Multiple sequence alignments play a central role in Bioinformatics. Most alignment representations are designed to facilitate knowledge extraction by human experts. Additionally statistical models like Profile Hidden Markov Models are used as representations. They offer the advantage to provide sound, probabilistic scores. The basic idea we present in this paper is to use the structure of a Profile Hidden Markov Model for propositionalisation. This way we get a simple, extendable representation of multiple sequence alignments which facilitates further analysis by Machine Learning algorighms

Holmes, Geoffrey

Mutter, Stefan

Pfahringer, Bernhard

English

Name not available

Propositionalisation of multiple sequence alignments using probabilistic models

Research Commons@Waikato

Propositionalisation of Multiple Sequence Alignmentsusing Probabilistic ModelsStefan Muttermutter@cs.waikato.ac.nzBernhard Pfahringerbernhard@cs.waikato.ac.nzGeoff Holmesgeoff@cs.waikato.ac.nzDepartment of Computer ScienceThe University of WaikatoHamilton, New ZealandABSTRACTMultiple sequence alignments play a central role in Bioin-formatics. Most alignment representations are designed tofacilitate knowledge extraction by human experts. Addition-ally statistical models like Profile Hidden Markov Models areused as representations. They offer the advantage to providesound, probabilistic scores. The basic idea we present in thispaper is to use the structure of a Profile Hidden MarkovModel for propositionalisation. This way we get a simple,extendable representation of multiple sequence alignmentswhich facilitates further analysis by Machine Learning algo-rithms.Categories and Subject DescriptorsI.2.6.g. [Artificial Intelligence]: Learning—Machine Learn-ing ; G.3.e. [Probability and Statistics]: Markov pro-cesses; J.3.a. [Life and Medical Sciences]: Biology andgeneticsKeywordsMultiple Sequence Alignment, Representation, Hidden MarkovModel, Propositionalisation1. INTRODUCTIONSequences are omnipresent in Biology. Their analysis is thekey to knowledge discovery in molecular and related sci-ences. From the earliest stages of research in Bioinformat-ics, sequence alignment techniques have played a central roleand have become essential for molecular research in Life Sci-ences.Ultimately, biological diversity between organisms can beexplained by differences in DNA, RNA and consequentlyamino acid sequences. Detecting differences and similaritiesin these sequences can be seen as string parsing, calculatingedit distances and judging similarities between strings basedon some metric. However, the sequences under considerationevolve in a complex molecular world. An alignment of se-quences is an explicit mapping between the positions in thesequences or respectively strings [11]. As the title of this re-search paper suggests, we focus on alignments. An examplefor a simple alignment is given in Figure 1. Alignments areused to identify common, conserved regions of interest. Thesignificance of an alignment is given by a score. A high scor-ing conserved sequence region in different organisms mayindicate a common functional purpose of that region. Be-cause of the complexity of sequence evolution, scoring analignment is not an easy task. A common example of us-age of an alignment is the identification of new members ofa protein family. The protein family under considerationis represented as an alignment and the sequence of interestwill be aligned to it. The score indicates whether or not thesequence belongs to the specific protein family.Sequence alignments allow biologist in general, along withother applications, to determine and understand sequencefamily membership, phylogenetic, functional or structuralrelationships [8]. They represent a compressed form of knowl-edge that needs to be further evaluated. Currently, this taskis mostly performed by biological experts. Therefore, thereexist representations that facilitate knowledge extraction byhuman experts. Our objective is a simple and flexible repre-sentation for multiple sequence alignments that can be easilyused as input for an arbitrary, propositional machine learn-ing algorithm. By regularising the input we can gain frommany recent advances that have been made in propositionalmachine learning.This paper is structured in the following way. In the nextsection we introduce multiple sequence alignments. Sec-tion 3 presents representations beginning with human read-able ones and introducing Profile Hidden Markov Models [3,9, 5, 4] as a representation for multiple sequence alignments.They offer the advantage of a sound, probabilistic score forthe alignment. The following section introduces the conceptof propositionalisation and explains how we use it togetherwith Profile Hidden Markov Models to build a new simplerepresentation for a multiple sequence alignment. The lastsection summarises the paper and gives an outlook of theresearch project.2. MULTIPLE SEQUENCE ALIGNMENTIn this paper we are investigating global, multiple sequencealignments (MSA). An MSA consists of at least three se-quences. In this research project we align sequences globally,meaning that the whole length of a sequence gets aligned.Figure 1: A global multiple sequence alignment forthree amino acid sequences.Figure 2: Three short, unaligned amino acid se-quences.Figure 1 shows an example of a global MSA for three aminoacid sequences. An MSA reflects the underlying sequenceevolutionary process. A sequence can evolve in three differ-ent ways:• A specific position in the sequence can be altered.In Biology this phenomena is called a point muta-tion. From a Computer Science viewpoint this is justa change of a single character in a string.• Parts of the sequence can get deleted.• Parts of the sequence can be newly inserted.If there is no difference in a specific region of a sequence,this region is labelled as conserved. Generally a conservedregion may indicate a conserved function or structure. Inan alignment these conserved sequence positions are calledmatches. Therefore in an alignment we can find matches,insertions and deletions. By convention, matches are dis-played as upper case letters, whereas inserts are representedas lower case letters. A deletion is symbolised by a dash.Alignments are always displayed with equal length so thatconserved regions are one below another. That is why inall sequence strings (except the longest ones) dots are in-serted. This representation makes it easier to human expertto analyse the alignment.3. REPRESENTATIONS FORAMULTIPLESEQUENCE ALIGNMENTFor the remainder of this paper we use a simple MSA of threeamino acid sequences to illustrate our approach. Figure 2shows the unaligned sequences.A major problem in sequence alignment construction is howto deal with point mutations. Mutations vary greatly intheir effect. A change at a sequence position doesn’t nec-essarily mean that there is a functional, structural or anyother consequence. This happens when a sequence elementmutates into a different one with very similar chemical prop-erties. In order to allow a biological expert to further investi-gate alignment colour coding schemes are used. Each colourFigure 3: An MSA enhanced with colour codes foramino acid properties. Blue represents acidic aminoacids, red small and hydrophobic ones. The aminoacids with one letter code R, H and K are colouredmagenta, whereas green is used for S, T, Y, H, C,N, G and Q.Figure 4: A sequence logo representation for oursimple example MSA. The logo is produced usingthe WebLogo software [2].represents a chemical property. We would expect match re-gions to have the same colour. Figure 3 shows a colour codedMSA for our example sequences.An alignment contains information about the distributionof DNA or RNA nucleotides or amino acids at each posi-tion. So far this information isn’t clearly represented. Asequence logo [10] as shown in Figure 4 gives a good visualoverview of the distribution of nucleotides or amino acids ineach sequence position. The height of a letter indicates itsfrequency at a given sequence position. In addition colourcodes for properties are used.It is a challenging and problem domain dependent task toscore alignments in a biologically meaningful way. A com-mon approach uses a probabilistic measure. Profile HiddenMarkov Models are a widely used representation for biolog-ical sequence alignments. They are probabilistic graphicalmodels. Thus, they allow the calculation a probability foreach sequence. We will introduce Profile Hidden MarkovModels in the next section.3.1 Profile Hidden Markov ModelsA Profile Hidden Markov Model [3, 9, 5, 4] is a probabilis-tic graphical model. It consists of three different sets ofstates: match, insert and delete states. Consequently, amatch states models a match in an alignment position. Inthe same way delete and insert states model deletions andinsertions. In each alignment position a sequence can ei-ther match, or there is a deletion or an insertion. This iswhy the structure of Profile Hidden Markov Models is or-ganised in columns. There is a column for each position inthe alignment and therefore, each column consists of exactlyone match, insert and delete state.In practice slightly different structures for Profile HiddenFigure 5: The general structure of a Profile Hidden Markov Model. This Figure is taken from [1].Markov Models exist. An example is the architecture ofHMMER [4], the most prominent Profile Hidden MarkovModel implementation. In our implementation the first andlast column are exceptions to the general rule (see Figure 5).Depending on the implementation there might also be slightdifferences concerning the state transitions. A discussion onthis topic can be found in Durbin et al. [3]. The structureof the Profile Hidden Markov Model that we used in ourexperiments is shown in Figure 5.Each match and insert state can emit sequence symbols.The set of all possible symbols of a sequence is called analphabet. There are probabilities for each transition and foreach emission in a match and insert state. A delete statedoes not emit a symbol.There are different learning algorithm for the transition andemission probabilities of a Profile Hidden Markov Model.We use he most common method, the Baum-Welch algo-rithm [3]. It is an EM derived algorithm and guarantiesconvergence to a local optima. This trained Profile HiddenMarkov Model represents an MSA.Traditionally, Profile Hidden Markov Models are used toconstruct and represent MSAs. In this paper we also usethe Profile Hidden Markov Model for propositionalisation.The next section introduces the basic idea.The Viterbi algorithm [3] allows to efficiently calculate themost probable path for a sequence to be aligned througha trained Profile Hidden Markov Model. This probabilityindicates whether or not the sequence belongs to the existingMSA.4. PROPOSITIONALISATIONPropositionalisation is an approach widely used in the Ma-chine Learning community [7]. Basically it is nothing morethan a change of representation. It refers to the transforma-tion of relational data into a feature-based, attribute valuerepresentation [6]. This representation is then consequentlycalled a propositional one. Traditionally, Machine Learningalgorithms like decision trees and support vector machineswork on propositionalised data. Therefore, propositionalisa-tion leads to a wide range of learning algorithm that can beapplied in a consecutive step. It decouples the feature con-struction from the model construction [6]. In many problemsettings propositionalisation leads to equivalent or better re-sults [7, 6].In this research project we change the representation of anMSA as Profile Hidden Markov Model to an attribute-valuerepresentation. This step is called the propositionalisationusing a Profile Hidden Markov Model and will be explainedin the following section.4.1 Propositionalisation using a Profile Hid-den Markov ModelThe propositionalisation is guided by the structure of theProfile Hidden Markov Model. Each column of the ProfileHidden Markov Model is transformed into two features orattributes. The first attribute is nominal and represents thematch and delete state of the column under consideration.Thus its values can either be any letter from the sequencealphabet or the deletion symbol. The type of the secondattribute is numerical and counts how many times the col-umn’s insert state was visited.As a first step an MSA is built and represented using aProfile Hidden Markov Model. To propositionalise a newsequence we simply calculate the Viterbi path for this se-quence.Figure 6 shows the propositional representation for our sim-ple example MSA.Therefore we use the Profile Hidden Markov Model to con-struct features for our new representation. This proposition-alised data is then consequently used for model constructionin the learning step.This is a very simple representation that can be used as inputFigure 6: The propositional representation of thesimple example MSAfor propositional machine learning algorithms. In additionthis representation can be extended. The Profile HiddenMarkov Model provides a probability for the Viterbi path.This value can be added as an extra attribute. Furthermoreemission and transition probabilities can be used as featuresas well.5. PROJECT STATUS AND OUTLOOKThis paper presents parts of the first author’s PhD project.We outline a technique to propositionalise a Profile HiddenMarkov Model that represents an MSA. This representationis simple and easy to use as input for learning algorithms.Therefore it is useful in further analysing in silico the infor-mation stored in an MSA.In this paper we use the Profile Hidden Markov Model inthree ways. It constructs, represents and propositionalisesan MSA. The advantage of this approach is that a Pro-file Hidden Markov Model allows the probabilistic scoringof aligning a new sequence. We can extend our simple rep-resentation to incorporate this score. In addition we can alsotake advantage of having emission and transition probabili-ties and include them as a feature in our representation.Generally, any MSA, not only the ones represented as Pro-file Hidden Markov Model, can be transformed into our basicsimple representation without probabilistic extensions.Preliminary results show that this approach leads to a bet-ter predictive accuracy than a pure Profile Hidden MarkovModel approach.The next stage of the project includes intense, experimen-tal testing to verify whether or not propositionalisation en-hances the models and therefore in silico analysis of MSAs.The experimental results will be presented at the conference.In the future we plan to apply semi-supervised learning tothis experimental setting to determine to what extent thistechnique can benefit from propositionalisation.AcknowledgementsThis work has been partially funded by the Marsden GrantUOW-306 of the Royal Society of New Zealand.6. 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A tutorial on hidden markov models and selected applications in speech recognition.

Biological sequence analysis: probabilistic models of proteins and nucleic acids.

Comparative evaluation of approaches to propositionalization.

Hmmer user’s guide: biological sequence analysis using proﬁle hidden markov models.

Proﬁle hidden markov models (review).

Propositionalization approaches to relational data mining.

Recent progresses in multiple sequence alignment: a survey.

Sequence alignment and database searching.

Sequence logos: a new way to display consensus sequences.

Weblogo: a sequence logo generator.

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.9544

Propositionalisation of multiple sequence alignments using probabilistic models

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