Optimization of de novo transcriptome assembly from next-generation sequencing data

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1、Downloadedfromgenome.cshlp.orgonNovember17,2012-PublishedbyColdSpringHarborLaboratoryPressOptimizationofdenovotranscriptomeassemblyfromnext-generationsequencingdataYannSurget-GrobaandJuanI.Montoya-BurgosGenomeRes.201020:1432-1440originallypublishedonlineAugust6,2010Acces

2、sthemostrecentversionatdoi:10.1101/gr.103846.109Supplementalhttp://genome.cshlp.org/content/suppl/2010/08/06/gr.103846.109.DC1.htmlMaterialReferencesThisarticlecites48articles,20ofwhichcanbeaccessedfreeat:http://genome.cshlp.org/content/20/10/1432.full.html#ref-list-1Art

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5、hearticleorclickhereTosubscribetoGenomeResearchgoto:http://genome.cshlp.org/subscriptions©2010,PublishedbyColdSpringHarborLaboratoryPressDownloadedfromgenome.cshlp.orgonNovember17,2012-PublishedbyColdSpringHarborLaboratoryPressMethodOptimizationofdenovotranscriptomeassem

6、blyfromnext-generationsequencingdata1YannSurget-GrobaandJuanI.Montoya-BurgosDepartmentofZoologyandAnimalBiology,UniversityofGeneva,1211Geneva4,SwitzerlandTranscriptomeanalysishasimportantapplicationsinmanybiologicalfields.However,assemblingatranscriptomewithoutaknownrefe

7、renceremainsachallengingtaskrequiringalgorithmicimprovements.Wepresenttwomethodsforsubstantiallyimprovingtranscriptomedenovoassembly.Thefirstmethodreliesontheobservationthattheuseofasinglek-merlengthbycurrentdenovoassemblersissuboptimaltoassembletranscriptomeswheretheseq

8、uencecoverageoftranscriptsishighlyheterogeneous.WepresenttheMultiple-kmethodinwhichvariousk-merlengthsa