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Knowledge-based expert systems

and a proof-of-concept case study

for multiple sequence alignment

construction and analysisMohamed Radhouene Aniba,Sophie Siguenza, Anne Friedrich,FredericPlewniak ,Olivier Poch,Aron Marchler-Bauer

and Julie Dawn Thompson

SB MIRZA

FA11-RBI-004

MS 2nd semester Bioinformatics

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ABSTRACT

Bioinformatic information being produced tohandle the various applications used to studythe information presents a major datamanagement and analysis challenge toresearchers.

It is impossible to analyse manually all theinformation.

And new approaches are needed that arecapable of processing the large-scaleheterogeneous data to extract the pertinentinformation.

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Cont

A general methodology for building knowledge-based expert systems is described, focusing onthe (Unstructured Information Management

Architecture), UIMA, which provides facilities forboth data and process management.

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New Challenges

Such system level studies necessitate acombination of experimental, theoretical andcomputational approaches.

A major challenge for bioinformaticians in thepost-genomic era is clearly the management,validation and analysis of this mass ofexperimental and predicted data.

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Approaches

One approach has been data warehousing,where all the relevant databases are stored in aunified format and mined through a uniform

interface Distributed systems implement software to

access heterogeneous databases that aredispersed over the internet and provide a query

facility to access the data.

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SRS and entrez are probably the mostwidely used database query andnavigation systems for the life science

community.

Semantic web based methods have beenintroduced which add meaning to the raw

data by using formal descriptions of theconcepts

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KNOWLEDGE-BASED EXPERTSYSTEMS

There are several forms of expert systems thathave been classified according to themethodology

rule-based systems use a set of rules to analyseinformation about a specific class of problemsand recommend one or more possible solution.

case-based reasoning systems adapt solutionsthat were used to solve previous problems anduse them to solve new problems.

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Cont

Neural networks implement software simulationsof massively parallel processes involving theprocessing of elements that are interconnected

in anetwork architecture.

Fuzzy expert systems use the method of fuzzylogic which deals with uncertainty and is used in

areas where the results are not always binary.

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The knowledge base contains domain expertise inthe form of facts that the expert system will use tomake determinations.

The working storage is a database containing dataspecific to a problem being solved.

The inference engine is the code at the core of thesystem which derives recommendations from theknowledge base and problem-specific data in theworking storage

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Cont

The knowledge acquisition module is usedto update or expand dynamic knowledgebases.

The user interface controls the dialogbetween the user and the system.

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Expert systems and their architecture

The sequence of steps used to analyse aparticular problem is not explicitlyprogrammed, but is defined dynamically

for each new case.

Expert systems allow more than one lineof reasoning to be pursued and presented

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Cont

Problem solving is accomplished by applyingspecific knowledge rather than a specifictechnique. when the expert system does not

produce the desired results, the solution is toexpand the knowledge base rather than to re-programme the procedures.

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Tools for implementation of expertsystems Expert system shell is a piece of software which

provides a development framework containingthe user interface.

(CLIPS)

(JESS)

The use of a shell can reduce the amount ofmaintenance required and increase reusability

and flexibility of the application. An alternative is toBuild a customised expert

system using conventional languages.

It has been recommended to use a

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Expert systems in bioinformatics

In the biocomputing domain, various expertsystems have been built for specific tasks.

Artificial neural networks have been used

successfully for pattern discovery in many areas,including DNA and protein sequence analysis andmicroarray data analysis.

Fuzzy logic approaches have also been applied tothe analysis of gene expression data.

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Cont

An active area of research has been thereconstruction of functional networks, such asexpression data or interaction data, and

intelligent systems, neural networks, geneticalgorithms, etc.

These approaches are also finding applications,e.g drug discovery and design or medical

diagnosis.

An important task in bioinformatics is theextraction of knowledge from the biomedical

literature.

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Cont

FIGENIX addresses the problems of automaticstructural and functional annotation under thesupervision of a rule-based expert system.

Case-based approaches and rule-basedsystems have also been applied widely.

But there is no standard architecture that wouldallow exchange of information code between themany different applications.

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A PROPOSED SOLUTION FORBIOINFORMATICS:UIMA The most difficult aspects of building an expert

system in this domain is information integration,because of the heterogeneity of traditional

biological data resources. The results of certain programmes, databaseannotations in natural language or the scientificliterature is stored in different formats.

The principal challenge with unstructuredinformation is needed to be analysed to identify,locate and relate the entities and relationships ofinterest.

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Cont

OpenNLP, GATE and UIMA, Open NLP is anumbrella structure to open source projects forthe treatment of natural language.

Open NLP and GATE, contain many powerful

and robust algorithms for the processing ofnatural language texts.

UIMA allows the analysis structured and

unstructured data. It provides powerful capabilities for distributed

computing through services and is scalable andextensible software architecture.

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Overview of UIMA

A Collection Reader (CR), which allows the CPEto treat all the data in a given collection.

A corpus: AEs and AAEs, possibly managed byFCs and CAS Consumer (CC), takes the resultsfrom the CAS and stores them in an exploitableformat

The first layer of a UIMA application, represents

the Working Storage and contains the systemsmemory and used to

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Cont

store the metadata associated with

each AE.

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Cont

AAEs and each module in the application layercan then update the metadata in the CAS ateach step of the data analysis.

In an expert system, the analysis pipeline is not

pre-defined by the developer or user, butdepends the previous experience gained by

the system.

The third layer corresponds to the User Interfacebetween the final user and the system.

It provides powerful capabilities for distributedcomputing through services and is scalable and

extensible software architecture.

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Cont

UIMA is now exploited by numerous applicationrelated to the biological domain.

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A CASE STUDY: THE ALEXSYS

MULTIPLE ALIGNM ENT EXPERTSYSTEM

The first formal algorithm for Multiple sequencealignment (MSA) was computationally expensiveand most programmes employ some kind ofheuristic approach.

The progressive alignment procedure exploits

the fact that homologous sequences areevolutionarily related.

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Cont

This method involves three main steps:1:Pairwise sequence alignment and

distance matrix calculation

2: guide tree construction

3:multiple alignment

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Different MSA tools

DbClustal

Tcoffee

MAFFT Muscle

3DCoffee

PRALINE Refiner

PipeAlign

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AlexSys prototype: objectives

Multiple sequence comparisons or alignmentsprovide the basis for most of the computationalmethods used in genomic analyses orproteomics projects.

The MSA programmes are capable of aligningdifficult, divergent sets of sequences withconsistently high quality.

Alignments Expert SYStem evaluate andoptimise all the steps involved in theconstruction and analysis of a multiplealignment.

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Design and implementation

The TSs represent the prototypes memory (the

UIMA CAS)

The prototype currently uses five TSs:

Sequence, Matrix, Tree, Parameter andAlignment.

With TS we can build the second applicationlayer containing the AEs, grouped together intoAAEs.

This is designed to perform three main tasks:input data handling, annotation and information

extraction and MSA construction.

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The modular structure of theAlexSys prototype.

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Clustal W alignment optimisationusing UIMA

The final enhancement to the ClustalW algorithm

involved the addition of a post-processingrefinement step

Many approaches are developed to improveMSA, based on an optimisation of the Sum of

Pairs objective function.

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