1. Genomic wide analysis of nucleic acids.
Lecture 9
Genomic wide analysis of nucleic acids.

2. A Genome Revolution in Biology and Medicine
We are in the midst of a "Golden Era" of biology
The Human Genome Project has produced a huge storehouse of data that will be used to change every aspect of biological research and medicine
The revolution is about treating biology as an information science, not about specific biochemical technologies. .

3. Historical Milestones Year. Milestone 1866
Historical Milestones Year Milestone Mendel’s discovery of genes Discovery of nucleic acids First protein sequence (insulin) Double helix structure of DNA 1960s Elucidation of the genetic code Advent of DNA sequencing First cloning of human genes Fully automated DNA sequencing First whole genome (Haemophilus Influenza) First human chromosome(Chr #22) Drosophila / Arabidopsis genomes Human and mouse genomes Much more genomes since them!!

4. Genomic data
Whole genome data sets. According to as at 30-sept-04
Archea – 19
Bacteria – 167
Eukaryota - 36
Organelles – 569
Phages – 137
Plasmids – 204
Viroids – 36
Viruses – 911
TOTAL:2079

5. The ….omics

6. Genomics The experimental study of complete genomes.
The application of high-throughput automated technologies to molecular biology.
The experimental study of complete genomes.

7. Genomics Technologies
Automated DNA sequencing
Automated annotation of sequences
DNA microarrays
gene expression (measure RNA levels)
single nucleotide polymorphisms (SNPs)
Protein chips (SELDI, etc.)
Protein-protein interactions

8. New Types of Biological Data
Microarrays - gene expression
Multi-level maps: genetic, physical, sequence splicing, expression, function
Networks of protein-protein interactions
Cross-species relationships
homologous genes
chromosome organization (synteny)
common regulatory sequences

9. Biological Information
Protein 2-D gel
mRNA Expression
Protein 3-D Structure
Mass Spec.
Genome sequence
The Cell

10. What is gene expression?
The amount of RNA produced from a gene.
Level of RNA produced from a gene is controlled by:
Transcription
Degradation
Transcriptome - Expressed transcripts in a cell under defined experimental conditions.
mRNA(5-10% of total RNA).
rRNA, tRNA - make up most of total RNA

11. Analysis of gene expression at the single gene level.
Northern Blots
Measure RNA levels by hybridization of a labeled probe to total RNA.
Reporter Genes
Use of an enzyme to measure the amount of transcription from a promoter.
Quantitative RT-PCR.

12. Assaying the regulation of 1000s of genes in a single experiment
DNA microarrays
DNA molecules printed at high density used to determine the level of RNA or DNA in a sample.
Can be thought of a “reverse Northern blots”
Other technologies
- SAGE
- Microbeads
SAGE = Systematic Analysis of Gene Expression
Microbeads = new technology. You can check it at

13. DNA Microarrays Spotted DNA arrays (glass slides)
Competitive binding of samples
Fluorescent detection - Cy3 and Cy5
Small sample sizes (10-30µl).
PCR or cDNA arrays
Long oligonucleotide arrays
Short oligonucleotide arrays
ex. Affymetrix
DNA spotted onto nylon membranes (macroarrays)

14. Applications of DNA microarrays
Expression profiling
Determining the relative levels of RNA in two or more samples.
DNA/DNA hybridizations
Investigate gene content between different strains
Determine gene dosage
16S arrays - microbial communities (being developed).
Identification of protein binding sites
ChIP-Chip. Immunoprecipitation of protein/DNA complexes. Assaying those interactions with microarrays.

15. cDNA spotted microarrays

16. Labeling RNA or DNA with Cy3 or Cy5.
Cy3 and Cy5 - most often used fluorescent molecules used to label samples for microarray analysis.
Absorb light at one wavelength and emit at another.
Emission and Excitation spectra do not overlap significantly.
In arrays Cy3 and Cy5 are usually false colored green (Cy3) and red (Cy5) for ease of visualization.

18. Affymetrix Gene Chips

19. Microarray Experiment - labeling, hybridizing, scanning

21. Each gene on an Affy chip is represented by a probe set
Affymetrix = Oligonucleotide Microarray
Each gene on an Affy chip is represented by a probe set
affymetrix arrays uses 25 base-pairs Oligonucleotides to probe genes. There are two types of probes, the perfect match and the mismatch probes of these probe pairs of PP and MM represent a genes.

22. Rationality of Affy analysis
MM probes are used to measure background signals due to non-specific sources and scanner offset.
Using a MM probe as an estimate of background seems them great in theory.
- The expression value for a gene is a combination of the (PM-MM) signals for each of the probes (i.e. the average)
The MM probes are designed to measure non-specific binding and optical noise components in PM.
Many expression measures are based on PM-MM, with the intention of correcting for non-specific binding and background noise.
•Problems:
–MMsare PMsfor some genes,
–removing the middle base does not make a difference for some probes .
–Subtracting MM adds variance. Especially at low end.

23. Microarray Data Analysis
Data mining and visualization
Controls and normalization of results
Statistical validation
Linkage between gene expression data and gene sequence/function/metabolic pathways databases
Clustering and pattern detection
Discovery of common sequences in co-regulated genes

25. Regulons and Stimulons
Operon - group of genes co-expressed on a single transcript.
One location of the genome
Regulon - genes that are regulated by a single transcription factor.
Genes and operons throughout the genome
Stimulon - collection of genes that are regulated in response to environmental changes.
Can be multiple regulons affected at once.
Regulatory network - alternative term for regulon.

30. Identifying genes
whose expression
changes at specific
stages of the cell cycle

33. RBK1 PHO87 BUD5 MATa2
MATa1 TSM1 YCR043C HO
12346 hr
Microarray analysis of 150 damage-regulated mRNAs after a single unrepaired
HO-induced DSB
4 kb/hr hr
YCR033W
YCR034W
FEN3
YCR035C
RRP43
YCR036W
RBK1
YCR037C
PHO87
YCR038C
BUD5
YCR039C
MATa 2
MATa 1
YCR040W
YCR041W
YCR042C
TSM1
YCR043C
YCR044C
YCR045C
Audrey Gasch
Moreshwar Vaze

34. Circadian Rhythms Genes whose expression changes during the day
day night day night day night
Circadian Rhythms
Genes whose
expression changes
during the day
in fruit flies

35. Cancer can be qualified from the transcriptome

36. Bioinformatics
Genomics produces high-throughput, high-quality data, and bioinformatics provides the analysis and interpretation of these massive data sets.
It is impossible to separate genomics laboratory technologies from the computational tools required for data analysis.

47. What type of data we can use to build a transcriptional network?
Protein-Protein interaction data
Expression data
ChIP data

48. CHIP ON CHIP

52. Comparative Genomics
The Assumption that underlies comparitive genomics is that the two genomes had a common ancestor and that each organism is a combination of the ancestor and the action of evolution.
Evolution can be broadly thought of as the combination of two processes: mutational forces that generate random mutations in the genome sequence, and selection pressures that
1. Eliminate random mutations (negative selection),
2. Have no effect on mutations (neutral selection) or,
2. Increase the frequency of mutant alleles in the population as a result of a gain in fitness (positive selection).
The combined action of mutation and selection is represented generally by a RATE MATRIX of base-pair changes between the two observed genomes.

53. Comparative Genomics Human Mouse Rat C.Elegans
Evolutionary relationship between metazoans that are sequenced, or due for sequencing.
Evolutionary distances are in millions of years.
C.Elegans

54. Comparative Genomics
Comparative genomics may be defined as the derivation of genomic information following comparison of the information content of 2 or more species genome sequences

55. The similarity is such that human chromosomes can be cut (schematically at least) into about 150 pieces (only about 100 are large enough to appear here), then reassembled into a reasonable approximation of the mouse genome.

57. Harnessing the genome to answer real problems
How do we control infectious disease?
How do we slow or stop the effects of cancer?
How can we detect and treat genetic disorders?
Only 2% of human diseases are due to single gene defects
the rest involve networks of gene expression.
Most pharmaceutical drugs act on individual proteins
or sets of proteins.

58. The study of the ‘proteome’
Proteomics
The study of the ‘proteome’
While an organism has only one genome,
it has many transcriptomes, proteomes and metabolomes

59. mRNA level  expressed protein level nor does it indicate the nature of the functional protein product
Functional
Protein
Product
Genomic
Sequence
Protein
Product
mRNA
Translational
Control
Transcriptional
Control
Post-Translational
Control

60. Temporal Changes in mRNA and protein
Gene
Expression t
When you measure expression affects what you find

61. Does mRNA level correlate with protein level?
20 liver proteins
and corresponding mRNAs
Glutathione-S-transferase
in 60 human cell lines x
Lung
Ovarian
CNS
Leukemia
Renal
Melanoma
Breast
0.1
1.0 10
100
1000
R = 0.43
Protein (Affinity-HPLC)
mRNA (Northern)
0.1 1 10
100
1000
R=0.48
Protein (2D gels)
mRNA (EST clones)
Anderson & Anderson
Electrophoresis :
From Tew et al 1996
Anderson & Seilhamer
Electrophoresis :

62. Genomics, proteomics era.
Lots of data (lots of real data and lots of noise!). Needs validation!
Dangers :
+ Become too descriptive and reductionist
+ Forget about the biological problem

63. Year by year we are becoming better equipped to accomplish the things we are striving for.
But what are we actually striving for? - Bertrand de Jouvenel,
Success is the ability to go from failure to failure without losing your enthusiasm. - Winston Churchill,