Transcript introduction_csc8309..

Gene Expression, Proteomics
CSC8309
Phillip Lord
Time table notes
Central Dogma
DNA  RNA  Protein
Transcription
Translation
In general, most regulation of gene expression occurs at the level of
transcription.
Genes can be regulated at many other times.
Transcriptional control is
important
Drosophila with mutated antp gene
- Legs appear in replacement of antenna
(a gene that regulates thoracic development called Antennapedia )
Is a gene active?
In situ hybridisation
Taken from doi:10.1371/journal.pbio.0060027.g001
Northern Blotting
Taken from doi:10.1371/journal.pbio.0000053.g001
Is a protein produced?
Western Blotting
Immunofluoresence microscopy
doi:10.1371/journal.pbio.0050067.g002
Which new genes are expressed
now?
• This question cannot be answered
practically with northerns or microscopy.
• Likewise, changes in protein composition
are hard with western.
• In both cases, you have to guess what
genes are going to be affected in the first
place.
Hence microarrays
doi:10.1371/journal.pbio.0020007.g001
And proteomics
•
doi:10.1371/journal.pbio.0020160.g001
Why both together
• Although the experimental techniques are
different, many of the questions are the
same
• Much of the bioinformatics analysis
overlaps.
An idealised experiment
• Have a biological question, which you can
answer by comparing two conditions.
• Design your experiment.
• Run your experiment
• Analyse the results
• Publish the results
Have a biological question…
• Is entirely obvious
• yet, frequently forgotten
Design your experiment
• How many conditions are there?
• How much variability is there, where does
it come from?
• How should I randomise my sampling?
• How do I enable tests for systematic
errors?
• How should I ensure consistency?
Run your experiment
• This bit, we don’t talk about much.
• Except to note that the experiment design
is not always stuck to
Analyse your results
• There are three important parts to results
analysis
– Statistics
– Statistics
– Statistics
• Three colour pictures of the statistics is
also good.
Analyse your results
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Increasingly, results have to look real.
Corroboration with additional knowledge
No "cherry-picking"
Data integration
Multi-source analysis
Publish your results
• The traditional mechanism
– Do lots of statistics
– Make a three colour picture, throwing away most of
the statistics results.
– Stick it in a paper, throwing away 99% of the value of
the data
– Throw away the original data; otherwise if someone
wants it, then finding the right CD can be painful
– Just in case, you can hide an excel spreadsheet on
the corner of your website.
Publish your data
• The new way
– Describe your experimental purpose
– Your experimental design
– Organise all your data, in a clearly describe
way.
– Lodge your data in a public repository.
What next?
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Lectures to cover “design”
Lectures to cover “running”
2 practical covers the “analysis”
Lectures covering “publishing”