Simulation 2014 John E. Matsson, Ph.D. SDC PUBLICATIONS Better Textbooks. Lower Prices. www.sdcpublications.com Powered by TCPDF (www.tcpdf.org) Visit the following websites

More information

Compares a sequence of protein to another sequence or database of a protein, or a sequence of DNA to another sequence or library of DNA.

Compares a sequence of protein to another sequence or database of a protein, or a sequence of DNA to another sequence or library of DNA. Compares a sequence of protein to another sequence or database of a protein, or a sequence of DNA to another sequence or library of DNA. Fasta is used to compare a protein or DNA sequence to all of the

More information

Reduced Order Models for Oxycombustion Boiler Optimization

Reduced Order Models for Oxycombustion Boiler Optimization Reduced Order Models for Oxycombustion Boiler Optimization John Eason Lorenz T. Biegler 9 March 2014 Project Objective Develop an equation oriented framework to optimize a coal oxycombustion flowsheet.

More information

Finding Topologically Associating Domains. Revealing hidden patterns in the three-dimensional structure of DNA

Finding Topologically Associating Domains. Revealing hidden patterns in the three-dimensional structure of DNA Finding Topologically Associating Domains Revealing hidden patterns in the three-dimensional structure of DNA Ivar Grytten Master s Thesis Spring 2015 Finding Topologically Associating Domains Ivar Grytten

More information

You will be re-directed to the following result page.

You will be re-directed to the following result page. ENCODE Element Browser Goal: to navigate the candidate DNA elements predicted by the ENCODE consortium, including gene expression, DNase I hypersensitive sites, TF binding sites, and candidate enhancers/promoters.

More information

Package enrich. September 3, 2013

Package enrich. September 3, 2013 Package enrich September 3, 2013 Type Package Title An R package for the analysis of multiple ChIP-seq data Version 2.0 Date 2013-09-01 Author Yanchun Bao , Veronica Vinciotti

More information

A Branch and Bound Algorithm for the Protein Folding Problem in the HP Lattice Model

A Branch and Bound Algorithm for the Protein Folding Problem in the HP Lattice Model Article A Branch and Bound Algorithm for the Protein Folding Problem in the HP Lattice Model Mao Chen* and Wen-Qi Huang School of Computer Science and Technology, Huazhong University of Science and Technology,

More information

Double Self-Organizing Maps to Cluster Gene Expression Data

Double Self-Organizing Maps to Cluster Gene Expression Data Double Self-Organizing Maps to Cluster Gene Expression Data Dali Wang, Habtom Ressom, Mohamad Musavi, Cristian Domnisoru University of Maine, Department of Electrical & Computer Engineering, Intelligent

More information

High School Science Acid and Base Concentrations (ph and poh) Lesson Objective: Subobjective 1: Subobjective 2: Subobjective 3: Subobjective 4:

High School Science Acid and Base Concentrations (ph and poh) Lesson Objective: Subobjective 1: Subobjective 2: Subobjective 3: Subobjective 4: High School Science Acid and Base Concentrations (ph and poh) Lesson Objective: The student will determine acid and base concentrations. Subobjective 1: The student will use the ph scale to characterize

More information

CSE 111 Bio: Program Design I Lecture 13: BLAST, while loops. Bob Sloan (CS) & Rachel Poretsky (Bio) University of Illinois, Chicago October 10, 2017

CSE 111 Bio: Program Design I Lecture 13: BLAST, while loops. Bob Sloan (CS) & Rachel Poretsky (Bio) University of Illinois, Chicago October 10, 2017 CSE 111 Bio: Program Design I Lecture 13: BLAST, while loops Bob Sloan (CS) & Rachel Poretsky (Bio) University of Illinois, Chicago October 10, 2017 Grace Hopper Celebration of Women in Computing Apply

More information

Nature Biotechnology: doi: /nbt Supplementary Figure 1

Nature Biotechnology: doi: /nbt Supplementary Figure 1 Supplementary Figure 1 Detailed schematic representation of SuRE methodology. See Methods for detailed description. a. Size-selected and A-tailed random fragments ( queries ) of the human genome are inserted

More information

Wavelets. Earl F. Glynn. Scientific Programmer Bioinformatics. 9 July Wavelets

Wavelets. Earl F. Glynn. Scientific Programmer Bioinformatics. 9 July Wavelets Wavelets Earl F. Glynn Scientific Programmer Bioinformatics 9 July 2003 1 Wavelets History Background Denoising Yeast Cohesins Binding Data Possible Applications of Wavelets Wavelet References and Resources

More information

Special course in Computer Science: Advanced Text Algorithms

Special course in Computer Science: Advanced Text Algorithms Special course in Computer Science: Advanced Text Algorithms Lecture 6: Alignments Elena Czeizler and Ion Petre Department of IT, Abo Akademi Computational Biomodelling Laboratory http://www.users.abo.fi/ipetre/textalg

More information

Technical Brief: Specifying a PC for Mascot

Technical Brief: Specifying a PC for Mascot Technical Brief: Specifying a PC for Mascot Matrix Science 8 Wyndham Place London W1H 1PP United Kingdom Tel: +44 (0)20 7723 2142 Fax: +44 (0)20 7725 9360 info@matrixscience.com http://www.matrixscience.com

More information

Variations on Genetic Cellular Automata

Variations on Genetic Cellular Automata Variations on Genetic Cellular Automata Alice Durand David Olson Physics Department amdurand@ucdavis.edu daolson@ucdavis.edu Abstract: We investigated the properties of cellular automata with three or

More information

TAIR User guide. TAIR User Guide Version 1.0 1

TAIR User guide. TAIR User Guide Version 1.0 1 TAIR User guide TAIR User Guide Version 1.0 1 Getting Started... 3 Browser compatibility and configuration.... 3 Additional Resources... 3 Finding help documents for TAIR tools... 3 Requesting Help....

More information

Miniaturizing Components by Reverse Engineering and Rapid Prototyping Techniques

Miniaturizing Components by Reverse Engineering and Rapid Prototyping Techniques Miniaturizing Components by Reverse Engineering and Rapid Prototyping Techniques L. Francis Xavier 1, Dr. D. Elangovan 2, N.Subramani 3, R.Mahesh 4 Assistant Professor, Department of Mechanical Engineering,

More information

Kyle Gettig. Mentor Benjamin Iriarte Fourth Annual MIT PRIMES Conference May 17, 2014

Kyle Gettig. Mentor Benjamin Iriarte Fourth Annual MIT PRIMES Conference May 17, 2014 Linear Extensions of Directed Acyclic Graphs Kyle Gettig Mentor Benjamin Iriarte Fourth Annual MIT PRIMES Conference May 17, 2014 Motivation Individuals can be modeled as vertices of a graph, with edges

More information

RiceFREND Ver 2.0 User Manual

RiceFREND Ver 2.0 User Manual RiceFREND Ver 2.0 User Manual About Coexpression Index Coexpression Search Options Coexpression Gene Network in Hyper Tree Coexpression Gene Network in Cytoscape Web (Single) Coexpression Gene Network

More information

Deciphering the Information Encoded in RNA Viral Genomes

Deciphering the Information Encoded in RNA Viral Genomes Deciphering the Information Encoded in RNA Viral Genomes Christine E. Heitsch Genome Center of Wisconsin and Mathematics Department University of Wisconsin Madison Detecting and Processing Regularities

More information

Constrained Sequence Alignment

Constrained Sequence Alignment Western Washington University Western CEDAR WWU Honors Program Senior Projects WWU Graduate and Undergraduate Scholarship 12-5-2017 Constrained Sequence Alignment Kyle Daling Western Washington University

More information

Metrics for Comparing 3D Neuron Segmentations in Expansion Microscopy Connectomics

Metrics for Comparing 3D Neuron Segmentations in Expansion Microscopy Connectomics Metrics for Comparing 3D Neuron Segmentations in Expansion Microscopy Connectomics By Albert Gerovitch Mentors: Adam Marblestone, Daniel Goodwin (Boyden Lab) Background Information Neurons Functional unit

More information

Locality. Cache. Direct Mapped Cache. Direct Mapped Cache

Locality. Cache. Direct Mapped Cache. Direct Mapped Cache Locality A principle that makes having a memory hierarchy a good idea If an item is referenced, temporal locality: it will tend to be referenced again soon spatial locality: nearby items will tend to be

More information

Tiling-Harmonic Functions

Tiling-Harmonic Functions Tiling-Harmonic Functions Yilun Du Vyron Vellis (Graduate Student Mentor) Sergiy Merenkov (Faculty Mentor) PRIMES-IGL Fourth Annual PRIMES Conference May 17, 2014 Table of Contents 1 Grid Harmonic Function

More information

Additive Manufacturing

Additive Manufacturing Additive Manufacturing Prof. J. Ramkumar Department of Mechanical Engineering IIT Kanpur March 28, 2018 Outline Introduction to Additive Manufacturing Classification of Additive Manufacturing Systems Introduction

More information

ssrna Example SASSIE Workflow Data Interpolation

ssrna Example SASSIE Workflow Data Interpolation NOTE: This PDF file is for reference purposes only. This lab should be accessed directly from the web at https://sassieweb.chem.utk.edu/sassie2/docs/sample_work_flows/ssrna_example/ssrna_example.html.

More information

Tutorial 3 Comparing Biological Shapes Patrice Koehl and Joel Hass

Tutorial 3 Comparing Biological Shapes Patrice Koehl and Joel Hass Tutorial 3 Comparing Biological Shapes Patrice Koehl and Joel Hass University of California, Davis, USA http://www.cs.ucdavis.edu/~koehl/ims2017/ What is a shape? A shape is a 2-manifold with a Riemannian

More information

Technical Notes. Considerations for Choosing SLC versus MLC Flash P/N REV A01. January 27, 2012

Technical Notes. Considerations for Choosing SLC versus MLC Flash P/N REV A01. January 27, 2012 Considerations for Choosing SLC versus MLC Flash Technical Notes P/N 300-013-740 REV A01 January 27, 2012 This technical notes document contains information on these topics:...2 Appendix A: MLC vs SLC...6

More information

THE EXAMPLE OF A PEN HOLDER PRODUCTION IN 3D PRINTER

THE EXAMPLE OF A PEN HOLDER PRODUCTION IN 3D PRINTER THE EXAMPLE OF A PEN HOLDER PRODUCTION IN 3D PRINTER IZZAT KASS HANNA Abstract This article describes the production of a simple shape (pen holder) by 3D printer using Fused Deposition Modelling, the features,

More information

Lecture 14. Resource Allocation involving Continuous Variables (Linear Programming) 1.040/1.401/ESD.018 Project Management.

Lecture 14. Resource Allocation involving Continuous Variables (Linear Programming) 1.040/1.401/ESD.018 Project Management. 1.040/1.401/ESD.018 Project Management Lecture 14 Resource Allocation involving Continuous Variables (Linear Programming) April 2, 2007 Samuel Labi and Fred Moavenzadeh Massachusetts Institute of Technology

More information

3.021J / 1.021J / J / J / 22.00J Introduction to Modeling and Simulation Spring 2008

3.021J / 1.021J / J / J / 22.00J Introduction to Modeling and Simulation Spring 2008 MIT OpenCourseWare http://ocw.mit.edu 3.021J / 1.021J / 10.333J / 18.361J / 22.00J Introduction to Modeling and Simulation Spring 2008 For information about citing these materials or our Terms of Use,

More information

Motif Scan Results. hamap, pat, freq_pat, pre, prf, pfam_fs, pfam_ls.

Motif Scan Results. hamap, pat, freq_pat, pre, prf, pfam_fs, pfam_ls. Page 1 Motif Scan Results search help user: GUEST log in Tools Hub Results Stored results Private area Misc Deprecated width: 600 settings Query Protein temporarily stored here. HAMAP profiles [hamap],

More information

MAKING PROPERTIES AND PROFITS

MAKING PROPERTIES AND PROFITS AKING PROPERTIES AND PROFITS Twin-Screw Compounding Workshop April 10-12, 2018 Workshop Leader: Adam Dreiblatt Polymers Center of Excellence Charlotte, NC Program Overview This three-day workshop focuses

More information

Self-formation, Development and Reproduction of the Artificial System

Self-formation, Development and Reproduction of the Artificial System Solid State Phenomena Vols. 97-98 (4) pp 77-84 (4) Trans Tech Publications, Switzerland Journal doi:.48/www.scientific.net/ssp.97-98.77 Citation (to be inserted by the publisher) Copyright by Trans Tech

More information

Chromatin immunoprecipitation sequencing (ChIP-Seq) on the SOLiD system Nature Methods 6, (2009)

Chromatin immunoprecipitation sequencing (ChIP-Seq) on the SOLiD system Nature Methods 6, (2009) ChIP-seq Chromatin immunoprecipitation (ChIP) is a technique for identifying and characterizing elements in protein-dna interactions involved in gene regulation or chromatin organization. www.illumina.com

More information

Some geometries to describe nature

Some geometries to describe nature Some geometries to describe nature Christiane Rousseau Since ancient times, the development of mathematics has been inspired, at least in part, by the need to provide models in other sciences, and that

More information

ROTS: Reproducibility Optimized Test Statistic

ROTS: Reproducibility Optimized Test Statistic ROTS: Reproducibility Optimized Test Statistic Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo fatsey (at) utu.fi March 3, 2016 Contents 1 Introduction 2 2 Algorithm overview 3 3 Input data 3 4 Preprocessing

More information

Second Session (only if there should still be places available after the first one)

Second Session (only if there should still be places available after the first one) Ph.D course in Astrophysics Early Universe Physics Galaxies Stellar Evolution Gravitation Theory High Energy Astrophysics Large Scale Structure Relativistic Astrophysics Prof. Carlo Baccigalupi Astrophysics

More information

Alignment of Long Sequences

Alignment of Long Sequences Alignment of Long Sequences BMI/CS 776 www.biostat.wisc.edu/bmi776/ Spring 2009 Mark Craven craven@biostat.wisc.edu Pairwise Whole Genome Alignment: Task Definition Given a pair of genomes (or other large-scale

More information

CPSC 340: Machine Learning and Data Mining. Density-Based Clustering Fall 2016

CPSC 340: Machine Learning and Data Mining. Density-Based Clustering Fall 2016 CPSC 340: Machine Learning and Data Mining Density-Based Clustering Fall 2016 Assignment 1 : Admin 2 late days to hand it in before Wednesday s class. 3 late days to hand it in before Friday s class. 0

More information

MIT Geometric Folding Algorithms Prof. Erik Demaine

MIT Geometric Folding Algorithms Prof. Erik Demaine MIT 6.849 Geometric Folding Algorithms Prof. Erik Demaine Lecture 6: Origami Art and Design Guest Lecturer: Jason Ku September 27, 2010 Jason Ku President of OrigaMIT Mechanical Engineering Bachelor's,

More information

Lab Activity #2- Statistics and Graphing

Lab Activity #2- Statistics and Graphing Lab Activity #2- Statistics and Graphing Graphical Representation of Data and the Use of Google Sheets : Scientists answer posed questions by performing experiments which provide information about a given

More information

Clustering. RNA-seq: What is it good for? Finding Similarly Expressed Genes. Data... And Lots of It!

Clustering. RNA-seq: What is it good for? Finding Similarly Expressed Genes. Data... And Lots of It! RNA-seq: What is it good for? Clustering High-throughput RNA sequencing experiments (RNA-seq) offer the ability to measure simultaneously the expression level of thousands of genes in a single experiment!

More information

Practical 4: ChIP-seq Peak calling

Practical 4: ChIP-seq Peak calling Practical 4: ChIP-seq Peak calling Shamith Samarajiiwa, Dora Bihary September 2017 Contents 1 Calling ChIP-seq peaks using MACS2 1 1.1 Assess the quality of the aligned datasets..................................

More information

Aerodynamic Design of a Tailless Aeroplan J. Friedl

Aerodynamic Design of a Tailless Aeroplan J. Friedl Acta Polytechnica Vol. 4 No. 4 5/2 Aerodynamic Design of a Tailless Aeroplan J. Friedl The paper presents an aerodynamic analysis of a one-seat ultralight (UL) tailless aeroplane named L2k, with a very

More information

A cell-cycle knowledge integration framework

A cell-cycle knowledge integration framework A cell-cycle knowledge integration framework Erick Antezana Dept. of Plant Systems Biology. Flanders Interuniversity Institute for Biotechnology/Ghent University. Ghent BELGIUM. erant@psb.ugent.be http://www.psb.ugent.be/cbd/

More information

Extra-Homework Problem Set

Extra-Homework Problem Set Extra-Homework Problem Set => Will not be graded, but might be a good idea for self-study => Solutions are posted at the end of the problem set Your adviser asks you to find out about a so far unpublished

More information

Outline. Motivation. Introduction of GAs. Genetic Algorithm 9/7/2017. Motivation Genetic algorithms An illustrative example Hypothesis space search

Outline. Motivation. Introduction of GAs. Genetic Algorithm 9/7/2017. Motivation Genetic algorithms An illustrative example Hypothesis space search Outline Genetic Algorithm Motivation Genetic algorithms An illustrative example Hypothesis space search Motivation Evolution is known to be a successful, robust method for adaptation within biological

More information

Cumulus: Filesystem Backup to the Cloud

Cumulus: Filesystem Backup to the Cloud Cumulus: Filesystem Backup to the Cloud 7th USENIX Conference on File and Storage Technologies (FAST 09) Michael Vrable Stefan Savage Geoffrey M. Voelker University of California, San Diego February 26,

More information

Sample study by 3D optical profiler Contour Elite K for KTH university.

Sample study by 3D optical profiler Contour Elite K for KTH university. Sample study by 3D optical profiler Contour Elite K for KTH university Samuel.lesko@bruker.com Objectives Objectives Main goals for the visit consist of evaluating 3D optical profiler: Confirm capability

More information

GPU Implementation of Implicit Runge-Kutta Methods

GPU Implementation of Implicit Runge-Kutta Methods GPU Implementation of Implicit Runge-Kutta Methods Navchetan Awasthi, Abhijith J Supercomputer Education and Research Centre Indian Institute of Science, Bangalore, India navchetanawasthi@gmail.com, abhijith31792@gmail.com

More information

Twin-Screw Food Extrusion: Control Case Study

Twin-Screw Food Extrusion: Control Case Study Twin-Screw Food Extrusion: Control Case Study Joel Schlosburg May 12 th,25 HOWARD P. ISERMANN DEPARTMENT OF CHEMICAL & BIOLOGICAL ENGINEERING RENSSELAER POLYTECHNIC INSTITUTE TROY, NY 1218 Contents Motivation

More information

Linear trees and RNA secondary. structuret's

Linear trees and RNA secondary. structuret's ELSEVIER Discrete Applied Mathematics 51 (1994) 317-323 DISCRETE APPLIED MATHEMATICS Linear trees and RNA secondary. structuret's William R. Schmitt*.", Michael S. Watermanb "University of Memphis. Memphis,

More information