• Aucun résultat trouvé

The South Green portal: A comprehensive resource for tropical and Mediterranean crop genomics

N/A
N/A
Protected

Academic year: 2021

Partager "The South Green portal: A comprehensive resource for tropical and Mediterranean crop genomics"

Copied!
4
0
0

Texte intégral

(1)

CurrentPlantBiology7–8(2016)6–9

ContentslistsavailableatScienceDirect

Current

Plant

Biology

j ourn a l h o m e pa g e :w w w . e l s e v i e r . c o m / l o c a t e / c p b

The

South

Green

portal:

a

comprehensive

resource

for

tropical

and

Mediterranean

crop

genomics

South

Green

collaborators

a,b,c,d,e,f,1

aBioversityInternational,ParcScientifiqueAgropolisII,34397MontpellierCedex5,France bUMRAGAPCIRAD/INRA/SupAgro,AvenueAgropolis,34398MontpellierCedex5,France

CUMRBGPICIRAD/INRA/SupAgro,CampusInternationaldeBaillarguet,34398Montpellier,Cedex5,France dUMRDIADEIRD/UM,AvenueAgropolis,34934MontpellierCedex5,France

eUMRInterTrypCIRAD/IRD,CampusInternationaldeBaillarguet,34398Montpellier,Cedex5,France fUMRIPMEIRD/UM/CIRAD,AvenueAgropolis,34394Montpellier,Cedex5,France

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received28October2016

Receivedinrevisedform3December2016 Accepted3December2016

Keywords: Cropdatabases Genomics Galaxy

NextGenerationSequencing Pipeline

Sequencevariants Workflow

a

b

s

t

r

a

c

t

TheSouthGreenWebportal(http://www.southgreen.fr/)providesaccesstoalargepanelofpublic databases,analyticalworkflowsandbioinformaticsresourcesdedicatedtothegenomicsoftropicaland Mediterraneancrops.Theportalcontainscurrentlyabout20informationsystemsandtoolsandtargets abroadrangeofcropssuchasBanana,Cacao,Cassava,Coconut,Coffee,Grape,RiceandSugarcane.

©2016TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

Analysisandvisualizationofmassivegenomicsdatasetsarean ongoingtrendinplantsciences,especiallyfortropicalcropswhere thissubjectcanhelptotacklechallengeslinkedtohumanactivities andClimateChange:conservationandanalysisofbiodiversity(loss becauseofhumanactivity),foodsecurity(the9billionpeople ques-tion),newusageofplantmaterial,etc.Intherecentyears,because ofsuccessivelylowercostsofgenomicsequencing,alarge num-berofgroupshavedevelopedmassiveresourcesanddatawhich requirehighperformancecomputationalresourcesandnew ana-lyticalapproaches[1,2].

TheSouthGreenportalisanecosystemoftoolsthatwere orig-inallydeveloped as independententities to fulfillthe need for specificprojectsorcrops,buthaveevolvedovertimetogeneric toolstocomprehensivelystudycropgenomics.Thosegenerictools areadaptabletoawiderangeofotherorganisms,especially culti-vated,wildororphanplants.

夽 Thisarticleispartofaspecialissueentitled“Genomicresourcesanddatabases”, publishedinthejournalCurrentPlantBiology7–8,2016.

1 ThelistofparticipantsandtheiraffiliationsareprovidedinAppendixA

2. Specializedresources

Some resources from the South Green portal are dedicated tospecializeddatasets.AmongtheGene-baseddatabases, Green-PhylDB[3]providesaccesstoresourcesforcomparativegenomics andorthologyidentificationinplantgenomes,andOryGenesDB[4]

isadatabasedevelopedforricereversegenetics,andcontaining FSTs (flanking sequence tags) of various mutagens and func-tionalgenomicsdata,collectedfrombothinternationalinsertion collectionsand theliterature.Otherresourcesare− (i) Marker-based databases like TropGeneDB [5] which connects data on molecularmarkers(e.g.SimpleSequenceRepeats,DiversityArrays Technology),Quantitative TraitLoci,geneticandphysicalmaps, phenotypingstudies,andinformationongeneticresources (geo-graphicorigin,parentage,collection),(ii)SNiPlay[6]whichallows queryingbothSNPs(Single-NucleotidePolymorphism)andInDels derivedfromNGS(Next-GenerationSequencing)projects (Whole-Genome Sequencing,Genotyping bySequencing, RNA-Seq) and computing a set of web analytical workflows for the resulting variants(diversity,populationstratification,Genome-Wide Associ-ationStudy),andproposesgraphicalrepresentationsoftheresults, and(iii) Gigwa[7]providingexploration ofverylarge genotyp-ingstudiesbyfilteringthembasedonnotonlyvariantfeatures

http://dx.doi.org/10.1016/j.cpb.2016.12.002

2214-6628/©2016TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4. 0/).

(2)

/CurrentPlantBiology7–8(2016)6–9 7

Fig.1. SouthGreenresourcesandstrategytoprocess,analyzeanddisplayNext-GenerationSequencingdatasetsinthecontextofgenomicvariationstudies.

(SNP,Indels),includingfunctionalannotations,butalsogenotype patterns,inordertoextractsubsetsinvariouspopularexport for-mats.Currentdevelopmentsareonthewayfordatabasesaimed tohost/pathogeninteractions,catalogsofplantpathogensstrains andsoon.Recently,basedonourexperiencewithWebSemantic technologies[8],wedevelopedAgroLD(www.agrold.org)anRDF (ResourceDescriptionFramework)knowledgebasethatconsistsof integrateddatafromavarietyofplantresourcesandontologies.

AcompletesummaryoftheSouthGreendatabaseswiththeir descriptionisavailableathttp://www.southgreen.fr/databases.

3. GenomeHubs

Our participation in several reference genome sequencing projects[9–11] hasledusto develop crop-specificinformation systems,socalledGenome Hubs, tomanagethecorresponding genomeannotationsandlinkeddatasets.Dataavailableareunder differentforms,fromcompletegenomesequencealongwithgene structure, gene product information, metabolicpathways, gene families, transcriptomicassays (ExpressedSequence Tags, RNA-Seq),geneticmarkersaswellasgeneticandphysicalmaps.Several GenomeHubs werereleased: Banana[12], Cassava,Cacao, Cof-fee[13],andothers(e.g.Rice,Magnaporthae)arecurrentlyunder development.GenomeHubsarepoweredbymajorGMOD(Generic ModelOrganismDatabase)components(i.e.Chado,Cmap,Jbrowse,

Tripal,Galaxy,Pathwaytools)andcomplementedbyresourcesand toolsdevelopedwithintheSouthGreenframeworksuchas Green-PhylDB,SNiPlayandTropGeneDB.

4. Workflowanalyses

Targetusersofbioinformaticapplicationsareusuallydivided betweenpeoplewhousecommand-lineandthosewhodonot.Our strategyhasbeentoaddressbothcategoriesbyoffering comple-mentarysolutionstoperformdataanalyses(Fig.1).

4.1. Galaxyworkflows

Galaxy [14] is a web-based service that allows an easy access to the bioinformatic applications and strongly supports reproducibility of analysis steps. Through its graphical inter-face, non-bioinformatician scientists are able to conduct small scale as well as medium range analyses in a user-friendly manner.Inadditiontothegenerictoolsprovidedwiththe stan-dard installation of Galaxy, the South Green Galaxy instance (http://galaxy.southgreen.fr/galaxy/)containsalargecollectionof exclusivetools.TheGalaxyToolShedallowsthesemodulestobe easily sharedwithany otherGalaxy instance. In addition, pre-configuredworkflowsweredesignedforrecurrentanalysesinplant genomicssuchasNGSmapping/cleaning,SNPcallingand

(3)

filter-8 /CurrentPlantBiology7–8(2016)6–9

ing,Genome-WideAssociation Study,basicpopulationgenetics, structuralvariationsandphylogenetics.

4.2. TOGGLE

TOGGLE[15]isacommand-linetooldesignedtoallow biolo-giststocreatelargeworkflowswithoutknowinghowtocodeit (https://github.com/SouthGreenPlatform/).Usingeditable config-urationfiles,itproposesamorein-depthcontrolofcustomizable pipelinesandallowsthemanagementofmanymoresamplesthan Galaxy(ex:massiveresequencingprojects,morethan50samples), aswellasthemanagement oftoolsversions.It canbeusedon widearrayofanalyses,suchasNGSdatacleaning,DNAseq,RNAseq, restrictionsite-associatedDNAsequencing(RADseq)/Genotyping bySequencing(GBS),genomeand transcriptomeassembly,SNP calling,structuralvariationsdetection,andsoon.Thecurrent ver-sioncanbedeployedonlaptop,largemachineaswellasaHPC cluster,usingnormalsystemofDockermachines,andis scheduler-aware.Alreadycustomizedpipelinesarealsoavailableforclassical NGSanalyses.

4.3. HandlingbigdatasetsinRice

Ourgoalwastosetupasimpleframeworktohelpuserstowork efficientlyandeasilywiththehugevolumeofgenomicresources generatedintheRicedatastorebasedonthe3,000ricegenomes

[16]andadditionalresources(i.e.High DensityRiceArray700k SNPsandIRIGIN projecthttp://irigin.org).Galaxymoduleswere implemented toquery efficiently millions of genomic variants, tofilterongenomicannotations, tofilteronlistof individuals, topredict coding effects of genomic variants, and toexport in severalformats (see Rice Variant Analysisin theTool Panel of

http://galaxy.southgreen.fr/galaxy/).Outputscanbeeasilysentto anyGalaxyworkflows.TheequivalentTOGGLEimplementationis alsoontheway.

5. Otherdevelopments

Wedevelopedalargenumberofothercodes,snippets, mod-ulesandpluginsfordatabasemanagements,duplicationdetection andrepresentation, fastafile manipulationand soon.Allthose codes are available under GPLv3 license on the GitHub, at

https://github.com/SouthGreenPlatform.

6. ExamplesofstudiesusingtheSouthGreenplatform

VariousgroupsusedtheSouthGreeninfrastructuretoobtain theirdataandresults,andwereabletopublishhigh-quality bio-logicalinformation,onCoffeegenome[9],Banana[10],Cocoa[11], Africanrice[17]orlargetranscriptomeresources[18].Tools devel-oped for these studies are adaptable to a wide range of other organisms.

7. Conclusion

TheSouthGreenportalisdevelopedbyalocalnetworkof sci-entistsgathering bioinformatics and genomicsskills locatedon theAgropoliscampus fromMontpellierasa jointcollaboration betweenCIRAD,IRD,INRA,SupAgroandBioversityinternational. Based on this strong local community in the field of agricul-ture,foodandbiodiversity,variousbioinformaticsapplicationsand resourcesdedicatedto genomicsoftropical and Mediterranean plantshavebeendeveloped,publishedandprovidedtothe inter-nationalcommunity. Sourcecode canbealso foundonGitHub (https://github.com/SouthGreenPlatform).

Funding

ThisworkwassupportedbyAgropolisFondation/AGROLabex (ID ARCAD 0900-001, ID RTB Multi-Genomes Hub 1403-018), the French Institute of Bioinformatics (IFB), ANR (GNPAnnot, MusaTract, CoffeaSeq,AfriCrop #NR-13-BSV7-0017), CIRAD and IRD (computer infrastructures), the CGIAR Research Programs on Roots, Tubers and Bananas and GRiSP/RICE, IRIGIN/France Génomique(Nationalinfrastructure,fundedaspartof “Investisse-mentd’avenir”programmanagedbyANR#ANR-10-INBS-09),and NUMEVLabex(LandPanToggle#2015-1-030-LARMANDE).

Acknowledgements

WethanktheScientificCommitteeofSouthGreenfortheir help-fuladviceandsuggestions,aswellasourdifferentinstitutionsfor theirsupport.

AppendixA.

Authors’information

Bioversity International: Guignon Valentin; Hueber Yann; RouardMathieu(email:m.rouard@cgiar.org).

UMR AGAP (CIRAD/INRA/SupAgro): Bocs Stephanie; Couvin David;deLamotteFrédéric;DrocGaëtan;DufayardJean-Franc¸ois; ElHassouniNordine;FarcyCédric;GkanogiannisAnestis;Hamelin Chantal;Larivière Delphine; MartinGuillaume;Ortega Enrique; PitollatBertrand;PointetStéphanie;RuizManuel(email:manuel. ruiz@cirad.fr);SarahGautier(email:gautier.sarah@supagro.inra. fr);SummoMarilyne;ThisDominique.

UMRBGPI(CIRAD):RavelSébastien.

UMR DIADE (IRD): Larmande Pierre; Monat Cécile; Sabot Franc¸ois;TandoNdomassi;Tranchant-DubreuilChristine(email:

christine.tranchant@ird.fr).

UMRInterTryp(CIRAD):SempéréGuilhem. UMRIPME(IRD):DereeperAlexis.

References

[1]O.Spjuth,E.Bongcam-Rudloff,J.Dahlberg,M.Dahlo,A.Kallio,L.Pireddu,F. Vezzi,E.Korpelainen,Recommendationsone-infrastructuresfor next-generationsequencing,GigaScience5(2016)26.

[2]R.R.Gullapalli,K.V.Desai,L.Santana-Santos,J.A.Kant,M.J.Becich,Next generationsequencinginclinicalmedicine:Challengesandlessonsfor pathologyandbiomedicalinformatics,JPatholInform3(2012)40.

[3]M.Rouard,V.Guignon,C.Aluome,M.-A.Laporte,G.Droc,C.Walde,C.M. Zmasek,C.Périn,M.G.Conte,GreenPhylDBv2.0:comparativeandfunctional genomicsinplants,NucleicAcidsRes.39(2011)D1095–1102.

[4]G.Droc,C.Périn,S.Fromentin,P.Larmande,OryGenesDB2008update: databaseinteroperabilityforfunctionalgenomicsofrice,NucleicAcidsRes. 37(2009)D992–995.

[5]C.Hamelin,G.Sempere,V.Jouffe,M.Ruiz,TropGeneDB,themulti-tropical cropinformationsystemupdatedandextended,NucleicAcidsRes.41(2013) D1172–1175.

[6]A.Dereeper,F.Homa,G.Andres,G.Sempere,G.Sarah,Y.Hueber,J.-F. Dufayard,M.Ruiz,SNiPlay3:aweb-basedapplicationforexplorationand largescaleanalysesofgenomicvariations,NucleicAcidsRes.43(2015) W295–300.

[7]G.Sempéré,F.Philippe,A.Dereeper,M.Ruiz,G.Sarah,P.Larmande, Gigwa-Genotypeinvestigatorforgenome-wideanalyses,GigaScience5 (2016)25.

[8]J.Wollbrett,P.Larmande,F.deLamotte,M.Ruiz,Clevergenerationofrich SPARQLqueriesfromannotatedrelationalschema:applicationtoSemantic WebServicecreationforbiologicaldatabases,BMCBioinformatics14(2013) 126.

[9]F.Denoeud,L.Carretero-Paulet,A.Dereeper,G.Droc,R.Guyot,M.Pietrella,C. Zheng,A.Alberti,F.Anthony,G.Aprea,etal.,Thecoffeegenomeprovides insightintotheconvergentevolutionofcaffeinebiosynthesis,Science345 (2014)1181–1184.

[10]A.D’Hont,D.AngéliqueD.h.France,A.Jean-Marc,B.Franc-Christophe,C. Franc¸oise,G.Olivier,N.Benjamin,B.Stéphanie,D.Gaëtan,etal.,Thebanana

(4)

/CurrentPlantBiology7–8(2016)6–9 9

(Musaacuminata)genomeandtheevolutionofmonocotyledonousplants, Nature488(2012)213–217.

[11]X.Argout,J.Salse,J.-M.Aury,M.J.Guiltinan,G.Droc,J.Gouzy,M.Allegre,C. Chaparro,T.Legavre,S.N.Maximova,etal.,ThegenomeofTheobromacacao, Nat.Genet.43(2011)101–108.

[12]G.Droc,D.Larivière,V.Guignon,N.Yahiaoui,D.This,O.Garsmeur,A. Dereeper,C.Hamelin,X.Argout,J.-F.Dufayard,etal.,Thebananagenomehub, Database2013(2013)bat035.

[13]A.Dereeper,S.Bocs,M.Rouard,V.Guignon,S.Ravel,C.Tranchant-Dubreuil,V. Poncet,O.Garsmeur,P.Lashermes,G.Droc,Thecoffeegenomehub:a resourceforcoffeegenomes,NucleicAcidsRes.43(2015)D1028–1035.

[14]J.Goecks,A.Nekrutenko,J.Taylor,T.Galaxy,Galaxy:acomprehensive approachforsupportingaccessible,reproducible,andtransparent computationalresearchinthelifesciences,GenomeBiol.11(2010)R86.

[15]C.Monat,C.Tranchant-Dubreuil,A.Kougbeadjo,C.Farcy,E.Ortega-Abboud,S. Amanzougarene,S.Ravel,M.Agbessi,J.Orjuela-Bouniol,M.Summo,etal., TOGGLE:toolboxforgenericNGSanalyses,BMCBioinformatics16(2015)374.

[16]r.g.project,The3000ricegenomesproject,GigaScience3(2014)7.

[17]B.Nabholz,G.Sarah,F.Sabot,M.Ruiz,H.Adam,S.Nidelet,A.Ghesquiere,S. Santoni,J.David,S.Glemin,Transcriptomepopulationgenomicsreveals severebottleneckanddomesticationcostintheAfricanrice(Oryza glaberrima),MolEcol23(2014)2210–2227.

[18]G.Sarah,F.Homa,S.Pointet,S.Contreras,F.Sabot,B.Nabholz,S.Santoni,L. Saune,M.Ardisson,N.Chantret,etal.,Alargesetof26newreference transcriptomesdedicatedtocomparativepopulationgenomicsincropsand wildrelatives,Molecularecologyresources.(2016).

Références

Documents relatifs

Diversity Arrays Technology (DArT) combines the ability to identify various types of DNA polymorphism with the low cost and high throughput platforms DArT offers several-fold gain

Various groups used the South Green infrastructure to obtain their data and results, and were able to publish high-quality biological information, on Coffee genome, Banana,

The most common data stored in TropGeneDB are molecular markers, quantitative trait loci, genetic and physical maps, genetic diversity, phenotypic diversity studies and.. information

Defence Defence-related transcript functions identified according to the KOG classification across the unigenes comprised nine germin/oxalate oxidases (OXOs), three regulators

However, when farmers have to replace lost seed, seed quality can be neither guaranteed nor tested (seed is not “transparent”), and “farmers depend largely on

We analyze how social anthropology, analyzing intermarriage, residence and seed inheritance, can contribute to studies of crop genetic diversity in situ, by

The four platforms provide similar general processes: Network discovery, Transaction creation, Block generation and submission (Mining and Chain con- sensus process) and

In order to enable users to quickly and easily discover datasets, we set up a portal for browsing the dataset. Naturally we set this up as a site that publishes the individual