Download Bioinfo primer - part 6/6

Christophe Roos - MediCel ltd
[email protected]
High throughput data acquisition
New issues in storage and analysis
Annotating genomes with functional
information: automatic but without errors?
Genome annotation
• Annotations is the sum of all non-sequence information that can be
connected to any sequence
Phylogenetic inference
Metabolic profiles
Sequence homologs in other genomes
Connectors to other maps
Cofactors and metabolites
Metabolic map locator
Gene
Sequence
Functional
chemistry
Experimental
data
Genome location
Expression info
Raw images
Numerical
values
Cluster genes
Christophe Roos - 6/6 Functional genomics
Structure
Raw
data
Electron
density
Structure
annotation
Spring 2002
SS
assignments
Genome annotation
• Primary sources of information about what genes do are laboratory
experiments. It may take several experiments for one data point.
• All that data should ideallically be associated – hyperlinked among DBs.
– Magpie is an environment for genome annotation
• Compare genomes to learn how their structure affects function
– Bacteria have modules of genes functioning together organised in ‘operons’
– Higher organisms need to pack the DNA to fit it in the nucleus. Activating a
gene means unpacking and is not efficient if it is done for each gene separately
Christophe Roos - 6/6 Functional genomics
Spring 2002
Functional genomics
• High throughput technologies give us long lists of the parts of systems
(chromosomes, genomes, cells, etc). We can now analyse how they work
together to produce the complexity of the organisms.
• The function of the genome is
– Metabolism: metabolic pathways convert chemical energy derived from food
into useful work in the cell.
– Regulation: regulatory pathways are biochemical mechanisms that control what
genomic DNA does. It switches genes on and off in a controlled way.
– Signalling: signalling pathways control the movement of information
(chemicals) from one component to another on many levels
– Construction
• Functional genomics tries to map these pathways
Christophe Roos - 6/6 Functional genomics
Spring 2002
Analysing the activity of the genome
• Genomics: look at transcriptional activity of genes
– Transcription: When a gene is transcriptionally active, it means that
messenger RNA (mRNA) is synthesised. The amount of mRNA from
each active gene varies over time.
– Turnover: Different mRNA species have different half-lives.
– Translation: When a mRNA is produced, it does not imply that the
corresponding protein is translated. Transcripts can also be produced for
storage and later use.
– Technically feasible: it is possible to isolate all mRNAs from cells and
to quantitate it within certain limits.
• Proteomics: look at proteins instead of transcripts
– Limited: Presently acceptable efficiency comes at the expenses of
incufficient quality
– Closer to ’reality’ since the proteins are the players
Christophe Roos - 6/6 Functional genomics
Spring 2002
EST: Expressed sequence tags
•
ESTs are partial sequences of cDNA clones. cDNA clones are DNA synthesised in
vitro using mRNA as template.
– Why?
cDNA is more stable than mRNA
– How?
cDNA can be made ‘en masse’ starting from total cellular mRNA
isolates. cDNA libraries are specific for tissue, developmental time, stimulation
etc.
– Therefore, looking at cDNA is looking at mRNA is looking at active genes.
– To look at cDNA means sequencing (part of) it.
• Clones are picked at random (10’000-200’000)
• Sequenced from one or both ends once (no proofreading)
• Sequences entered into EST sequence databases
Christophe Roos - 6/6 Functional genomics
Spring 2002
EST: Expressed sequence tags
•
•
•
•
constucting a clone by inserting a piece
of DNA into a ’vector’.
the vector and its insert will behave as
an independent unit (’plasmid’) in the
bacterial host and carries some
additional genes to allow for selection
(only those bacterial with the vector
will survive on antibiotics)
Amplify and sequence
Iterate (in parallell)
Christophe Roos - 6/6 Functional genomics
Spring 2002
DNA hybridisation
• DNA is a double-helix and can be separated by denaturing treatment into
two strands. Each strand becomes ’sticky’ and attempts to renature with
homologous single-strand sequences to form hybrids.
• Single-strand DNA from all known genes of a given species can be attached
to a matrix, then probed with labelled cDNA molecules from a given
sample. Only complementary probes will hybridise and can be detected if
they have been previously labelled (radioactivity, fluorescent stain, ...)
• The technique can be multiplexed:
– High density arrays carrying sticky probes from a full genome
– Parallel hybridisation with cDNA from various sources
Christophe Roos - 6/6 Functional genomics
Spring 2002
The process of using microarrays
Building the Chip:
PCR PURIFICATION
and PREPARATION
MASSIVE PCR
PREPARING SLIDES
PRINTING
Preparing RNA:
CELL CULTURE
AND HARVEST
Hybridising the Chip:
POST PROCESSING
ARRAY HYBRIDIZATION
RNA ISOLATION
DATA ANALYSIS
cDNA PRODUCTION
Christophe Roos - 6/6 Functional genomics
PROBE LABELING
Spring 2002
The output: the image raw data
cDNA is prepared from two samples (in this example) and labelled, each sample
with a distinct color. Then the array is hybridised with the doubble probe and the
signal is recorded as images
overlay images and normalise
scanning
laser 2
laser 1
emission
Christophe Roos - 6/6 Functional genomics
analysis
Spring 2002
Problems in image analysis
• Noise
• Spot detection and intensity
• Alignment if overlay
Christophe Roos - 6/6 Functional genomics
Spring 2002
A set of experiments on yeast...
• Each row represents one gene
• Each column represents one
experiment
– The columns have been
organised into related sets of
experiments (ALPH, ELU,...)
• The colors indicate gene
activity (from high to absent)
Christophe Roos - 6/6 Functional genomics
Spring 2002
Clustering the resulting data
• Looking at 10’000 genes is not
easy
• Group genes into clusters of
genes that behave the same way
over a set of several
experiments
–
–
–
–
Hierarchical clustering
K-means clustering
Self-organising maps (SOM)
Etc.
Christophe Roos - 6/6 Functional genomics
Spring 2002
The overall process with microarrays
• Microarray data
has to be used
in a larger frame
of
experimentation
Christophe Roos - 6/6 Functional genomics
Spring 2002
Making a model of the data
Sequence
Interaction
Genome
1.
2.
3.
Elements
Binary relations
Networks
Christophe Roos - 6/6 Functional genomics
 Structure
 Network
 Transcriptome
 Function
 Function
 Proteome
Assembly
Neighbour
Cluster
Pathway
Genome
Hierarchical Tree
Spring 2002
Comparing networks
Pathway vs. Pathway
• Gain new biological information
by comparison of networks
• What is the metrics?
• How is it done? Is it simply a
problem of graph isomorphism
Pathway vs. Genome
Genome vs. Genome
Cluster vs. Pathway
Christophe Roos - 6/6 Functional genomics
Spring 2002
Biological graph comparison
• Search heuristically
for clusters of
correspondence
Graph 1
A
C
D
B
G
E
I
Correspondences
K
H
F
J
A
C
D
E
I
H
A
B
C
D
.
.
.
.
a
b
c
d
.
.
Graph 2
a
d
i
h
K
f
j
a
c
i
j
Spring 2002
k
h
F
b
g
e
J
Christophe Roos - 6/6 Functional genomics
k
d
G
b
g
e
Clustering
algorithm
B
c
f
Example: genomic, metabolic, structural
Genome-pathway comparison, which reveals the correlation of physical
coupling of genes in the genome - operon structure (a) and functional
coupling (b) of gene products in the pathway
E. coli genome
hisL
hisG
hisD
yefM
Christophe Roos - 6/6 Functional genomics
hisC
hisB
hisH
hisA
hisF
hisI
yzzB
Spring 2002
Example: genomic, metabolic, structural
HISTIDINE METABOLISM
Pentose phosphate cycle
5P-D-1-ribulosylformimine
3.5.1.-
Phosphoribosyl-AMP
PRPP
3.6.1.31
2.4.2.17
3.5.4.19
PhosphoribulosylFormimino-AICARP
2.4.2.-
5.3.1.16
PhosphoribosylFormimino-AICAR-P
Phosphoriboxyl-ATP
2.6.1.-
Imidazoleacetole P
2.6.1.9
4.2.1.19
ImidazoleGlicerol-3P
3.1.3.15
L-Histidinol-P
5P Ribosyl-5-amino 4Imidazole carboxamide
(AICAR)
1.1.1.23
1-MethylL-histidine
3.4.13.5
Aneserine
6.3.2.11
Purine metabolism
2.1.1.-
6.3.2.11
3.4.13.3
3.5.3.5
Imidazolone
acetate
3.5.2.-
Imidazole4-acetate
1.14135
Christophe Roos - 6/6 Functional genomics
3.4.13.2
0
Imidazole
acetaldehyde
1.2.1.3
Histamine
1.4.3.6
L-Hisyidinal
2.1.1.22
Carnosine
N-Formyl-Lspartate
L-Hisyidinal
1.1.1.23
6.1.1
Hercyn
4.1.1.22
4.1.1.28
L-Histidine
Spring 2002
Example: genomic, metabolic, structural
SCOP hierarchical tree……..NE, TYROSINE AND TRYPTOPHAN BIOSYNTHESIS
1.
2.
3.
All alpha
All beta
Alpha and beta (a/b)
3.1 beta/alpha (TIM)-barrel
3.2 Cellulases
. . . . . . .
3.74 Thiolase
3.75 Cytidine deaminase
4. Alpha and beta (a+b)
5. Multi-domain (alpha and beta)
6. Membrane and cell surface pro
7. Small proteins
RNA
8. Peptides
9. Designed proteins
10. Non-protein
2.5.1.19
3-deoxyD-arabinoheptonate
1.1.1.24
1.3.1.43
4.2.1.51
4.2.1.10
4.2.1.11
1.1.9925
2.6.1.57
Pretyrosine
4.2.1.91
1.4.1.20
6.1.1.20
2.6.1.5
Phenylalanine
Phenylpyruvate
2.6.1.1
2.6.1.9
2.6.1.57
4.1.3.27
Histidine
1.1.9925
2.6.1.9
2.6.1.57
4-Aminobenzoate
2.6.1.5
2.6.1.9
2.6.1.57
Prephenate
4.2.1.51
Indole
5.4.99.5
2.4.2.18
N-(5-Phosphob-v-ribosyl)anthranilate
4.2.1.20
5.3.1.24
4.1.1.48
1-(2- CarboxyPhenylamino)1-deoxy-D-ribulose
5-phosphate
4.2.1.20
(3-Indolyl)Glycerol
phosphate
L-Tryptophan
Tryptophan
metabolism
Ubiquinone biosynthesis
3-Dehydro- Protocatechuate
shikimate
Folate
biosynthesis
Christophe Roos - 6/6 Functional genomics
1.4.3.2
2.6.1.1
4.2.1.91
4.1.3.-
4.2.1.10
2.6.1.5
4.2.1.20
1.4.3.2
4.6.1.4
2.6.1.1
4-Hydroxyphenylpyruvate
1.14.16.1
Shikimate
1.1.1.25
Alkaloid biosynthesis I
6.1.1.1
Tyrosine
Anthranilate
4.6.1.3
3-Dehydroquinate
Tyr-tRNA
Chorismate
2.7.1.71
Tyrosine metabolism
Spring 2002
More challenges?
The list of genes being
activated or
inactivated or that are
unaffected when
comparing two
samples becomes
more informative if
the genes can be
mapped onto maps
from which functions
can be deduced.
Christophe Roos - 6/6 Functional genomics
Spring 2002
More challenges?
Christophe Roos - 6/6 Functional genomics
Spring 2002