A MULTI-AGENT TOOL TO ANNOTATE BIOLOGICAL

SEQUENCES

C´elia Ghedini Ralha, Hugo Wruck Schneider, Maria Emilia M. T. Walter

Department of Computer Science, University of Bras´ılia, Campus Universit´ario Darcy Ribeiro

PO Box 4466, Bras´ılia, ZipCode 70.904-970, Brazil

Marcelo M. Br´ıgido

Institute of Biology, University of Bras´ılia, Campus Universit´ario Darcy Ribeiro

Bras´ılia, ZipCode 70.910-900, Brazil

Keywords:

Multi-agent system, Annotation, Genome sequencing projects, BioAgents.

Abstract:

Nowadays, great challenges are imposed by the existence of enormous volume of DNA and RNA sequences,

which are continuously being discovered by genome sequencing projects, through the automatic sequencers

based on massively parallel sequencing technologies. Thus, the task of identifying biological function for

these sequences is a key activity in these high-throuput sequencing projects, where the automatic annotation

must be signiﬁcantly improved. In this context, this paper presents a multi-agent approach to address the

important issue of automatic annotation in genome projects. We developed a sophisticated prototype named

BioAgents, which simulates biologists knowledge and experience to annotate DNA or RNA sequences in

genome sequencing projects, where different specialized intelligent agents work together to accomplish the

annotation process.

1 INTRODUCTION

Artiﬁcial Intelligence (AI) techniques are being em-

ployed in bioinformatics with increasing success. In

this context, a MAS takes goal-oriented approach in

which agents can act cooperatively to reach a goal.

Recently, new massively parallel sequencing tech-

nologies (Mardis, 2008), that can produce billions of

bases in a veryshort time, havedramatically increased

the amount of biological data, and have presented new

bioinformatics challenges (Pop and Salzberg, 2008).

A key activity in these genome projects is the annota-

tion of these enormous volume of sequences.

The annotation phase of a genome project has

the objective of assigning biological functions to the

identiﬁed DNA and RNA sequences. In addition,

ab initio gene ﬁnding programs (programs that ﬁnd

genes based on biological and chemical properties)

to predict protein-coding genes are being largely em-

ployed, using annotation techniques that include com-

parisons among the investigated sequences and se-

quences of related species, available in public biolog-

ical data bases, like GenBank (Benson et al., 2008).

In the annotation phase, computational methods

to infer biological functions to each sequence are nor-

mally accomplished by approximate string matching

algorithms, like BLAST (Altschul et al., 1990). These

algorithms run on data bases containing the sequences

and their already identiﬁed functions, or methods to

identify noncoding RNAs (Eddy and Durbin, 1994).

The annotation process can be completed by biolo-

gists, who using their knowledge analyze and correct

the function suggested by the programs.

The annotation phase in the context of massively

parallel DNA pyrosequencing could be certainly im-

proved by computational tools. Thus, this paper fo-

cuses on the use of AI techniques to bioinformatics,

with a MAS approach implemented in a prototype

called BioAgents. This version of BioAgents, has a

Web interface, uses BLAST and BLAT (Kent, 2002)

algorithms, a method to identify noncoding RNAs –

PORTRAIT (Arrial et al., 2007), and an open source

rule engine in Java – Drools.

The rest of this work is divided into ﬁve sections.

In Section 2, we discuss previous work; while in Sec-

tion 3, the related work. In Section 4, we present the

architecture, BioAgents new prototype and MAS fea-

tures. In Section 5, we discuss the experiments. Fi-

226

Ghedini Ralha C., Wruck Schneider H., Emilia M. T. Walter M. and M. Brígido M..

A MULTI-AGENT TOOL TO ANNOTATE BIOLOGICAL SEQUENCES.

DOI: 10.5220/0003179102260231

In Proceedings of the 3rd International Conference on Agents and Artiﬁcial Intelligence (ICAART-2011), pages 226-231

ISBN: 978-989-8425-41-6

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

nally, in Section 6, we conclude and suggest future

work.

2 PREVIOUS WORK

We have already presented the ﬁrst prototype of

BioAgents (Lima, 2007). It was a tool to be used with

the traditional Sanger technology, with the objective

to assist the biologists during the manual annotation

phase on genome sequencing projects. BioAgents

was developed with a three layer architecture using

JADE framework (Bellifemine et al., 2007), compar-

ing algorithms such as BLAST and FASTA (Pearson

and Lipman, 1988), using Jess inference engine (Hill,

2003).

During this ﬁrst stage of our research project, the

MAS approach has proved to allow the interaction

of specialized software agents in the reach of an ob-

jective (Weiss, 2000; Wooldridge, 2009). Different

agents using speciﬁc algorithms, which interact to

each other in order to reach a common objective, have

accomplished well the process of annotation. Thus,

BioAgents was used for supporting manual annota-

tion on three different genome sequencing projects:

Paracoccidioides brasilienses fungus, Paullinia cu-

pana (guaran´a) plant and Anaplasma marginale rick-

ettsia. The obtained results were encouraging at the

traditional Sanger technology (Ralha et al., 2008).

Considering an ongoing research work, BioA-

gents was described to illustrate the recent research

ﬁeld of Agent-Mining Interaction and Integration

(AMII) (Ralha, 2009). In another perspective, we

have implemented a reinforcement learning (RL)

method to BioAgents, where we have used together

with the genome sequencing projects previously ex-

perimented – Paracoccidioides brasiliensis (Pb fun-

gus) and Paullinia cupana (Guaran´a plant), together

with two reference genomes – Caenorhabditis ele-

gans and Arabidopsis thaliana, respectively, for Pb

and Guaran´a. The results obtained with the learning

layer were better when compared to the system with-

out the proposed method (Ralha et al., 2010).

3 RELATED WORK

Bioinformatics is a research area concerned with the

investigation of tools and techniques from computer

science to solve problems from molecular biology

(see Setubal and Meidanis (1997) for details). Many

projects on bioinformatics use AI techniques on dif-

ferent bioinformatics tasks such as analysis and pre-

diction of gene function. We cite some of these initia-

tives, but not being exhaustive.

Another MAS tool is the MASKS environ-

ment (Schroeder and Bazzan, 2002), that improves

symbolic learning through knowledge exchange. The

motivation is to mimic human interaction in order to

reach better solutions to data classiﬁcation. The tool

Agent-based environmenT for aUtomatiC annotation

of Genomes - ATUCG is based on an agent architec-

ture, and aims to support the biologists by using the

concept of re-annotation (do Nascimento and Bazzan,

2004).

Finally, transcriptome and regulome sequenc-

ing projects, as well as metagenomics projects, se-

quenced with massively parallel sequencing tech-

nologies, have been successfully annotated with tra-

ditional annotation with Sanger technology, being

slightly modiﬁed to adequately treat the enourmous

volumes of output of these sequencers (Moore1 et al.,

2006; Solda et al., 2009; Iacono et al., 2008; Wang

et al., 2009).

Comparing to the related works cited, BioAgents

simulates biologists knowledge and experience to an-

notate DNA or RNA sequences in different genome

sequencing projects. See Section 5, where we present

our experiments conducted with a fungus, a plant and

a rickettsia project. The annotation tools cited focus

speciﬁc annotation organisms such as virus (BioMAS,

DECAF). The ATUCG focus the re-annotation pro-

cess in the traditional annotation form, while MASKS

knowledge approach is an interesting initiative, since

data classiﬁcation is still an increasing problem to

genome annotation methods with massively parallel

sequencing technologies; which we believe can be

improved through the use of DM and ML techniques

(see Section 6 of future work to BioAgents).

To conclude, annotation methods for genomes

will pursue reasonably accuracy for genes presented

in other species, since sequence comparison methods

can deal well with errors, even if the genes are frag-

mented. But genes that belong uniquely to an organ-

ism will be difﬁcult to be annotated with traditional

annotation methods, and the small size of sequences

assemblies of massively parallel sequencing projects

will increase this problem (Pop and Salzberg, 2008).

4 BIOAGENTS

Considering the new scenario of massively parallel

automatic sequencers, where billions of little frag-

ments are produced in an increasing velocity, BioA-

gents can strongly help to improve the quality of the

annotation process with its reasoning mechanism to

A MULTI-AGENT TOOL TO ANNOTATE BIOLOGICAL SEQUENCES

227

gene annotation. Figure 1 presents the architecture

of BioAgents, considering the Web prototype version,

which is divided into three layers: interface, collab-

orative and physical. Since the three layers and the

agents do not differ from previous publications, for a

more detailed explanation see (Ralha et al., 2008).

Collaborative

Layer

Interface

Layer

Interface Module

Queries Results

User 2

Queries Results

User n

Conflict Resolution Agent (CR)

Blast MR Agent Blat MR Agent

Portrait Specific

Agent (SA)

ANL Agent ANL Agent

Physical

Layer

ANL Agent ANL Agent

COG

KOG

Swiss−

Prot

other

data

bases

User 1

Queries Results

Figure 1: The three layer architecture of BioAgents.

The collaborative layer is the architecture core.

It has specialized manager agents to execute par-

ticular algorithms, like BLAST and BLAT, that in-

teract with analyst agents for treating data bases,

like nr-GenBank or kog. We deﬁned special-

ized agents to deal with different algorithms and

speciﬁc data bases. Finally, this layer sug-

gests annotations to be sent to the interface layer

through the conﬂict resolution agent. The physical

layer is formed by different public biological data

bases. Figure 2 presents the BioAgents Web inter-

face (http://bioinformatica.cenargen.embrapa.br:8080/

bioagents/bioagents.html).

4.1 The Prototype

BioAgents Web prototype version was rewritten to be

time efﬁcient in execution by using separated threads.

It was implemented with a framework for MAS devel-

opment known as Java Agent DEvelopment Frame-

work - JADE, version 3.6.1. JADE uses Java lan-

guage, and Eclipse SDK, version 3.4.1, was used as

the development environment.

JADE offers class libraries of pattern interaction

protocols, ready to be used and extended. As its plat-

form is ready to use, it is not necessary to imple-

ment agents functionalities, agent management on-

tologies and transportation mechanism for message

parsing. We have used FIPA Agent Communication

Language - FIPA ACL for message interchange and

contract net interaction protocol. BioAgents parsers

Figure 2: BioAgents Web interface.

used by the ANL agents were implemented using

some libraries of the framework BioJava, version

1.6 (http://biojava.org/wiki/Main Page). BioJava of-

fers objects to manipulate biological sequences and

parsers to ﬁles of biological sequences, among other

functionalities.

BioAgents uses Drools as a production rule sys-

tem (http://www.jboss.org/drools/). Drools is an

open source rule engine implementation written in

Java, and it is based on Charles Forgy’s Rete al-

gorithm (Forgy and Shepard, 1987) tailored for the

Java language. Drools allows pluggable language im-

plementations. With Drools we deﬁned the biolo-

gists knowledge through the use of production rules

(declarative rules), according to the parameters de-

ﬁned for the speciﬁc genome project.

To analyze the outputs from BLAST and BLAT,

MR and ANL agents used two parameters, the e-value

and score, according to the following rules: (i) Ver-

ify if there are alignments having e-value less than or

equal to 10

−5

(value adopted by the biologists on the

three genome projects of our experiments, but this is

a parameter of the system easily changeable);(ii) Se-

lect the lower e-value, among the alignments present-

ing the previous restriction; (iii) Select the alignment

with the higher score, if the e-values are equal.

Figure 3 presents the conﬂict resolution ﬂow used

by the CR Agent. The CR agent uses BLAST and

BLAT results if at least one of them ﬁnds an e-

value less than or equal to 10

−5

. Otherwise, the CR

agent calls the PORTRAIT agent to identify noncod-

ing RNAs. PORTRAIT is a method to identify non-

coding RNA in transcriptomes of poorly character-

ized species (Arrial et al., 2007).

MR and ANL agents interpretthe results produced

by the comparison algorithms, according to the agents

knowledge formalized by production rules (presented

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

228

Figure 3: The set of Drools rules to analyze the outputs of

BLAST, BLAT and PORTRAIT.

Submission

Sequence

Interface Layer

Conflict

Resolution

Agent (CR)

Blast

MR Agent

ANL

Agent

Physical Layer

2 3 4

567

Collaborative Layer

Figure 4: BioAgents workﬂow, noting that the numbers in-

dicate the execution order.

in Section 4.1). Every agent work like an expert sys-

tem, where the basic parameters used by the MR and

ANL agents rules are e-value and score. CR agent

decides the suggestion based on the best results given

by the MR agents to recommend the annotation. All

this procedure is done at the collaborative layer. Fig-

ure 4 shows the pipeline of BioAgents executing for a

particular program (BLAST) and data base (nr) with

a gene identiﬁed.

5 EXPERIMENTS

In order to validate BioAgents new version, we used

data from three genome sequencing projects devel-

oped at the MidWest Region of Brazil: Functional and

Differential Genome from the Paracoccidioides bra-

siliensis (Pb) fungus (https://dna.biomol.unb.br/Pb-

eng/), Genome Project of Paullinia cupana plant

(guaran´a) (https://dna.biomol.unb.br/GR/) and

Genome Project of the Anaplasma marginale rick-

ettsia (https://www.biomol.unb.br/anaplasma/servlet/

IndexServlet).

Considering the Genome Project Pb, the analyzed

data were extracted from BLAST executed with nr,

COG and GO data bases; and from BLAT with S.

cereviseae and S. pombee fungi data bases. For the

Genome Project Guaran´a, we used BLAST executed

with nr, KOG and SwissProt data bases. We have used

the same nr data base adopted for the annotation on

both projects, Pb and Guaran´a, in order to compare

with BioAgents suggestions.

Since the Genome Project Anaplasma was not

manually annotated, we used BioAgents to support

the annotation task. For this project we used BLAST

with nr and Anaplasma marginalis St. Maries data

base. For the three projects, PORTRAIT was used if

BLAST and BLAT did not ﬁnd any similar sequences

as presented in Section 4.1.

From the Genome Project Pb, 6, 107 sequences

were analyzed (Table 1). From these, 2, 820 genes

were manually annotated by the biologists, and 3, 287

were not. Note that 3,040 annotations were sug-

gested by BioAgents, being 1,746 correct when com-

pared to the 2,820 manual annotations of the Genome

Project Pb, which corresponds to 57.48% of correct

suggestions. Observe that for the 3,287 not manu-

ally annotated genes, 533 were suggested by BioA-

gents. From the 3,067 not identiﬁed as putative pro-

teins, 447 were identiﬁed as ncRNA. According to

the biologists, these are good results that can be even

improved as the agent knowledge bases are reﬁned.

Also, we consider the correctness method used very

naive (three equal strings), which demands semantic

improvement.

Table 1: Results of BioAgents applied to the Genome

Project Pb.

Number of genes 6,107

Number of genes manually annotated 2,820

Number of annotations suggested by

BioAgents 3,040

Number of annotations correctly 1,746/

indicated by BioAgents 3,040

Percentage of correct suggestions

(related to the manual annotations) 57.48%

Number of annotations suggested for 533/

genes not manually annotated 3,287

Number of sequences not identiﬁed as

putative proteins 3,067

Number of ncRNAs 447

Percentage of ncRNAs 14.57%

We analyzed 8,597 sequences of the Genome

Project Guaran´a (Table 2). From these, 7,725 genes

were manually annotated by the biologists and 872

were not. Note that 6,354 annotations were suggested

A MULTI-AGENT TOOL TO ANNOTATE BIOLOGICAL SEQUENCES

229

by BioAgents, being 3,626 correct when compared to

the 7,725 manual annotations, which corresponds to

57.07% of correct suggestions. From the 2, 243 not

identiﬁed as putative proteins, 1,217 were identiﬁed

as ncRNAs. The ncRNAs results of 54.25% proved

the importance to use a ncRNA algorithm like POR-

TRAIT in the Guaran´a Project.

Table 2: Results of BioAgents applied on the Genome

Project Guaran´a.

Number of genes 8,597

Number of genes manually annotated 7,725

Number of annotations suggested by

BioAgents 6,354

Number of annotations correctly 3, 626/

indicated by BioAgents 6,354

Percentage of correct suggestions

(related to the manual annotations) 57.07%

Number of annotations suggested for 367/

genes not manually annotated 872

Number of sequences not identiﬁed as

putative proteins 2,243

Number of ncRNAs 1,217

Percentage of ncRNAs 54.25%

For the Genome Project Anaplasma, BioAgents

suggested 2,401 annotations for a total of 3,214

ORFs (Table 3), corresponding to 74.70% of sugges-

tions. This was an expected result since one of the

used data base was from the same already annotated

organism Anaplasma marginalis St. Maries. From

the 813 not identiﬁed as putative proteins, 502 were

identiﬁed as ncRNAs, which corresponds to 61.74%.

Table 3: Results of BioAgents applied on the Genome

Anaplasma Project.

Number of contigs 773

Number of ORFs at the contigs 1,541

Number of annotations to ORFs

(contigs) suggested by BioAgents 1,343

Number of singlets 1,041

Number of ORFs on singlets 1,673

Number of annotations to ORFs

(singlets) suggested by BioAgents 1,058

Number of ORFs 3,214

Number of ORF annotations

suggested by BioAgents 2,401

Percentage of suggestions 74.70%

Number of not identiﬁed as putative proteins 813

Number of ncRNAs 502

Percentage of ncRNAs 61.74%

Figure 5 shows the results of Genome Project Pb

and Genome Project Guaran´a according to Tables 1

and 2. Note that the adopted rules use only the e-value

and score computed by BLAST and BLAT. Particu-

larly, we do not consider a minimum percentage of the

score, since this information was not used by the biol-

ogists in their manual annotation. Programs to iden-

tify ncRNAs were not used in the genome projects

used in our experiments, although BioAgents have

used PORTRAIT. In addition, the adopted basic rules

lead to good results.

Figure 5: Comparisons among the results of Genome

Project Pb and Genome Project Guaran´a.

6 CONCLUSIONS AND FUTURE

WORK

In this article, we presented a new version of a so-

phisticated prototype called BioAgents, a MAS to

annotate biological sequences in genome projects.

The annotation process is based on heterogeneous

and dynamic environment, and biologists can ana-

lyze sequences of interest in order to conﬁrm com-

putational results. BioAgents uses different and dis-

tributed databases, with data being continuously mod-

iﬁed, which ﬁts well to the multi-agent approach.

BioAgents has agents specialized on distinct tasks, so

that they can act independently,using their knowledge

represented through speciﬁc inference rules.

As mentioned before, considering the new sce-

nario of massively parallel automatic sequencers,

with billions of little fragments, BioAgents can

strongly help to improve the quality of the annota-

tion process. As far as we know, the majority of the

systems developed to support annotation like the ones

cited in Section 3 are organisms speciﬁc (virus or pro-

teins) and do not have a reasoning mechanism to sug-

gest annotation. In addition, they do consider ncR-

NAs during the annotation phase.

BioAgents can be improved in many different

ways. Including other algorithms that analyse dif-

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

230

ferent characteristics of the sequences, as FASTA for

example. The improvement of agents knowledge is

necessary to achieve a higher accuracy for the sug-

gestions proposed by BioAgents. Also more complex

semantic and ontological methods would improve the

suggestions in BioAgents. Finally, BioAgents can

be conﬁgurable to a more distributed implementation

system using clusters, grids or cloud computing re-

sources. In the experimental aspects, we plan to use

BioAgents in high throuput genome projects, that are

beginning in the MidWest Region of Brazil.

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