bioinformatics the prediction of life
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Bioinformatics The Prediction of Life. Tony C Smith Department of Computer Science University of Waikato [email protected]. Bioinformatics. Computation with biological data Data: genes, proteins, microarrays, mass spectra, written documents, populations of organisms … - PowerPoint PPT PresentationTRANSCRIPT
BioinformaticsBioinformatics
The Prediction of LifeThe Prediction of Life
Tony C SmithTony C SmithDepartment of Computer ScienceDepartment of Computer Science
University of WaikatoUniversity of [email protected]@cs.waikato.ac.nz
Bioinformatics Tony C Smith
BioinformaticsBioinformatics
Computation with biological dataComputation with biological data
Data:Data: genes, proteins, microarrays, mass genes, proteins, microarrays, mass spectra, written documents, populations of spectra, written documents, populations of
organisms …organisms …
Goal:Goal: knowledge discovery knowledge discovery
Bioinformatics Tony C Smith
The The essenceessence is prediction … is prediction … My dog is very littlMy 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. what’s 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!)
Bioinformatics Tony C Smith
Prediction in bioinformaticsPrediction in bioinformatics
Predicting the location of genes in DNAPredicting the location of genes in DNAPredicting the function of proteinsPredicting the function of proteinsPredicting diseases from molecular samplesPredicting diseases from molecular samplesPredicting population dynamicsPredicting population dynamicsAnything that involves “making a judgment”; Anything that involves “making a judgment”; typically expressible as a yes/no decision about typically expressible as a yes/no decision about some sample datumsome sample datum
Bioinformatics Tony C Smith
RepresentationRepresentation
W e e t – B i xW e e t – B i x
0101011101100101011001010111010000101101 …
… to the computer, everything is binary!
Bioinformatics Tony C Smith
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 sequences—like a genetic sequence
Bioinformatics Tony C Smith
A genetic prediction problemA genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaagcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcataacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagccacccacaccagttatatagagacgaactcgcatcagc
Bioinformatics Tony C Smith
A genetic prediction problemA genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcccaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcatttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctatcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcaccgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacaggctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgccgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgcttacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgccatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatataatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactcgagacgaactc
Bioinformatics Tony C Smith
A genetic prediction problemA genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcacaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcgacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacggctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttaccagttatatagagacgaactcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttaatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatctacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactaagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagcgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacgggaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgacgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactccatcagtgttgcgcacccacaccagttatatagagacgaactc
Bioinformatics Tony C Smith
A genetic prediction problemA 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 problemA genetic prediction problem
encoding region untranslated region
transcription
factor RNARNARNARNARNA
Bioinformatics Tony C Smith
A genetic prediction problemA genetic prediction problem
untranslated region
Bioinformatics Tony C Smith
A genetic prediction problemA genetic prediction problem
untranslated regionttgcaatcggcgctacgcttcaaaatttattatattcccggcttgcaatcggcgctacgcttcaaaatttattatattcccggc
Bioinformatics Tony C Smith
A genetic prediction problemA genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggcttgcaatcggcgctacgcttcaaaatttattatattcccggc
What transcription factors bind to this gene?
Where is the transcription factor binding site?
Bioinformatics Tony C Smith
A genetic prediction problemA genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggcttgcaatcggcgctacgcttcaaaatttattatattcccggc
Clues: A binding site is often a short general pattern
E.g. CCGATNATCGG
Bioinformatics Tony C Smith
A genetic prediction problemA genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggcttgcaatcggcgctacgcttcaaaatttattatattcccggc
Clues: The patterns are often reverse complements
E.g. CCGATNATCGGGGCTANTAGCC
Bioinformatics Tony C Smith
A genetic prediction problemA genetic prediction problem
ttgcaatcggcgctacgcttcaaaatttattatattcccggcttgcaatcggcgctacgcttcaaaatttattatattcccggc
Clues: Where there is one binding site, often there is another nearby.
Bioinformatics Tony C Smith
A genetic prediction problemA 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.
Bioinformatics Tony C Smith
proteomicsproteomics
Three consecutive nucleotides in the coding regionform 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!
Bioinformatics Tony C Smith
proteomicsproteomics
Glycine: GGA, GGC, GGG, GGTTyrosine: TAT, TACMethionine: ATG
There is quite a bit of redundancy in codons.
Bioinformatics Tony C Smith
Amidegroup
Carboxylgroup
R group
Amino AcidAmino Acid
Bioinformatics Tony C Smith
Amino AcidAmino Acid
glycine
tyrosine
Bioinformatics Tony C Smith
Primary structure: MSALVSTTPSLLAGVRNVDB …..
Bioinformatics Tony C Smith
Tertiary Structure
Bioinformatics Tony C Smith
Secondary Structure
Bioinformatics Tony C Smith
Signal peptideSignal peptide
A relatively short sequence of amino A relatively short sequence of amino residues at the N-terminus of the nascent residues at the N-terminus of the nascent proteinprotein
typically 15-50 residuestypically 15-50 residues
MAGPRPSPWARLLLAALISVSLSGTLAMAGPRPSPWARLLLAALISVSLSGTLARCKKAPVSKKCETCVGQAALTGL …RCKKAPVSKKCETCVGQAALTGL …
Cleaved off as protein passes through Cleaved off as protein passes through membrane membrane (operates like a pass key)(operates like a pass key)
Knowing signal peptide helps determine Knowing signal peptide helps determine protein function in the organismprotein function in the organism
Bioinformatics Tony C Smith
How do we do it?How do we do it? see any patterns?see any patterns?
ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaatttcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccacgcccagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaatttcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcaatttattatagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacggctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtaacgcatcagactctcgtcgcgttcgcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtaacgcatcagactctcgtcgcgttcgcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgctacgcttcaaaatttattatattcccggcggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgctacgcttcaaaatttattatattcccggcggcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcaaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcaacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttatttattatattcccggcgcgcccacaccagttatatagagacgaactcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttatttattatattcccggcgcggctacgttcatcccagcattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgctacgttcatcccagcattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacggtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcaggacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcaggacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagatgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagatgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctactcatatcgcagctacagcgcacatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctactcatatcgcagctacagcgcatcagacgcatacgacgacgaagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgctcagacgcatacgacgacgaagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaagcagcgattttaaaattaacgcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacgaactcgcatcagtgcaatcggccggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacgaactcgcatcagtgcaatcggccggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgccatcttttactacgacggcgcctacgcatcgcagcatacgattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactcttagaggcgaggacatcatcatatcgcagctacagcgcatcagttagaggcgaggacatcatcatatcgcagctacagcgcatcagttagaggcgaggacatcatcatatgcatcagtgttgcgcacccacaccagttatatagagacgaactcttagaggcgaggacatcatcatatcgcagctacagcgcatcagttagaggcgaggacatcatcatatcgcagctacagcgcatcagttagaggcgaggacatcatcatatcgccgc
Bioinformatics Tony C Smith
Local biases in residues around the cleavage site
Sequence regularities can be
exploited by statistical and pattern-based
models
Bioinformatics Tony C Smith
Proteomic predictionProteomic 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. α-helixes and β-sheets)• motifs combine to make proteins• proteins combine to make toenails (and ultimately people)
Bioinformatics Tony C Smith
ApproachApproach
Problem is stated as two-class:Problem is stated as two-class:
an amino acid is either the first residue of an amino acid is either the first residue of the mature protein or it is notthe mature protein or it is not
Each residue is described by a single Each residue is described by a single document, which includes as many document, which includes as many electrochemical, structural or contextual electrochemical, structural or contextual facts as are available (desirable)facts as are available (desirable)
Bioinformatics Tony C Smith
Properties of amino acidsProperties of amino acids
Bioinformatics Tony C Smith
Residue as a documentResidue as a document
E.g.E.g. CysteineCysteine CysCys CC
aliphatic [aliphatic [yesyes], aromatic [], aromatic [nono], hydrophobic [], hydrophobic [yesyes], ], charge [charge [--], polarized [], polarized [yesyes]],, small [ small [nono], number of ], number of nitrogen atoms [nitrogen atoms [11], contains sulphur [], contains sulphur [yesyes], has a ], has a carbon ring [carbon ring [nono], ionized [], ionized [yesyes], valence [], valence [22], cbeta ], cbeta [[nono], covalent [], covalent [yesyes], h-bond [], h-bond [yesyes], ],
etc. (whatever else experimenter wants to include)etc. (whatever else experimenter wants to include)
Bioinformatics Tony C Smith
Sample documentSample document PRNUM:1. AANUM:21.PRNUM:1. AANUM:21.
AMINO[-8]:L. ALIPH[-8]:-. AROMA[-8]:-. CBETA[-8]:-. CHARG[-8]:-. COVAL[-8]:-. HBOND[-8]:-. HPHOB[-8]:+. IONIZ[-8]:-. NITRO[-8]:1. AMINO[-8]:L. ALIPH[-8]:-. AROMA[-8]:-. CBETA[-8]:-. CHARG[-8]:-. COVAL[-8]:-. HBOND[-8]:-. HPHOB[-8]:+. IONIZ[-8]:-. NITRO[-8]:1. POLAR[-8]:-. POSNG[-8]:0. SMALL[-8]:-. SULPH[-8]:-. TEENY[-8]:-. CRING[-8]:-. VALEN[-8]:2. AMINO[-7]:L. ALIPH[-7]:-. AROMA[-7]:-. POLAR[-8]:-. POSNG[-8]:0. SMALL[-8]:-. SULPH[-8]:-. TEENY[-8]:-. CRING[-8]:-. VALEN[-8]:2. AMINO[-7]:L. ALIPH[-7]:-. AROMA[-7]:-. CBETA[-7]:-. CHARG[-7]:-. COVAL[-7]:-. HBOND[-7]:-. HPHOB[-7]:+. IONIZ[-7]:-. NITRO[-7]:1. POLAR[-7]:-. POSNG[-7]:0. SMALL[-7]:-. CBETA[-7]:-. CHARG[-7]:-. COVAL[-7]:-. HBOND[-7]:-. HPHOB[-7]:+. IONIZ[-7]:-. NITRO[-7]:1. POLAR[-7]:-. POSNG[-7]:0. SMALL[-7]:-. SULPH[-7]:-. TEENY[-7]:-. CRING[-7]:-. VALEN[-7]:2. AMINO[-6]:F. ALIPH[-6]:+. AROMA[-6]:+. CBETA[-6]:-. CHARG[-6]:-. COVAL[-6]:-. SULPH[-7]:-. TEENY[-7]:-. CRING[-7]:-. VALEN[-7]:2. AMINO[-6]:F. ALIPH[-6]:+. AROMA[-6]:+. CBETA[-6]:-. CHARG[-6]:-. COVAL[-6]:-. HBOND[-6]:-. HPHOB[-6]:+. IONIZ[-6]:-. NITRO[-6]:1. POLAR[-6]:-. POSNG[-6]:0. SMALL[-6]:-. SULPH[-6]:-. TEENY[-6]:-. CRING[-6]:+. HBOND[-6]:-. HPHOB[-6]:+. IONIZ[-6]:-. NITRO[-6]:1. POLAR[-6]:-. POSNG[-6]:0. SMALL[-6]:-. SULPH[-6]:-. TEENY[-6]:-. CRING[-6]:+. VALEN[-6]:2. AMINO[-5]:A. ALIPH[-5]:-. AROMA[-5]:-. CBETA[-5]:-. CHARG[-5]:-. COVAL[-5]:-. HBOND[-5]:-. HPHOB[-5]:-. IONIZ[-5]:-. VALEN[-6]:2. AMINO[-5]:A. ALIPH[-5]:-. AROMA[-5]:-. CBETA[-5]:-. CHARG[-5]:-. COVAL[-5]:-. HBOND[-5]:-. HPHOB[-5]:-. IONIZ[-5]:-. NITRO[-5]:1. POLAR[-5]:-. POSNG[-5]:0. SMALL[-5]:+. SULPH[-5]:-. TEENY[-5]:+. CRING[-5]:-. VALEN[-5]:2. AMINO[-4]:T. ALIPH[-4]:+. NITRO[-5]:1. POLAR[-5]:-. POSNG[-5]:0. SMALL[-5]:+. SULPH[-5]:-. TEENY[-5]:+. CRING[-5]:-. VALEN[-5]:2. AMINO[-4]:T. ALIPH[-4]:+. AROMA[-4]:-. CBETA[-4]:+. CHARG[-4]:-. COVAL[-4]:-. HBOND[-4]:+. HPHOB[-4]:-. IONIZ[-4]:-. NITRO[-4]:1. POLAR[-4]:+. POSNG[-AROMA[-4]:-. CBETA[-4]:+. CHARG[-4]:-. COVAL[-4]:-. HBOND[-4]:+. HPHOB[-4]:-. IONIZ[-4]:-. NITRO[-4]:1. POLAR[-4]:+. POSNG[-4]:0. SMALL[-4]:+. SULPH[-4]:-. TEENY[-4]:-. CRING[-4]:-. VALEN[-4]:2. AMINO[-3]:C. ALIPH[-3]:+. AROMA[-3]:-. CBETA[-3]:-. CHARG[-4]:0. SMALL[-4]:+. SULPH[-4]:-. TEENY[-4]:-. CRING[-4]:-. VALEN[-4]:2. AMINO[-3]:C. ALIPH[-3]:+. AROMA[-3]:-. CBETA[-3]:-. CHARG[-3]:-. COVAL[-3]:+. HBOND[-3]:+. HPHOB[-3]:+. IONIZ[-3]:+. NITRO[-3]:1. POLAR[-3]:+. POSNG[-3]:-. SMALL[-3]:-. SULPH[-3]:+. 3]:-. COVAL[-3]:+. HBOND[-3]:+. HPHOB[-3]:+. IONIZ[-3]:+. NITRO[-3]:1. POLAR[-3]:+. POSNG[-3]:-. SMALL[-3]:-. SULPH[-3]:+. TEENY[-3]:-. CRING[-3]:-. VALEN[-3]:2. AMINO[-2]:I. ALIPH[-2]:-. AROMA[-2]:-. CBETA[-2]:+. CHARG[-2]:-. COVAL[-2]:-. HBOND[-2]:-. TEENY[-3]:-. CRING[-3]:-. VALEN[-3]:2. AMINO[-2]:I. ALIPH[-2]:-. AROMA[-2]:-. CBETA[-2]:+. CHARG[-2]:-. COVAL[-2]:-. HBOND[-2]:-. HPHOB[-2]:+. IONIZ[-2]:-. NITRO[-2]:1. POLAR[-2]:-. POSNG[-2]:0. SMALL[-2]:-. SULPH[-2]:-. TEENY[-2]:-. CRING[-2]:-. VALEN[-2]:2. HPHOB[-2]:+. IONIZ[-2]:-. NITRO[-2]:1. POLAR[-2]:-. POSNG[-2]:0. SMALL[-2]:-. SULPH[-2]:-. TEENY[-2]:-. CRING[-2]:-. VALEN[-2]:2. AMINO[-1]:A. ALIPH[-1]:-. AROMA[-1]:-. CBETA[-1]:-. CHARG[-1]:-. COVAL[-1]:-. HBOND[-1]:-. HPHOB[-1]:-. IONIZ[-1]:-. NITRO[-1]:1. AMINO[-1]:A. ALIPH[-1]:-. AROMA[-1]:-. CBETA[-1]:-. CHARG[-1]:-. COVAL[-1]:-. HBOND[-1]:-. HPHOB[-1]:-. IONIZ[-1]:-. NITRO[-1]:1. POLAR[-1]:-. POSNG[-1]:0. SMALL[-1]:+. SULPH[-1]:-. TEENY[-1]:+. CRING[-1]:-. VALEN[-1]:2. POLAR[-1]:-. POSNG[-1]:0. SMALL[-1]:+. SULPH[-1]:-. TEENY[-1]:+. CRING[-1]:-. VALEN[-1]:2. AMINO[0]:R. ALIPH[0]:+. AROMA[0]:-. AMINO[0]:R. ALIPH[0]:+. AROMA[0]:-. CBETA[0]:-. CHARG[0]:+. COVAL[0]:-. HBOND[0]:+. HPHOB[0]:-. IONIZ[0]:+. NITRO[0]:4. POLAR[0]:+. POSNG[0]:+. SMALL[0]:-. CBETA[0]:-. CHARG[0]:+. COVAL[0]:-. HBOND[0]:+. HPHOB[0]:-. IONIZ[0]:+. NITRO[0]:4. POLAR[0]:+. POSNG[0]:+. SMALL[0]:-. SULPH[0]:-. TEENY[0]:-. CRING[0]:-. VALEN[0]:3.SULPH[0]:-. TEENY[0]:-. CRING[0]:-. VALEN[0]:3. AMINO[1]:H. ALIPH[1]:+. 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Bioinformatics Tony C Smith
Artificial IntelligenceArtificial Intelligence
Computers do things Computers do things only human brains only human brains can otherwise docan otherwise do
expert expert
Bioinformatics Tony C Smith
Artificial IntelligenceArtificial Intelligence
Computers do things Computers do things only human brains only human brains can otherwise docan otherwise do
expertsystem
expert
Bioinformatics Tony C Smith
Artificial IntelligenceArtificial Intelligence
Computers do things Computers do things only human brains only human brains can otherwise docan otherwise do
learningsystem
expertsystem
Bioinformatics Tony C Smith
Machine learningMachine learning
creating computer programs that get better with experiencecreating computer programs that get better with experiencelearn how to make expert judgmentslearn how to make expert judgmentsdiscover previously hidden, potentially useful information (data discover previously hidden, potentially useful information (data mining)mining)
What is machine learning?
How does it work?user provides learning system with examples of concept to be learneduser provides learning system with examples of concept to be learned
induction algorithm infers a characteristic model of the examplesinduction algorithm infers a characteristic model of the examples
model is used to predict whether or not future novel instances are also model is used to predict whether or not future novel instances are also examples – and it does this very consistently, and very, very quickly!examples – and it does this very consistently, and very, very quickly!
Bioinformatics Tony C Smith
BioinformaticsBioinformatics
Biologists know proteins, computer scientists Biologists know proteins, computer scientists know machine learningknow machine learning
Together, they can find hidden and potentially Together, they can find hidden and potentially useful information about genes and proteinsuseful information about genes and proteins
Biotechnology is a multi-billion dollar industryBiotechnology is a multi-billion dollar industry
Biotechnology is one of the best funded areas of Biotechnology is one of the best funded areas of scientific researchscientific research
Shortage of people educated in bioinformaticsShortage of people educated in bioinformatics
Bioinformatics Tony C Smith
The University of WaikatoThe University of Waikato
Waikato University is ranked first in the country Waikato University is ranked first in the country in computer science and in molecular, cellular, in computer science and in molecular, cellular, and whole-organism biologyand whole-organism biology
centre of the universe for machine learningcentre of the universe for machine learning
Bioinformatics Tony C Smith
The University of WaikatoThe University of Waikato
If you’re interested in getting involved in If you’re interested in getting involved in bioinformatics, or indeed any other area bioinformatics, or indeed any other area
along the leading edge of computer along the leading edge of computer science and/or biology, then …science and/or biology, then …
Waikato wants You!Waikato wants You!