The challenge of bioinformatics Chris Glasbey Biomathematics & Statistics Scotland.

Slides:

Advertisements

Similar presentations

BiGCaT Bioinformatics Hunting strategy of the bigcat.

Advertisements

High-dimensional data analysis: Microarrays and multiple testing Mark van de Wiel 1,2 1. Dep. of Mathematics, VU University Amsterdam 2. Dep. of Biostatistics.

1 MicroArray -- Data Analysis Cecilia Hansen & Dirk Repsilber Bioinformatics - 10p, October 2001.

Microarray technology and analysis of gene expression data Hillevi Lindroos.

Proteomics: Its Function and Methods Ryan Victor.

CISC667, F05, Lec26, Liao1 CISC 667 Intro to Bioinformatics (Fall 2005) Genetic networks and gene expression data.

Additional Powerful Molecular Techniques Synthesis of cDNA (complimentary DNA) Polymerase Chain Reaction (PCR) Microarray analysis Link to Gene Therapy.

Inferring the nature of the gene network connectivity Dynamic modeling of gene expression data Neal S. Holter, Amos Maritan, Marek Cieplak, Nina V. Fedoroff,

Introduce to Microarray

Problems at the interface between statistics, biomathematics and bioinformatics Chris Glasbey Biomathematics & Statistics Scotland.

\department of mathematics and computer science Supervised microarray data analysis Mark van de Wiel.

By Moayed al Suleiman Suleiman al borican Ahmad al Ahmadi

Analysis of microarray data

Lecture 18, Chapter 11 Analysis of transgenic plants part I Mat Halter 3/27/12 Plant Genetics, Breeding and Biotechnology (PLSC 452/552), University of.

1 Normalization Methods for Two-Color Microarray Data 1/13/2009 Copyright © 2009 Dan Nettleton.

Statistical Bioinformatics QTL mapping Analysis of DNA sequence alignments Postgenomic data integration Systems biology.

CDNA Microarrays Neil Lawrence. Schedule Today: Introduction and Background 18 th AprilIntroduction and Background 25 th AprilcDNA Mircoarrays 2 nd MayNo.

(2) Ratio statistics of gene expression levels and applications to microarray data analysis Bioinformatics, Vol. 18, no. 9, 2002 Yidong Chen, Vishnu Kamat,

Biotechnology. DNA technology DNA diagnostics DNA therapy.

Analyzing your clone 1) FISH 2) “Restriction mapping” 3) Southern analysis : DNA 4) Northern analysis: RNA tells size tells which tissues or conditions.

DNA MICROARRAYS WHAT ARE THEY? BEFORE WE ANSWER THAT FIRST TAKE 1 MIN TO WRITE DOWN WHAT YOU KNOW ABOUT GENE EXPRESSION THEN SHARE YOUR THOUGHTS IN GROUPS.

Applying statistical tests to microarray data. Introduction to filtering Recall- Filtering is the process of deciding which genes in a microarray experiment.

Bioinformatics Brad Windle Ph# Web Site:

Statistical Bioinformatics Genomics Transcriptomics Proteomics Systems Biology.

ARK-Genomics: Centre for Comparative and Functional Genomics in Farm Animals Richard Talbot Roslin Institute and R(D)SVS University of Edinburgh Microarrays.

PerkinElmer Life Sciences Production Company Meeting - 1st February 2002 Progenesis John Hoyland Product Manager - Bioinformatics.

What Is Microarray A new powerful technology for biological exploration Parallel High-throughput Large-scale Genomic scale.

DNA microarray. Biology Statistics (data analysis) Engineering (microfluidic, microfabrication, robotic automation…) Chemistry (surface modification)

Gene expression. The information encoded in a gene is converted into a protein  The genetic information is made available to the cell Phases of gene.

Sequencing DNA 1. Maxam & Gilbert's method (chemical cleavage) 2. Fred Sanger's method (dideoxy method) 3. AUTOMATED sequencing (dideoxy, using fluorescent.

Extracting quantitative information from proteomic 2-D gels Lecture in the bioinformatics course ”Gene expression and cell models” April 20, 2005 John.

Microarray Technology. Introduction Introduction –Microarrays are extremely powerful ways to analyze gene expression. –Using a microarray, it is possible.

DNA Sequencing.

Overview of Microarray. 2/71 Gene Expression Gene expression Production of mRNA is very much a reflection of the activity level of gene In the past, looking.

ANALYSIS OF GENE EXPRESSION DATA. Gene expression data is a high-throughput data type (like DNA and protein sequences) that requires bioinformatic pattern.

Molecular Genetic Technologies Gel Electrophoresis PCR Restriction & ligation Enzymes Recombinant plasmids and transformation DNA microarrays DNA profiling.

Disease Diagnosis by DNAC MEC seminar 25 May 04. DNA chip Blood Biopsy Sample rRNA/mRNA/ tRNA RNA RNA with cDNA Hybridization Mixture of cell-lines Reference.

No reference available

DNA Microarray Overview and Application. Table of Contents Section One : Introduction Section Two : Microarray Technique Section Three : Types of DNA.

Bio SS integration meeting with SEERAD Programme 2 23rd November 2006.

DNA Sequencing 8.2. Polymerase Chain Reaction (PCR) a direct method of making many copies of a DNA sequence exponential increase because each cycle doubles.

Statistical Analysis for Expression Experiments Heather Adams BeeSpace Doctoral Forum Thursday May 21, 2009.

Other uses of DNA microarrays

Notes: Human Genome (Right side page)

Higher Human Biology Unit 1 Human Cells KEY AREA 5: Human Genomics.

1st workshop of the Aberdeen Microarray Analysis Network (AMAN)

Vectors Bacteria, viruses or liposomes into which DNA can be inserted. These can be used to grow genes, harvest the proteins they code for or deliver them.

2d dige proteomics Differential in Gel Electrophoresis (2d dige proteomics ) is a technique to monitor the differences in proteomic profile between cells.

Protein Characterization/Purification

Genomics A Systematic Study of the Locations, Functions and Interactions of Many Genes at Once.

Functional Genomics in Evolutionary Research

Microarray Technology and Applications

 The human genome contains approximately genes.  At any given moment, each of our cells has some combination of these genes turned on & others.

Normalization Methods for Two-Color Microarray Data

Lecture 11 By Shumaila Azam

Example of a DNA Array (note green, yellow red colors; also note that only part of the total array is depicted)

Chapter 14 Bioinformatics—the study of a genome

DNA Technology.

CHAPTER 12 DNA Technology and the Human Genome

Gene Expression Analysis and Proteins

DNA Chip Data Interpretation Tools: Genmapp & Dragon View

CISC 841 Bioinformatics (Spring 2006) Inference of Biological Networks

KEY CONCEPT Entire genomes are sequenced, studied, and compared.

Protease digestion of the purified major allele MBP-CBS fusion protein

Frequency distribution of the GRAVY of the theoretical proteins (open bars) and of 110 genes encoding proteins identified on a 2-D electrophoresis gel,

CISC 667 Intro to Bioinformatics (Spring 2007) Genetic networks and gene expression data CISC667, S07, Lec24, Liao.

Label propagation algorithm

Topic Cloning and analyzing oxalate degrading enzymes to see if they dissolve kidney stones with Dr. VanWert.

Changes of gene expression induced by 1 mmol/L valproic acid in vitro.

Presentation transcript:

The challenge of bioinformatics Chris Glasbey Biomathematics & Statistics Scotland

Talk plan 1. DNA 2. mRNA 3. Protein 4. Genetic networks

1. DNA

Frank Wright et al BioSS

1.DNA

TOPALi

2. mRNA Prepare cDNA targets Label with fluorescent dyes Combine Equal Amounts Hybridise for hours Scanning

2. mRNA Scanner’s PMT setting is one of the sources of contamination. Scanner’s setting is to be raised to a certain level to make the weakly expressed genes visible. This may cause highly expressed genes to get censored (at 2 16 –1= 65535) expression values.

2. mRNA Censored spot Imputed values With GTI (Edinburgh)

2. mRNA Multiple scans

Mizan Khondoker

2. mRNA Jim McNicol

3. Proteins Electrophoresis gel Lars Pedersen DTU, Denmark

3. Proteins Protein separation by 1.pH 2.Mol. Wt.

3. Proteins gel 1 gel 2 How to compare gels 1 and 2?

3. Proteins John Gustafsson, Chalmers University, Sweden WARP

3. Proteins Two gels superimposed (in different colours)

3. Proteins  Statistical Design  3 complete reps of  15 treatment combinations.  (3 ecotypes by 5 heavy metals)  Maximum of 1400 protein spots per gel  Statistical Analyses  Filter data – remove spots with low intensity values and low quality scores (leaving ~290 spots)  Individual proteins – ANOVA, main effects and interactions

3. Proteins  Principal Components Analysis  Identify groups of proteins that are affected in a consistent manner by treatments Protein identity Loadings Jim McNicol

4. Genetic networks

Is it possible to infer the network from gene expression data such as these? Dirk Husmeier

4. Genetic networks Bayesian network

4. Genetic networks truth inferred

“I genuinely believe that we are living through the greatest intellectual moment in human history.” (Matt Ridley, Genome, 1999) “Grand Unified Systems Biology”