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Expression Analysis Platforms. Friday's Class. 4:00-5:00 140 SH Work in the Laboratory of Signal Processing of the EESC / USP revolves around modeling biological systems. Currently, our lines of research are: diagnosing speech pathology, ultrasound signal processing, and

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Friday s class
Friday's Class

4:00-5:00

140 SH

Work in the Laboratory of Signal Processing of the EESC / USP revolves around modeling biological systems. Currently, our lines of research are: diagnosing speech pathology, ultrasound signal processing, and

bioinformatics (particularly for phylogeny). All lines of research involve clustering algorithms.

The work on clustering seeks to determine the natural number of groups and to validate the clustering algorithms. Several techniques have been applied to genomic databases, among them are: resampling, analysis of missing data, using assumptions about a priori information. Now we are focusing on probabilistic models and validation of structural models. The studies are conducted on information generated by electrophoresis of species with agricultural applications, and are provided by Embrapa (www.embrapa.br). Now we are working with 3 doctoral students focusing on the area of binder phenotypic and genotypic information for varieties of corn.


Other courses
Other Courses

  • Intro to Informatics (in CS)

  • Intro to Bioinformatics (51:121)

    • provides a first exposure to some available computational techniques and resources

    • however, the emphasis is on utilization

  • In this course (51:123) -- I try to emphasize tools and techniques that you would use to go about developing your own computational resources (software, systems, tools, etc).

  • Computational Methods in Molecular Biology (51:122 -- Casavant, Bair)

    • advanced topics


Bioinformatics certificate
Bioinformatics Certificate

  • Offered by the Graduate College (MS/PhD)

  • http://informatics.grad.uiowa.edu/bioinformatics/


Final exam
Final Exam

  • 25 questions – mostly short answer/T/F

    • 1 paper

    • 1 genome sequencing

    • 2 Ensembl

    • 1 references

    • 1 array

    • 2 programming

    • 1 pattern matching

    • 2 expression

    • 3 other

    • 3 p-genes

    • 1 Blast/Blat

    • 5 Hash questions

    • 1 N-W

    • 1 sequencing


Outline
Outline

  • What is expression

  • Platforms

    • ChIP on Chip

    • Gene expression

    • Exon arrays

    • Tiling arrays

    • SNP chips

  • Applied S/W for Expression "Library" -- OTDB

  • Alternative Splicing

  • Association Study Example -- AMD

  • How to analyze


What is expression
What is expression?

Gene expression

mRNA - transcription

- microRNAs

Protein - translation


A typical experiment
A Typical Experiment

Case vs. Control

Ex. Retina cells +/- 7-keto-cholesterol

3x redundancy

Look for differentially expressed genes

t-test, ANOVA

fold-change

Result --> set of genes


But there s so much more
But there’s so much more…

  • Differential expression of genes

  • Time-courses

  • Alternative splicing

  • ChIP-on-chip

  • High-density SNP genotyping

  • Using chips to select genomic fragments for re-sequencing

  • Additional annotation/analyses


Definition of microarray
Definition of Microarray

  • What is a gene expression array?

    • “A microarray is a small analytical device that allows genomic exploration with speed and precision unprecedented in the history of biology” - Schena 2003





Advantages to arrays
Advantages to Arrays

  • A single array permits monitoring thousands of genes in parallel

  • Provides information at genomic scale

    • Reveals gene function and gene interactions

    • Identifies relationship between genetic and biochemical pathways

    • Identifies traits associated with multigenic origins

  • Caveat - further modifications may occur

    • Post-transcriptional

    • Translational

    • Protein


Microarray research
Microarray Research

  • Ubiquitous in biology & agriculture research

  • Interdisciplinary disciplines

    • Biology

    • Computer Science

    • Statistics

  • Experiments require teams of individuals

  • Analysis presents many obstacles that need to be overcome


Statement of the problem
Statement of the Problem

  • Obstacles impeding analysis process

    • Analysis is complex with multiple steps

    • Requires multiple discipline expertise

      • Bio - understand underlying biology

      • Stats - normalization & statistical measures

      • Comp Sci - programmatic solutions, computation resources

  • Necessity for centralized analysis system

    • Robust

    • Extensible

    • Portable


Platforms
Platforms

Gene Expression Arrays

Exon Arrays

Tiling Arrays

SNP chips

Venders: Affymetrix, Nimblegene, Agilent, others


An aside
An Aside

  • State-of-the-art sequencing technology + microarray == ?

  • 454-, pyro-, pyrophosphate sequencing




Gene chip sequencing
Gene Chip + Sequencing

454, pyro- or pyrophosphate sequencing

Genome sequencing in microfabricated high-density picolitre reactors, Margulies, et. al, Nature, 2005

Nature 2007


Sequence capture
Sequence Capture

http://www.nimblegen.com/products/seqcap/index.html


Gene expression arrays
Gene Expression Arrays

Traditional method, typically provides one or more probes that interrogate the expression level of a gene.

U133Plus2 - 54,000 probes


Exon arrays
Exon Arrays

Target each exon of a gene individually

1,400,000 probe sets

Different levels of confidence/quality

300,000 exons from full-length mRNAs

880,000+ exons from gene predictions

500,000+ “control” exons

Available for human, mouse and rat


Tiling array
Tiling Array

http://www.affymetrix.com/products/arrays/specific/human_tiling.affx


Tiling arrays
Tiling Arrays

Covering the entire genome with probes

Probes every 35 bp across the genome

7-14 chips (depending on the application)

… or can focus on a specific area

10,000 bp proximal promoter of every gene

1 chip


Tiling arrays applications
Tiling Arrays - Applications

Applications

expression

protein-DNA interaction

DNA modifications

methylation

acetylation

Anywhere in the genome!



Encode project

ENCODE Project

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

Nature, V 447, June 14, 2007


Transcript connectivity
Transcript Connectivity

  • protein-coding loci are more transcriptionally complex than previously thought

  • 19% of pseudogenes transcribed

  • genes had, on average, 10 different transcriptional start sites


Chip on chip
ChIP on Chip

http://www.chem.agilent.com/Scripts/generic.asp?lpage=37461&indcol=N&prodcol=N


Snp chips
SNP chips

SNPs - single nucleotide polymorphisms

Affymetrix 6.0 Array

  • 906,600 SNPs

  • 946,000 (non-polymorphic) "monomorphic" SNPs

    Applications:

    Linkage

    Association Studies

    Changes in Copy Number (deletions/duplications)


Association
Association

populations Unaffected Affected

allele frequencies A1 A2 A1 A2

SNP 1 0.74 0.26 0.75 0.25

SNP 2 0.70 0.30 0.10 0.90

Power increases with more samples, and more SNPs


Alternative splicing in the eye
Alternative Splicing in the Eye

GOAL:

To identify the splicing variants expressed in retina, retinal pigment epithelia, and optic nerve head. (3x biological replicates)

Motivation:

To guide/focus screening efforts to those exons that are expressed.

In collaboration with Rob Mullins



Ocular tissue expression database
Ocular Tissue Expression Database

Survey of 10 ocular tissues

GOAL: catalog which genes are expressed across tissues of specific interest in ocular

In collaboration with Abe Clark at Alcon





Amd association study
AMD Association Study

GOAL:

Identify the major susceptibility regions for age-related macular degeneration.

Several regions have been reported

  • How may susceptibility regions are there?

    Genotyping 400 AMD patients and controls with high-density SNP chips

    400,000,000 genotypes


Association1
Association

populations Unaffected Affected

allele frequencies A1 A2 A1 A2

SNP 1 0.74 0.26 0.75 0.25

SNP 2 0.70 0.30 0.10 0.90

Power increases with more samples, and more SNPs


How to analyze the data
How to analyze the data?

First step is acquiring the data!

Normalization

Analysis


Analysis
Analysis

Differential Expression

t-test

ANOVA

Fold-change

Time series (all of the above)

Correlation of expression

Early response vs. late response


Analysis1
Analysis

DAVID Database for Annotation, Visualization and Integrated Discovery

http://david.abcc.ncifcrf.gov/

  • Look for conservation of a particular function or annotation in the set of differentially expressed genes.

    GSEA Gene Set Enrichment Analysis

    http://www.broad.mit.edu/gsea/software/software_index.html

  • Look for annotations that are differentially expressed (as a group).

    Ex. Tour de France


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