Introduction to Bioinformatics Data analysis of bio-activities for drug discovery Course 341...
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Transcript of Introduction to Bioinformatics Data analysis of bio-activities for drug discovery Course 341...
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
7.627.58
7.547.57.46
7.42
7.38
7.34
7.3
7.26
7.227.18
7.147.17.06
7.026.986.94
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
5.425.38
5.34
5.3
5.26
5.22
5.185.14
5.15.065.02
4.584.544.54.464.42
4.38
4.34
4.3
4.264.22
4.18
4.14
4.1
4.06
4.023.98
3.94
3.9
3.863.82
3.78
3.74
3.7
3.663.62
3.58
3.54
3.5 3.46
3.42
3.38
3.34
3.3
3.26
3.22
3.18
3.143.1
3.063.02
2.982.942.92.86
2.82
2.78
2.74
2.72.66 2.62
2.582.542.5
2.462.422.38
2.342.3
2.262.222.18
2.142.1
2.06
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1.98
1.94 1.91.861.821.78
1.741.71.66 1.621.581.541.5 1.46
1.42 1.38
1.34
1.31.26
1.22
1.181.14 1.1
1.06 1.02
0.98
0.94
0.9
0.86
0.82
0.78 0.740.7
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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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)