Download Info Day 2007 (bioinformatics) - Department of Computer Science

Bioinformatics
bits of Life
Dr. Tony C Smith
Department of Computer Science
University of Waikato
[email protected]
The essence is prediction from context
…
My dog is very littl_ ?
 We know that letters do not occur in English at random
(e.g. ‘t’ is generally more common than ‘x’)
 We know that context changes the probability of a letter
(e.g. ‘x’ is more common than ‘t’ after the sequence “I eat
Weet-Bi_”)
Predicting symbols is fundamental to a wide range of
important applications (e.g. encryption, compression)
Bioinformatics
Tony C Smith
Prediction in bioinformatics
Predicting the location of genes in DNA
Predicting gene roles in an organism
Predicting errors in a genetic transcription
Predicting the function of proteins
Predicting diseases from molecular samples
Anything that involves “making a judgment”; a
yes/no decision about whether some sample
datum ‘does’ or ‘does not’ have some property.
Bioinformatics
Tony C Smith
Representation
Weet–Bix
0101011101100101011001010111010000101101 …
… to the computer, everything is binary!
Bioinformatics
Tony C Smith
0101011101100101011001010111010000101101
0101101100100111111011010011010000101101
A AC G T CA T T C GA T GAT T C GA
Just as we can teach a computer to predict
things about a sequence of letters in English
prose, we can also teach it to predict things
about a other sequences—like a genetic
sequence
Bioinformatics
Tony C Smith
A genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggc
gcggctacgttcatcccagcagcagcgattttaaaattaa
cgcatcagactctcgtcgcgttcgtcgcctttattcacgcta
atggacgacatcttttactacgacggcgcctacgcatcg
cagcatacgacgcccagcatagtattttagaggcgagg
acatcatcatatcgcagctacagcgcatcagacgcata
cgacgacgactacgacgacactaacgacgatgttgcg
cacccacaccagttatatagagacgaactcgcatcagc
Bioinformatics
Tony C Smith
A genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcg
cctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgc
agctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgc
aatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgccttt
attcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagct
acagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcg
gcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattca
cgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacag
cgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgct
acgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgcta
atggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcat
cagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgct
tcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatgga
cgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcaga
cgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaa
aatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgac
atcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcat
acgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttat
tatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatctttt
actacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacga
cgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatatt
cccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactac
gacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacg
actacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactc
Bioinformatics
Tony C Smith
A genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcg
cagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagct
gcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgc
agcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgc
aatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcag
catacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaat
cggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcat
acgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcg
gcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatac
gacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggc
gctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacga
cgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgct
acgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacg
cccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctac
gcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcc
cagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacacc
agttatatagagacgaactcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttacta
cgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatag
agacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactac
gacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatataga
gacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacga
cggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagaga
cgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacg
gcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacg
aactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggc
gcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaa
ctcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgc
ctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaact
cgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcct
acgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcg
catcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctac
gcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgca
tcagtgttgcgcacccacaccagttatatagagacgaactc
Bioinformatics
Tony C Smith
A genetic prediction problem
 A gene encodes a protein
 It is a blueprint that provides biochemical
instructions on how to construct a sequence of
amino acids so as to make a working protein
that will perform some function in the organism
Bioinformatics
Tony C Smith
A genetic prediction problem
untranslated region
encoding region
transcription
factor
Bioinformatics
RNA
RNA
RNA
RN
Tony C Smith
R
A genetic prediction problem
untranslated region
Bioinformatics
Tony C Smith
A genetic prediction problem
untranslated region
ttgcaatcggcgctacgcttcaaaatttattatattcccggc
Bioinformatics
Tony C Smith
A genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggc
What transcription factors bind to this gene?
Where is the transcription factor binding site?
Bioinformatics
Tony C Smith
A genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggc
It is like predicting a phrase in a sentence:
Mary saw _______ with her binoculars.
Bioinformatics
Tony C Smith
A genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggc
Clues:
A binding site is often a short general pattern
E.g.
Bioinformatics
CCGATNATCGG
Tony C Smith
A genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggc
Clues:
The patterns are often reverse complements
E.g.
Bioinformatics
CCGATNATCGG
GGCTANTAGCC
Tony C Smith
A genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggc
Clues:
Bioinformatics
Where there is one binding site, often there is
another nearby.
Tony C Smith
A genetic prediction problem
All of these properties are the kinds of things for
which computer science has developed
algorithms and data structures, and therefore it
is exactly the kind of problem computer
scientists should be able to solve …
… though we haven’t yet!
Bioinformatics
Tony C Smith
Artificial Intelligence
Human brains can be
expert at very hard
tasks
expert
Bioinformatics
expert
Tony C Smith
Artificial Intelligence
Computers can be
programmed to do a
lot of hard things …
often better!
expert
system
Bioinformatics
expert
Tony C Smith
Artificial Intelligence
But people can start
from nothing, then get
better and better …
and they are versatile!
expert
system
Bioinformatics
learner
Tony C Smith
Artificial Intelligence
Machine learning is
about creating
programs that can get
better and better
learning
system
Bioinformatics
learner
Tony C Smith
Machine learning
What is machine learning?
creating computer programs that get better with experience
learn how to make expert judgments
discover previously hidden, potentially useful information (data
mining)
How does it work?
user provides learning system with examples of concept to be
learned
induction algorithm infers a characteristic model of the examples
that model can represent “new knowledge” that was previously
hidden, and/or it can be used to make predictions/decisions about
future novel data
Bioinformatics
Tony C Smith
proteomics
Three consecutive nucleotides in the coding region
form a ‘codon’ … i.e. encode an amino acid.
A string of amino acids makes a protein.
3 nucleotides, 4 possibilities each {A, C, G, T}:
43 = 64 possible codons
But there are only 20 amino acids!
Bioinformatics
Tony C Smith
proteomics
There is quite a bit of redundancy in codons.
Glycine:
GGA, GGC, GGG, GGT
Tyrosine:
TAT, TAC
Methionine: ATG
Bioinformatics
Tony C Smith
Amino Acid
R group
Amide
group
Bioinformatics
Carboxyl
group
Tony C Smith
Amino Acid
tyrosine
glycine
Bioinformatics
Tony C Smith
Bioinformatics
Tony C Smith
Bioinformatics
Tony C Smith
Bioinformatics
Tony C Smith
Biotechnology
Biologists know proteins, computer
scientists know machine learning
Together, they can find out a lot of hidden
information about genes and proteins
Biotechnology is a multi-billion dollar
industry
Biotechnology is one of the best funded
areas of scientific research
Bioinformatics
Tony C Smith
The University of Waikato
Waikato University is the centre of the
universe for machine learning
The Machine Learning Group is a large,
globally active, well-funded research group
The WEKA workbench of ML tools is used
around the world
Professors at Waikato University literally
wrote the book on sequence modeling
Bioinformatics
Tony C Smith
The University of Waikato
If you’re seriously interested in machine
learning, in getting involved in
bioinformatics research, or indeed any
other area along the leading edge of
computer science, then university is the
only place to be, and
Waikato wants You!
Bioinformatics
Tony C Smith