1

Armstrong, 2006

Bioinformatics 2

Genomics & Proteomics

Armstrong, 2006

Biological Profiling

• Microarrays– cDNA arrays– oligonucleotide arrays– whole genome arrays

• Proteomics– yeast two hybrid– PAGE techniques

Armstrong, 2006

Gene expression and microarrays

• Annealing properties of DNA• ‘probes’ of known DNA on a substrate• Extract mRNA from biological sample• Convert mRNA->DNA• Label the target DNA• Add the labeled target DNA to substrate• Look for co-localisation of label and known DNA

spot on substrate.

Armstrong, 2006

Array technology

• Not really a new technology• Old methods used nylon or nitrocellulose

filter squares• 100 x 100 spots on a square just larger than

a CD cover• Required large amount of sample DNA• Limited number of spots on substrate.

Armstrong, 2006

Microarrays

• Glass substrate• Very high density of probes• Two main methods:

– robot spotted system– substrate synthesized system (affymetrix)

Armstrong, 2006

Why microarrays?

• What genes are expressed in a tissue andhow does that tissue respond to one of anumber of factors:– change in physical environment– experience– pharmacological manipulation– influence of specific mutations

2

Armstrong, 2006

What do we actually get?

• A snap-shot of the mRNA profile in abiological sample

• With the correct experimental conditions wecan compare two situations

• Not all biological processes are regulatedthrough mRNA expression levels

Armstrong, 2006

What can we learn?

• Identify functionally related genes• Find promoter regions (common regulation)• Predict genetic interactions• If we change one variable a network of gene

responses should compensate• Homeostasis is a fundamental principle of biology

- almost all biological systems exist in a controlledstate of negative feedback.

Armstrong, 2006

Robot spotting

• Robotically controlled print head with asmall array of fine needles

• Needles dipped into DNA probes• Needles spotted onto glass substrate.• Clean needles, get more probe and offset

next set of spots• Needle mismatches are propagated over

entire matrix.Armstrong, 2006

From medir.ohsu.edu/~geneview/education/ Normalization%20Presentation%2011-7-2001.ppt

Armstrong, 2006

Robot spotted array.

Armstrong, 2006

Spot detection software review:http://ihome.cuhk.edu.hk/~b400559/arraysoft_image.html

http://experimental.act.cmis.csiro.au/Spot/index.php

3

Armstrong, 2006

Spot Detection

Signal (includes area inside)Background

From medir.ohsu.edu/~geneview/education/ Normalization%20Presentation%2011-7-2001.ppt

Armstrong, 2006

From medir.ohsu.edu/~geneview/education/ Normalization%20Presentation%2011-7-2001.ppt

Armstrong, 2006

Affymetrix system

• Proprietary technology• Synthesize oligonucleotides on the substrate• Allows much higher density of probes• Probe length is limited• Multiple probes used per gene• Reverse complement controls included• Has the ability to look at splice variants

Armstrong, 2006

The experiment

• Tissue/biological sample from anexperiment is obtained

• RNA is extracted from the tissue• RNA->DNA and possibly amplified• DNA is labeled with coloured dyes• DNA is hybridised to the array

Armstrong, 2006

Analysis of microarray data

• Image processing– Locate all the probe spots and identify them– Measure the spot intensity

• Normalising for experimental Artifacts– Spot variability– Dye pairing bias– Background noise

Armstrong, 2006

Replication

• Gene expression levels are often multi-factorial

• Expression levels will vary independentlyof any experimentally controlled variable

• Multiple samples or mixed samples willgive an overall baseline

• Limited by cost and sample availability

4

Armstrong, 2006

Data analysis

• The data is noisy• The sensitivity is low• 1.4 fold changes in gene expression are the

normal threshold• Start by looking for significant changes in

individual gene expression levels betweenthe two conditions or time points.

Armstrong, 2006

Clustering

• Take the relative changes in geneexpression per gene

• Cluster these changes into sets (normallyaround 100-1000)

• Gives us sets of genes that are affected to asimilar degree by the experimental variable

• Can be used to help look for promoterregions

Armstrong, 2006

Dealing with microarray data

• Many experiments focus on a few samplesover a few conditions to examine geneexpression changes in response to anenvironmental change.

• Can we reuse this data to learn aboutnetworks of genes?

Armstrong, 2006

Predicting gene networks

• Very active area of research.• Multiple microarrays contain information

that we can use to predict networks• The data from microarray experiments was

not collected for that express purpose.• The Microarray Gene Expression Database

group are proposing management systemsto help

Armstrong, 2006

MGED

• Microarray Gene Expression Database Group• MIAME• MAGE• Ontologies• Normalization

• http://www.mged.org/Armstrong, 2006

MIAME

• Minimum amount of information requiredto unambiguously describe a microarrayexperiment.– relevant details of the experiment.– biological repetition– reuse of the data– comparison across experiments.

5

Armstrong, 2006 Armstrong, 2006

MIAME Express

• Under development at EBI• GUI interface to the MIAME database

system

• http://www.ebi.ac.uk/microarray/MIAMExpress/miamexpress.html

Armstrong, 2006

MAGE

• Microarray And Gene Expression• Developing standards for representing and

exchanging microarray data.

• MAGE-OM Object Model for microarray datadeveloped using Unified Modelling Language(UML)

Armstrong, 2006

MAGE

• MAGE-ML - XML based model forcommunication

• MAGE-STK - toolkit for handling andconverting between MAGE-OM andMAGE-XML on a variety of platforms– Java and PERL APIs available

Armstrong, 2006

Excerpts from a Sample Descriptioncourtesy of M. Hoffman, S. Schmidtke, Lion BioSciences

Organism: mus musculus [ NCBI taxonomy browser ]Cell source: in-house bred mice (contact: norma.howells@itg.fzk.de)Sex: female [ MGED ]Age: 3 - 4 weeks after birth [ MGED ]Growth conditions: normal

controlled environment20 - 22 oC average temperaturehoused in cages according to German and EU legislationspecified pathogen free conditions (SPF)14 hours light cycle10 hours dark cycle

Developmental stage: stage 28 (juvenile (young) mice) [ GXD "Mouse Anatomical Dictionary" ]Organism part: thymus [ GXD "Mouse Anatomical Dictionary" ]Strain or line: C57BL/6 [International Committee on Standardized Genetic Nomenclature for Mice]Genetic Variation: Inbr (J) 150. Origin: substrains 6 and 10 were separated prior to 1937. Thissubstrain is now probably the most widely used of all inbred strains. Substrain 6 and 10 differ at the H9,Igh2 and Lv loci. Maint. by J,N, Ola. [International Committee on Standardized Genetic Nomenclaturefor Mice ]Treatment: in vivo [MGED] intraperitoneal injection of Dexamethasone into mice, 10 microgram per25 g bodyweight of the mouseCompound: drug [MGED] synthetic glucocorticoid Dexamethasone, dissolved in PBS Armstrong, 2006

Biomaterial Concepts• Environmental or experimental history: A description of the conditions the

organism has been exposed to that are not one of the variables under study.– Culture conditions: A description of the isolated environment used to grow

organisms or parts of the organism.• atmosphere, humidity, temperature• light: The photoperiod and type (e.g., natural, restricted wavelength) of light exposure.• nutrients: The food provided to the organism (e.g., chow, fertilizer, DEMM 10%FBS,

etc.).• medium: The physical state or matrix used to provide nutrients to the organism (e.g.,

liquid, agar, soil)• density range: The concentration range of the organism.• contaminant organisms: Organisms present that were not planned as part of the study

(e.g., mycoplasma).• removal of contaminants: Steps taken to eliminate contaminant organisms.• host organism or organism parts: Organisms or organism parts used as a designed

part of the culture (e.g., red blood cells, stromal cells).– Generations: The number of cell divisions if the organism or organism part that is

cultured is unicellular otherwise the number of breedings.– Clinical history: The organism's (i.e., the patient's) medical record.– Husbandry: water, bedding, barrier facility, pathogen test results– Preservation: seed dormancy, frozen storage

6

Armstrong, 2006

Biomaterial Concepts• Treatment: The manipulation of the biomaterial for the purposes

of generating one of the variables under study.– somatic modification: The organism has had parts removed, added,or

rearranged.– genetic modification: The organism has had genes removed, added, or

rearranged.– starvation: The organism (or organism part) has been deprived of nutrients.– infection: The organism (or organism part) has been exposed to a virus or

pathogen.– behavioral stimulus: The organism is forced to respond to a stimulus with some

behavior (e.g., avoidance, obtaining a reward, etc.)– agent-based treatment: The treatment is effected by a defined chemical,

biological, or physical agent.– agent type: chemical (drugs), biological (macromolecule), physical

(stress from light, temperature, etc.)• agent application: In vivo, in vitro, in situ; qualitative or quantitative

– treatment protocol: method of treatment– treatment parameters: constant, variable– treatment duration: length of treatment Armstrong, 2006

Biomaterial Concepts• Biomaterial preparation: A description of the state

and condition of the biomaterial.– Time of day when the biomaterial was generated (i.e.,

sampled). Pathological staging: pre or post mortem atsampling

– state at start of treatment (age, time of day)– physio-chemical composition of the sample: amount of

material, number of cells, purity.– Extraction: Chemical extraction, Physical extraction– protocol: method used.– Pool types:

• Multiple: Biomaterial prepared from multiple specimens, butsame Organism, Genotype, Phenotype and treatment.

• Individually: Biomaterial prepared from individually specimen,but same Organism, Genotype, Phenotype and treatment

Armstrong, 2006

MGED Ontologies

• Develop standard ontologies for describingexperimental procedures associated withmicroarray data.

• Ontologies specific for:– sample (e.g. species, anatomical location etc)– by concept– array design

Armstrong, 2006

MGED Normalization

• Working group to discus standards fornormalization in microarray experiments– Differences in labeling efficiency between dyes– Differences in the power of the two lasers.– Differing amounts of RNA labeled between the 2

channels.– Spatial biases in ratios across the surface of the

microarray.

Armstrong, 2006

The Transcriptome

• Microarrays work by revealing DNA-DNAbinding.

• Transcriptional activators also bind DNA• Spot genomic DNA onto glass slides• Label protein extracts• Hybridise to the genomic probes• Reveals domains that include promoter

regionsArmstrong, 2006

ChIP to ChipChromatin Immunoprecipitation to Microarray (Chip)

Protein-DNA interactionsde-novo prediction has many false positivesWhich DNA sites do actually bind a specific TF?Requires an antibody to the protein

7

Armstrong, 2006

http://proteomics.swmed.edu/chiptochip.htm

Armstrong, 2006

http://proteomics.swmed.edu/chiptochip.htm

Armstrong, 2006

pause…

Gene networks and network inference

Armstrong, 2006

What is a gene network

• Genes do not act alone.• Gene products interact with other genes

– Inhibitors– Promoters

• The nature of genetic interactions in complex– Takes time– Can be binary, linear, stochastic etc– Can involve many different genes

Armstrong, 2006

What makes boys boys and girls girls?

Sugar, Spice and synthetic Oestrogens?

Armstrong, 2006

Sex determination: a genecascade (in flies…)

RuntSisterlessScute

DaughterlessDeadpanExtramachrochaete

6 Genes detect X:A ratioFemales Males

8

Armstrong, 2006

Sex determination(in flies…)

RuntSisterlessScute

DaughterlessDeadpanExtramachrochaete

6 Genes regulate ‘Sexlethal’

Sexlethal

+ effect

- effect

Armstrong, 2006

Sex determination(in flies…)

RuntSisterlessScute

DaughterlessDeadpanExtramachrochaete

Sexlethal can then regulate itself...

Sexlethal

Armstrong, 2006

Sex determination(in flies…)

Downstream cascade builds...

RuntSisterlessScute

DaughterlessDeadpanExtramachrochaete

Sexlethal transformer doublesex

Armstrong, 2006

Gene expression and time1 Runt2 Sisterless3 Scute

4 Daughterless5 Deadpan6 Extramachrochaete

7 Sexlethal 8 transformer 9 doublesex

Armstrong, 2006

Gene microarrays

time

Armstrong, 2006

Gene sub networks

• Do all types of connections exist in genenetworks?

• Milo et al studied the transcriptionalregulatory networks in yeast and E.Coli.

• Calculated all the three and four genecombinations possible and looked at theirfrequency

9

Armstrong, 2006

Milo et al. 2002 Network Motifs: Simple Building Blocks of ComplexNetworks. Science 298: 824-827

Biological Networks

Three node possibilities

Armstrong, 2006

Gene sub networks

Heavy bias in both yeast and E.coli towards these two subnetwork architectures

Armstrong, 2006 Armstrong, 2006

Gene Network Inference

• Gene micro-array data• Learning from micro-array data• Unsupervised Methods• Supervised Methods• Edinburgh Methods

Armstrong, 2006

Gene Network Inference

• Gene micro-array data– Time Series array data– Tests under ranges of conditions

• Unlike example - 1000s genes• Lots of noise• Clustering would group many of these genes

together• Aim: To infer as much of the network as possible

Armstrong, 2006

Learning from Gene arrays

• Big growth industry but difficult problem• Initial attempts based on unsupervised

methods:– Basic clustering analysis - related genes– Principal Component Analysis– Self Organising Maps– Bayesian Networks

10

Armstrong, 2006

Bayesian ‘gene’ networks

• Developed by Nir Friedman and Dana Pe’er• Can be easily adapted to a supervised

method

Armstrong, 2006

Learning Gene Networks

• The field is generally moving towards moresupervised methods:– Bayesian networks can use priors– Support Vector machines– Neural Networks– Decision Trees

• New local approach uses modified geneticprogramming

Armstrong, 2006

GAGA

• Genetic Algorithms for Gene Analysis• Represent the network sub-components in a string• Mutate this string to produce new networks• Assign a rough linear weight to each edge• Select the best of these• Refine the weights of each edge using back

propagation• Select the fittest and start over

Armstrong, 2006

Island Model

• Set up individual islands for computation (servers)• Allow these to interbreed from time to time• Massively increased performance• More accurate since we can use a huge initial

search space

Armstrong, 2006

• Scale free architecture– Chance of new edges is proportional to existing ones– Highly connected nodes may well be known to be

lethal

• Network motifs– Constrain the types of sub networks

• Prior Knowledge– Many sub networks already known

Can we combine networkknowledge with gene inference?

Armstrong, 2006

Conclusions

• Network analysis is a big growth area• Several promising fields starting to converge

– Complex systems analysis– Using prior knowledge– Application of advance machine learning algorithms– AI approaches show promise