Comparative genomic hybridization Because arrays are more than just a tool for expression analysis
|
|
- Shanna Stafford
- 7 years ago
- Views:
Transcription
1 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 H. Willenbrock) Media glna tnra GlnA TnrA C2 glnr C3 C5 C6 K GlnR C1 C4 C7
2 Outline Introduction to comparative genomic hybridization (CGH) and array CGH Data analysis approaches Breakpoint detection Loss and gain analysis Real data example: Comparative genomic profiling of bacterial strains
3 Outline Introduction to comparative genomic hybridization (CGH) and array CGH Data analysis approaches Breakpoint detection Loss and gain analysis Real data example: Comparative genomic profiling of bacterial strains
4 Comparative Genomic Hybridization Study types : Gain or loss of genetic material To find variations in the genetic material Purposes: Study of chromosomal aberrations often found in cancer and developmental abnormalities Study of variations in the baseline sequence in a microbial population (microbial comparative genomics) 4
5 Genetic Alterations and Disease A Variety of Genetic Alterations Underlie Developmental Abnormalities and Disease Inappropriate gene activation or inactivation can be caused by: Mutation Epigenetic gene silencing (e.g. addition of methyl groups) Reciprocal translocation (exchange of fragments between two nonhomologous chromosomes) Gain or loss of genetic material Any of the above may lead to an oncogene activation or to inactivation of a tumor suppressor
6 Detecting structural abnormalities Albertson and Pinkel, Human Molecular Genetics, 2003
7 Microarrays for copy number analysis BAC arrays Affymetrix SNP chip (500 K) Representational oligonucleotide microarray analysis (ROMA) Whole genome tiling arrays Own design (NimbleGen/NimbleExpress)
8 Array CGH Array CGH Maps DNA Copy Number Alterations to Positions in the Genome Test Genomic DNA Reference Genomic DNA Cot-1 DNA Gain of DNA copies in tumor Loss of DNA copies in tumor Ratio Position on Sequence
9 Structural abnormalities * *HSR: homogeneously staining region Albertson and Pinkel, Human Molecular Genetics, 2003
10 Advantages over Expression Arrays Hybridization of DNA to microarray (DNA is much more stable) Little normalization is necessary Use of spatial coherence in the analysis Only 1 sample is necessary to draw conclusions it is still necessary with biological replicates to be able to draw general conclusions regarding a certain biological subtype Results may be easier interpretable and correlated with sample phenotypes e.g. loss of oncogene repressor -> certain cancer subtype
11 Outline Introduction to comparative genomic hybridization (CGH) and array CGH Data analysis approaches Breakpoint detection Loss and gain analysis Real data example: Comparative genomic profiling of bacterial strains
12 Analysis of array CGH Goal: To partition the clones into sets with the same copy number and to characterize the genomic segments in terms of copy number. Biological model: genomic rearrangements lead to gains or losses Sizable contiguous parts of the genome, possibly spanning entire chromosomes Or, alternatively, to focal high-level amplifications
13 Copy Number Profiles of a Tumor
14 Varying genomic complexity Breakpoints
15 Observed clone value and spatial coherence Useful to make use of the physical dependence of the nearby clones, which translates into copy number dependence N(-.3,.08^2) N(.6,.1^2)??
16 Expected log 2 ratio A function of copy number change, normal cell contamination and ploidy Reference ploidy= % Reference ploidy=3 50% %
17 Simulation of Array CGH Data Real biological variation considered: Breast cancer data used as model data Segment length and copy number is taken from the empirical distribution observed in breast cancer data (DNAcopy segmentation). Mixture of cells (sample is not pure) Each sample was assigned a value, P t : proportion of tumor cells, between 0.3 and 0.7 from a uniform distribution. Experimental noise is Gaussian Standard deviations drawn from a uniform distribution between 0.1 and 0.2 to imitate real data where the noise may vary between experiments. Cancer subtypes are heterogeneous Certain aberrations characteristic for a cancer subtype may only exist in a percentage of the patients with that cancer subtype. Thus, in each sample, segments with copy number alterations (copy number not 2) was removed at random with probability 30%. Willenbrock and Fridlyand; Bioinformatics 2005
18 Comparison Scheme Use of simulated data, where the truth is known and the noise is controlled True breakpoint false predicted breakpoint
19 Methods for Segmentation HMM: Hidden Markov Model (acgh package) Fit HMMs in which any state is reachable from any other state (Fridlyand et al, JMVA, 2004). CBS: Circular binary segmentation (DNAcopy package) Tertiary splits of the chromosomes into contiguous regions of equal copy number and assesses significance of the proposed splits by using a permutation reference distribution (Olshen et al, Biostatistics, 2004). GLAD: Gain and Loss Analysis of DNA (GLAD package) Detects chromosomal breakpoints by estimating a piecewise constant function that is based on adaptive weights smoothing (Hupe et al, Bioinformatics, 2004). Willenbrock and Fridlyand; Bioinformatics 2005
20 Breakpoint Detection Accuracy
21 Conclusions so far Signal2noise: CBS consistently the best performance HMM has the highest FDR GLAD is least sensitive
22 Outline Introduction to comparative genomic hybridization (CGH) and array CGH Data analysis approaches Breakpoint detection Loss and gain analysis Application of segmentation to testing Real data example: Comparative genomic profiling of bacterial strains
23 Merging segments Note: that all procedures operate on individual chromosomes, therefore resulting in a large number of segments with mean values close to each other Additional Challenge: reduce number of segments by merging the ones that are likely to correspond to the same copy number This will facilitate inference of altered regions
24 Merging For estimating actual copy number levels from segmentations
25 Segmentation and Merging
26 ROC Curves Identification of copy number alterations for varying thresholds
27 Using segmentation for testing (phenotype association studies) Case: Find clones (or whole segments) that are significantly differing in copy number between two cancer subtypes. Task: Investigate whether incorporating spatial information (segmentation) into testing for differential copy number increases detection power. Data type: Samples with either of 2 different phenotypes (e.g. 2 different cancer subtypes) How: Comparison of sensitivity and specificity using: 1. Original test statistic (no use of spatial information) 2. Segmented T-statistic derived from original log 2 ratios 3. T-statistic computed from segmented log 2 ratios 27
28 Testing samples (original values) Red: True different clones 28
29 Correction for multiple testing? standard p-value cutoff for alpha=0.05 => Many false positives 29
30 The maxt Multiple Testing Correction By repeating random class assigningment and testing, e.g. 100 times, the following permutation reference distribution of maximum absolute test statistic is obtained (maxt distribution): We wish to control the family wise error rate (FWER) at alpha=0.05 (5% chance of 1 false positive). Therefore, the cut-off should be such that only in 5% of the random cases, we will get one false positive (95 percentile): cutoff = 5 standard significance threshold MaxT multiple testing corrected threshold 30
31 Testing samples (original values) maxt p- value cutoff for alpha = 0.05 standard p-value cutoff for alpha=
32 Testing: Segmenting test statistics Reference 32
33 Testing segmented samples Segmentation of individual samples... 33
34 Testing segmented samples Reference 2. T-statistic from segmented individual samples... 34
35 Detecting regions with differential copy number Willenbrock and Fridlyand. Bioinformatics 2005; 21(22):
36 Outline Introduction to comparative genomic hybridization (CGH) and array CGH Data analysis approaches Breakpoint detection Loss and gain analysis Real data example: Comparative genomic profiling of bacterial strains
37 Real Data Example: Comparative genomic profiling of several Escherichia coli strains The microarray design included probes for: 7 known E. coli strains 39 known E. coli bacteriophages 104 known E. coli virulence genes Experimentally: 2 sequenced control strains (W3110 and EDL933), 3 replicates 2 non-sequenced strains (D1 and 3538), 3 replicates Bacteriophage: φ3538 (Δstx2::cat), 2 replicates Willenbrock et al.; J. Bacteriology
38 Comparative Genomic Profiling: Challenges Ratio problems: some genes might be present on query strain but not on the known reference strain Single channel microarrays or dual channel microarrays? In this case, we used an Affymetrix single channel custommade array (NimbleExpress) Partly present genes versus similar but distinct genes 38
39 The 7 E. coli strains included on the microarray Very high similarity between the two K-12 strains and between the two O157:H7 strains. Percentage of homologues for E. coli genomes in columns found in E. coli genomes in rows. Willenbrock et al. Journal of Bacteriology Nov;188(22):
40 BLAST Atlas Willenbrock et al. Journal of Bacteriology Nov;188(22):
41 Hybridization Atlases Probe hybridizations for experiments (samples) result in a similar pattern as expected from the BLAST atlas Willenbrock et al. Journal of Bacteriology Nov;188(22):
42 Mapping the phage Φ3538 (Δstx2::cat) Willenbrock et al. Journal of Bacteriology Nov;188(22):
43 Zoom of phage Φ3538 (Δstx2::cat) The hybridization pattern is very similar for the phage, strain 3538 and strain D1 Willenbrock et al. Journal of Bacteriology Nov;188(22):
44 Hierarchical Cluster Analysis D1 is very similar to the K-12 type strains (W MG1655) K-12
45 E. coli virulence genes D1 is probably still a commensal strain An organism participating in a symbiotic relationship from which it benefits while the other is unaffected Willenbrock et al. Journal of Bacteriology Nov;188(22):
46 Summary Comparative genomic profiling of two E. coli strains 0175:H16 D1 0157:H Identification of virulence genes and phage elements Conclusions: D1 is similar to the K-12 type strains Characterization of D1 and 3538 genes: Identification of a number of genes involved in DNA transfer and recombination 46
47 Summary Numerous methods have been introduced for segmentation of DNA copy number data and breakpoint identification. Important to benchmark against existing methods (however, only feasible if the software is publicly available) Currently, CBS (DNAcopy package) has the best overall performance Merging of segmentation results improves copy number phenotype characterization Study types: Study of copy number in cancer samples Comparison of bacterial strains Etc.
Microarray Data Analysis Workshop. Custom arrays and Probe design Probe design in a pangenomic world. Carsten Friis. MedVetNet Workshop, DTU 2008
Microarray Data Analysis Workshop MedVetNet Workshop, DTU 2008 Custom arrays and Probe design Probe design in a pangenomic world Carsten Friis Media glna tnra GlnA TnrA C2 glnr C3 C5 C6 K GlnR C1 C4 C7
More informationCore Facility Genomics
Core Facility Genomics versatile genome or transcriptome analyses based on quantifiable highthroughput data ascertainment 1 Topics Collaboration with Harald Binder and Clemens Kreutz Project: Microarray
More informationGAIA: Genomic Analysis of Important Aberrations
GAIA: Genomic Analysis of Important Aberrations Sandro Morganella Stefano Maria Pagnotta Michele Ceccarelli Contents 1 Overview 1 2 Installation 2 3 Package Dependencies 2 4 Vega Data Description 2 4.1
More informationPREDA 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 informationMolecular typing of VTEC: from PFGE to NGS-based phylogeny
Molecular typing of VTEC: from PFGE to NGS-based phylogeny Valeria Michelacci 10th Annual Workshop of the National Reference Laboratories for E. coli in the EU Rome, November 5 th 2015 Molecular typing
More informationHow many of you have checked out the web site on protein-dna interactions?
How many of you have checked out the web site on protein-dna interactions? Example of an approximately 40,000 probe spotted oligo microarray with enlarged inset to show detail. Find and be ready to discuss
More informationGene Expression Analysis
Gene Expression Analysis Jie Peng Department of Statistics University of California, Davis May 2012 RNA expression technologies High-throughput technologies to measure the expression levels of thousands
More informationMolecular Genetics: Challenges for Statistical Practice. J.K. Lindsey
Molecular Genetics: Challenges for Statistical Practice J.K. Lindsey 1. What is a Microarray? 2. Design Questions 3. Modelling Questions 4. Longitudinal Data 5. Conclusions 1. What is a microarray? A microarray
More informationCHROMOSOMES Dr. Fern Tsien, Dept. of Genetics, LSUHSC, NO, LA
CHROMOSOMES Dr. Fern Tsien, Dept. of Genetics, LSUHSC, NO, LA Cytogenetics is the study of chromosomes and their structure, inheritance, and abnormalities. Chromosome abnormalities occur in approximately:
More informationThe following chapter is called "Preimplantation Genetic Diagnosis (PGD)".
Slide 1 Welcome to chapter 9. The following chapter is called "Preimplantation Genetic Diagnosis (PGD)". The author is Dr. Maria Lalioti. Slide 2 The learning objectives of this chapter are: To learn the
More informationAnalysis of gene expression data. Ulf Leser and Philippe Thomas
Analysis of gene expression data Ulf Leser and Philippe Thomas This Lecture Protein synthesis Microarray Idea Technologies Applications Problems Quality control Normalization Analysis next week! Ulf Leser:
More informationSimplifying Data Interpretation with Nexus Copy Number
Simplifying Data Interpretation with Nexus Copy Number A WHITE PAPER FROM BIODISCOVERY, INC. Rapid technological advancements, such as high-density acgh and SNP arrays as well as next-generation sequencing
More informationrestriction enzymes 350 Home R. Ward: Spring 2001
restriction enzymes 350 Home Restriction Enzymes (endonucleases): molecular scissors that cut DNA Properties of widely used Type II restriction enzymes: recognize a single sequence of bases in dsdna, usually
More informationHierarchical Bayesian Modeling of the HIV Response to Therapy
Hierarchical Bayesian Modeling of the HIV Response to Therapy Shane T. Jensen Department of Statistics, The Wharton School, University of Pennsylvania March 23, 2010 Joint Work with Alex Braunstein and
More informationAn unsupervised fuzzy ensemble algorithmic scheme for gene expression data analysis
An unsupervised fuzzy ensemble algorithmic scheme for gene expression data analysis Roberto Avogadri 1, Giorgio Valentini 1 1 DSI, Dipartimento di Scienze dell Informazione, Università degli Studi di Milano,Via
More informationLESSON 3.5 WORKBOOK. How do cancer cells evolve? Workbook Lesson 3.5
LESSON 3.5 WORKBOOK How do cancer cells evolve? In this unit we have learned how normal cells can be transformed so that they stop behaving as part of a tissue community and become unresponsive to regulation.
More informationPackage empiricalfdr.deseq2
Type Package Package empiricalfdr.deseq2 May 27, 2015 Title Simulation-Based False Discovery Rate in RNA-Seq Version 1.0.3 Date 2015-05-26 Author Mikhail V. Matz Maintainer Mikhail V. Matz
More informationPersonalized Predictive Medicine and Genomic Clinical Trials
Personalized Predictive Medicine and Genomic Clinical Trials Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute http://brb.nci.nih.gov brb.nci.nih.gov Powerpoint presentations
More informationRecombinant DNA and Biotechnology
Recombinant DNA and Biotechnology Chapter 18 Lecture Objectives What Is Recombinant DNA? How Are New Genes Inserted into Cells? What Sources of DNA Are Used in Cloning? What Other Tools Are Used to Study
More informationFact Sheet 14 EPIGENETICS
This fact sheet describes epigenetics which refers to factors that can influence the way our genes are expressed in the cells of our body. In summary Epigenetics is a phenomenon that affects the way cells
More informationRT 2 Profiler PCR Array: Web-Based Data Analysis Tutorial
RT 2 Profiler PCR Array: Web-Based Data Analysis Tutorial Samuel J. Rulli, Jr., Ph.D. qpcr-applications Scientist Samuel.Rulli@QIAGEN.com Pathway Focused Research from Sample Prep to Data Analysis! -2-
More informationNATIONAL GENETICS REFERENCE LABORATORY (Manchester)
NATIONAL GENETICS REFERENCE LABORATORY (Manchester) MLPA analysis spreadsheets User Guide (updated October 2006) INTRODUCTION These spreadsheets are designed to assist with MLPA analysis using the kits
More informationLecture 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 informationExploratory data analysis for microarray data
Eploratory data analysis for microarray data Anja von Heydebreck Ma Planck Institute for Molecular Genetics, Dept. Computational Molecular Biology, Berlin, Germany heydebre@molgen.mpg.de Visualization
More informationMicroarray Technology
Microarrays And Functional Genomics CPSC265 Matt Hudson Microarray Technology Relatively young technology Usually used like a Northern blot can determine the amount of mrna for a particular gene Except
More informationArabidopsis. A Practical Approach. Edited by ZOE A. WILSON Plant Science Division, School of Biological Sciences, University of Nottingham
Arabidopsis A Practical Approach Edited by ZOE A. WILSON Plant Science Division, School of Biological Sciences, University of Nottingham OXPORD UNIVERSITY PRESS List of Contributors Abbreviations xv xvu
More informationVaxign Reverse Vaccinology Software Demo Introduction Zhuoshuang Allen Xiang, Yongqun Oliver He
Vaxign Reverse Vaccinology Software Demo Introduction Zhuoshuang Allen Xiang, Yongqun Oliver He Unit for Laboratory Animal Medicine Department of Microbiology and Immunology Center for Computational Medicine
More informationCancer Biostatistics Workshop Science of Doing Science - Biostatistics
Cancer Biostatistics Workshop Science of Doing Science - Biostatistics Yu Shyr, PhD Jan. 18, 2008 Cancer Biostatistics Center Vanderbilt-Ingram Cancer Center Yu.Shyr@vanderbilt.edu Aims Cancer Biostatistics
More informationNext 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 informationGene Enrichment Analysis
a Analysis of DNA Chips and Gene Networks Spring Semester, 2009 Lecture 14a: January 21, 2010 Lecturer: Ron Shamir Scribe: Roye Rozov Gene Enrichment Analysis 14.1 Introduction This lecture introduces
More informationThe Human Genome Project
The Human Genome Project Brief History of the Human Genome Project Physical Chromosome Maps Genetic (or Linkage) Maps DNA Markers Sequencing and Annotating Genomic DNA What Have We learned from the HGP?
More informationGene expression analysis. Ulf Leser and Karin Zimmermann
Gene expression analysis Ulf Leser and Karin Zimmermann Ulf Leser: Bioinformatics, Wintersemester 2010/2011 1 Last lecture What are microarrays? - Biomolecular devices measuring the transcriptome of a
More informationmicrornas Non protein coding, endogenous RNAs of 21-22nt length Evolutionarily conserved
microrna 2 micrornas Non protein coding, endogenous RNAs of 21-22nt length Evolutionarily conserved Regulate gene expression by binding complementary regions at 3 regions of target mrnas Act as negative
More informationTECHNOLOGIES, PRODUCTS & SERVICES for MOLECULAR DIAGNOSTICS, MDx ABA 298
DIAGNOSTICS BUSINESS ANALYSIS SERIES: TECHNOLOGIES, PRODUCTS & SERVICES for MOLECULAR DIAGNOSTICS, MDx ABA 298 By ADAMS BUSINESS ASSOCIATES MAY 2014. May 2014 ABA 298 1 Technologies, Products & Services
More informationTutorial for proteome data analysis using the Perseus software platform
Tutorial for proteome data analysis using the Perseus software platform Laboratory of Mass Spectrometry, LNBio, CNPEM Tutorial version 1.0, January 2014. Note: This tutorial was written based on the information
More informationINTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN USE Q5B
INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN USE ICH HARMONISED TRIPARTITE GUIDELINE QUALITY OF BIOTECHNOLOGICAL PRODUCTS: ANALYSIS
More informationAnalysis of Illumina Gene Expression Microarray Data
Analysis of Illumina Gene Expression Microarray Data Asta Laiho, Msc. Tech. Bioinformatics research engineer The Finnish DNA Microarray Centre Turku Centre for Biotechnology, Finland The Finnish DNA Microarray
More informationBIOINF 525 Winter 2016 Foundations of Bioinformatics and Systems Biology http://tinyurl.com/bioinf525-w16
Course Director: Dr. Barry Grant (DCM&B, bjgrant@med.umich.edu) Description: This is a three module course covering (1) Foundations of Bioinformatics, (2) Statistics in Bioinformatics, and (3) Systems
More informationAppendix 2 Molecular Biology Core Curriculum. Websites and Other Resources
Appendix 2 Molecular Biology Core Curriculum Websites and Other Resources Chapter 1 - The Molecular Basis of Cancer 1. Inside Cancer http://www.insidecancer.org/ From the Dolan DNA Learning Center Cold
More informationOverview of Genetic Testing and Screening
Integrating Genetics into Your Practice Webinar Series Overview of Genetic Testing and Screening Genetic testing is an important tool in the screening and diagnosis of many conditions. New technology is
More informationSingle-Cell Whole Genome Sequencing on the C1 System: a Performance Evaluation
PN 100-9879 A1 TECHNICAL NOTE Single-Cell Whole Genome Sequencing on the C1 System: a Performance Evaluation Introduction Cancer is a dynamic evolutionary process of which intratumor genetic and phenotypic
More informationIntroduction To Real Time Quantitative PCR (qpcr)
Introduction To Real Time Quantitative PCR (qpcr) SABiosciences, A QIAGEN Company www.sabiosciences.com The Seminar Topics The advantages of qpcr versus conventional PCR Work flow & applications Factors
More informationMicroarray Data Analysis. A step by step analysis using BRB-Array Tools
Microarray Data Analysis A step by step analysis using BRB-Array Tools 1 EXAMINATION OF DIFFERENTIAL GENE EXPRESSION (1) Objective: to find genes whose expression is changed before and after chemotherapy.
More informationDNA 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 informationGene Expression Assays
APPLICATION NOTE TaqMan Gene Expression Assays A mpl i fic ationef ficienc yof TaqMan Gene Expression Assays Assays tested extensively for qpcr efficiency Key factors that affect efficiency Efficiency
More informationA Primer of Genome Science THIRD
A Primer of Genome Science THIRD EDITION GREG GIBSON-SPENCER V. MUSE North Carolina State University Sinauer Associates, Inc. Publishers Sunderland, Massachusetts USA Contents Preface xi 1 Genome Projects:
More informationBAPS: Bayesian Analysis of Population Structure
BAPS: Bayesian Analysis of Population Structure Manual v. 6.0 NOTE: ANY INQUIRIES CONCERNING THE PROGRAM SHOULD BE SENT TO JUKKA CORANDER (first.last at helsinki.fi). http://www.helsinki.fi/bsg/software/baps/
More informationThe genetic screening of preimplantation embryos by comparative genomic hybridisation
Vol. 11, Suppl. 3 51 The genetic screening of preimplantation embryos by comparative genomic hybridisation Maria V Traversa 1, James Marshall, Steven McArthur, Don Leigh Genea, Sydney, Australia Received:
More informationCHAPTER 6: RECOMBINANT DNA TECHNOLOGY YEAR III PHARM.D DR. V. CHITRA
CHAPTER 6: RECOMBINANT DNA TECHNOLOGY YEAR III PHARM.D DR. V. CHITRA INTRODUCTION DNA : DNA is deoxyribose nucleic acid. It is made up of a base consisting of sugar, phosphate and one nitrogen base.the
More informationSingle-Cell DNA Sequencing with the C 1. Single-Cell Auto Prep System. Reveal hidden populations and genetic diversity within complex samples
DATA Sheet Single-Cell DNA Sequencing with the C 1 Single-Cell Auto Prep System Reveal hidden populations and genetic diversity within complex samples Single-cell sensitivity Discover and detect SNPs,
More informationGenetic 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 informationArray Comparative Genomic Hybridisation (CGH)
Array Comparative Genomic Hybridisation (CGH) Exceptional healthcare, personally delivered What is array CGH? Array CGH is a new test that is now offered to all patients referred with learning disability
More informationHuman Genome Organization: An Update. Genome Organization: An Update
Human Genome Organization: An Update Genome Organization: An Update Highlights of Human Genome Project Timetable Proposed in 1990 as 3 billion dollar joint venture between DOE and NIH with 15 year completion
More informationDNA Copy Number and Loss of Heterozygosity Analysis Algorithms
DNA Copy Number and Loss of Heterozygosity Analysis Algorithms Detection of copy-number variants and chromosomal aberrations in GenomeStudio software. Introduction Illumina has developed several algorithms
More informationCCR 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 informationLECTURE 6 Gene Mutation (Chapter 16.1-16.2)
LECTURE 6 Gene Mutation (Chapter 16.1-16.2) 1 Mutation: A permanent change in the genetic material that can be passed from parent to offspring. Mutant (genotype): An organism whose DNA differs from the
More informationFactors for success in big data science
Factors for success in big data science Damjan Vukcevic Data Science Murdoch Childrens Research Institute 16 October 2014 Big Data Reading Group (Department of Mathematics & Statistics, University of Melbourne)
More informationGenetics 301 Sample Final Examination Spring 2003
Genetics 301 Sample Final Examination Spring 2003 50 Multiple Choice Questions-(Choose the best answer) 1. A cross between two true breeding lines one with dark blue flowers and one with bright white flowers
More informationEuropean Medicines Agency
European Medicines Agency July 1996 CPMP/ICH/139/95 ICH Topic Q 5 B Quality of Biotechnological Products: Analysis of the Expression Construct in Cell Lines Used for Production of r-dna Derived Protein
More informationCourse on Functional Analysis. ::: Gene Set Enrichment Analysis - GSEA -
Course on Functional Analysis ::: Madrid, June 31st, 2007. Gonzalo Gómez, PhD. ggomez@cnio.es Bioinformatics Unit CNIO ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA
More informationMUTATION, 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 informationA and B are not absolutely linked. They could be far enough apart on the chromosome that they assort independently.
Name Section 7.014 Problem Set 5 Please print out this problem set and record your answers on the printed copy. Answers to this problem set are to be turned in to the box outside 68-120 by 5:00pm on Friday
More informationInvestigating the genetic basis for intelligence
Investigating the genetic basis for intelligence Steve Hsu University of Oregon and BGI www.cog-genomics.org Outline: a multidisciplinary subject 1. What is intelligence? Psychometrics 2. g and GWAS: a
More informationQuality Assessment of Exon and Gene Arrays
Quality Assessment of Exon and Gene Arrays I. Introduction In this white paper we describe some quality assessment procedures that are computed from CEL files from Whole Transcript (WT) based arrays such
More informationFrom Reads to Differentially Expressed Genes. The statistics of differential gene expression analysis using RNA-seq data
From Reads to Differentially Expressed Genes The statistics of differential gene expression analysis using RNA-seq data experimental design data collection modeling statistical testing biological heterogeneity
More information1 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 informationGenetic Technology. Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question.
Name: Class: Date: Genetic Technology Multiple Choice Identify the choice that best completes the statement or answers the question. 1. An application of using DNA technology to help environmental scientists
More informationAnalyzing the Effect of Treatment and Time on Gene Expression in Partek Genomics Suite (PGS) 6.6: A Breast Cancer Study
Analyzing the Effect of Treatment and Time on Gene Expression in Partek Genomics Suite (PGS) 6.6: A Breast Cancer Study The data for this study is taken from experiment GSE848 from the Gene Expression
More informationMeasuring gene expression (Microarrays) Ulf Leser
Measuring gene expression (Microarrays) Ulf Leser This Lecture Gene expression Microarrays Idea Technologies Problems Quality control Normalization Analysis next week! 2 http://learn.genetics.utah.edu/content/molecules/transcribe/
More informationBioruptor NGS: Unbiased DNA shearing for Next-Generation Sequencing
STGAAC STGAACT GTGCACT GTGAACT STGAAC STGAACT GTGCACT GTGAACT STGAAC STGAAC GTGCAC GTGAAC Wouter Coppieters Head of the genomics core facility GIGA center, University of Liège Bioruptor NGS: Unbiased DNA
More informationMeDIP-chip service report
MeDIP-chip service report Wednesday, 20 August, 2008 Sample source: Cells from University of *** Customer: ****** Organization: University of *** Contents of this service report General information and
More informationRETRIEVING 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 informationGenomic instability in cancers and cancer predispositions. Popova Tatiana Inserm U830 Institut Curie
Genomic instability in cancers and cancer predispositions Popova Tatiana Inserm U830 Institut Curie Time-scale in a tumor genome discovery Bovery HYP Cancer genome Knudson 2 hit HYP Tumor DNA has transforming
More informationHeuristics for the Sorting by Length-Weighted Inversions Problem on Signed Permutations
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
More informationLesson 3 Reading Material: Oncogenes and Tumor Suppressor Genes
Lesson 3 Reading Material: Oncogenes and Tumor Suppressor Genes Becoming a cancer cell isn t easy One of the fundamental molecular characteristics of cancer is that it does not develop all at once, but
More informationSupplementary Information
Supplementary Information S1: Degree Distribution of TFs in the E.coli TRN and CRN based on Operons 1000 TRN Number of TFs 100 10 y = 619.55x -1.4163 R 2 = 0.8346 1 1 10 100 1000 Degree of TFs CRN 100
More informationCircular binary segmentation for the analysis of array-based DNA copy number data
Biostatistics (00), 5,,pp. 557 57 doi: 10.109/biostatistics/kxh008 Circular binary segmentation for the analysis of array-based DNA copy number data ADAM B. LSHEN, E. S. VENKATRAMAN Department of Epidemiology
More informationBasic Analysis of Microarray Data
Basic Analysis of Microarray Data A User Guide and Tutorial Scott A. Ness, Ph.D. Co-Director, Keck-UNM Genomics Resource and Dept. of Molecular Genetics and Microbiology University of New Mexico HSC Tel.
More informationAnalysis of the DNA Methylation Patterns at the BRCA1 CpG Island
Analysis of the DNA Methylation Patterns at the BRCA1 CpG Island Frédérique Magdinier 1 and Robert Dante 2 1 Laboratory of Molecular Biology of the Cell, Ecole Normale Superieure, Lyon, France 2 Laboratory
More informationStep-by-Step Guide to Basic Expression Analysis and Normalization
Step-by-Step Guide to Basic Expression Analysis and Normalization Page 1 Introduction This document shows you how to perform a basic analysis and normalization of your data. A full review of this document
More informationCurrent Motif Discovery Tools and their Limitations
Current Motif Discovery Tools and their Limitations Philipp Bucher SIB / CIG Workshop 3 October 2006 Trendy Concepts and Hypotheses Transcription regulatory elements act in a context-dependent manner.
More informationDNA Mapping/Alignment. Team: I Thought You GNU? Lars Olsen, Venkata Aditya Kovuri, Nick Merowsky
DNA Mapping/Alignment Team: I Thought You GNU? Lars Olsen, Venkata Aditya Kovuri, Nick Merowsky Overview Summary Research Paper 1 Research Paper 2 Research Paper 3 Current Progress Software Designs to
More informationInterpret software. User guide. version 11
Interpret software User guide version 11 This protocol booklet and its contents are Oxford Gene Technology (Operations) Limited 2008. All rights reserved. Reproduction of all or any substantial part of
More informationBioinformatics 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 informationIGV Hands-on Exercise: UI basics and data integration
IGV Hands-on Exercise: UI basics and data integration Verhaak, R.G. et al. Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA,
More informationStep by Step Guide to Importing Genetic Data into JMP Genomics
Step by Step Guide to Importing Genetic Data into JMP Genomics Page 1 Introduction Data for genetic analyses can exist in a variety of formats. Before this data can be analyzed it must imported into one
More informationSchool of Nursing. Presented by Yvette Conley, PhD
Presented by Yvette Conley, PhD What we will cover during this webcast: Briefly discuss the approaches introduced in the paper: Genome Sequencing Genome Wide Association Studies Epigenomics Gene Expression
More informationStatistical issues in the analysis of microarray data
Statistical issues in the analysis of microarray data Daniel Gerhard Institute of Biostatistics Leibniz University of Hannover ESNATS Summerschool, Zermatt D. Gerhard (LUH) Analysis of microarray data
More informationacghviewer: A Generic Visualization Tool For acgh data
acghviewer: A Generic Visualization Tool For acgh data APPLICATION NOTE Ganesh Shankar 1, Michael R. Rossi 1, Devin E. McQuaid 1, Jeffrey M. Conroy 1, Daniel G. Gaile 2, John K. Cowell 1, Norma J. Nowak
More informationAP Biology Essential Knowledge Student Diagnostic
AP Biology Essential Knowledge Student Diagnostic Background The Essential Knowledge statements provided in the AP Biology Curriculum Framework are scientific claims describing phenomenon occurring in
More informationJust 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 informationRecombinant DNA & Genetic Engineering. Tools for Genetic Manipulation
Recombinant DNA & Genetic Engineering g Genetic Manipulation: Tools Kathleen Hill Associate Professor Department of Biology The University of Western Ontario Tools for Genetic Manipulation DNA, RNA, cdna
More informationConsistent Assay Performance Across Universal Arrays and Scanners
Technical Note: Illumina Systems and Software Consistent Assay Performance Across Universal Arrays and Scanners There are multiple Universal Array and scanner options for running Illumina DASL and GoldenGate
More informationChapter 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 informationWhen 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 informationModule 1. Sequence Formats and Retrieval. Charles Steward
The Open Door Workshop Module 1 Sequence Formats and Retrieval Charles Steward 1 Aims Acquaint you with different file formats and associated annotations. Introduce different nucleotide and protein databases.
More informationCancer Genomics: What Does It Mean for You?
Cancer Genomics: What Does It Mean for You? The Connection Between Cancer and DNA One person dies from cancer each minute in the United States. That s 1,500 deaths each day. As the population ages, this
More informationData Integration. Lectures 16 & 17. ECS289A, WQ03, Filkov
Data Integration Lectures 16 & 17 Lectures Outline Goals for Data Integration Homogeneous data integration time series data (Filkov et al. 2002) Heterogeneous data integration microarray + sequence microarray
More informationData deluge (and it s applications) Gianluigi Zanetti. Data deluge. (and its applications) Gianluigi Zanetti
Data deluge (and its applications) Prologue Data is becoming cheaper and cheaper to produce and store Driving mechanism is parallelism on sensors, storage, computing Data directly produced are complex
More informationAdvances in RainDance Sequence Enrichment Technology and Applications in Cancer Research. March 17, 2011 Rendez-Vous Séquençage
Advances in RainDance Sequence Enrichment Technology and Applications in Cancer Research March 17, 2011 Rendez-Vous Séquençage Presentation Overview Core Technology Review Sequence Enrichment Application
More information