Abstract Background Microarrays allow genome-wide assays of gene expression. There is a need for user-friendly software to visualise and analyse these data. Analysing microarray data in the context of biological pathways is now common, and several tools exist. Results We describe the use of MAPPFinder, a component of GenMAPP to characterise the biological pathways affected in chickens infected with the protozoan parasite <it>Eimeria. </it>Several pathways were significantly affected based on the unadjusted p-value, including several immune-system pathways. Conclusion GenMAPP/MAPPFinder provides a means to rapidly visualise pathways affected in microarray studies. However, it relies on good genome annotation and having genes reliably linked to pathway objects. We show that GenMAPP/MAPPFinder can produce useful results, and as the annotation of the chicken genome improves, so will the level of information gained.</p

AR Pico

Dennis Prickett

F Al-Shahrour

J Hedegaard

M van Iersel

MD Prickett

Michael Watson

N Salomonis

N Yeung

RB Williams

SW Doniger

English

PubMed

Crossref

Use of GenMAPP and MAPPFinder to analyse pathways involved in chickens infected with the protozoan parasite Eimeria

Springer - Publisher Connector

BioMed CentralBMC Proceedings
ssOpen AcceResearch
Use of GenMAPP and MAPPFinder to analyse pathways involved in 
chickens infected with the protozoan parasite Eimeria
Dennis Prickett* and Michael Watson
Address: Bioinformatics Group, Institute for Animal Health (IAH), Compton, Newbury, RG20 7NN, UK
Email: Dennis Prickett* - dennis.prickett@bbsrc.ac.uk; Michael Watson - michael.watson@bbsrc.ac.uk
* Corresponding author 
Abstract
Background: Microarrays allow genome-wide assays of gene expression. There is a need for user-
friendly software to visualise and analyse these data. Analysing microarray data in the context of
biological pathways is now common, and several tools exist.
Results: We describe the use of MAPPFinder, a component of GenMAPP to characterise the
biological pathways affected in chickens infected with the protozoan parasite Eimeria. Several
pathways were significantly affected based on the unadjusted p-value, including several immune-
system pathways.
Conclusion: GenMAPP/MAPPFinder provides a means to rapidly visualise pathways affected in
microarray studies. However, it relies on good genome annotation and having genes reliably linked
to pathway objects. We show that GenMAPP/MAPPFinder can produce useful results, and as the
annotation of the chicken genome improves, so will the level of information gained.
Background
Microarrays provide information on the expression of
many thousands of transcripts in a single assay. The gen-
eration of such data is rapid and increasingly cost-effec-
tive. Pathway analysis tools can be used to connect gene
expression data from microarrays with existing biological
pathways by using specific database identifiers that link
reporters with elements in the pathways. This provides a
biological context to visualise the results and can be useful
for generating testable hypotheses.
There are a number of public sources of pathway informa-
tion, such as KEGG [1], MetaCyc [2], Reactome [3] and
WikiPathways [4]. Software systems that can analyse
quantitative data based on these resources include Gen-
MAPP [5], Cytoscape [6], PathVisio [7] and KegArray [8],
as well as online tools such as those at Reactome and
FatiGO [9].
In this study we applied pathway analysis to identify path-
ways involved in Eimeria infected chickens. Eimeria is an
obligate intracellular protozoan parasite of chickens of
economic importance to the poultry industry [10]. To
identify affected pathways we used the software package
MAPPFinder [11], a component of the free pathways anal-
ysis tool, GenMapp, to identify pathways involved in
from EADGENE and SABRE Post-analyses Workshop
Lelystad, The Netherlands. 12–14 November 2008
Published: 16 July 2009
BMC Proceedings 2009, 3(Suppl 4):S7 doi:10.1186/1753-6561-3-S4-S7
<supplement> <title> EADGENE and SABRE Post-analyses Workshop </title> <editor>Dirk-Jan de Koning</editor> <sponsor> <note>The publication of these proceedings was supported by the EC-funded Network of Excellence EADGENE (EC contract number FOOD-CT-2004-506416).</note> </sponsor> <note>Proceedings</note> <url>http://www.biomedcentral.com/content/pdf/1753-6561-3-S4-info.pdf</url> </supplement>
This article is available from: http://www.biomedcentral.com/1753-6561/3/S4/S7
© 2009 Prickett and Watson; licensee BioMed Central Ltd. 
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 4
(page number not for citation purposes)
BMC Proceedings 2009, 3(Suppl 4):S7 http://www.biomedcentral.com/1753-6561/3/S4/S7Eimeria infected chickens. This paper is part of a workshop
[12] with the aim to present and compare several different
methods for the post analysis of microarray data, the
results of which have been published in conjunction with
this paper [13].
Results
From 20,465 unique probes on the array, 12,038 unique
Ensembl genes were identified. Of those, 1175 genes
(9.8%) could be mapped to the 85 inferred chicken path-
ways available through GenMAPP.
The distribution of the ratio of genes mapped to total
genes in the pathway is shown in Figure 1 and has a mean
of 0.67. This shows that in most cases the majority of the
entities in a pathway are represented in the data by at least
one gene.
Using an adjusted p-value cut-off of 0.05, two pathways,
"ribosomal proteins" (PM8 and MA8) and small ligand
GPCRs (PM8), were significantly affected. Using an unad-
justed p-value cut-off of 0.05, 18 pathways were signifi-
cantly affected in one or more experiments (Figure 2).
Monoamine GPCRs appear consistently upregulated and
small-ligand GPCRs consistently downregulated. The IL2,
IL4 and IL7 pathways are upregulated in MM24. The IL3
pathway is upregulated in PM8 but is not so during subse-
quent infections. The TGF beta signalling pathways is
upregulated in MM8 and MA8, but not PA8 nor MM24.
The ribosomal proteins pathway is both up- and down-
regulated in all experiments.
Discussion
We were able to visualise the effects of an Eimeria infec-
tion on chickens using the pathway analysis tool MAPP-
Finder. Using the adjusted p value the results of the
pathway analysis were inconclusive, however using the
unadjusted p value we were able to note effects in several
different pathways. The biological interpretation of these
results were compared with other methods and are pre-
sented in a summary paper published in conjunction with
this paper [13].
We included data using unadjusted p-values as the lack of
significant results using the adjusted p-values results from
a lack of power when performing the statistical test for
over-representation. For example, when looking at the
Ribosomal Proteins pathway, there are 56 objects in the
pathway, with 42 being represented by probes on our
array. However, at PM8, only 8 of those genes matched
Distribution of pathway coverage in GenMAPPFigure 1
Distribution of pathway coverage in GenMAPP. The 
histogram shows the log2 of the ratio of number of genes 
mapped to total number of genes on the pathway. The 
majority of pathways have a value greater than 0.5, which 
represents a figure of 50% of genes in the pathway are 
mapped to probes on the array.
Up- and down- regulated pathwaysFigure 2
Up- and down- regulated pathways. Heatmap of up- and 
down- regulated pathways using an unadjusted p-value cut-off 
of p <= 0.05. Red signifies up-regulation, green down-regula-
tion and yellow indicates the pathway was both up- and 
down- regulated. White indicates no significance for that 
pathway in that experiment.Page 2 of 4
(page number not for citation purposes)
BMC Proceedings 2009, 3(Suppl 4):S7 http://www.biomedcentral.com/1753-6561/3/S4/S7the criteria. This was typical throughout the results, with
only a small fraction of the genes in the pathway fitting
the criteria, thus hugely reducing the power of the analy-
sis. It is possible that we must conclude from this that
nothing of significance is occurring in our experiment,
however, this seems unlikely given that it is well known
that Eimeria induces an immune response in chickens. In
the light of this knowledge, the weak results from the
analyses would appear to be due to a poor mapping of
genes to pathways combined with the problem that we are
measuring transcription. An increase or decrease in tran-
scription is only one way in which a cell can activate cer-
tain pathways, with others being post-transcriptional
regulation of transcripts or proteins, post-translational
modification of proteins, and biochemical activation of
proteins, none of which are measured by microarrays.
The quality of pathway analysis could be increased by
improvements in the quality of annotation of the chicken
genome, in microarray design, and in the pathways used
for analysis. Pathway analysis was based on less than 10%
of the genes that could be assigned an Ensembl gene id.
The microarray used in this study was designed in 2005
and since then there has been an additional chicken
genome assembly and 20 additional versions of the
Ensembl database. Microarrays based on current assem-
blies may increase the coverage of the pathways. Addition-
ally the inferred pathways for chickens have fewer genes
represented on the pathways than human GenMapp path-
ways. Improvements in the pathway mapps would also
improve the quality of pathway analysis.
Conclusion
In this study we were able to identify several pathways
affected in chickens infected with the protozoan parasite
Eimeria using GenMapp/MAPPFinder. This demonstrates
that this pathway analysis tool could be useful for micro-
array experiments using chickens. Further improvements
in annotation, microarray design, and pathway design
would help to enhance the ability to use pathway analysis
to understand biological processes.
Materials and methods
Microarray dataset
The microarray used in this study was the Arkgenomics
chicken 20 K oligo microarray [14] which consists of
20,460 probes annotated using the IMAD system [15].
This system uses NCBI BLAST to match microarray probes
to Ensembl transcripts, from which Ensembl gene ids
were derived.
Experiment
Two-week-old chickens infected with Eimeria maxima
were challenged two weeks later either with Eimeria
maxima (MM), Eimeria acervulina (MA), or PBS (PM).
Samples were taken for analysis at 8 hours (MM8, MA8,
and PM8) and 24 hours (MM24) after the challenge. This
allows us to analyse the differences between a primary and
secondary response (MM8/PM8), differences due to a
challenge with a different species (MM8/MA8), and
changes over time (MM8/MM24).
MAPPFinder Analysis
For the characterisation of the biological processes
affected in this study, we used GenMAPP 2.1 and MAPP-
Finder 2.1 to produce lists of significantly regulated path-
way [5,11,16]. For the analysis of chicken microarray data,
GenMAPP has 85 inferred mapps based on human path-
ways. Values used in this study were normalised log-ratios
obtained by lowess normalisation. MappFinder analysis
of these datasets used two criteria, either an increase (=>
1.5) or a decrease <= 1.5) in gene expression to generate
lists of significantly affected pathways. MappFinder anal-
ysis provides two p values, one using the Westfall-Young
adjustment calculation for the family-wise error rate for
multiple testing and the unadjusted p value [11]. A path-
way was defined as significantly affected if the p value <=
0.05
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
DP carried out the analysis and assisted in the writing of
the paper. MW advised on the analysis and assisted in the
writing of this paper.
Acknowledgements
This work was supported by the EADGENE network of excellence (EU 
Contract No. FOODCT-2004-506416), and by the core strategic grant of 
the Institute for Animal Health, provided by the BBSRC.
This article has been published as part of BMC Proceedings Volume 3 Sup-
plement 4, 2009: EADGENE and SABRE Post-analyses Workshop. The full 
contents of the supplement are available online at http://www.biomedcen 
tral.com/1753-6561/3?issue=S4.
References
1. Kanehisa M, et al.: KEGG for linking genomes to life and the
environment. Nucleic Acids Res 2008:D480-484.
2. Caspi R, et al.: The MetaCyc Database of metabolic pathways
and enzymes and the BioCyc collection of Pathway/Genome
Databases. Nucleic Acids Res 2008:D623-631.
3. Matthews L, et al.: Reactome knowledgebase of human biolog-
ical pathways and processes. Nucleic Acids Res 2009:D619-622.
4. Pico AR, et al.: WikiPathways: pathway editing for the people.
PLoS Biol 2008, 6(7):e184.
5. Salomonis N, et al.: GenMAPP 2: new features and resources
for pathway analysis. BMC Bioinformatics 2007, 8:217.
6. Yeung N, et al.: Exploring biological networks with Cytoscape
software. Curr Protoc Bioinformatics 2008, Chapter 8(Unit 8):13.
7. van Iersel M, et al.: Presenting and exploring biological path-
ways with PathVisio. BMC Bioinformatics 2008, 9:399.
8. Okuda S, et al.: KEGG Atlas mapping for global analysis of met-
abolic pathways. Nucleic Acids Res 2008:W423-426.Page 3 of 4
(page number not for citation purposes)
BMC Proceedings 2009, 3(Suppl 4):S7 http://www.biomedcentral.com/1753-6561/3/S4/S7Publish with BioMed Central and every 
scientist can read your work free of charge
"BioMed Central will be the most significant development for 
disseminating the results of biomedical research in our lifetime."
Sir Paul Nurse, Cancer Research UK
Your research papers will be:
available free of charge to the entire biomedical community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central 
yours — you keep the copyright
Submit your manuscript here:
http://www.biomedcentral.com/info/publishing_adv.asp
BioMedcentral
9. Al-Shahrour F, Diaz-Uriarte R, Dopazo J: FatiGO: a web tool for
finding significant associations of Gene Ontology terms with
groups of genes. Bioinformatics 2004, 20(4):578-80.
10. Williams RB: Tracing the emergence of drug-resistance in coc-
cidia (Eimeria spp.) of commercial broiler flocks medicated
with decoquinate for the first time in the United Kingdom.
Vet Parasitol 2006, 135(1):1-14.
11. Doniger SW, et al.: MAPPFinder: using Gene Ontology and
GenMAPP to create a global gene-expression profile from
microarray data. Genome Biol 2003, 4(1):R7.
12. EADGENE Annotation Workshop [http://www.eadgene.inf-
NewsandEvents/EADGENEEvents/EADGENEandSABREPostan
alysesWorkshop/AnnotationWorkshopResults/tabid/345/
Default.aspx]
13. Hedegaard J, et al.: Methods for interpreting lists of affected
genes obtained in a DNA microarray experiment . BMC Pro-
ceedings 2009, 3(Suppl 4):S5.
14. Arkgenomics [http://www.arkgenomics.org/index.php]
15. Prickett MD, Watson M: IMAD: flexible annotation of microar-
ray sequences. BMC Proceedings 2009, 3(Suppl 4):S2.
16. GenMAPP [http://www.genmapp.org]Page 4 of 4
(page number not for citation purposes)

Use of GenMAPP and MAPPFinder to analyse pathways involved in chickens infected with the protozoan parasite 

Background: Microarrays allow genome-wide assays of gene expression. There is a need for userfriendlysoftware to visualise and analyse these data. Analysing microarray data in the context ofbiological pathways is now common, and several tools exist.Results: We describe the use of MAPPFinder, a component of GenMAPP to characterise thebiological pathways affected in chickens infected with the protozoan parasite Eimeria. Severalpathways were significantly affected based on the unadjusted p-value, including several immunesystempathways.Conclusion: GenMAPP/MAPPFinder provides a means to rapidly visualise pathways affected inmicroarray studies. However, it relies on good genome annotation and having genes reliably linkedto pathway objects. We show that GenMAPP/MAPPFinder can produce useful results, and as theannotation of the chicken genome improves, so will the level of information gained

Prickett, D.

Watson, Mick

Edinburgh Research Explorer

Edinburgh Research Explorer Use of GenMAPP and MAPPFinder to analyse pathways involvedin chickens infected with the protozoan parasite EimeriaCitation for published version:Prickett, D & Watson, M 2009, 'Use of GenMAPP and MAPPFinder to analyse pathways involved inchickens infected with the protozoan parasite Eimeria' BMC Proceedings, vol. 3, no. Suppl 4, S7. DOI:10.1186/1753-6561-3-S4-S7Digital Object Identifier (DOI):10.1186/1753-6561-3-S4-S7Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordPublished In:BMC ProceedingsPublisher Rights Statement:This is an open access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/2.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019BioMed CentralBMC ProceedingsssOpen AcceResearchUse of GenMAPP and MAPPFinder to analyse pathways involved in chickens infected with the protozoan parasite EimeriaDennis Prickett* and Michael WatsonAddress: Bioinformatics Group, Institute for Animal Health (IAH), Compton, Newbury, RG20 7NN, UKEmail: Dennis Prickett* - dennis.prickett@bbsrc.ac.uk; Michael Watson - michael.watson@bbsrc.ac.uk* Corresponding author AbstractBackground: Microarrays allow genome-wide assays of gene expression. There is a need for user-friendly software to visualise and analyse these data. Analysing microarray data in the context ofbiological pathways is now common, and several tools exist.Results: We describe the use of MAPPFinder, a component of GenMAPP to characterise thebiological pathways affected in chickens infected with the protozoan parasite Eimeria. Severalpathways were significantly affected based on the unadjusted p-value, including several immune-system pathways.Conclusion: GenMAPP/MAPPFinder provides a means to rapidly visualise pathways affected inmicroarray studies. However, it relies on good genome annotation and having genes reliably linkedto pathway objects. We show that GenMAPP/MAPPFinder can produce useful results, and as theannotation of the chicken genome improves, so will the level of information gained.BackgroundMicroarrays provide information on the expression ofmany thousands of transcripts in a single assay. The gen-eration of such data is rapid and increasingly cost-effec-tive. Pathway analysis tools can be used to connect geneexpression data from microarrays with existing biologicalpathways by using specific database identifiers that linkreporters with elements in the pathways. This provides abiological context to visualise the results and can be usefulfor generating testable hypotheses.There are a number of public sources of pathway informa-tion, such as KEGG [1], MetaCyc [2], Reactome [3] andWikiPathways [4]. Software systems that can analysequantitative data based on these resources include Gen-MAPP [5], Cytoscape [6], PathVisio [7] and KegArray [8],as well as online tools such as those at Reactome andFatiGO [9].In this study we applied pathway analysis to identify path-ways involved in Eimeria infected chickens. Eimeria is anobligate intracellular protozoan parasite of chickens ofeconomic importance to the poultry industry [10]. Toidentify affected pathways we used the software packageMAPPFinder [11], a component of the free pathways anal-ysis tool, GenMapp, to identify pathways involved infrom EADGENE and SABRE Post-analyses WorkshopLelystad, The Netherlands. 12–14 November 2008Published: 16 July 2009BMC Proceedings 2009, 3(Suppl 4):S7 doi:10.1186/1753-6561-3-S4-S7<supplement> <title> EADGENE and SABRE Post-analyses Workshop </title> <editor>Dirk-Jan de Koning</editor> <sponsor> <note>The publication of these proceedings was supported by the EC-funded Network of Excellence EADGENE (EC contract number FOOD-CT-2004-506416).</note> </sponsor> <note>Proceedings</note> <url>http://www.biomedcentral.com/content/pdf/1753-6561-3-S4-info.pdf</url> </supplement>This article is available from: http://www.biomedcentral.com/1753-6561/3/S4/S7© 2009 Prickett and Watson; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 4(page number not for citation purposes)BMC Proceedings 2009, 3(Suppl 4):S7 http://www.biomedcentral.com/1753-6561/3/S4/S7Eimeria infected chickens. This paper is part of a workshop[12] with the aim to present and compare several differentmethods for the post analysis of microarray data, theresults of which have been published in conjunction withthis paper [13].ResultsFrom 20,465 unique probes on the array, 12,038 uniqueEnsembl genes were identified. Of those, 1175 genes(9.8%) could be mapped to the 85 inferred chicken path-ways available through GenMAPP.The distribution of the ratio of genes mapped to totalgenes in the pathway is shown in Figure 1 and has a meanof 0.67. This shows that in most cases the majority of theentities in a pathway are represented in the data by at leastone gene.Using an adjusted p-value cut-off of 0.05, two pathways,"ribosomal proteins" (PM8 and MA8) and small ligandGPCRs (PM8), were significantly affected. Using an unad-justed p-value cut-off of 0.05, 18 pathways were signifi-cantly affected in one or more experiments (Figure 2).Monoamine GPCRs appear consistently upregulated andsmall-ligand GPCRs consistently downregulated. The IL2,IL4 and IL7 pathways are upregulated in MM24. The IL3pathway is upregulated in PM8 but is not so during subse-quent infections. The TGF beta signalling pathways isupregulated in MM8 and MA8, but not PA8 nor MM24.The ribosomal proteins pathway is both up- and down-regulated in all experiments.DiscussionWe were able to visualise the effects of an Eimeria infec-tion on chickens using the pathway analysis tool MAPP-Finder. Using the adjusted p value the results of thepathway analysis were inconclusive, however using theunadjusted p value we were able to note effects in severaldifferent pathways. The biological interpretation of theseresults were compared with other methods and are pre-sented in a summary paper published in conjunction withthis paper [13].We included data using unadjusted p-values as the lack ofsignificant results using the adjusted p-values results froma lack of power when performing the statistical test forover-representation. For example, when looking at theRibosomal Proteins pathway, there are 56 objects in thepathway, with 42 being represented by probes on ourarray. However, at PM8, only 8 of those genes matchedDistribution of pathway coverage in GenMAPPFigure 1Distribution of pathway coverage in GenMAPP. The histogram shows the log2 of the ratio of number of genes mapped to total number of genes on the pathway. The majority of pathways have a value greater than 0.5, which represents a figure of 50% of genes in the pathway are mapped to probes on the array.Up- and down- regulated pathwaysFigure 2Up- and down- regulated pathways. Heatmap of up- and down- regulated pathways using an unadjusted p-value cut-off of p <= 0.05. Red signifies up-regulation, green down-regula-tion and yellow indicates the pathway was both up- and down- regulated. White indicates no significance for that pathway in that experiment.Page 2 of 4(page number not for citation purposes)BMC Proceedings 2009, 3(Suppl 4):S7 http://www.biomedcentral.com/1753-6561/3/S4/S7the criteria. This was typical throughout the results, withonly a small fraction of the genes in the pathway fittingthe criteria, thus hugely reducing the power of the analy-sis. It is possible that we must conclude from this thatnothing of significance is occurring in our experiment,however, this seems unlikely given that it is well knownthat Eimeria induces an immune response in chickens. Inthe light of this knowledge, the weak results from theanalyses would appear to be due to a poor mapping ofgenes to pathways combined with the problem that we aremeasuring transcription. An increase or decrease in tran-scription is only one way in which a cell can activate cer-tain pathways, with others being post-transcriptionalregulation of transcripts or proteins, post-translationalmodification of proteins, and biochemical activation ofproteins, none of which are measured by microarrays.The quality of pathway analysis could be increased byimprovements in the quality of annotation of the chickengenome, in microarray design, and in the pathways usedfor analysis. Pathway analysis was based on less than 10%of the genes that could be assigned an Ensembl gene id.The microarray used in this study was designed in 2005and since then there has been an additional chickengenome assembly and 20 additional versions of theEnsembl database. Microarrays based on current assem-blies may increase the coverage of the pathways. Addition-ally the inferred pathways for chickens have fewer genesrepresented on the pathways than human GenMapp path-ways. Improvements in the pathway mapps would alsoimprove the quality of pathway analysis.ConclusionIn this study we were able to identify several pathwaysaffected in chickens infected with the protozoan parasiteEimeria using GenMapp/MAPPFinder. This demonstratesthat this pathway analysis tool could be useful for micro-array experiments using chickens. Further improvementsin annotation, microarray design, and pathway designwould help to enhance the ability to use pathway analysisto understand biological processes.Materials and methodsMicroarray datasetThe microarray used in this study was the Arkgenomicschicken 20 K oligo microarray [14] which consists of20,460 probes annotated using the IMAD system [15].This system uses NCBI BLAST to match microarray probesto Ensembl transcripts, from which Ensembl gene idswere derived.ExperimentTwo-week-old chickens infected with Eimeria maximawere challenged two weeks later either with Eimeriamaxima (MM), Eimeria acervulina (MA), or PBS (PM).Samples were taken for analysis at 8 hours (MM8, MA8,and PM8) and 24 hours (MM24) after the challenge. Thisallows us to analyse the differences between a primary andsecondary response (MM8/PM8), differences due to achallenge with a different species (MM8/MA8), andchanges over time (MM8/MM24).MAPPFinder AnalysisFor the characterisation of the biological processesaffected in this study, we used GenMAPP 2.1 and MAPP-Finder 2.1 to produce lists of significantly regulated path-way [5,11,16]. For the analysis of chicken microarray data,GenMAPP has 85 inferred mapps based on human path-ways. Values used in this study were normalised log-ratiosobtained by lowess normalisation. MappFinder analysisof these datasets used two criteria, either an increase (=>1.5) or a decrease <= 1.5) in gene expression to generatelists of significantly affected pathways. MappFinder anal-ysis provides two p values, one using the Westfall-Youngadjustment calculation for the family-wise error rate formultiple testing and the unadjusted p value [11]. A path-way was defined as significantly affected if the p value <=0.05Competing interestsThe authors declare that they have no competing interests.Authors' contributionsDP carried out the analysis and assisted in the writing ofthe paper. MW advised on the analysis and assisted in thewriting of this paper.AcknowledgementsThis work was supported by the EADGENE network of excellence (EU Contract No. FOODCT-2004-506416), and by the core strategic grant of the Institute for Animal Health, provided by the BBSRC.This article has been published as part of BMC Proceedings Volume 3 Sup-plement 4, 2009: EADGENE and SABRE Post-analyses Workshop. The full contents of the supplement are available online at http://www.biomedcen tral.com/1753-6561/3?issue=S4.References1. Kanehisa M, et al.: KEGG for linking genomes to life and theenvironment. Nucleic Acids Res 2008:D480-484.2. Caspi R, et al.: The MetaCyc Database of metabolic pathwaysand enzymes and the BioCyc collection of Pathway/GenomeDatabases. Nucleic Acids Res 2008:D623-631.3. Matthews L, et al.: Reactome knowledgebase of human biolog-ical pathways and processes. Nucleic Acids Res 2009:D619-622.4. Pico AR, et al.: WikiPathways: pathway editing for the people.PLoS Biol 2008, 6(7):e184.5. Salomonis N, et al.: GenMAPP 2: new features and resourcesfor pathway analysis. BMC Bioinformatics 2007, 8:217.6. Yeung N, et al.: Exploring biological networks with Cytoscapesoftware. Curr Protoc Bioinformatics 2008, Chapter 8(Unit 8):13.7. van Iersel M, et al.: Presenting and exploring biological path-ways with PathVisio. BMC Bioinformatics 2008, 9:399.8. Okuda S, et al.: KEGG Atlas mapping for global analysis of met-abolic pathways. Nucleic Acids Res 2008:W423-426.Page 3 of 4(page number not for citation purposes)BMC Proceedings 2009, 3(Suppl 4):S7 http://www.biomedcentral.com/1753-6561/3/S4/S7Publish with BioMed Central and every scientist can read your work free of charge"BioMed Central will be the most significant development for disseminating the results of biomedical research in our lifetime."Sir Paul Nurse, Cancer Research UKYour research papers will be:available free of charge to the entire biomedical communitypeer reviewed and published immediately upon acceptancecited in PubMed and archived on PubMed Central yours — you keep the copyrightSubmit your manuscript here:http://www.biomedcentral.com/info/publishing_adv.aspBioMedcentral9. Al-Shahrour F, Diaz-Uriarte R, Dopazo J: FatiGO: a web tool forfinding significant associations of Gene Ontology terms withgroups of genes. Bioinformatics 2004, 20(4):578-80.10. Williams RB: Tracing the emergence of drug-resistance in coc-cidia (Eimeria spp.) of commercial broiler flocks medicatedwith decoquinate for the first time in the United Kingdom.Vet Parasitol 2006, 135(1):1-14.11. Doniger SW, et al.: MAPPFinder: using Gene Ontology andGenMAPP to create a global gene-expression profile frommicroarray data. Genome Biol 2003, 4(1):R7.12. EADGENE Annotation Workshop [http://www.eadgene.inf-NewsandEvents/EADGENEEvents/EADGENEandSABREPostanalysesWorkshop/AnnotationWorkshopResults/tabid/345/Default.aspx]13. Hedegaard J, et al.: Methods for interpreting lists of affectedgenes obtained in a DNA microarray experiment . BMC Pro-ceedings 2009, 3(Suppl 4):S5.14. Arkgenomics [http://www.arkgenomics.org/index.php]15. Prickett MD, Watson M: IMAD: flexible annotation of microar-ray sequences. BMC Proceedings 2009, 3(Suppl 4):S2.16. GenMAPP [http://www.genmapp.org]Page 4 of 4(page number not for citation purposes)

et al.: GenMAPP 2: new features and resources for pathway analysis.

et al.: MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol

et al.: Reactome knowledgebase of human biological pathways and processes.

FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics

IMAD: flexible annotation of microarray sequences.

KEGG for linking genomes to life and the environment.

Methods for interpreting lists of affected genes obtained in a DNA microarray experiment .

Presenting and exploring biological pathways with PathVisio.

The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases.

Tracing the emergence of drug-resistance in coccidia (Eimeria spp.) of commercial broiler flocks medicated with decoquinate for the first time in the United Kingdom. Vet Parasitol

WikiPathways: pathway editing for the people. PLoS Biol

https://www.pure.ed.ac.uk/ws/files/7837697/Use_of_GenMAPP_and_MAPPFinder_to_analyse_pathways_involved_in_chickens_infected_with_the_protozoan_parasite_Eimeria.pdf

Use of GenMAPP and MAPPFinder to analyse pathways involved in chickens infected with the protozoan parasite Eimeria

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