Gene expression analysis summary Where are we now?

Slides:



Advertisements
Similar presentations
Biological pathway and systems analysis An introduction.
Advertisements

D ISCOVERING REGULATORY AND SIGNALLING CIRCUITS IN MOLECULAR INTERACTION NETWORK Ideker Bioinformatics 2002 Presented by: Omrit Zemach April Seminar.
Darwinian Genomics Csaba Pal Biological Research Center Szeged, Hungary.
Gene regulation in cancer 11/14/07. Overview The hallmark of cancer is uncontrolled cell proliferation. Oncogenes code for proteins that help to regulate.
The Central Dogma & Data DNA mRNA Transcription Protei n Translation Metabolite Cellular processes Phenotype Embryology Organismal Biology Genetic Data.
1 MicroArray -- Data Analysis Cecilia Hansen & Dirk Repsilber Bioinformatics - 10p, October 2001.
Work Process Using Enrich Load biological data Check enrichment of crossed data sets Extract statistically significant results Multiple hypothesis correction.
Getting the numbers comparable
Systems Biology Biological Sequence Analysis
DNA Microarray Bioinformatics - #27611 Program Normalization exercise (from last week) Dimension reduction theory (PCA/Clustering) Dimension reduction.
Gene ontology & hypergeometric test Simon Rasmussen CBS - DTU.
DNA Microarray Bioinformatics - #27612 Normalization and Statistical Analysis.
Dimension reduction : PCA and Clustering Agnieszka S. Juncker Slides: Christopher Workman and Agnieszka S. Juncker Center for Biological Sequence Analysis.
27803::Systems Biology1CBS, Department of Systems Biology Schedule for the Afternoon 13:00 – 13:30ChIP-chip lecture 13:30 – 14:30Exercise 14:30 – 14:45Break.
Bioinformatics: a Multidisciplinary Challenge Ron Y. Pinter Dept. of Computer Science Technion March 12, 2003.
Schedule for the Afternoon 13:00 – 13:30ChIP-chip lecture 13:30 – 14:30Exercise 14:30 – 14:45Break 14:45 – 15:15Regulatory pathways lecture 15:15 – 15:45Exercise.
Dimension reduction : PCA and Clustering by Agnieszka S. Juncker
Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae Speaker: Zhu YANG 6 th step, 2006.
Dimension reduction : PCA and Clustering Slides by Agnieszka Juncker and Chris Workman.
Systems Biology Biological Sequence Analysis
Indiana University Bloomington, IN Junguk Hur Computational Omics Lab School of Informatics Differential location analysis A novel approach to detecting.
EGRIN Session II Broadening the Model Baliga lab retreat 2010.
Introduction to Systems Biology. Overview of the day Background & Introduction Network analysis methods Case studies Exercises.
27803::Systems Biology1CBS, Department of Systems Biology Schedule for the Afternoon 13:00 – 13:30ChIP-chip lecture 13:30 – 14:30Exercise 14:30 – 14:45Break.
Systems Biology Biological Sequence Analysis
Dimension reduction : PCA and Clustering Christopher Workman Center for Biological Sequence Analysis DTU.
CISC667, F05, Lec24, Liao1 CISC 667 Intro to Bioinformatics (Fall 2005) DNA Microarray, 2d gel, MSMS, yeast 2-hybrid.
Modeling Functional Genomics Datasets CVM Lesson 1 13 June 2007Bindu Nanduri.
Introduction to molecular networks Sushmita Roy BMI/CS 576 Nov 6 th, 2014.
Genetics: From Genes to Genomes
Introduction to DNA microarrays DTU - January Hanne Jarmer.
Epistasis Analysis Using Microarrays Chris Workman.
Analysis of GO annotation at cluster level by H. Bjørn Nielsen Slides from Agnieszka S. Juncker.
Why microarrays in a bioinformatics class? Design of chips Quantitation of signals Integration of the data Extraction of groups of genes with linked expression.
Pathway Informatics 6 th July, 2015 Ansuman Chattopadhyay, PhD Head, Molecular Biology Information Services Health Sciences Library System University of.
Analysis of microarray data
Computational Molecular Biology Biochem 218 – BioMedical Informatics Gene Regulatory.
Systems Biology The search for the syntax of biological information, that is, the study of the dynamic networks of interacting biological elements. The.
Mapping protein-DNA interactions by ChIP-seq Zsolt Szilagyi Institute of Biomedicine.
Chapter 14 Genomes and Genomics. Sequencing DNA dideoxy (Sanger) method ddGTP ddATP ddTTP ddCTP 5’TAATGTACG TAATGTAC TAATGTA TAATGT TAATG TAAT TAA TA.
Does gene order matter? Cis-regulatory elements, proteins, and messengers are integrated into biological circuits. Does gene location in the genome affect.
Detecting enriched regions (Chip- seq, RIP-seq) Statistical evaluation of enriched regions Data displayed in Genome Browser Detection of enriched motifs.
CDNA Microarrays MB206.
Introduction to DNA microarrays DTU - May Hanne Jarmer.
A New Oklahoma Bioinformatics Company. Microarray and Bioinformatics.
Finish up array applications Move on to proteomics Protein microarrays.
Microarrays and Their Uses Brad Windle, Ph.D
GTL User Facilities Facility IV: Analysis and Modeling of Cellular Systems Jim K. Fredrickson.
Introduction to Systems Biology. Overview of the day Background & Introduction Network analysis methods Case studies Exercises.
Dimension reduction : PCA and Clustering Slides by Agnieszka Juncker and Chris Workman modified by Hanne Jarmer.
Biological Signal Detection for Protein Function Prediction Investigators: Yang Dai Prime Grant Support: NSF Problem Statement and Motivation Technical.
Analysis of GO annotation at cluster level by Agnieszka S. Juncker.
Bioinformatics MEDC601 Lecture by Brad Windle Ph# Office: Massey Cancer Center, Goodwin Labs Room 319 Web site for lecture:
Proteomics Session 1 Introduction. Some basic concepts in biology and biochemistry.
Central dogma: the story of life RNA DNA Protein.
Idea: measure the amount of mRNA to see which genes are being expressed in (used by) the cell. Measuring protein might be more direct, but is currently.
Statistical Analysis of Microarray Data By H. Bjørn Nielsen.
Introduction to Microarrays. The Central Dogma.
Genome Biology and Biotechnology The next frontier: Systems biology Prof. M. Zabeau Department of Plant Systems Biology Flanders Interuniversity Institute.
Introduction to biological molecular networks
ANALYSIS OF GENE EXPRESSION DATA. Gene expression data is a high-throughput data type (like DNA and protein sequences) that requires bioinformatic pattern.
Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network Science, Vol 292, Issue 5518, , 4 May 2001.
Modeling the cell cycle regulation by the RB/E2F pathway Laurence Calzone Service de Bioinformatique U900 Inserm / Ecoles de Mines / Institut Curie Collaborative.
Biological Networks. Can a biologist fix a radio? Lazebnik, Cancer Cell, 2002.
1 Genomics Advances in 1990 ’ s Gene –Expressed sequence tag (EST) –Sequence database Information –Public accessible –Browser-based, user-friendly bioinformatics.
Microarray: An Introduction
 Facilities Open House Functional Genomics Facility Molishree Joshi, Ph.D. 6/1/2015 Contact Information:
Dimension reduction : PCA and Clustering by Agnieszka S. Juncker
Analysis of GO annotation at cluster level by Agnieszka S. Juncker
Dimension reduction : PCA and Clustering
Presentation transcript:

Gene expression analysis summary Where are we now?

Sample Preparation Hybridization Array design Probe design Experimental Design Buy standard Chip / Array Statistical Analysis Fit to Model (time series) Expression Index Calculation Advanced Data Analysis ClusteringPCAGene Annotation AnalysisPromoter Analysis ClassificationMeta analysisSurvival analysisRegulatory Network Comparable Gene Expression Data Normalization Image analysis The DNA Microarray Analysis Pipeline Question/hypothesis

DNA microarray analysis PCA (using SVD) Cluster analysis Normalization Before After

High-throughput applications of microarrays Gene expression * DNA re-sequencing (relative to reference) * SNP analysis * Competitive growth assays * ChIP-chip (interaction data) * Array CGH Whole genome tiling arrays Peptide arrays (interaction data, not DNA based) * De novo DNA sequencing (short)

Tiling microarrays Huber W, et al., Bioinformatics 2006

Motivation for Systems Biology

Interest in Systems Biology? Human genome completed PubMed abstracts

Systems biology and emerging properties

Transcriptional regulation of the Cell Cycle Simon et al. Cell 2001

Boehringer Mannheim metabolic map

Mathematical abstraction of biochemistry

Metabolic models

“Genome scale” metabolic models Genes708 Metabolites584 –Cytosolic559 –Mitochondrial164 –Extracellular121 Reactions1175 –Cytosolic702 –Mitochondrial124 –Exchange fluxes349 Forster et al. Genome Research 2003.

One framework for Systems Biology 1.The components. Discover all of the genes in the genome and the subset of genes, proteins, and other small molecules constituting the pathway of interest. If possible, define an initial model of the molecular interactions governing pathway function (how?). 2.Pathway perturbation. Perturb each pathway component through a series of genetic or environmental manipulations. Detect and quantify the corresponding global cellular response to each perturbation.

One framework for Systems Biology 3.Model Reconciliation. Integrate the observed mRNA and protein responses with the current, pathway-specific model and with the global network of protein-protein, protein-DNA, and other known physical interactions. 4.Model verification/expansion. Formulate new hypotheses to explain observations not predicted by the model. Design additional perturbation experiments to test these and iteratively repeat steps (2), (3), and (4).

From model to experiment and back again

Systems biology paradigm Aebersold R, Mann M, Nature, 2003.