Bioinformatics The Prediction of Life

1
BioinformaticsThe Prediction of Life

• Tony C Smith
• Department of Computer Science
• University of Waikato
• tcs_at_cs.waikato.ac.nz

2
Bioinformatics

• Computation with biological data
• Data genes, proteins, microarrays, mass spectra,
  written documents, populations of organisms
• Goal knowledge discovery

3
The essence is prediction

• My dog is very littl_ ?
• We know that letters do not occur in English at
  random not all letters are equally common (e.g.
  e is more common than x)
• We know that context changes the probability of a
  letter (e.g. whats the most likely letter after
  the sequence I eat Weet-Bi_)
• Prediction is important in many applications
  (e.g. encryption, compression, communication,
  graphics, simulation and bioinformatics!)

4
Prediction in bioinformatics

• Predicting the location of genes in DNA
• Predicting the function of proteins
• Predicting diseases from molecular samples
• Predicting population dynamics
• Anything that involves making a judgment
  typically expressible as a yes/no decision about
  some sample datum

5
Representation

• W e e t B i x
• 0101011101100101011001010111010000101101
• to the computer, everything is binary!

6

• 0101011101100101011001010111010000101101
• 0101101100100111111011010011010000101101
• A A C G T C A T T C G A T G A T T C G
  A
• 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 sequenceslike a genetic sequence

7
A genetic prediction problem

• ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggcta
  cgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtc
  gcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacg
  gcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcga
  ggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgac
  tacgacgacactaacgacgatgttgcgcacccacaccagttatatagaga
  cgaactcgcatcagc

8
A genetic prediction problem

• ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggcta
  cgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtc
  gcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacg
  gcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcga
  ggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgac
  tacgacgacactaacgacgatgttgcgcacccacaccagttatatagaga
  cgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatatt
  cccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgca
  tcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatc
  ttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagt
  attttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgc
  atacgacgacgactacgacgacactaacgacgatgttgcgcacccacacc
  agttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaa
  atttattatattcccggcgcggctacgttcatcccagcagcagcgatttt
  aaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgcta
  atggacgacatcttttactacgacggcgcctacgcatcgcagcatacgac
  gcccagcatagtattttagaggcgaggacatcatcatatcgcagctacag
  cgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttg
  cgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcg
  ctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagc
  agcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcct
  ttattcacgctaatggacgacatcttttactacgacggcgcctacgcatc
  gcagcatacgacgcccagcatagtattttagaggcgaggacatcatcata
  tcgcagctacagcgcatcagacgcatacgacgacgactacgacgacacta
  acgacgatgttgcgcacccacaccagttatatagagacgaactcgcatca
  gtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctac
  gttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcg
  cgttcgtcgcctttattcacgctaatggacgacatcttttactacgacgg
  cgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgag
  gacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgact
  acgacgacactaacgacgatgttgcgcacccacaccagttatatagagac
  gaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcc
  cggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatc
  agactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatctt
  ttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtat
  tttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcat
  acgacgacgactacgacgacactaacgacgatgttgcgcacccacaccag
  ttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaat
  ttattatattcccggcgcggctacgttcatcccagcagcagcgattttaa
  aattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaat
  ggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgc
  ccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcg
  catcagacgcatacgacgacgactacgacgacactaacgacgatgttgcg
  cacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgct
  acgcttcaaaatttattatattcccggcgcggctacgttcatcccagcag
  cagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgccttt
  attcacgctaatggacgacatcttttactacgacggcgcctacgcatcgc
  agcatacgacgcccagcatagtattttagaggcgaggacatcatcatatc
  gcagctacagcgcatcagacgcatacgacgacgactacgacgacactaac
  gacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagt
  gttgcgcacccacaccagttatatagagacgaactc

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A genetic prediction problem

• ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggcta
  cgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtc
  gcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacg
  gcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcga
  ggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgac
  tacgacgacactaacgacgatgttgcgcacccacaccagttatatagaga
  cgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatatt
  cccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgca
  tcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatc
  ttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagt
  attttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgc
  atacgacgacgactacgacgacactaacgacgatgttgcgcacccacacc
  agttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaa
  atttattatattcccggcgcggctacgttcatcccagcagcagcgatttt
  aaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgcta
  atggacgacatcttttactacgacggcgcctacgcatcgcagcatacgac
  gcccagcatagtattttagaggcgaggacatcatcatatcgcagctacag
  cgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttg
  cgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcg
  ctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagc
  agcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcct
  ttattcacgctaatggacgacatcttttactacgacggcgcctacgcatc
  gcagcatacgacgcccagcatagtattttagaggcgaggacatcatcata
  tcgcagctacagcgcatcagacgcatacgacgacgactacgacgacacta
  acgacgatgttgcgcacccacaccagttatatagagacgaactcgcatca
  gtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctac
  gttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcg
  cgttcgtcgcctttattcacgctaatggacgacatcttttactacgacgg
  cgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgag
  gacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgact
  acgacgacactaacgacgatgttgcgcacccacaccagttatatagagac
  gaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcc
  cggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatc
  agactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatctt
  ttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtat
  tttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcat
  acgacgacgactacgacgacactaacgacgatgttgcgcacccacaccag
  ttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaat
  ttattatattcccggcgcggctacgttcatcccagcagcagcgattttaa
  aattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaat
  ggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgc
  ccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcg
  catcagacgcatacgacgacgactacgacgacactaacgacgatgttgcg
  cacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgct
  acgcttcaaaatttattatattcccggcgcggctacgttcatcccagcag
  cagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgccttt
  attcacgctaatggacgacatcttttactacgacggcgcctacgcatcgc
  agcatacgacgcccagcatagtattttagaggcgaggacatcatcatatc
  gcagctacagcgcatcagacgcatacgacgacgactacgacgacactaac
  gacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagt
  gttgcgcacccacaccagttatatagagacgaactcttgcaatcggcgct
  acgcttcaaaatttattatattcccggcgcggctacgttcatcccagcag
  cagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgccttt
  attcacgctaatggacgacatcttttactacgacggcgcctacgcatcgc
  agcatacgacgcccagcatagtattttagaggcgaggacatcatcatatc
  gcagctacagcgcatcagacgcatacgacgacgactacgacgacactaac
  gacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagc
  tgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacg
  ttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgc
  gttcgtcgcctttattcacgctaatggacgacatcttttactacgacggc
  gcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgagg
  acatcatcatatcgcagctacagcgcatcagacgcatacgacgacgacta
  cgacgacactaacgacgatgttgcgcacccacaccagttatatagagacg
  aactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattccc
  ggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatca
  gactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttt
  tactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtatt
  ttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcata
  cgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagt
  tatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatt
  tattatattcccggcgcggctacgttcatcccagcagcagcgattttaaa
  attaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatg
  gacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcc
  cagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgc
  atcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgc
  acccacaccagttatatagagacgaactcgcatcagtgcaatcggcgcta
  cgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagc
  agcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgccttta
  ttcacgctaatggacgacatcttttactacgacggcgcctacgcatcgca
  gcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcg
  cagctacagcgcatcagacgcatacgacgacgactacgacgacactaacg
  acgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtg
  caatcggcgctacgcttcaaaatttattatattcccggcgcggctacgtt
  catcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgt
  tcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgc
  ctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggac
  atcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacg
  acgacactaacgacgatgttgcgcacccacaccagttatatagagacgaa
  ctcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccgg
  cgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcaga
  ctctcgtcgcgttcgtcgcctttattcacgctaatggacgacatctttta
  ctacgacggcgcctacgcatcgcagcatacgacgcccagcatagtatttt
  agaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacg
  acgacgactacgacgacactaacgacgatgttgcgcacccacaccagtta
  tatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaattta
  ttatattcccggcgcggctacgttcatcccagcagcagcgattttaaaat
  taacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatgga
  cgacatcttttactacgacggcgcctacgcatcgcagcatacgacgccca
  gcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcat
  cagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcac
  ccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacac
  cagttatatagagacgaactc

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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

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A genetic prediction problem

encoding region
untranslated region
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A genetic prediction problem

untranslated region
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A genetic prediction problem

ttgcaatcggcgctacgcttcaaaatttattatattcccggc
untranslated region
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A genetic prediction problem

ttgcaatcggcgctacgcttcaaaatttattatattcccggc
What transcription factors bind to this
gene? Where is the transcription factor binding
site?
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A genetic prediction problem

ttgcaatcggcgctacgcttcaaaatttattatattcccggc
Clues
A binding site is often a short general
pattern E.g. CCGATNATCGG
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A genetic prediction problem

ttgcaatcggcgctacgcttcaaaatttattatattcccggc
Clues
The patterns are often reverse complements E.g. C
CGATNATCGG GGCTANTAGCC
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A genetic prediction problem

ttgcaatcggcgctacgcttcaaaatttattatattcccggc
Clues
Where there is one binding site, often there is
another nearby.
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A genetic prediction problem

All of these properties are the kinds of things
for which computer science has developed
algorithms and data structures to identify
quickly and efficiently, and therefore it is
exactly the kind of problem computer scientists
should be able to solve.
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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 for each, so
43 64 possible codons But there are only
20 amino acids!
20
proteomics
There is quite a bit of redundancy in codons.
Glycine GGA, GGC, GGG, GGT Tyrosine TAT,
TAC Methionine ATG
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Amino Acid
R group
Amide group
Carboxyl group
22
Amino Acid
tyrosine
glycine
23
Primary structure MSALVSTTPSLLAGVRNVDB ..
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Tertiary Structure
25
Secondary Structure
26
Signal peptide

• A relatively short sequence of amino residues at
  the N-terminus of the nascent protein
• typically 15-50 residues
• MAGPRPSPWARLLLAALISVSLSGTLARCKKAPVSKKCETCVGQAALT
  GL
• Cleaved off as protein passes through membrane
  (operates like a pass key)
• Knowing signal peptide helps determine protein
  function in the organism

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How do we do it?

• see any patterns? ttgcaatcggcgctacgcttcaaaatttat
  tatattcccggcgcggctacgttcatcccagcagcagcgattttaaaatt
  aacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggac
  gacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccag
  catagtattttagaggcgaggacatcatcatatcgcagctacagcgcatc
  agacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacc
  cacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacg
  cttcaaaatttattatattcccggcgcggctacgttcatcccagcagcag
  cgattttaaaatttcgcctttattcacgctaatggacgacatcttttact
  acgacggcgcctacgcatcgcagcatacgacgcccacgcccagcatagta
  ttttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgca
  tacgacgacgactacgacgacactaacgacgatgttgcgcacccacacca
  gttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaa
  tttattatagcatagtattttagaggcgaggacatcatcatatcgcagct
  acagcgcatcagacgcatacgacgacgactacgacgacactaacgacgat
  gttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatc
  ggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcc
  cagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtc
  gcctttattcacgctaatggacgacatcttttactacgacggcgcctacg
  catcgcagcatacgacgcccagcatagtattttagaggcgaggacatcat
  catatcgcagctacagcgcatcagacgcatacgacgacgactacgacgac
  actaacgacgatgttgcgcacccacaccagttatatagagacgaactcgc
  atcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcgg
  ctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctc
  gtaacgcatcagactctcgtcgcgttcgcgcgttcgtcgcctttattcac
  gctaatggacgacatcttttactacgacggcgcctacgcatcgcagcata
  cgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagct
  acagcgcatcagacgcatacgacgacgactacgacgacactaacgacgat
  gttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatc
  ggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcc
  cagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtc
  gcctttattcacgctaatggacgacatcttttactacgacggcgcctacg
  catcgcagcatacgacgcccagcatagtattttagaggcgaggacatcac
  tacgacgacactaacgacgatgttgcgcacccacaccagttatatagaga
  cgaactcgcatcagtgctacgcttcaaaatttattatattcccggcggca
  atcggcgctacgcttcaaaatttattatattcccggcgcggctacgttca
  tcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttc
  gtcgcctttattcacgctaatggacgacatcttttactacgacggcgcct
  acgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacat
  catcatatcgcagctacagcgcatcagacgcatacgacgacgactacgac
  gacactaacgacgatgttgcgcacccacaccagttatatagagacgaact
  cgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcg
  cggctacgttcatcccagcagcagcgattttaaaattaacgcatcagact
  ctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttact
  acgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttag
  aggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgac
  gacgactacgacgacactaacgacgatgttgcgcacccacaccagttata
  tagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttatt
  atattcccggcgcggctacgttcatcccagcagcagcgattttaaaatta
  acgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacg
  acatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagc
  atagtattttagaggcgaggacatcatcatatcgcagctacagcgcatca
  gacgcatacgacgacgactacgacgacactaacgacgatgttgcgcaccc
  acaccagttatatagagacgaactcgcatcagtgttgcgcacccacacca
  gttatatagagacgaactcttgcaatcggcgctacgcttcaaaatttatt
  atattcccggcgcggctacgttcatcccagcagcagcgattttaaaatta
  acgcatcagactctcgtcgcgttcgtcgcctttatttattatattcccgg
  cgcggctacgttcatcccagcattcacgctaatggacgacatcttttact
  acgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttag
  aggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgac
  gacgactacgacgacactaacgacgatgttgcgcacccacaccagttata
  tagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttat
  tatattcccggcgcggctacgttcatcccagcagcagcgattttaaaatt
  aacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggac
  gacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccag
  catagtattttagaggcgaggacatcatcatatcgcagctacagcgcatc
  agacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacc
  cacaccagttatatagagacgaactcgcatcaggacatcttttactacga
  cggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggc
  gaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacg
  actacgacgacactaacgacgatgttgcgcacccacaccagttatataga
  tgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacg
  ttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgc
  gttcgtcgcctttattcacgctaatggacgacatcttttactacgacggc
  gcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgagg
  acatcatcatatcgcagctactcatatcgcagctacagcgcatcagacgc
  atacgacgacgaagcgcatcagacgcatacgacgacgactacgacgacac
  taacgacgatgttgcgcacccacaccagttatatagagacgaactcgcat
  cagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggct
  acgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgt
  cgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgac
  ggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcg
  aggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacga
  ctacgacgacactaacgacgatgttgcgcacccacaccagttatatagag
  acgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatatt
  cccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgca
  tcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatc
  ttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagt
  attttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgc
  atacgacgacgactacgacgacactaacgacgatgttgcgcacccacacc
  agttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaa
  agcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcct
  ttattcacgctaatggacgacgaactcgcatcagtgcaatcggccggcta
  cgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtc
  gcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacg
  gcgcctacgcatcgcagcatacgattcccggcgcggctacgttcatccca
  gcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgc
  ctttattcacgctaatggacgacatcttttactacgacggcgcctacgca
  tcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatca
  tatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacac
  taacgacgatgttgcgcacccacaccagttatatagagacgaactcgcat
  cagtgttgcgcacccacaccagttatatagagacgaactcttagaggcga
  ggacatcatcatatcgcagctacagcgcatcagttagaggcgaggacatc
  atcatatcgcagctacagcgcatcagttagaggcgaggacatcatcatat
  cgc

28
Local biases in residues around the cleavage site
Sequence regularities can be exploited by
statistical and pattern-based models
29
Proteomic prediction
Language letters combine to form words
words combine to form phrases phrases combine
to form sentences sentences combine to form
sentences (and ultimately Harry Potter
books) Proteins amino acids combine to form
peptides peptides combine to form secondary
motifs (e.g. a-helixes and ß-sheets)
motifs combine to make proteins proteins
combine to make toenails (and ultimately
people)
30
Approach

• Problem is stated as two-class
• an amino acid is either the first residue of the
  mature protein or it is not
• Each residue is described by a single document,
  which includes as many electrochemical,
  structural or contextual facts as are available
  (desirable)

31
Properties of amino acids
32
Residue as a document

• E.g. Cysteine Cys C
• aliphatic yes, aromatic no, hydrophobic
  yes, charge -, polarized yes, small no,
  number of nitrogen atoms 1, contains sulphur
  yes, has a carbon ring no, ionized yes,
  valence 2, cbeta no, covalent yes, h-bond
  yes,
• etc. (whatever else experimenter wants to
  include)

33
Sample document

• PRNUM1. AANUM21.
• AMINO-8L. ALIPH-8-. AROMA-8-.
  CBETA-8-. CHARG-8-. COVAL-8-.
  HBOND-8-. HPHOB-8. IONIZ-8-.
  NITRO-81. POLAR-8-. POSNG-80.
  SMALL-8-. SULPH-8-. TEENY-8-.
  CRING-8-. VALEN-82. AMINO-7L.
  ALIPH-7-. AROMA-7-. CBETA-7-.
  CHARG-7-. COVAL-7-. HBOND-7-.
  HPHOB-7. IONIZ-7-. NITRO-71.
  POLAR-7-. POSNG-70. SMALL-7-.
  SULPH-7-. TEENY-7-. CRING-7-.
  VALEN-72. AMINO-6F. ALIPH-6.
  AROMA-6. CBETA-6-. CHARG-6-.
  COVAL-6-. HBOND-6-. HPHOB-6.
  IONIZ-6-. NITRO-61. POLAR-6-.
  POSNG-60. SMALL-6-. SULPH-6-.
  TEENY-6-. CRING-6. VALEN-62.
  AMINO-5A. ALIPH-5-. AROMA-5-.
  CBETA-5-. CHARG-5-. COVAL-5-.
  HBOND-5-. HPHOB-5-. IONIZ-5-.
  NITRO-51. POLAR-5-. POSNG-50.
  SMALL-5. SULPH-5-. TEENY-5.
  CRING-5-. VALEN-52. AMINO-4T.
  ALIPH-4. AROMA-4-. CBETA-4.
  CHARG-4-. COVAL-4-. HBOND-4.
  HPHOB-4-. IONIZ-4-. NITRO-41.
  POLAR-4. POSNG-40. SMALL-4.
  SULPH-4-. TEENY-4-. CRING-4-.
  VALEN-42. AMINO-3C. ALIPH-3.
  AROMA-3-. CBETA-3-. CHARG-3-.
  COVAL-3. HBOND-3. HPHOB-3.
  IONIZ-3. NITRO-31. POLAR-3.
  POSNG-3-. SMALL-3-. SULPH-3.
  TEENY-3-. CRING-3-. VALEN-32.
  AMINO-2I. ALIPH-2-. AROMA-2-.
  CBETA-2. CHARG-2-. COVAL-2-.
  HBOND-2-. HPHOB-2. IONIZ-2-.
  NITRO-21. POLAR-2-. POSNG-20.
  SMALL-2-. SULPH-2-. TEENY-2-.
  CRING-2-. VALEN-22. AMINO-1A.
  ALIPH-1-. AROMA-1-. CBETA-1-.
  CHARG-1-. COVAL-1-. HBOND-1-.
  HPHOB-1-. IONIZ-1-. NITRO-11.
  POLAR-1-. POSNG-10. SMALL-1.
  SULPH-1-. TEENY-1. CRING-1-.
  VALEN-12. AMINO0R. ALIPH0. AROMA0-.
  CBETA0-. CHARG0. COVAL0-. HBOND0.
  HPHOB0-. IONIZ0. NITRO04. POLAR0.
  POSNG0. SMALL0-. SULPH0-. TEENY0-.
  CRING0-. VALEN03. AMINO1H. ALIPH1.
  AROMA1. CBETA1-. CHARG1. COVAL1-.
  HBOND1. HPHOB1-. IONIZ1. NITRO13.
  POLAR1. POSNG1. SMALL1-. SULPH1-.
  TEENY1-. CRING1. VALEN13. AMINO2Q.
  ALIPH2. AROMA2-. CBETA2-. CHARG2-.
  COVAL2-. HBOND2. HPHOB2-. IONIZ2-.
  NITRO22. POLAR2. POSNG20. SMALL2-.
  SULPH2-. TEENY2-. CRING2-. VALEN22.
  AMINO3Q. ALIPH3. AROMA3-. CBETA3-.
  CHARG3-. COVAL3-. HBOND3. HPHOB3-.
  IONIZ3-. NITRO32. POLAR3. POSNG30.
  SMALL3-. SULPH3-. TEENY3-. CRING3-.
  VALEN32. AMINO4R. ALIPH4. AROMA4-.
  CBETA4-. CHARG4. COVAL4-. HBOND4.
  HPHOB4-. IONIZ4. NITRO44. POLAR4.
  POSNG4. SMALL4-. SULPH4-. TEENY4-.
  CRING4-. VALEN43. AMINO5Q. ALIPH5.
  AROMA5-. CBETA5-. CHARG5-. COVAL5-.
  HBOND5. HPHOB5-. IONIZ5-. NITRO52.
  POLAR5. POSNG50. SMALL5-. SULPH5-.
  TEENY5-. CRING5-. VALEN52. AMINO6Q.
  ALIPH6. AROMA6-. CBETA6-. CHARG6-.
  COVAL6-. HBOND6. HPHOB6-. IONIZ6-.
  NITRO62. POLAR6. POSNG60. SMALL6-.
  SULPH6-. TEENY6-. CRING6-. VALEN62.
  AMINO7Q. ALIPH7. AROMA7-. CBETA7-.
  CHARG7-. COVAL7-. HBOND7. HPHOB7-.
  IONIZ7-. NITRO72. POLAR7. POSNG70.
  SMALL7-. SULPH7-. TEENY7-. CRING7-.
  VALEN72. AMINO8Q. ALIPH8. AROMA8-.
  CBETA8-. CHARG8-. COVAL8-. HBOND8.
  HPHOB8-. IONIZ8-. NITRO82. POLAR8.
  POSNG80. SMALL8-. SULPH8-. TEENY8-.
  CRING8-. VALEN82. MULT37. MULT54.
  MULT73. MULT92. 2GRAMIA. GRAM2HQ. 3GRAMCIA.
  GRAM3HQQ.

34
Artificial Intelligence

• Computers do things only human brains can
  otherwise do

expert
expert
35
Artificial Intelligence

• Computers do things only human brains can
  otherwise do

expert system
expert
36
Artificial Intelligence

• Computers do things only human brains can
  otherwise do

learning system
expert system
37
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
• model is used to predict whether or not future
  novel instances are also examples and it does
  this very consistently, and very, very quickly!

38
Bioinformatics

• Biologists know proteins, computer scientists
  know machine learning
• Together, they can find hidden and potentially
  useful information about genes and proteins
• Biotechnology is a multi-billion dollar industry
• Biotechnology is one of the best funded areas of
  scientific research
• Shortage of people educated in bioinformatics

39
The University of Waikato

• Waikato University is ranked first in the country
  in computer science and in molecular, cellular,
  and whole-organism biology
• centre of the universe for machine learning

40
The University of Waikato

• If youre interested in getting involved in
  bioinformatics, or indeed any other area along
  the leading edge of computer science and/or
  biology, then
• Waikato wants You!