vsDNA一般在整個(gè)組上具有近似的深度，而轉(zhuǎn)錄組的深度可能會(huì)有若干數(shù)量級(jí)組測雙鏈，而RNA-seq可以是鏈特異性同一個(gè)的不同轉(zhuǎn)錄本會(huì)共享外顯子，這就為 -first?method:firstalignallthereadstoareferenceandthenmergesequenceswith tools:Scripture, umsensitivity,butdependoncorrectread-to-referencealignment,thereferencegenomeisassembly-firsttools:??:donotrequireanyread-referencealignments,importantwhent omicsequenceisnotavailable,isgapped,highlyfragmentedorsubstantiallyaltered,asincancercellsTrinity,developedattheBroadInstitutetheHebrewUniversityofJerusalem(耶路撒冷希伯萊大學(xué)representsanovelmethodfortheefficientandrobustdenovoreconstrucionoftranscriptomesfromRNA-seqdataTrinitycombinesthreeindependentsoftwaremodules:Inchworm,Chrysalis,andButterfly,appliedsequentiallytoprecesslargevolumesofRNA-seqreads. 分 “25-mersworkverywellforbothhighlyandexpresseddeBruijn--seqType--JM(JellyfishMemory)(~1GRAMper1Mpairs?k-mersthathaveidenticalk-1prefixes,differingonlyattheirterminalnumleotide,andremovingthosek-mersthatare<5%abundantascomparedtothemosthighlyabundantk-merofthegroup?error-containingkmerswillbegreatlyenrichedwithinthelowabundancekmercountsandcanbeexcludedwithminimalloss--min_kmer_cov選擇seedk-標(biāo)準(zhǔn)：1.coverage最高；2.高復(fù)雜性；3.在read中至少出現(xiàn)2次無回文Seedk-mer延 迭代到?jīng)]有符合條件的kmer,每使用一個(gè)kmer，將其?thesequenceyieldedfromthebidirectionalseedmerextensionisreportedasadrafttranscript重復(fù)seedselectionbidirectionalk-merextension直到k-mer字典耗盡With agek-mercoverageof?Lengthatleast48(2*(k-InchwormdoesaverygoodjobatreconstructingtranscriptsfromRNA-seqdata,butsinceitleveragesonlyuniquek-mersforcontigconstruction,itcanonlyreportthepartsofalternativelysplicedisoformsthatareunique.SubsequentTrinitystepsreconstructthefulllengthalternativelysplicedtranscripts.由cnti的構(gòu)法各ti不可能共kmcgs源 等情，rsis將那些有 的gs聚，構(gòu)成cntsct為變剪切isf源 合groupscontigsintoconnectedcommonk-1basesperfect?Readsupport:therearenreadsthatspanacrossapotentialjunction(welds)andextendperfectmatchesby(k-1)/2basesoneachside;--min_glue(default:buildadeBruijngraphforeach?K-1mer→Kmer→reads?thereadsarethenmappedtocomponentsbyselectingthecomponentthatsharesthemostk-1-merswiththeread.Chrysalisalsocountsallk-mers(inassignedreads)andstoresthemas'edgeweight'(fordeBruijnconstructedinlaststep)toindicatetheirsupportinthereadset.ComponentswithlessthanaminimumnumberofnodesarediscardedButterflyresolvesalternativelysplicedandparalogoustranscriptsgraphmergingconsecutivenodesinlinearpathsinthedeBruijngraphtoformnodesthatrepresentlongersequencespruningedgesthatrepresentminordeviations(supportedbycomparativelyfewreads),whichlikelycorrespondtosequencingerrorspathtracesthepathsthatreadsandpairsofreadstakewithinthegraphandreportsthemostprobabletranscriptsasafastafile.InchwormassemblestheRNA-seqdataintouniquesequencesoftranscripts,oftengeneratingfull-lengthtranscriptsforadominantisoform,butthenreportsjusttheuniqueportionsofalternativelysplicedtranscripts.ChrysalisclusterstheInchwormcontigsintoclustersandconstructscompletedeBruijngraphsforeachcluster.Eachclusterrepresentsthefulltranscriptonalcomplexityforagivengene(orsetsofgenesthatsharesequencesincommon).Chrysalisthenpartitionsthefullreadsetamongthesedisjointgraphs.Butterflythenprocessestheindividualgraphsinparallel,tracingthepathsthatreadsandpairsofreadstakewithinthegraph,ultima yreportingfull-lengthtranscriptsforalternativelysplicedisoforms,andteasingaparttranscriptsthatcorrespondstoparalogousgenes.其他參?Ifdataarederivedfromagene-densecompactgenome,suchasfromfungalgenomes,wheretranscriptsmayoftenoverlapinUTRregions.Trinitywillexaminetheconsistencyofreadpairingsandfragmenttranscriptsatpositionsthathavelittleread-pairingsupport.?PairedRF:firstread(/1)offragmentpairissequencedasantisense(reverse(R)),andsecondread(/2)isinthesensestrand(forward(F));typicalofsequencingmethod.theFR:firstread(/1)offragmentpairissequencedassense(forward),andsecondread(/2)isintheantisense?Unpaired(single)F:thesinglereadisinthesense(forward)R:thesinglereadisintheantisense(reverse)其他問>comp2_c0_seq1len=2364path=[0:0-588:588-10761146:1077-comp2:sequenceisderivedfromcomponent#c0:sequencealsocorrespondstoponent#0(duringgraphcompactionandpruning,somecomponentsarepartitionedinto seq1:sequencecountfromchrysaliscomponent2, ponentzero.Ifthis yieldsmultiplesequences,thesewillhavedifferentseqnumbers.?len:lengthofthetranscript單獨(dú)拼接和混合拼 eralideaistocombineallyourrna-seqdataandgenerateoneassembly.Then,toalignthereadsfromthedifferentsamplessepara totheTrinityassemblies,computingabundanceestimatesbasedoneachreadset.Finally,youdothedifferentialexpression ysistoidentifythoseTrinityassembliesthatareofinterest.Thisisalloutlinedinthedownstream sectionofthe ation.”--BrianJ.WhatcomputingresourcesareIdeally,youwillhaveaccesstoalarge-memoryserver,having~1GofRAMper1Mreadstobeassembled(butoften,muchlessmemorymayberequired).5GBasespersample=50Mreads=50GHowlongshouldthisItdependsonanumberoffactorsincludingthenumberofreadstobeassembledandthecomplexityofthetranscriptgraphs.Theassemblyfromstarttofinishcantakeanywherefrom~1/2hourto1hourpermillionreads.5Gbasespersample=50Mreads=50一些--jaccard_clip(se