Introduction to Bioinformatics Data analysis of bio-activities for drug discovery

Course 341Department of ComputingImperial College, London

Yike Guo Vasa CurcinHenry Morris

Recommended Texts

For this part of the course– Lecture Notes– Handouts

General overview of microarray data analysis– “Microarray Gene Expression Data Analysis: A Beginner’s

Guide” (Causton, Quakenbush and Brazma)– “Microarray Bioinformatics” (Stekel)

Data Mining– “Data Mining: Concepts and Techniques” (Han)

Goal:

Understand the basic bioarray technology including microarray technology for gene expression, NMR spectroscopy and other high throughout devices

Learn the basic analytical technology and its applications to the bioarray information

Learn the analysis processes of processing and analysing bioarray data (e.g. gene expression analysis)

Lecture Overview

Lecture One : BioArray Informatics in Drug Discovery Lecture Two : BioArray Technology Lecture Three : Analysis Technology (1)—Data

Normalisation and Transformation Lecture Four : Analysis Technology (2)--Clustering

Lecture Five : Analysis Technology (3)– Classification and Ontology

Lecture Six : Integrative Analysis of gene expression data Lecture Seven: Kernel Method Lecture Eight : Analysis for NMR Metabinomics Data

The Drug Discovery Process

database /genes

proteintargets

chemicaldiversity

identify‘hit’

optimize‘hit’ structure test safety/efficacy

animals humans

The aim is to translate new information into new therapies

10,000 Drug Candidates

10,000 Drug Candidates

1 Drug Molecule1 Drug

Molecule

Valid Biomedical

Hypothesis?

Complexity of Drug DiscoveryFinding a Molecule that Satisfies Multiple Criteria

manufacturablemanufacturable

physically stablephysically stable

solublesoluble

permeablepermeable

metabolically stablemetabolically stable

non-inducingnon-inducing

reversiblereversible

durabledurable

targetedtargeted

selectiveselectivepotentpotent

non-mutagenicnon-mutagenic

non-teratogenic

patentable

Complexity of Drug DiscoveryFinding a Molecule that Satisfies Multiple Criteria

10 Drug Molecules10 Drug

Molecules

1 Drug Launch1 Drug Launch

All discovery criteria metAll discovery criteria met

Safe and active in lab and animal modelsSafe and active in lab and animal models

FormulationFormulation

Trial sites and investigatorsTrial sites and investigators

Dosing rangesDosing ranges

StabilityStability

Side effect profile Side effect profile

Long-term safetyLong-term safety

EfficacyEfficacy

Competitive profileCompetitive profile

Regulatory filingRegulatory filing

Patient recruitmentPatient recruitment

Carcinogenicity studiesCarcinogenicity studies

Cost-effective manufacturingCost-effective manufacturing

Bioarray : High Throughput Measurement of Biological Activities

Gene Expression Protein Expression SNP Metabonomic Expression Chemical Hits

RNA

Metabolites

DNA Protein

Growth rateExpression

Interactions Environment

A Dynamics in BioArray Informatics

Gene Expression

Cells are different because of differential gene expression.

About 40% of human genes are expressed at one time.

Gene is expressed by transcribing DNA into single-stranded mRNA

mRNA is later translated into a protein Microarrays measure the level of mRNA expression

Gene Expression Measurement

mRNA expression represents dynamic aspects of cell

mRNA expression can be measured with latest technology

mRNA is isolated and labeled with fluorescent protein

mRNA is hybridized to the target; level of hybridization corresponds to light emission which is measured with a laser

Gene Expression Microarrays

The main types of gene expression microarrays: Short oligonucleotide arrays (Affymetrix); cDNA or spotted arrays (Brown/Botstein). Long oligonucleotide arrays (Agilent Inkjet); Fiber-optic arrays ...

Affymetrix Microarrays

50um

1.28cm

~107 oligonucleotides, half Perfectly Match mRNA (PM), half have one Mismatch (MM)Raw gene expression is intensity difference: PM - MM

Raw image

BioArray Informatics: BioArray is the data, everything else is Informatics

Data Engineering Data Warehousing Data Integration Data Analysis Knowledge Discovery Discovery Integration Discovery Validation Knowledge Integration

Quantitative Analysis

Reproducibility

confidence intervals

to find significant

deviations

Data SourcesExternalData SourcesOperational Data Sources

Data Warehousing:

KEGGKEGG

UnigeneUnigene

GenbankGenbank

StructureAnnotationsStructure

Annotations

FunctionAnnotationsFunction

Annotations

Sample & ClinicalData

Sample & ClinicalData

Experimental/SampleDatabase

Experimental/SampleDatabase

BioArray Data

BioArray Data

Expression Database

Expression Database

Data Warehousing

Data Schema in Warehousing :A Gene Expression Example

Gene ExpressionWarehouse

ProteinDisease

SNP

Enzyme

Pathway

Known Gene

SequenceCluster

Affy Fragment

Sequence

LocusLink

MGD

ExPASySwissProt

PDBOMIM

NCBIdbSNP

ExPASyEnzyme

KEGG

SPAD

UniGene

Genbank

NMR

Metabolite

A Workflow of Gene Expression Database

GXDWSet FoldChange

(e.g., > 2X)User defined

dataset

Set higher avg difference value (e.g., >200)

A->P/ P->A stringency (e.g., 80%)

Data in analysis

Data Reduction Queries Warehousing Output

Advanced Gene Expression

Analysis

Comparisons between 2 samples

Comparisons between multiple

samples

Profile Report

Visualisation

Query to the data – Which genes are linked ?– Which genes are expressed similarly to my gene XYZ?– Which genes are co-expressed in differing conditions ?– classification (of tumors, diseased tissues etc.): which patterns are

characteristic for a certain class of samples, which genes are involved?– functional classification of genes: Are changes clustered in particular

classes? – metabolic pathway information: Is a certain pathway/route in a pathway

affected?– disease information & clinical follow up: correlation to expression

patterns. – phenotype information for mutants: Are there correlations between

particular phenotypes and expression patterns?

Queries, Queries…..

Gene Expression Data Analysis Work Flow

Data in analysis

Cluster by genes

Study outliers

Correlate clinical measurements

Defined subsets of genes

Classic drug targets

Known disease association

Cross species indices

[Examples, not exhaustive]

Literature analysis

Interactive Analysis Procedures Knowledge Deliverables

Time course analysis

Microarray Data Analysis Types

Gene Selection– find genes for therapeutic targets– avoid false positives (FDA approval ?)

Classification (Supervised)– identify disease – predict outcome / select best treatment

Clustering (Unsupervised)– find new biological classes / refine existing ones– exploration

…

Microarray Data Mining Challenges

too few records (samples), usually < 100 too many columns (genes), usually > 1,000 Too many columns likely to lead to False positives for exploration, a large set of all relevant genes is

desired for diagnostics or identification of therapeutic

targets, the smallest set of genes is needed model needs to be explainable to biologists

Classification

desired features:– robust in presence of false positives– understandable– return confidence/probability– fast enough

simplest approaches are most robust advanced approaches can be more accurate

Microarray Data Classification

Prediction: ALL or AML

Gene ValueD26528_at 193D26561_cds1_at -70D26561_cds2_at 144D26561_cds3_at 33D26579_at 318D26598_at 1764D26599_at 1537D26600_at 1204D28114_at 707

Class Sno D26528 D63874 D63880 …ALL 2 193 4157 556ALL 3 129 11557 476ALL 4 44 12125 498ALL 5 218 8484 1211AML 51 109 3537 131AML 52 106 4578 94AML 53 211 2431 209…

Data Miningmodel

Newsample

Microarray chips Images scanned by laser

Datasets

FALSE POSITIVES PROBLEM

Not enough records (samples), usually < 100 Too many columns (genes), usually >>1,000 FALSE POSITIVES are very likely because of

few records and many columns

Popular Classification Methods

Decision Trees/Rules– find smallest gene sets, but not robust false positives

Neural Nets - work well for reduced # of genes K-nearest neighbor - robust for small # genes TreeNet from authors of CART and MARS

– networks of simple trees; very robust against outliers Support Vector Machines (SVM)

– good accuracy, does its own gene selection, but hard to understand

...

Microarrays: An Example

Leukemia: Acute Lymphoblastic (ALL) vs Acute Myeloid (AML), Golub et al, Science, v.286, 1999– 72 examples (38 train, 34 test), about 7,000 genes– well-studied (CAMDA-2000), good test example

ALL AML

Visually similar, but genetically very different

Results on the test data

Genes selected and model trained on Train set ONLY!

Best Clementine neural net model used 10 genes per class

Evaluation on test data (34 samples) gives – 1 or 2 errors (94-97% accuracy),– Note: all methods give error on sample 66, believed

to be mis-classified by a pathologist

Clustering

Goals Find natural classes in the data Identify new classes / gene correlations Refine existing taxonomies Support biological analysis / discovery Different Methods

– Hierarchical clustering, SOM's, etc

Yeast SOM Clusters

Yeast Cell Cycle SOM. www.pnas.org/cgi/content/full/96/6/2907

(a) 6 × 5 SOM. The 828 genes that passed the variation filter were grouped into 30 clusters. Each cluster is represented by the centroid (average pattern) for genes in the cluster. Expression level of each gene was normalized to have mean = 0 and SD = 1 across time points. Expression levels are shown on y-axis and time points on x-axis. Error bars indicate the SD of average expression. n indicates the number of genes within each cluster. Note that multiple clusters exhibit periodic behavior and that adjacent clusters have similar behavior. (b) Cluster 29 detail. Cluster 29 contains 76 genes exhibiting periodic behavior with peak expression in late G1. Normalized expression pattern of 30 genes nearest the centroid are shown. (c) Centroids for SOM-derived clusters 29, 14, 1, and 5, corresponding to G1, S, G2 and M phases of the cell cycle, are shown.

Yeast SOM Clusters

BioArray Informatics: Data Analysis of Bioarray Data within the Biological Context

ATGCAAGTCCCTAAGATTGCATAAGCTCGCTCAGTT

polymorphismpatient recordsepidemiology

linkage mapscytogenetic maps

physical maps

sequences alignments

expression patternsphysiology

receptorssignals

pathways

secondary structuretertiary structure

An illustration of iterative analysis of Bio-activities

7

8

Receptor Ligand9

4,5,6

2 3

1Gene

Protein

Relations

1- gene homologs

2- gene encodes a protein

3- protein can regulate the expression of a gene

4- protein phosphorylates another protein

5- protein binds to another protein

6- protein lyses another protein

7- Proteins can sometimes be receptors

8- Receptors bind a ligand

9- Receptors (if bound) activate other proteins

Advanced Analysis

Discovery Annotation and Validation– E.X. Annotating a set of co-expressed genes with

some conserved regulatory motifs – E.X. Scoring a co-expression pattern with pathways – E.X. Literature analysis to annotate biological meaning

Integrative Analysis– E.X. Multi-modality Analysis– E.X. Cross Annotation of Discovered Patterns

Modelling and Simulation– E.X. Pathway Synthesis– E.X. Virtual Cell Modelling

Multi-Modality Analysis

“INPUTS”NOXIOUS AGENT/STRESSOR

“OUTPUTS”“BIOLOGICAL END-POINTS”

PATHOLOGY

“REAL WORLD”

Gene Profile

Protein Profile

Metabolic Profile

Time

Time

Time

Time

Time

“-OMICS WORLD”

ALTERED PHYSIOLOGYAND METABOLISM

A mathematical model

time

event

mRNA

protein

metabolites

forwards-propagatedcorrelations

-0.20

-0.10

0.00

0.10

0.20

-0.10 0.00 0.10

w*c

[2]

w*c[1]

all_osc1.M3 (PLS), pls(par), Work setLoadings: w*c[1]/w*c[2]

9.989.949.99.869.829.789.749.79.669.629.589.549.59.469.429.389.349.39.269.229.189.149.19.069.028.988.948.98.868.828.788.748.78.668.628.588.548.58.468.428.388.34

8.38.268.22 8.188.148.18.06

8.02 7.987.94

7.97.867.827.78

7.747.77.66

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7.42

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7.147.17.06

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6.9

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6.76.666.626.586.546.56.466.426.386.346.36.266.226.186.146.16.066.025.985.945.9

5.865.825.785.745.75.665.625.585.54

5.55.46

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5.34

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5.15.065.02

4.584.544.54.464.42

4.38

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4.264.22

4.18

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4.06

4.023.98

3.94

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3.863.82

3.78

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3.063.02

2.982.942.92.86

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2.582.542.5

2.462.422.38

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1.741.71.66 1.621.581.541.5 1.46

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1.34

1.31.26

1.22

1.181.14 1.1

1.06 1.02

0.98

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0.66

0.62 0.580.54 0.50.460.420.380.340.30.260.220.180.14

0.10.060.02

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AFFX-LysX-5_

160948_at94940_at95752_at98451_at104314_r_at

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A3

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95733_at

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103101_at101934_at101527_at94078_at

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95044_at

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93923_at98527_at

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160792_at93336_at99931_at

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100022_at

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104303_i_at95717_at98477_s_at

160777_at

98069_s_at

AFFX-BioDn-5

97345_at160900_at

160400_at

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94048_at

102002_at

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95939_i_at94319_at96774_at

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102720_at96119_s_at

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94552_at

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93320_at98924_at

A4

96321_at

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97485_atA5

101097_at98064_at

103646_at

95457_at

93498_s_at104234_at94869_at

AFFX-BioB-3_

96909_at

102776_at

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160869_at94925_at96042_at95282_at95756_at97570_at

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102313_at101073_at98508_s_at

99988_at

99970_atAFFX-BioB-M_

93077_s_at

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101112_g_at98550_at

95095_at96641_at

94768_at102413_at

93967_at103926_at

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94878_at

102000_f_at

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93592_at

160742_at

103263_at

97773_at

103429_i_at

104155_f_atAFFX-TrpnX-3

93962_at102042_at

96936_at

AFFX-MURINE_

102098_at160152_at

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AFFX-PheX-3_

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160849_at94897_at98007_at

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101027_s_at

102714_at

160688_at

AFFX-BioDn-3

97160_at

101867_at

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97500_g_at99336_at

103526_at

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Simca-P 7.01 by Umetri AB 2002-01-29 15:36

Integrated Analysis of Metabonomic (plasma) versus Muscle Gene Expression Data for Insulin Resistance

(Prof. Jeremy Nicolson et.al).

Red = mRNA expression levels for each gene/ESTBlack = individual quantitative single pulse plasma NMR spectral descriptors

Strongly weightingvariables WT > KO

zones showing high

gene-metabolite correlations

Strongly weightingvariables KO > WT

Minimally weightingvariables

Integration of RNA and NMR results

Discovery of causal processes

A long term goal of Systems Biology is to discover the causal processes among genes, proteins, and other molecules in cells

Can this be done (in part) by using data from High Throughput experiments, such as microarrays?

Bayesian Causal Network Structure

P(GAL4)P(GAL2 | GAL4)P(Intracellular Galactose |

GAL2)Each variable is independent

of its distant causes given all of its direct causes.

GAL4

IntracellularGalactose

GAL2

Thanks to Greg Cooper, U. Pitt

Bayesian Network Learned for Yeast

Hartemink et al, Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models,

PSB 2002 psb.stanford.edu/psb-online

Integrate biological knowledge when analyzing microarray data (from Cheng Li, Harvard SPH)

Right picture: Gene Ontology: tool for the unification of biology, Nature Genetics, 25, p25

Enjoy the lecture so you can find a drug (or many jobs)