bioinformatics the application of computer science to biological data

BioinformaticsBioinformatics

The application of computer science to The application of computer science to biological databiological data

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

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 (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)


Prediction in bioinformaticsPrediction in bioinformatics

Predicting the location of genes in DNAPredicting the location of genes in DNA

Predicting gene roles in an organismPredicting gene roles in an organism

Predicting errors in a genetic transcriptionPredicting errors in a genetic transcription

Predicting the function of proteinsPredicting the function of proteins

Predicting diseases from molecular samplesPredicting diseases from molecular samples

Anything that involves “making a judgment”; a Anything that involves “making a judgment”; a yes/no decision about whether some sample yes/no decision about whether some sample datum ‘does’ or ‘does not’ have some property.datum ‘does’ or ‘does not’ have some property.


RepresentationRepresentation

W e e t – B i xW e e t – B i x

0101011101100101011001010111010000101101 …

… to the computer, everything is binary!


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


A genetic prediction problemA genetic prediction problem

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tatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactcgagacgaactc



ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcacaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcgacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacggctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttaccagttatatagagacgaactcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttaatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatctacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactaagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagcgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacgggaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgacgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactccatcagtgttgcgcacccacaccagttatatagagacgaactc



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



encoding region untranslated region

transcription

factor RNARNARNARNARNA



untranslated region



untranslated regionttgcaatcggcgctacgcttcaaaatttattatattcccggcttgcaatcggcgctacgcttcaaaatttattatattcccggc



ttgcaatcggcgctacgcttcaaaatttattatattcccggcttgcaatcggcgctacgcttcaaaatttattatattcccggc

What transcription factors bind to this gene?

Where is the transcription factor binding site?




Clues: A binding site is often a short general pattern

E.g. CCGATNATCGG




Clues: The patterns are often reverse complements

E.g. CCGATNATCGGGGCTANTAGCC




Clues: Where there is one binding site, often there is another nearby.



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.


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 each:

43 = 64 possible codons

But there are only 20 amino acids!


proteomicsproteomics

Glycine: GGA, GGC, GGG, GGTTyrosine: TAT, TACMethionine: ATG

There is quite a bit of redundancy in codons.


Amidegroup

Carboxylgroup

R group

Amino AcidAmino Acid


Amino AcidAmino Acid

glycine

tyrosine


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


Local biases in residues around the cleavage site

Sequence regularities can be

exploited by statistical and pattern-based

models


Existing solutionsExisting solutions

Partial alignmentsPartial alignments (Altschul & Gish, 1996)(Altschul & Gish, 1996)

Neural networksNeural networks (Nielsen at al., 1997)(Nielsen at al., 1997)

Hidden Markov modelsHidden Markov models (Nielsen et al., 1999)(Nielsen et al., 1999)

Polypeptide probabilitiesPolypeptide probabilities (Chou, 2001)(Chou, 2001)

Maximum entropyMaximum entropy (Clote, 2002)(Clote, 2002)


SignalP SignalP (Nielsen et al., 1997-2004)(Nielsen et al., 1997-2004)

HMMs (or NNs) used to predict cleavage point


Existing methods all perform reasonably Existing methods all perform reasonably well and with about the same accuracy well and with about the same accuracy (90% eukaryotes, 87% gram-, 85% gram+)(90% eukaryotes, 87% gram-, 85% gram+)

Do not offer a transparent explanatory Do not offer a transparent explanatory framework as to the underlying biologyframework as to the underlying biology

Many other learning algorithms do!Many other learning algorithms do!(WEKA data mining tools, Waikato University)(WEKA data mining tools, Waikato University)


From sequences to textFrom sequences to text

Primary sequence data has many Primary sequence data has many similarities with textsimilarities with text– Amino residues (letters)Amino residues (letters)– Polypeptides (words)Polypeptides (words)– Secondary structures (phrases/sentences)Secondary structures (phrases/sentences)


From sequences to textFrom sequences to text

Primary sequence data looks like textPrimary sequence data looks like text– Amino residues (letters)Amino residues (letters)– Polypeptides (words)Polypeptides (words)– Secondary structures (phrases/sentences)Secondary structures (phrases/sentences)– Tertiary structure (whole documents)Tertiary structure (whole documents)

Approach: Approach: transform a sequence into a transform a sequence into a set of pseudo-text documentsset of pseudo-text documents


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)


Properties of amino acidsProperties of amino acids


Free facts about amino acidsFree facts about amino acids


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)


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]:-. 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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. 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. MULT7:3. MULT9:2. 2GRAM:IA. GRAM2:HQ. 3GRAM:CIA. GRAM3:HQQ.


demodemo


Concluding remarksConcluding remarks

A [pseudo] text classification approach to sequence A [pseudo] text classification approach to sequence prediction problems can perform as well as the state-of-prediction problems can perform as well as the state-of-the-art stochastic methodsthe-art stochastic methods

Allows miscellaneous facts (i.e. any textual description of Allows miscellaneous facts (i.e. any textual description of relevant information) to be includedrelevant information) to be included

A ranked list of features from the text classifier provides A ranked list of features from the text classifier provides insights into the underlying biologyinsights into the underlying biology

Features could be used for text generationFeatures could be used for text generation


BiotechnologyBiotechnology

Biologists know proteins, computer Biologists know proteins, computer scientists know machine learningscientists know machine learning

Together, they can find out a lot of hidden Together, they can find out a lot of hidden information about genes and proteinsinformation about genes and proteins

Biotechnology is a multi-billion dollar Biotechnology is a multi-billion dollar industryindustry

Biotechnology is one of the best funded Biotechnology is one of the best funded areas of scientific research areas of scientific research


The University of WaikatoThe University of Waikato

Waikato University is the centre of the Waikato University is the centre of the universe for machine learninguniverse for machine learning

The Machine Learning Group is a large, The Machine Learning Group is a large, globally active, well-funded research groupglobally active, well-funded research group

The WEKA workbench of ML tools is used The WEKA workbench of ML tools is used around the worldaround the world

Professors at Waikato University literally Professors at Waikato University literally wrote the book on sequence modelingwrote the book on sequence modeling


The University of WaikatoThe University of Waikato

If you’re seriously interested in machine If you’re seriously interested in machine learning, in getting involved in learning, in getting involved in

bioinformatics research, or indeed any bioinformatics research, or indeed any other area along the leading edge of other area along the leading edge of

computer science, then university is the computer science, then university is the only place to be, andonly place to be, and

Waikato wants You!Waikato wants You!

bioinformatics the application of computer science to biological data

Documents

t t c g

c g t c

sequence of letters

english prose

location of genes

organismpredicting errors

dnapredicting gene roles

predicting symbols