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Bioinformatics The application of computer science to biological data Tony C Smith Department of Computer Science University of Waikato

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1 Bioinformatics The application of computer science to biological data Tony C Smith Department of Computer Science University of Waikato

2 Bioinformatics Tony C Smith The essence is prediction … My dog is very littl_ My dog is very littl_ ?   We know that letters do not occur in English at random (e.g. ‘t’ is 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)

3 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.

4 Bioinformatics Tony C Smith Representation W e e t – B i x … … to the computer, everything is binary!

5 Bioinformatics Tony C Smith 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

6 Bioinformatics Tony C Smith A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc gcggctacgttcatcccagcagcagcgattttaaaattaa cgcatcagactctcgtcgcgttcgtcgcctttattcacgcta atggacgacatcttttactacgacggcgcctacgcatcg cagcatacgacgcccagcatagtattttagaggcgagg acatcatcatatcgcagctacagcgcatcagacgcata cgacgacgactacgacgacactaacgacgatgttgcg cacccacaccagttatatagagacgaactcgcatcagc ttgcaatcggcgctacgcttcaaaatttattatattcccggc gcggctacgttcatcccagcagcagcgattttaaaattaa cgcatcagactctcgtcgcgttcgtcgcctttattcacgcta atggacgacatcttttactacgacggcgcctacgcatcg cagcatacgacgcccagcatagtattttagaggcgagg acatcatcatatcgcagctacagcgcatcagacgcata cgacgacgactacgacgacactaacgacgatgttgcg cacccacaccagttatatagagacgaactcgcatcagc

7 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 ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcg cctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgc agctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgc aatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgccttt attcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagct acagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcg gcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattca cgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacag cgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgct acgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgcta atggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcat cagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgct tcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatgga cgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcaga cgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaa aatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgac atcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcat acgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttat tatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatctttt actacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacga cgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatatt cccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactac gacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacg actacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactc

8 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 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

9 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

10 Bioinformatics Tony C Smith A genetic prediction problem encoding region untranslated region transcription factor RNA

11 Bioinformatics Tony C Smith A genetic prediction problem untranslated region

12 Bioinformatics Tony C Smith A genetic prediction problem untranslated region ttgcaatcggcgctacgcttcaaaatttattatattcccggc

13 Bioinformatics Tony C Smith A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc What transcription factors bind to this gene? Where is the transcription factor binding site?

14 Bioinformatics Tony C Smith A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc Clues:A binding site is often a short general pattern E.g. CCGATNATCGG

15 Bioinformatics Tony C Smith A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc Clues:The patterns are often reverse complements E.g.CCGATNATCGG GGCTANTAGCC

16 Bioinformatics Tony C Smith A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc Clues:Where there is one binding site, often there is another nearby.

17 Bioinformatics 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 to identify quickly and efficiently, and therefore it is exactly the kind of problem computer scientists should be able to solve.

18 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: 4 3 = 64 possible codons But there are only 20 amino acids!

19 Bioinformatics Tony C Smith proteomics Glycine:GGA, GGC, GGG, GGT Tyrosine:TAT, TAC Methionine:ATG There is quite a bit of redundancy in codons.

20 Bioinformatics Tony C Smith Amide group Carboxyl group R group Amino Acid

21 Bioinformatics Tony C Smith Amino Acid glycine tyrosine

22 Bioinformatics Tony C Smith

23

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

25 Bioinformatics Tony C Smith Local biases in residues around the cleavage site Sequence regularities can be exploited by statistical and pattern-based models

26 Bioinformatics Tony C Smith Existing solutions Partial alignments (Altschul & Gish, 1996) Neural networks (Nielsen at al., 1997) Hidden Markov models (Nielsen et al., 1999) Polypeptide probabilities (Chou, 2001) Maximum entropy (Clote, 2002)

27 Bioinformatics Tony C Smith SignalP (Nielsen et al., ) HMMs (or NNs) used to predict cleavage point

28 Bioinformatics Tony C Smith Existing methods all perform reasonably well and with about the same accuracy (90% eukaryotes, 87% gram-, 85% gram+) Do not offer a transparent explanatory framework as to the underlying biology Many other learning algorithms do! (WEKA data mining tools, Waikato University)

29 Bioinformatics Tony C Smith From sequences to text Primary sequence data has many similarities with text –Amino residues (letters) –Polypeptides (words) –Secondary structures (phrases/sentences)

30 Bioinformatics Tony C Smith

31 From sequences to text Primary sequence data looks like text –Amino residues (letters) –Polypeptides (words) –Secondary structures (phrases/sentences) –Tertiary structure (whole documents) Approach: transform a sequence into a set of pseudo-text documents

32 Bioinformatics Tony C Smith 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)

33 Bioinformatics Tony C Smith Properties of amino acids

34 Bioinformatics Tony C Smith Free facts about amino acids

35 Bioinformatics Tony C Smith Residue as a document E.g.CysteineCysC 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)

36 Bioinformatics Tony C Smith Sample 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. 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]:-. 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]:+. 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]:+. 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[- 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]:-. 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. POLAR[-1]:-. POSNG[-1]:0. SMALL[-1]:+. SULPH[-1]:-. TEENY[-1]:+. CRING[-1]:-. VALEN[-1]:2. 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]:-. SULPH[0]:-. TEENY[0]:-. CRING[0]:-. VALEN[0]:3. AMINO[1]:H. ALIPH[1]:+. AROMA[1]:+. CBETA[1]:-. CHARG[1]:+. COVAL[1]:-. HBOND[1]:+. HPHOB[1]:-. IONIZ[1]:+. NITRO[1]:3. POLAR[1]:+. POSNG[1]:+. SMALL[1]:-. SULPH[1]:-. TEENY[1]:-. CRING[1]:+. VALEN[1]:3. AMINO[2]:Q. ALIPH[2]:+. AROMA[2]:-. CBETA[2]:-. CHARG[2]:-. COVAL[2]:-. HBOND[2]:+. HPHOB[2]:-. IONIZ[2]:-. NITRO[2]:2. POLAR[2]:+. POSNG[2]:0. SMALL[2]:-. SULPH[2]:-. TEENY[2]:-. CRING[2]:-. VALEN[2]:2. AMINO[3]:Q. ALIPH[3]:+. AROMA[3]:-. CBETA[3]:-. CHARG[3]:-. COVAL[3]:-. HBOND[3]:+. HPHOB[3]:-. IONIZ[3]:-. NITRO[3]:2. POLAR[3]:+. POSNG[3]:0. SMALL[3]:-. SULPH[3]:-. TEENY[3]:-. CRING[3]:-. VALEN[3]:2. AMINO[4]:R. ALIPH[4]:+. AROMA[4]:-. CBETA[4]:-. CHARG[4]:+. COVAL[4]:-. HBOND[4]:+. HPHOB[4]:-. IONIZ[4]:+. NITRO[4]:4. POLAR[4]:+. POSNG[4]:+. SMALL[4]:-. SULPH[4]:-. TEENY[4]:-. CRING[4]:-. VALEN[4]:3. AMINO[5]:Q. ALIPH[5]:+. AROMA[5]:-. CBETA[5]:-. CHARG[5]:-. COVAL[5]:-. HBOND[5]:+. HPHOB[5]:-. IONIZ[5]:-. NITRO[5]:2. POLAR[5]:+. POSNG[5]:0. SMALL[5]:-. SULPH[5]:-. TEENY[5]:-. CRING[5]:-. VALEN[5]:2. AMINO[6]:Q. ALIPH[6]:+. AROMA[6]:-. CBETA[6]:-. CHARG[6]:-. COVAL[6]:-. HBOND[6]:+. HPHOB[6]:-. IONIZ[6]:-. NITRO[6]:2. POLAR[6]:+. POSNG[6]:0. SMALL[6]:-. SULPH[6]:-. TEENY[6]:-. CRING[6]:-. VALEN[6]:2. AMINO[7]:Q. ALIPH[7]:+. AROMA[7]:-. CBETA[7]:-. CHARG[7]:-. COVAL[7]:-. HBOND[7]:+. HPHOB[7]:-. IONIZ[7]:-. NITRO[7]:2. POLAR[7]:+. POSNG[7]:0. SMALL[7]:-. SULPH[7]:-. TEENY[7]:-. CRING[7]:-. VALEN[7]:2. AMINO[8]:Q. ALIPH[8]:+. AROMA[8]:-. CBETA[8]:-. CHARG[8]:-. COVAL[8]:-. HBOND[8]:+. HPHOB[8]:-. IONIZ[8]:-. NITRO[8]:2. POLAR[8]:+. POSNG[8]:0. SMALL[8]:-. SULPH[8]:-. TEENY[8]:-. CRING[8]:-. VALEN[8]:2. MULT3:7. MULT5:4. MULT7:3. MULT9:2. 2GRAM:IA. GRAM2:HQ. 3GRAM:CIA. GRAM3:HQQ.

37 Bioinformatics Tony C Smith demo

38 Concluding remarks A [pseudo] text classification approach to sequence prediction problems can perform as well as the state-of- the-art stochastic methods Allows miscellaneous facts (i.e. any textual description of relevant information) to be included A ranked list of features from the text classifier provides insights into the underlying biology Features could be used for text generation

39 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

40 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

41 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!


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