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Transcript
Genomics and other “omics”
• Genome sequencing - individual organism
(genomics), community of organisms
(metagenomics)
• Searching the databases
• Transcriptional analysis (transcriptomics)
• Proteomics
• Metabolomics (detect small metabolites)
1
Genomic analysis: Step 1. Predicting open
reading frames (orfs) by computer algorithms
2
Genomic analysis: Step 1 (cont.). Predicting
open reading frames by computer algorithms
• Advantages
– Gives a readout of large open reading frames
• Limitations
– Some genes have start codons that are not ATG
– Ignores very small open reading frames. May
miss hormone-like peptides, small regulatory
peptides, quorum sensing peptides.
– Does not detect small regulatory RNAs.
3
Genomic analysis: Step 2. Database
searches
• DNA sequence alignments
– Best for finding nearly identical genes
– Find sequence motifs (e.g., helix-turn-helix in DNA binding
proteins)
• Linear amino acid sequence alignments
– Best for finding homologs that may be more distantly
related
– Annotation can be ambiguous
• Example: Elongation factors and tetracycline resistance genes
(ribosomal protection type)
• Example: Enzymes that are not present in an organism
• Annotations are hypotheses!!!
• Structural predictions – structural homologs
4
BLASTP 2.2.6 [Apr-09-2003]
SusA-8-03
Query=
(565 letters)
Database: Completed Bacteroides thetaiotaomicron VPI-5482;
1,480,858 sequences; 476,119,222 total letters
Distribution of 26 Blast Hits on the Query Sequence
Sequences producing significant alignments:
gi|29349112|ref|NP_812615.1|
gi|29349106|ref|NP_812609.1|
gi|29350098|ref|NP_813601.1|
gi|29347073|ref|NP_810576.1|
gi|29350097|ref|NP_813600.1|
gi|29346181|ref|NP_809684.1|
gi|29346183|ref|NP_809686.1|
gi|29346689|ref|NP_810192.1|
gi|29347520|ref|NP_811023.1|
gi|29345677|ref|NP_809180.1|
gi|29346515|ref|NP_810018.1|
gi|29347070|ref|NP_810573.1|
gi|29348342|ref|NP_811845.1|
gi|29349419|ref|NP_812922.1|
gi|29348421|ref|NP_811924.1|
gi|29346850|ref|NP_810353.1|
gi|29345906|ref|NP_809409.1|
gi|29347285|ref|NP_810788.1|
Score
E
(bits) Value
alpha-amylase (neopullulanase)...
alpha-amylase, susG [Bacteroid...
alpha-amylase precursor [Bacte...
pullulanase precursor [Bactero...
pullulanase precursor [Bactero...
1,4-alpha-glucan branching enz...
alpha-amylase 3 [Bacteroides t...
putative anti-sigma factor [Ba...
hypothetical protein [Bacteroi...
two-component system sensor hi...
phosphoglycerate mutase 1 [Bac...
phosphoglycerate mutase [Bacte...
Methionyl-tRNA synthetase [Bac...
DNA-methyltransferase [Bactero...
putative outer membrane protei...
putative outer membrane protei...
TonB-dependent receptor [Bacte...
putative outer membrane protei...
1076
79
67
61
59
45
38
35
33
30
29
29
28
28
28
28
27
27
0.0
1e-15
6e-12
2e-10
2e-09
1e-05
0.002
0.019
0.094
0.47
1.0
1.0
2.3
2.3
2.3
3.0
4.0
5.2
5
Protein Structure Prediction
6
“Transcriptomics” – Measuring gene
expression directly (mRNA)
• Types of analysis
– Microarray – measures expression of many genes at a time
– RT-PCR – measures expression of one gene at a time
• Advantages
– Microarrays, like transposon mutagenesis, find previously
unsuspected genes of interest
– Not necessary to make fusions to every gene
• Disadvantages (compared to fusions)
– Microarray data needs to be checked by RT-PCR
– Fusions can be made to monitor translation
7
Microarray - Measuring Gene Expression
of Many Genes at a Time
8
New variations of the microarray
approach
• Make a few labeled DNA copies of each
mRNA using RT-PCR – increases
sensitivity
• DNA copies of mRNA from cells grown
under different conditions labeled with
different fluorophores (e.g. red for low
iron, green for high iron), then mixture
is placed on a single slide
9
10
Uses of microarrays
• Compare gene expression under different
conditions
• Determine effects of mutations, eg, in
regulatory proteins – effect may be more
complex than you thought!
• Effects of overexpression of certain genes –
less commonly done
11
Metagenomics – genome sequencing of
entire bacterial populations
• Sample contains bacterial population (e.g. water
sample, human colon contents)
• Total DNA extracted, non-DNA impurities removed
• High throughput sequencing (e.g. 454 sequencing)
• Limitations
– Assembly
– Interpretation!!
• Transcriptome
– RT-PCR amplifies messages as DNA, sequence DNA
– Limitation: lots of rRNA, random priming of RT-PCR
12
Proteomics
• Detects proteins produced under different conditions
• Two dimensional gel creates an array of protein spots
– First dimension: isoelectric focusing (pH gradient)
– Second dimension: SDS denaturing gel
• Proteins extracted individually, fragmented by proteases, run
through a mass spectrometer – matched with fragments
predicted from DNA sequence.
• Advantages
– Detect proteins not RNA (post transcsriptional regulation
• Limitations
–
–
–
–
Only the most highly expressed proteins are detected
Overlapping spots may be difficult to resolve
Need to go through the MS step
Not likely to be useful in metagenomics
13
Conclusions
(according to AAS)
• Availability of new technologies is forcing a
shift from single gene-single pathway
thinking to a more global way of thinking.
• Increased need to focus on a specific
biological question
• Most technologies now provided by
centralized services – technology itself is
uninteresting, only interesting thing is what
you can do with it!!
14