1. The Bioinformatics of Microarrays
Microarray Outreach Team Fall 2005

2. Outline Biology, Statistics, Data mining common term definitions
Transcriptome caveats and limitations
Experimental Design
Scan to intensity measures
Low level analysis
Data mining – how to interpret > 6000 measures
Databases
Software
Techniques
Comparing to prior HT studies, across platforms? Issues

3. Bioinformatics, Computational Biology, Data Mining
Bioinformatics is an interdisciplinary field about the information processing problems in computational biology and a unified treatment of the data mining methods for solving these problems.
Computational Biology is about modeling real data and simulating unknown data of biological entities, e.g.
Genomes (viruses, bacteria, fungi, plants, insects,…)
Proteins and Proteomes
Biological Sequences
Molecular Function and Structure
Data Mining is searching for knowledge in data
Knowledge mining from databases
Knowledge extraction
Data/pattern analysis
Data dredging
Knowledge Discovery in Databases (KDD)

4. Basic Terms in Biology Example:
The human body contains ~100 trillion cells
Inside each cell is a nucleus
Inside the nucleus are two complete sets of the human genome (except in egg, sperm cells and blood cells)
Each set of genomes includes 30,000-80,000 genes on the same 23 chromosomes
Gene – A functional hereditary unit that occupies a fixed location on a chromosome, has a specific influence on phenotype, and is capable of mutation.
Chromosome – A DNA containing linear body of the cell nuclei responsible for determination and transmission of hereditary characteristics

5. Basic Terms in Data Mining
Data Mining:A step in the knowledge discovery process consisting of particular algorithms (methods) that under some acceptable objective, produces a particular enumeration of patterns (models) over the data.
Knowledge Discovery Process: The process of using data mining methods (algorithms) to extract (identify) what is deemed knowledge according to the specifications of measures and thresholds, using a database along with any necessary preprocessing or transformations.
A pattern is a conservative statement about a probability distribution.
Webster: A pattern is (a) a natural or chance configuration, (b) a reliable sample of traits, acts, tendencies, or other observable characteristics of a person, group, or institution

6. Problems in Bioinformatics Domain
Data production at the levels of molecules, cells, organs, organisms, populations
Integration of structure and function data, gene expression data, pathway data, phenotypic and clinical data, …
Prediction of Molecular Function and Structure
Computational biology: synthesis (simulations) and analysis (machine learning)

7. Subcellular Localization, Provides a simple goal for genome-scale functional prediction
Determine how many of the ~6000 yeast proteins go into each compartment

8. Subcellular Localization, a standardized aspect of function
Cytoplasm
Nucleus
Membrane ER
Extra- cellular [secreted]
Golgi
Mitochondria

9. "Traditionally" subcellular localization is "predicted" by sequence patterns
Cytoplasm
NLS
Nucleus
Membrane
TM-helix ER
HDEL
Extra- cellular [secreted]
Golgi
Import Sig.
Mitochondria
Sig. Seq.

10. [Expression Level in Copies/Cell]
Subcellular localization is associated with the level of gene expression
[Expression Level in Copies/Cell]
Cytoplasm
Nucleus
Membrane ER
Extra- cellular [secreted]
Golgi
Mitochondria

11. [Expression Level in Copies/Cell]
Combine Expression Information & Sequence Patterns to Predict Localization
[Expression Level in Copies/Cell]
Cytoplasm
NLS
Nucleus
Membrane
TM-helix ER
HDEL
Extra- cellular [secreted]
Golgi
Import Sig.
Mitochondria
Sig. Seq.

12. The central dogma of molecular biology???
Major Objective: Discover a comprehensive theory of life’s organization at the molecular level
The major actors of molecular biology: the nucleic acids, DeoxyriboNucleic acid (DNA) and RiboNucleic Acids (RNA)
The central dogma of molecular biology???
Proteins are very complicated molecules with 20 different amino acids.

14. Dynamic Nature of Yeast Genome
eORF= essential
kORF= known
hORF= homology identified
shORF= short
tORF= transposon identified
qORF= questionable
dORF= disabled
First published sequence claimed 6274 genes– a # that has been revised many times, why?

15. The Affy detection oligonucleotide sequences are frozen at the time of synthesis, how does this impact downstream data analysis?

16. Microarray Data Process
Experimental Design
Image Analysis – raw data
Normalization – “clean” data
Data Filtering – informative data
Model building
Data Mining (clustering, pattern recognition, et al)
Validation

17. Experimental Design A good microarray design has 4 elements
A clearly defined biological question or hypothesis
Treatment, perturbation and observation of biological materials should minimize systematic bias
Simple and statistically sound arrangement that minimizes cost and gains maximal information
Compliance with MIAME
The goal of statistics is to find signals in a sea of noise
The goal of exp. design is to reduce the noise so signals can be found with as small a sample size as possible

18. Observational Study vs. Designed Experiment
Investigator is a passive observer who measures variables of interest, but does not attempt to influence the responses
Designed Experiment-
Investigator intervenes in natural course of events
What type is our DMSO exp?

19. Experimental Replicates
Why?
In any exp. system there is a certain amount of noise—so even 2 identical processes yield slightly different results
Sources?
In order to understand how much variation there is it is necessary to repeat an exp a # of independent times
Replicates allow us to use statistical tests to ascertain if the differences we see are real

21. Technical vs. Biological Replicates
As we progress from the starting material to the scanned image we are moving from a system dominated by biological effects through one dominated by chemistry and physics noise
Within Affy platform the dominant variation is usually of a biological nature thus best strategy is to produce replicates as high up the experimental tree as possible

23. From probe level signals to gene abundance estimates

24. From probe level signals to gene abundance estimates
The job of the expression summary algorithm is to take a set of Perfect Match (PM) and Mis-Match (MM) probes, and use these to generate a single value representing the estimated amount of transcript in solution, as measured by that probeset.
To do this, .DAT files containing array images are first processed to produce a .CEL file, which contains measured intensities for each probe on the array.
It is the .CEL files that are analysed by the expression calling algorithm.

25. PM and MM Probes
The purpose of each MM probe is to provide a direct measure of background and stray-signal(perhaps due to cross-hybridisation) for its perfect-match partner. In most situations the signal from each probepair is simply the difference PM - MM.
For some probepairs, however, the MM signal is greater than the PM value; we have an apparently impossible measure of background.

26. Signal Intensity
Following these calculations, the MAS5 algorithm now has a measure of the signal for each probe in a probeset.
Other algortihms, ex RMA, GCRMA, dCHIP and others have been developed by academic teams to improve the precision and accuracy of this calculation
In our Exp we will use RMA and GCRMA

27. Low level data analysis / pre-processing
Varying biological or cellular composition among sample types.
Differences in sample preparation, labeling or hybridization
Non specific cross-hybridization of target to probes.
Lead to systemic differences between individual arrays
GMC scientists
Scott
Scott
Anjie
Anjie
Raw Data Quality Control
Scaling
Normalization and filtering.
GeneSpring, R-language, Bioconductorb
GMC scientists + entire UVM outreach team

30. Data processing is completed now what?

41. Overview of Microarray Problem
Biology Application Domain
Validation
Data Analysis
Microarray Experiment
Image Analysis
Data Mining
Experiment
Design and
Hypothesis
Data Warehouse
Artificial Intelligence (AI)
Knowledge discovery in databases (KDD)
Statistics

42. Back to Biology
Do the changes you see in gene expression make sense BIOLOGICALLY?
How do we know?
If they don’t make sense, can you hypothesize as to why those genes might be changing?
Leads to many, many more experiments

43. A Common Language for Annotation of Genes from
The Gene Ontologies
A Common Language for Annotation of Genes from
Yeast, Flies and Mice
…and Plants and Worms
…and Humans
…and anything else!

44. Gene Ontology Objectives
GO represents concepts used to classify specific parts of our biological knowledge:
Biological Process
Molecular Function
Cellular Component
GO develops a common language applicable to any organism
GO terms can be used to annotate gene products from any species, allowing comparison of information across species
GO is the designation of a project as well as the product of the project.
Starting with the cellular level, we are not distinguishing cell types, organs,
etc.
Gene Ontology is a collaboration between the fly (FlyBase), mouse (MGD) genome databases, and yeast (SGD). All three groups had started independent projects to produce controlled vocabularies for the biology of their organisms.
You will all be familiar with hierarchical system to classify enzymes (EC) or functions (YPD, SwissPROT, MIPS, …).
We have divided our project into the creation of three ontologies. These are not necessarily hierarchical rather they can be a network of associations -- a directed acyclic graph (DAG).
Process: cell cycle, nutrient transport, behavior,
Function: alcohol dehydrogenase,
Cellular Location: organelle, protein complex, subcellular compartment

45. Sriniga Srinivasan, Chief Ontologist, Yahoo!
The ontology. Dividing human knowledge into a clean set of categories is a lot like trying to figure out where to find that suspenseful black comedy at your corner video store. Questions inevitably come up, like are Movies part of Art or Entertainment? (Yahoo! lists them under the latter.) -Wired Magazine, May 1996

46. The 3 Gene Ontologies Molecular Function = elemental activity/task
the tasks performed by individual gene products; examples are carbohydrate binding and ATPase activity
Biological Process = biological goal or objective
broad biological goals, such as mitosis or purine metabolism, that are accomplished by ordered assemblies of molecular functions
Cellular Component = location or complex
subcellular structures, locations, and macromolecular complexes; examples include nucleus, telomere, and RNA polymerase II holoenzyme
GO is the designation of a project as well as the product of the project.
Starting with the cellular level, we are not distinguishing cell types, organs,
etc.
Gene Ontology is a collaboration between the fly (FlyBase), mouse (MGD) genome databases, and yeast (SGD). All three groups had started independent projects to produce controlled vocabularies for the biology of their organisms.
You will all be familiar with hierarchical system to classify enzymes (EC) or functions (YPD, SwissPROT, MIPS, …).
We have divided our project into the creation of three ontologies. These are not necessarily hierarchical rather they can be a network of associations -- a directed acyclic graph (DAG).
Process: cell cycle, nutrient transport, behavior,
Function: alcohol dehydrogenase,
Cellular Location: organelle, protein complex, subcellular compartment

47. Example: Gene Product = hammer
Function (what) Process (why)
Drive nail (into wood) Carpentry
Drive stake (into soil) Gardening
Smash roach Pest Control
Clown’s juggling object Entertainment
Stakes into vampires
Nails into wood is reversible with this model of hammer, you can remove the nails with the claw.
Cytoskeletal structural protein
Storage protein
WHY -- Process
WHAT -- Function

48. Biological Examples Biological Process Biological Process
Molecular Function
Molecular Function
Cellular Component
Cellular Component

49. Terms, Definitions, IDs
term: MAPKKK cascade (mating sensu Saccharomyces)
goid: GO:
definition: OBSOLETE. MAPKKK cascade involved in transduction of mating pheromone signal, as described in Saccharomyces.
definition_reference: PMID:
comment: This term was made obsolete because it is a gene product specific term. To update annotations, use the biological process term 'signal transduction during conjugation with cellular fusion ; GO: '.
definition: MAPKKK cascade involved in transduction of mating pheromone signal, as described in Saccharomyces

50. SGD

53. SGD public microarray data sets available for public query

54. Homework
Go to and find 3 candidate genes of known f(x) and one of undefined f(x) that you might predict to be altered by DMSO treatment
What GO biological processes and molecular mechanisms are associated with your candidate genes?
Where, subcellularly does the protein reside in the cell?
What other proteins are known or inferred to interact with yours? How was this interaction determined? Is this a genetic or physical interaction?
Find the expression of at least one of your known genes in another public ally deposited microarray data set?
Name of data set and how you found it?
What is the largest Fold change observed for this gene in the public study?
Now that you are microarray technology experts can you give me 3 reasons why the observed transcript level difference may not be confirmed through a second technology like RTQPCR?

55. Suggested Reading