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Wat bevat die finale (Illumina) RNA-volgorde-biblioteek? ss cdNA of ds cDNA?

Wat bevat die finale (Illumina) RNA-volgorde-biblioteek? ss cdNA of ds cDNA?


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Ek dink die oligo's op Illumina se vloeisel hibridiseer slegs met enkelstrengige cDNA's (sscDNA).

Wanneer ek egter die TruSeq-protokol lees (gegoogle: TruSeq® RNAsample Preparation v2 Guide) by die PCR-stap (AmpPCR, bladsy 41) lyk dit of die laaste siklus (d) net verlenging is.

Daarom, aangesien geen denaturasie (skeiding van dscDNA-stringe) plaasvind nie, behoort die finale biblioteekinvoer na Illumina slegs dscDNA's te bevat wat nie met vloeisel se oligo's hibridiseer nie!

My vraag is dan: wanneer gebeur denaturasie? Binne Illumina? Of mis ek iets?


'N Geoptimaliseerde protokol vir generering en ontleding van Ion Proton-volgorde lees vir RNA-Seq

Vorige studies het bedryfskoste, tyd en ander prestasiemaatstawwe van gewilde volgorde-platforms vergelyk. Omvattende assessering van biblioteekkonstruksie- en ontledingsprotokolle vir Proton-volgordebepalingsplatform bly egter onontgin. In teenstelling met Illumina-volgorde-platforms, is Proton-lesings heterogeen in lengte en kwaliteit. Wanneer opeenvolging van data vanaf verskillende platforms gekombineer word, kan dit lei tot leeswerk met verskillende leeslengtes. Of die prestasie van die algemeen gebruikte sagteware vir die hantering van sulke data bevredigend is, is onbekend.

Resultate

Deur universele menslike verwysings -RNA as die aanvanklike materiaal te gebruik, het RNaseIII en chemiese fragmenteringsmetodes in biblioteekkonstruksie 'n soortgelyke resultaat getoon in die ontdekking van gene en aansluiting en die geskatte akkuraatheid van die uitdrukkingsvlak. Daarteenoor beïnvloed die kwaliteit van die volgorde, leeslengte en die keuse van sagteware die karteringstempo in 'n baie groter mate. Ongeplaasde aligner TMAP behaal die hoogste karteringstempo (97,27 % tot genoom, 86,46 % tot transkriptoom), hoewel 47,83 % van die gekarteerde lesings geknip is. Langlesings kan paradoksaal genoeg kartering in aansluitings verminder. Met die verwysingsaantekeninggids het die karteringstempo van TopHat2 aansienlik toegeneem van 75.79 tot 92.09 %, veral vir lang (& gt150 bp) lesings. Sailfish, 'n op k-mer gebaseerde geenuitdrukkingskwantifiseerder, het baie konsekwente resultate behaal met dié van TaqMan-skikking en die hoogste sensitiwiteit.

Afsluiting

Ons het vir die eerste keer die verwysingstatistieke van biblioteekvoorbereidingsmetodes, geenopsporing en -kwantifisering en aansluitingontdekking vir RNA-Seq deur die Ion Proton-platform verskaf. Chemiese fragmentasie het ewe goed gevaar met die ensiemgebaseerde. Die optimale Ion Proton -volgorde -opsies en ontledingsagteware is geëvalueer.


DNA Sequencing — Illumina biblioteek konstruksiedienste

Namate volgorde-uitset toeneem en eksperimentele skale groei, is die generering van biblioteke vir volgordebepaling dikwels die tempo-beperkende stap. Die Core gebruik vloeistofhanteringsrobotte om monsterhanteringsvariasie te minimaliseer en om vinnige omkeertye te verskaf. Ons biblioteekvoorbereidingsdienste sluit biblioteek -QC, biblioteekkwantifisering en biblioteekpoel in. Al ons biblioteke is met strepieskode (enkelindeks of uniek dubbel-geïndekseer).
Ons bespreek graag die opsies en protokolle wat geskik is vir u spesifieke navorsingsprojekte. Ons berei standaard volgordebiblioteke sowel as baie gespesialiseerde biblioteke voor, en kan u ook help met aangepaste projekte. Ons bied tans:

  • Genomiese DNA-biblioteke (heel-genoom haelgeweer biblioteke)
  • Doelgerigte volgordebepaling: amplicon -volgorde, exome -opname -volgorde, SPE -volgorde
  • Metiel-Seq: WGBS (Hele genoom Bisulfite Seq) en RRBS (Verminderde voorstelling Bisulfite Seq)
  • ChIP-Seq, volgorde van verminderde voorstelling en aangepaste projekte
  • Hoë deurset (HT) biblioteekvoorbereidings
  • Mate-Pair-biblioteke
  • CHIP-sek
  • Verminderde verteenwoordiging biblioteke (bv. vir genotipering deur opeenvolging [GBS] aansoeke)
  • PCR-vrye biblioteke
  • BluePippin/PippinHT biblioteekgrootte -keuse
  • Pasgemaakte biblioteke (bv. Tn-Seq)

Ons gebruik outomatiese biblioteekvoorbereidingstelsels wat tot 96 strepieskode -biblioteke kan genereer met behulp van die Perkin Elmer Sciclone NGS G3 robotte vir konstante kwaliteit en vinnige ommeswaai. Ons kan ook opleiding en toegang tot die robotte verskaf as jy die instrumente self vir grootskaalse projekte wil gebruik.
Ons voer nie DNA-isolasies uit vir Illumina-opeenvolging in ons laboratorium nie, maar wel die PCR-laboratorium in die aangrensende gebou. Kontak hulle gerus.

DNA/RNA monster kwantifisering en suiwerheid

  • Alle monsters moet vergesel wees van toepaslike QC -dokumentasie van die monster (bv. Bioanalyzer -spore of agarose -gelelektroforese -beelde) en Nanodrop 260/280 nm en 260/230 nm absorpsieverhoudingmetings. Dit versnel die proses as QC so vroeg as moontlik in jou laboratorium uitgevoer kan word. Ons is egter bly om die monster -QC vir u teen 'n fooi uit te voer. (kyk op 'n agarosegel) en opgelos in EB buffer, TLE buffer (sien onderaan die bladsy vir buffersamestellings), of water van molekulêre biologie.

Die invoer -DNA- en RNA -hoeveelhede wat hieronder en op hierdie bladsy gespesifiseer word, is van toepassing as die monsters gekwantifiseer word volgens 'n fluorometriese metode (bv. Qubit, PicoGreen, RiboGreen). Fluorometrie bied voordele in presisie en spesifisiteit (bv. DNA -kleurstowwe bind nie aan/meet RNA nie). As 'n spektrofotometer (bv. Nanodrop) gebruik word, stel ons voor dat twee keer die gevraagde hoeveelheid monster ingedien word, aangesien hierdie tipe meting dikwels onbetroubaar is. In elk geval sal monsterhoeveelhede hoër as die minimum vereistes die biblioteekkompleksiteit verbeter. Spektrometerlesings is baie nuttig om die suiwerheid van monsters te bepaal. Vir DNA-monsters die 260/280 -verhouding moet tussen 1.8 en 2.0 wees en die 260/230 -verhouding moet hoër as 2.0 wees. Vir RNA monsters die 260/280 verhouding moet tussen 1,8 en 2,1 wees en die 260/230 verhouding moet hoër as 1,5 wees. Waardes buite hierdie reekse dui kontaminasie aan. Die Real-time PCR Core bied DNS- en RNA-ekstraksiedienste.
Beginmateriaal vir Illumina-biblioteekkonstruksie kan dubbelstrengs (ds) DNA van enige bron wees: genomiese DNA, BAC's, PCR-amplikone, chipmonsters, enige tipe RNA wat in ds cDNA (mRNA, genormaliseerde totale RNA, smRNA's), ens. Hierdie dsDNA word dan gefragmenteer (as dit nie reeds is nie, soos in ChIP). Die gemiddelde fragmentlengte mag nie 600 bp (MiSeq) of 400 bp (HiSeq 4000, NovaSeq) oorskry nie. Die punte word herstel en 'A' gestert, adapters word vasgemaak, grootte word uitgevoer, dan word PCR uitgevoer om die finale biblioteek gereed te maak vir QC en volgorde. Verskillende biblioteektipes kan in die besonderhede verskil (soos PCR-vrye biblioteek).

Sien die uitgebreide voorbeeldvereiste -bladsy en raadpleeg ons algemene vrae vir tegniese vrae

Hoë-deurvoer biblioteekvoorbereidings (HT-verwerking)

Ons bied hoë-deurset volgordebepaling biblioteek voorbereidings vir genomiese DNA biblioteek voorbereidings, sowel as vir RNA-volg biblioteek voorbereidings.
HT verwerking impliseer dat die monsters gereed-vir-gebruik ingedien word en dat ons nie individuele monsters kan vertroetel nie (bv. suiwer of konsentreer dit). Kortom, die geskiktheid van die monster is die verantwoordelikheid van die laboratorium wat die monsters indien.

  • Alle monsters vir die hoë-deurset biblioteek voorbereidings (teen verminder HT dosisse) moet genormaliseer word tot +- 20% van die gemiddelde monster konsentrasie.
  • Maak asseblief seker dat al jou monsters aan die monstersuiwerheidsvereistes en monsterkonsentrasiebiblioteekvereistes voldoen.
  • Die HT verwerking tariewe sluit nie herhalings in nie van biblioteekvoorbereidings wat misluk as gevolg van monsterontoereikendheid.

DNA-volgorde-biblioteke

Riglyne vir die indiening van biblioteekwaardige DNA
Gee 1 ug of meer hoë kwaliteit DNA (konsentrasie & gt 50 ng/ul, OD 260/280 naby 1,8 260/230 verhouding & gt2,0) in EB -buffer, of water met molekulêre biologie. Daar kan ook gepoog word om biblioteekbou te bou uit minder insetmateriaal, met voorbehoud. Vir PCR-vrye biblioteke word monsterhoeveelhede van 1 ug DNA aanbeveel om met minder te werk.
DNS -monsters moet wees RNA-vry. Op 'n etidiumbromied-gekleurde agarosegel sal RNA-kontaminasie sigbaar wees as 'n halo-agtige smeer in die reeks van 50 tot 200 bp. Dien asseblief 'n agarosegel-beeld van die DNA in saam met die monsters. DNA -monsters vir Illumina -volgorde moet geïsoleer word spin-kolom protokolle (bv. DNeasy bied verskeie verskaffers soortgelyke kits aan). Sulke stelle is ook beskikbaar in plaatformaat vir hoë-deursetverwerking. Die DNS-kwaliteit moet deur agarosegel-elektroforese geverifieer word. Stuur 'n gel -beeld vir ons voordat u die monsters stuur. Ons kan die agarose gels vir jou laat loop teen 'n fooi.

Whole Genome Shotgun Libraries (HT-biblioteek voorbereidings)
Ons gebruik die Covaris -gefokusde sonicator om die DNA fisies te fragmenteer tot 'n noue omvang wat geskik is vir Illumina -biblioteekvoorbereidings. Biblioteke wat met sulke ultraklankskeer gegenereer word, lei tot die mees eweredige genoomdekking. Probeer om 1 ug DNA per monster te verskaf. Die nuutste biblioteekvoorbereidingsprotokolle laat biblioteekvoorbereiding toe uit nanogramhoeveelhede. Sulke superlae insette het egter gevolge en is dalk nie geskik vir jou projek nie, bespreek asseblief lae insetprojekte vooraf met ons. Monsters moet genormaliseer word tot +- 20% van die gemiddelde monster konsentrasie.

NUUT: Gepoolde super-hoë deurvoer haelgeweerbiblioteke (SHT)
Hierdie baie goedkoop biblioteekvoorbereidingsprotokol genereer biblioteke vir 'n volledige monster van 96 putjies en is byvoorbeeld ideaal vir Skim-Seq-toepassings. Tot 960 biblioteke (10 plate) kan saamgevoeg en gerangskik word saam met gepaarde-end 150 bp lees. Die protokol gebruik dubbele indeksering, maar nie UDI -indeksering nie. Die protokol genereer biblioteke van voldoende kompleksiteit vir tot 10 miljoen leespare per monster (gelykstaande aan 1x dekking van 'n 3 Gb genoom) vir elke monster. Die eerste stap van biblioteekvoorbereiding voeg 'n monster-spesifieke strepieskode by elke monster op 'n bord, dan word alle monsters saamgevoeg vir die volgende stappe, wat beteken dat die finale biblioteke slegs beskikbaar is as 'n poel van 96 monsters. Dus, dit is belangrik dat alle monsters is in 'n soortgelyke toestand – geïsoleer met dieselfde protokol, van hoë suiwerheid algehele, en genormaliseer voor monster indiening. Wisselende DNS-kwaliteit sal wisselende leesgetalle tot gevolg hê. Die DNA-monsters moet geïsoleer word met draaikolomprotokolle (CTAB-protokolle is nie geskik). Dien 20 ul in vir elke DNA -monster in EB -buffer (sien hieronder nie TE nie buffer) of water in 'n goed verseëlde plaat met 96 putte. Die konsentrasie van DNA moet 10 ng/ul wees vir elk van die monsters gemeet deur fluorometrie (Qubit, Quantus, PicoGreen op plaatleser). Die protokol is ietwat self-normaliserend. Dus, wisselende monsterkonsentrasies van 4ng/ul tot 20 ng/ul is bruikbaar. Egter ons kan nie instaan ​​vir die leesverdelings nie voortspruitend uit sulke verskillende, semi-genormaliseerde monsters. Sien asseblief die Riglyne vir die indiening van biblioteekwaardige DNA hierbo vir vereiste monstersuiwerheid, UV-absorbansie, metrieke.

ChIP-volgende biblioteke
Ons bied biblioteekkonstruksie uit chromatien -immuun neerslagmateriaal. Vir hierdie meer komplekse eksperimente word besprekings met Kernpersoneel oor die geskiktheid van beginmateriaal en konstruksiestrategie aanbeveel. Geen waarborge word aangebied met hierdie biblioteekdiens nie, behalwe dat ons ons bes sal doen! Die ChIP-Seq Data Tegniese Nota en ChIP-Seq DataSheet van Illumina verskaf 'n paar agtergrondinligting.

  • Maak seker dat u vooraf die ingangskontroles op 'n Bioanalyzer of agarose -gel uitvoer, en stuur 'n e -pos na hierdie e -pos.
  • U sal ook een invoerbeheer per sellyn/steekproeftipe wil volg.
  • Ons beveel sterk aan dat u qPCR gebruik om die verryking van u belangstellingsstreke te verifieer, bv. promotorstreke teenoor die kontrolemonsters, voordat die monsters vir volgordebepaling ingedien word.

Die vereiste leesgetal per monster sal wissel van teiken tot teiken. Vir die bestudering van puntbron-transkripsiefaktore beveel die ENCODE-projek aan om ten minste 20 miljoen lesings (unieke kartering) te ontleed (http://genome.cshlp.org/content/22/9/1813.long#boxed-text-2). Afhangende van die kwaliteit van u voorbereiding, kan 75% van die lesings op unieke wyse verwag word. ENCODE is geneig om aan die hoë kant te fouteer met hul aanbevelings. So, ongeveer 20 miljoen lesings per monster behoort aanvaarbaar te wees, maar dit is waarskynlik die minimum getal.

Ons bied die inkorporering van in-lyn-UMI's wanneer ChIP-seq biblioteke gegenereer word. UMI's kan nuttig wees vir alle NGS-kwantifiseringstoepassings. Die oorwegings is soortgelyk soos verduidelik vir RNA-seq in hierdie algemene vrae.

Verminderde opeenvolgingsvolgorde
RR-volgmetodes gebruik verterings -ensiemvertering om reproduceerbare subgroepe van rye te genereer wat deur die genoom versprei word, dit wil sê verminderde voorstellings. In die meeste gevalle word verminderde voorstellings van ongeveer 1% of 2% van die genoomgrootte gevolgorde en ontleed vir SNP-merkers. Sien asseblief hierdie resensie vir 'n bespreking van metodes en toepassings. Ons gebruik die metilering sensitief ApeKI ensiem wat, vir plantgenome, uitsluiting van die meeste herhalende volgordes van die volgordebepaling toelaat. Ons berei biblioteke voor vir 95 genomiese DNA-monsters gelyktydig.
As u spesie van belang nie voorheen deur RR-volgorde bestudeer is nie, moet ons eers vasstel dat die ApeKI ensiem is geskik. E-pos ons asseblief gelbeelde (1% agarosegelelektroforese met Ethidiumbromied of GelRed-kleuring) van verteenwoordigende monsters voordat enige monsters gestuur word. Die gelbeeld moet ongeskonde DNA-monsters toon, asook dieselfde monsters wat verteer word met 'n nie-metilering sensitiewe beperkingsensiem (bv. HindIII). Indien die monsters goed verteer, dien toetsmonsters in (

5 ug by >50 ng/ul) sodat ons bykomende toetse kan doen om te sien of ApeKI gepas is (toetsverterings, adapterligasies, $75). By 'n suksesvolle toets vereis ons dat RR-monsters in 'n plaat met 96 putte ingedien word. Een of twee putte moet leeg gelaat word vir negatiewe kontroles. Monsterkonsentrasie moet genormaliseer word tot 50 ng/ul soos getoets deur 'n interkalerende kleurstof (fluorometrie op Qubit of plaatleser). Die UV-absorpsieverhoudings moet 260/280nm 1.8 tot 2.0 wees, en 260/230 > 2.0. 'N Volume van 20 ul per monster is voldoende. Die DNS-monsters moet met behulp van 'n CTAB-vrye protokol onttrek word (gebruik altyd spinkolomprotokolle), aangesien baie presiese DNS-monsterkwantifisering van kritieke belang is vir die sukses van die protokol. Die biblioteke is tipies in volgorde op 'n enkele HiSeq 4000 baan met enkellees 90 bp lees.

Mate-paar-biblioteke (let wel: vir die oorgrote meerderheid projekte is PacBio- of Nanopore-volgordedata beter as paar-biblioteke)
Die opeenvolging van Mate Pair-biblioteke genereer lang-insetsels met 'n paar insetsels. Die biblioteke word gegenereer deur self-ligering van lang DNA-fragmente en etikettering van die aansluitingsplekke om chimere biblioteekmolekules te genereer wat rye wat oorspronklik 2 kb tot 12 kb uitmekaar was, saambring. Ons gebruik die Illumina Nextera Mate Pair-stel wat 'n transposase-ensiem gebruik om te fragmenteer en die DNA in 'n enkele stap te merk. Die merkers is gebiotinileer en maak dus voorsiening vir die seleksie van aansluitingsplekke wat fragmente bevat. In teenstelling met ouer maatpaar-biblioteekprotokolle, is die Nextera-stel baie betroubaar, met die uitsondering van die grootte van die aanvanklike fragmente. Soos met alle ander lang DNA-fragmentontledings, maak die DNS-kwaliteit saak. E-pos ons asseblief 'n gel-beeld voordat u die DNA-monsters indien. Die monsters moet as 'n band van 20 kb of langer op agarose -gels uitgevoer word.
Die Nextera-kit bied twee protokolle: die 'gelvrye' weergawe (1 ug insette), wat meestal interessant is as slegs min inset-DNA beskikbaar is. Die groottes van die makkersfragmente uit hierdie protokol wissel gewoonlik van 1,5 kb tot 10 kb. Die SSPACE -steierhouer kan verbasend nog steeds met hierdie data werk.
Die "gel-plus" weergawe benodig 'n minimum van 4 ug inset-DNA (en 4 keer die reagense) en gebruik gelekstraksies om geselekteerde fragmente binne 'n reeks van +- 700 bp vir korter maats en binne +- 2kb vir langer maats tot 10 tot 12 kb. Dien asseblief ten minste twee keer die hoeveelheid monster in weens die onsekerhede van die fragmentasie.
In teorie is die fragmentgroottes as gevolg van die etikettering slegs afhanklik van die inset -DNA -hoeveelheid. In die praktyk wissel die fragmentlengtes aansienlik tussen verskillende DNA -monsters van soortgelyke hoeveelhede. Hierdie veranderlikheid tussen monsters kan waargeneem word selfs na presiese DNA -kwantifisering deur fluorometrie. Die reaksies is egter instelbaar vir aliquots van dieselfde monster. Veral as baie spesifieke groottereekse verlang word, is dit dikwels nodig om die tagmenteringsreaksie met aangepaste DNA-hoeveelhede te herhaal. Ons kan dan gel -ekstraksiefraksies van dieselfde grootte uit twee tagmenteringsreaksies kombineer om biblioteke van hoë kompleksiteit vir die gewenste grootte bereik te genereer. Laat ons weet hoe belangrik die spesifieke insetselgroottes vir u projek is.
As gevolg van die probleme om die fragmentgroottes te voorspel, haal ons herlaadtariewe van maatpaar aan, insluitend twee tagmenteringsreaksies. As ons die gewenste biblioteek met 'n enkele tagmentasie kan genereer, hef ons die laer tarief van die biblioteekvoorbereiding vir een-tagmentasie.

Doelverryking
Talle ondernemings lewer dienste en platforms wat hele eksome of teikenversterking genereer. Ons bied die Fluidigm Access Array aan, wat nanofluïdika gebruik vir koste-effektiewe doelwitkeuse om strepieskode-amplikonbiblioteke te genereer wat gereed is vir Illumina-opeenvolging. Volgorde-opname biblioteke Dit is gebiede waarin spesifieke genomiese streke verryk word na die genereer en volgorde van biblioteke. Hierdie strategie laat gefokusde, baie diep opeenvolging toe en kan geïmplementeer word vir 'n aantal toepassings. Verskeie ondernemings bied platforms aan wat sulke materiaal kan genereer, waaronder Illumina, RainDance, Agilent, NimbleGen en Fluidigm. Tegniese inligting oor die Agilent-, Nimblegen-, RainDance- en Qiagen -stelsels is beskikbaar (wat 'n PCR -gebaseerde, nie hibridisasie -opnamestrategie vir verryking gebruik nie). Ons gebruik aas van IDT ’s xGen Exome Research Panel v1.0 gekombineer met gewone Illumina biblioteekvoorbereiding.


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Funksionele genomika deur geïntegreerde analise van transkriptome van patats (Ipomoea batatas (L.) Lam.) Tydens wortelvorming

Patats word maklik deur steggies gepropageer. Maar die molekulêre biologiese meganisme van bywortelvorming is nog nie duidelik nie.

Doel

Om die molekulêre meganismes van afwykende wortelvorming deur stingels in patats te verstaan.

Metodes

RNA-volgorde-analise is uitgevoer met behulp van ongewortelde stam (0 dag) en gewortelde stam (3 dae). Gene Ontology (GO) verrykingsanalise, Kyoto Encyclopedia of Genes and Genomes (KEGG) -weg, vergelyking met Arabidopsis transkripsiefaktore (TF's) van DEG's is uitgevoer om die kenmerke van gene en TF's betrokke by wortelvorming te ondersoek. Boonop is qRT-PCR-analise met wortels op 0, 3, 6, 9 en 12 dae na plant uitgevoer om RNA-seq-betroubaarheid en verwante gene-uitdrukking te bevestig.

Resultate

42 459 verteenwoordigende transkripsies en 2092 DEG's is verkry deur die RNA-volgorde-analise. Die DEG's dui die GO-terme aan wat verband hou met die metabolisme van die enkele organisme en selperiferie, en wat betrokke is by die biosintese van sekondêre metaboliete, en fenielpropanoïede biosintese in KEGG-paaie. Die vergelyking met Arabidopsis thaliana TF -databasis het getoon dat 3 TF's (WRKY, NAC, bHLH) betrokke is by wortelvorming van patats. qRT-PCR analise, wat uitgevoer is om die betroubaarheid van RNA-volg analise te bevestig, het aangedui dat sommige metabolismes insluitend oksidatiewe stres en wond, vervoer, hormoon betrokke kan wees by bywortelvorming.

Gevolgtrekkings

Die opgespoorde gene wat verband hou met sekondêre metabolisme, een of ander hormoon (uksien, gibberellien), transporte, ens. en 3 TF's (WRKY, NAC, bHLH) kan funksies hê in bywortelvorming. Hierdie resultate verskaf waardevolle hulpbronne vir toekomstige navorsing oor die bywortelvorming van patats.


Resultate

Ons het bruikbare rye vir die mtDNA en vier van die ses kernlokale (BDNF, R35, TB02 en TB07 Tabel 2). Loci TB53 en TB95 het op herhaalde pogings elektroferogramme met oormatige geraas gegenereer, en dit is dus uitgesluit. In die faseering van die kern -DNA -data word alle monsters vir BDNF het 'n minimum paarwaarskynlikheid van 0,996 gehad en almal is vir stroomaf-ontledings geselekteer. Vir R35 is alles behalwe twee monsters gekies met 'n minimum paar waarskynlikheid van 0,942. Vir TB02 is alle monsters aanvaar, drie pare het onder 'n waarskynlikheid van 0.922 geval, maar is steeds ingesluit. Nie een van hierdie monsters het unieke haplotipes gehad wat nie in 'n ander monster verteenwoordig is nie. Vir TB07 het 51 monsters 100% paarwaarskynlikhede en is gekies vir analise. 18 monsters het paarwaarskynlikhede naby 0,50 tussen twee pare haplotipes (13 monsters met dieselfde twee moontlike pare haplotipes betrokke) G. agassizii of Sonoran-tipe G. morafkai vyf monsters van G. morafkai wat in Mexiko versamel is, het dieselfde twee dubbelsinnige pare gedeel). Beide voorbeelde het dieselfde dubbelsinnigheid in dieselfde SNP verteenwoordig en elk van die vier haplotipes onder die dubbelsinnige pare is in die ander 51 monsters verteenwoordig. Vir hierdie lokus kies ons die paar met die hoogste Bl waarde om te gebruik vir stroomaf-ontledings.

Onder die kernlokale was haplotipes beide afstammeling-spesifiek en wêreldwyd gevind in spesies van Gopherus (Tabel S3, fig. 2). Haplotipe diversiteit, nukleotied polimorfisme en nukleotied diversiteit skattings het oor lokusse en tussen steekproef groeperings gewissel en het nie sterk neigings voorgestel nie. Byvoorbeeld, die haplotipe diversiteit was groter in G. agassizii dan G. morafkai by sommige lokusse maar nie ander nie, en Tajima se D was positief vir sommige lokusse en negatief vir ander (Tabel S4).

Filogenetiese analise

Die matrilineêre genealogie (mtDNA-boom) het sterk ondersteuning gehad oor ontledings vir afsonderlike Mojave/Sonoran/Sinaloan matrilyne. Nieteenstaande is die geografies verafgeleë Mojawiese en Sinaloaanse matrilyn as sustertaksa opgelos, en saam was hulle die sustergroep van die tussenliggende Sonoraanse matrilyn (Fig. 2A). Die multilocus -ontledings het dieselfde topologie getoon vir beide streng en ontspanne log normale horlosies met spesieverhoudings in ooreenstemming, maar met die belangrike verwagting dat die aangrensende matrilines Sinaloan en Sonoran saamgevoeg het en die sustergroep van die Mojave gevorm het. Geskatte TMRCA's vertoon wye standaardafwykings (Fig. 3). Kwalitatiewe analise met behulp van DensiTree dui sterk ooreenstemming tussen iterasies vir die gevolglike boomtopologie aan, maar met minder presisie rondom diepte van knope (Fig. 3B).

Vir toetse van seleksie op die mtDNA lokus, ons skattings van ω verskillende modelle vir takke van die boom gebruik (Tabel 3). Eerstens, die veronderstelling van 'n uniform ω vir alle vertakkings van die vyf spesies/lyne van Gopherus, ω na raming 0.6192, wat aansienlik minder as een was (Bl < 0,05). Hierdie resultaat dui daarop dat hierdie geen onder die sterk sterk suiwerende seleksie was. Vervolgens het ons 'n model toegepas waarin elke tak sy eie gehad het ω. Hierdie model was aansienlik meer effektief as een-verhouding model (Bl & lt 0,05 Tabel 3), wat daarop dui ω wissel tussen die verskillende afstammelinge. Die waarde van ω op die tak wat na SIN (Sinaloan-lyn-haplogroep) lei, is geskat as groter as een (1.1), wat aandui dat positiewe seleksie hierdie afstamming kan beïnvloed het (Tabel 3).

Seleksie modelle npa a Aantal parameters.
lnLb b Die natuurlike logaritme van die waarskynlikheidswaarde.
ω (dN./dS) Modelle vergelyk Bl waardes
A. Alle takke het een ω 9 −2463.7 ω = 0.62
B. Alle takke het dieselfde ω = 1 8 −2469.4 ω = 1 A vs. B <0,001
C. Elke tak het sy eie ω 15 −2454.6 Veranderlik ω per tak A vs. C <0.01

Netwerk analise

Ons netwerkrekonstruksies van allele (Fig. 2) het middelpuntworteling van die grootste afstand gebruik, en nie uitgroepanalise nie, om boomagtige assosiasies aan te bied. As sodanig is die terminale allele, en nie individue nie. Alleliese assosiasies van individue is in Tabel S3 gelys. In een lokus, BDNF, alle spesies skilpaaie het die meeste allele gedeel. In teenstelling hiermee was unieke allele beperk tot spesies woestynskilpaaie in R35, TB02 en TB07, hoewel 'n paar alternatiewe en vermoedelik primitiewe allele in meer as een spesie voorgekom het.

Transkripsiesamestelling

Ons het 111 635 751 afgesnyde lesings van die totale RNA van volbloed bymekaargemaak in 'n gesamentlike G. agassizii en G. morafkai samestelling wat 235 412 contigs bevat het (Tabel 4). Die gekombineerde samestelling wat deur die ontploffing gefiltreer is, bevat 40,341 transkripsies met 'n contig N50 van 3010 bp en 'n gemiddelde lengte van 1957 bp. Nadat ons die ses individue in lyn gebring het met die gekombineerde samestelling en die variant -allele geïdentifiseer het, het ons 95,220 polimorfiese plekke gekenmerk waarvoor ons genotipe -inligting vir alle individue het. Die PCA -assessering toon uiters sterk groepering van individue binne elke afstamming en relatief ewe verre differensiasie tussen afstammelinge (Fig. 4).

Drie-eenheid vergadering Nee lees Aantal transkripsies N50 Gemiddelde lengte (bp)
G. agassizii (Mojave) 44,068,129 150,135 1190 709
G. morafkai (Sonoran) 22,528,007 202,778 2500 1093
G. morafkai (Sinaloan) 45,039,615 138,380 1994 899
Gekombineerde samestelling 111,635,751 235,412 1302 718
Gekombineerde gefiltreerde samestelling 40,341 3010 1957

Demografiese modellering

Ons het die allele-frekwensiespektrum (AFS) -gebaseerde afleidingsinstrument ∂a∂i (Gutenkunst et al. 2009) gebruik om die gesamentlike demografiese geskiedenis van die drie afstammelinge van woestynskilpaaie uit ons transkriptome af te lei. Omdat bewys word dat AFS-gebaseerde demografiese afleiding sensitief is vir genotiperingsfoute (Gutenkunst et al. 2009) en seleksie (Williamson et al. 2005), het ons slegs sinonieme variante wat al ses individue suksesvol genoem het, oorweeg, met 'n AFS met 20,126 sinonieme variante. van 7665 kontiges.

Om die ontwikkeling van drie-populasie modelle te lei, het ons eers eenvoudiger twee-populasie modelle oorweeg. Aanvanklike tweebevolkingsmodelle sonder genevloei (modellering van allopatriese spesies) het konsekwent 'n groter effektiewe populasiegrootte vir die Sonoraanse bevolking opgelewer as die ander en 'n meer onlangse verskil tussen die Sinaloan- en Sonoran-populasies as tussen een van die populasies en die Mojave (Tabel S1 ). In ooreenstemming met hierdie resultaat, het die beste gepaste drie bevolkingsmodelle wat betrokke was, onlangs uiteenlopende Sinaloan- en Sonoran-bevolkings (Tabel S2). Hierdie resultaat het onafhanklike ondersteuning gebied vir die *BEAST spesiesboom. Toe ons geenvloei bygevoeg het (H-MSgf, H-SSgf) in die modelle, hetsy as deurlopende vloei, soos deur parapatriese spesiasie met voortdurende kontak, of vertraagde vloei, soos as gevolg van fietsryperiodes in refugia gevolg deur introgressie tydens sekondêre kontak, het die maksimum saamgestelde waarskynlikheidskattings vir die geenvloeiparameter saamgevloei tot nul (tabel S1). Ons het dus geen bewyse van geenvloei tussen enige paar van die drie populasies gevind nie en het dus hipoteses H-MS verwerpgf en H-SSgf.

Gebaseer op ons twee-bevolking-ontledings, het ons drie-bevolkingsmodelle oorweeg waarin die Sinaloan- en Sonoran-bevolkings susters was en daar geen geenvloei tussen hulle was nie. Figuur 5 toon die twee beste pasmodelle onder die wat ons getoets het (saamgestelde log-waarskynlikheid: −887 vir die 6-parameter model teenoor −909 vir die 5-parameter model). Terwyl die twee modelle dieselfde boomtopologie gehad het, het die 6-parameter model 'n addisionele gratis parameter vir die effektiewe bevolkingsgrootte van die kontemporêre Mojave-bevolking gehad. Kwalitatief het hierdie modelle soortgelyke alleelfrekwensiespektra en residue geproduseer in vergelyking met die data (Fig. 5C, D), maar die 6-parameter is verkies in 'n saamgestelde waarskynlikheidsverhoudingtoets (aangepaste waarskynlikheidsverhouding 9,242, Bl = 0.0024, chi-squared test with df = 1). To estimate parameter uncertainty while accounting for linkage among variants, we used conventional bootstrapping. Table 5 showed the confidence intervals for the parameters of our 6-parameter demographic model. This best-fit model suggested that the Mojave and Sinaloan populations have similar effective sizes (128,000 and 150,000 individuals, respectively), but the effective size of the Sonoran population is much larger (600,084 individuals). The two divergence times in our model are also similar (Table 5), suggesting a trichotomy among these populations.

Demographic parameter Skat 95% C.I.
N.a: size of Gopherus ancestral population 336,200 328,000–344,000
N.moj: size of contemporary Mojave population 128,400 122,000–135,000
N.sin-son: size of contemporary Sinaloan population 149,600 143,000–156,000
N.seun: size of contemporary Sonoran population 600,000 548,000–668,000
Tdiv-1: time of Mojave divergence 5,900,000 5,597,000–6,183,000
Tdiv-2: time of Sinaloan and Sonoran divergence 5,650,000 5,376,000–5,967,000

Inleiding

The innate immune system carries a substantial burden of defense against viral pathogens. The study of this response across animal species in recent years, as well as the examination of the phylogenetic conservation of these responses has changed our concept of innate immunity however, much of this work has been performed in mammals. Major explorations of antiviral innate immune responses in non-mammalian species remains very limited, but may be critical to fully comprehend the complexity of the mammalian innate immune response to viral infection, and the discovery of novel antiviral therapeutics (1).

Mammalian cells have been shown to orchestrate elaborate defense mechanisms to detect and inhibit viral replication. Immediately after viral sensing by the host cells, the innate immune response is initiated by germline-encoded molecules termed pattern recognition receptors (PRRs). PRPs recognize conserved features of viruses and other microorganisms, known as pathogen-associated molecular patterns (PAMPs), which are small molecular motifs recognized as non-self, such as microbial nucleic acids, proteins, and carbohydrates (2). Following the recognitions of PAMPs, PRRs initiate a set of signaling cascades, which ultimately result in the production of interferon (IFN) and the upregulation of hundreds of interferon-stimulated genes (ISGs) (3). The expression of these ISGs is known to limit pathogens, in particularly viral pathogens, although the exact role of the majority of these ISGs remains unknown (4), more specifically we have very little understanding of how this response is orchestrated in non-mammalian vertebrate species.

There is an enormous lack of information surrounding antiviral innate immunity in the Reptilia class, which represents a bridge between fish and mammals. Both type I and type III IFNs are known to be central cytokines in the antiviral response in mammals, inducing the upregulation of hundreds of antiviral effector genes (5). To date, both functional type I and III IFNs have been found in the genomes of amphibians (6, 7), with type I IFNs also being found in fish (8). In reptiles, type III IFN has recently been found in the genome of lizards (9) however, the pathways that upregulate their production have not been described to date, and there is very little information on the downstream antiviral effector genes that may be responsible for viral control in reptiles. Recent transcriptomics work performed on non-infected tissue in the lizard has been able to identify the presence of multiple known PRRs in the reptile (10) with recent studies by our group also showing a number of known ISGs to be upregulated in the presence of viral infection in a reptile in vitro (11). The ISGs, viperin, 2′-5′-oligoadenylate synthetase (OASL) and IFN-induced GTP-binding protein Mx1 were demonstrated to be upregulated in saltwater crocodile, C. porosus LV-1 cells in the presence of the viral mimics, dsRNA, and dsDNA, as well as in the presence of replicating dengue virus (11). This study also demonstrated that crocodile viperin retained its antiviral activity and was able to inhibit dengue viral replication in vitro. Given the large number of viruses described to infect reptiles (12), these studies only give a very small insight into the induction of antiviral pathways in the reptile.

The world’s largest living reptile species, the saltwater crocodile, is a member of the prehistoric order Crocodylia, evolved from the archosauria clade that includes the dinosaurs, pterosaurs, crocodilians, and birds, the latter two being the only extant members of the clade (13). In recent years, two novel herpesviruses, crocodyline herpesvirus 1 and crocodyline herpesvirus 2 (CrHV-1 and -2, respectively) (14, 15), adenovirus, and poxvirus have emerged as significant viral infections of the crocodile, with multiple bacterial and fungal pathogens also being detected in this saltwater crocodile (16). However, very little is known about the ability to control these pathogens by the host immune system. The recent advancement of the crocodilian genome sequence (17) has given the opportunity to unveil the innate immune pathways in this ancient species, in particular its response to viral pathogens.

In recent years, transcriptomes profiling using high-throughput RNA sequencing (RNA-seq) technology has provided unprecedented opportunities to study the host response to infection against a wide range of viral and bacterial infections (18�). RNA-seq technology is both an efficient and accurate tool to reveal the systemic changes in host gene expression in response to infectious pathogens, which could help to unearth a better understanding of innate immune pathways in Reptilia. In the present study, we have used RNA-seq technology to comprehensively study the host transcriptomic profile following viral mimic stimulation of crocodile cell lines using both dsRNA and dsDNA. This study provides a global view for the first time, of nucleic acid-specific and post-stimulation time-specific mRNA profiles in the saltwater crocodile (C. porosus), adding significantly to the body of knowledge surrounding the early innate host response of reptiles to a virus.


Agtergrond

Drought stress is one of the most threatening environmental constraints that adversely affect plant growth and yield [1]. However, with global climate change, the frequency and intensity of drought have continuously increased [2, 3]. Drought might cause metabolic imbalance in plant cells and influence the optical energy absorption of plant leaves, destroying the photosynthetic organs of plants [4]. Moreover, drought can lead to the accumulation of active oxygen substances in the leaves, which may accelerate the peroxidation of biological membrane lipid to produce toxic products, thereby inhibiting plant growth [5]. For plants exposed to drought stress, the total primary productivity is not only closely related to their resistance and tolerance to drought stress, but also shows an important relationship with the ability of plants to recover from damage after the elimination of the stress [6, 7]. Therefore, the recovery ability after rehydration is important for the successful adaptation of plants to arid environments. Rehydration helps plants recover their physiological functions, and it can offset plant damage from drought stress to a certain extent [7]. However, the compensation of rehydration to plant growth after drought stress is often limited. The recovery degree of plant growth might be related to the degree and duration of drought stress before rehydration and drought resistance of plants [8].

To cope with drought stress, plants have adapted various self-protection and defense mechanisms in the long-term evolution process [9]. Plants decrease the photosynthetic capacity of mesophyll by closing the stomata to adjust the photosynthetic process of leaves [8]. By adjusting the in vivo antioxidation system, plants can eliminate excess active oxygen and maintain in vivo redox equilibrium. The intracellular water potential can be increased by increasing the substances of cell osmotic adjustment to maintain a certain expansion, thereby protecting the continuous growth of plants under drought stress [10]. Plant hormones regulate their own response mechanism through synergistic or antagonistic action in response to arid states. Various expressed genes have been reported in response to drought stress [11]. These genes include stabilizing membrane proteins, heat shock proteins and late embryogenesis abundant proteins, which play an important role in stabilizing protein structure and enhancing cell’s water binding capacity. Early drought-induced proteins protect plants by producing certain metabolic proteins and regulating gene expression through precise signal transduction during drought stress. Dehydrin genes (Dhn), which are among the most frequently observed proteins in plants, protect the cells from water deficit. Additionally, a large number of genes change their expression through the regulation of transcription factors (TFs) [12]. Several TFs also provide response under drought stress, including abscisic acid-responsive element (ABREs) binding factors (ABFs, AREBs, or DPBFs) [13,14,15], dehydration-responsive element binding factors [16], myeloblastosis (MYB), and SNF1-related kinase 2 [17]. Therefore, knowledge about the various genes translated and expressed in response to drought stress conditions will help elucidate the water deficit tolerance mechanisms and facilitate the development of new plant cultivation tools to combat climate change.

Giant Juncao is an ideal Gramineae C4 plant for water-soil conservation, wind prevention, and sand fixation due to its large biomass, disease resistance, and developed root system. This plant has been widely applied for the comprehensive environmental management of regions with vulnerable ecology [18]. However, drought environment influences the yield and restricts its large-scale plantation. To improve its productivity and performance in water deficit conditions, the response mechanisms of Giant Juncao to drought should be elucidated. Recently, the development of molecular biological methods has facilitated the discovery of potential plant response mechanisms to environmental stresses. RNA sequencing (RNA-seq) can quickly and comprehensively obtain the gene expression of a specific cell or tissue in a certain state, so as to determine the molecular mechanism of physiological metabolic responses of plants under abiotic stress conditions [19]. RNA-seq data could provide insights into the discovery of new genes, including annotation genes and differentially expressed genes (DEGs), and molecular markers [20]. Compared with traditional sequencing methods, RNA-seq provides high-throughput sequencing results, is inexpensive, has high sensitivity, and can detect low abundance expressed genes [21]. A large quantity of DEGs associated with drought stress response have been reported in various Gramineae species by RNA-seq, such as wheat [22], maize [23], rice [24], sorghum [25], and foxtail millet [26]. This method can obtain transcripts from different developmental stages, tissues, and organs, so it is a fundamental and efficient method for discovering functional genes.

Herein, we aimed to identify the genes involved in the response of Giant Juncao to drought stress and rehydration treatment using Illumina sequencing. We analyzed DEGs in Giant Juncao seedlings to elucidate its molecular response mechanism to drought stress. Furthermore, we employed the physiological indices to understand the drought response mechanism of Giant Juncao. As only a few studies have identified genes that respond to drought stress in Pennisetum spp., our study provides an important transcriptomic database for further targeted gene modifications in grasses.


MATERIAAL EN METODES

Biological materials

Adult zebrafish (strain ABxTL) were handled in compliance with local animal welfare regulations and maintained according to standard protocols (http://zfin.org). The breeding of adult fish was approved by the local animal welfare committee (DEC) of the University of Leiden, The Netherlands. All protocols adhered to the international guidelines specified by the EU Animal Protection Directive 86/609/EEC. Adult zebrafish and oocytes were flash-frozen in liquid nitrogen and stored at −80°C. Before freezing, fish were put under anesthesia using 0.02% buffered 3-aminobenzoic acid ethyl ester (Tricaine).

Total RNA and sRNA isolation

Whole zebrafish or isolated tissues were pulverized in liquid nitrogen with a mortar and pestle, after which sRNA isolation was performed using the miRNeasy Mini Kit (Qiagen). In brief, powdered tissue was homogenized in TRIzol Reagent (Life Technologies) and 1-bromo-3-chloropropane (BCP) was added. After centrifugation RNA partitioned to the upper aqueous phase, which was carefully removed and subjected to column-based sRNA isolation according to the manufacturers’ instructions. RNA concentration was measured on a NanoDrop ND-2000 (Thermo Scientific) and RNA integrity was examined on a 2200 TapeStation instrument using R6K and High Sensitivity R6K ScreenTapes (Agilent Technologies).

Design of the data-normalization and size-range quality control spike-in sets

A list of candidate random RNA sequences of the desired length was generated using the following constraints: maximum homopolymer length of two nucleotides, GC-content between 40 and 60%, ΔG for hairpin formation >−0.5 kcal/mol at 37°C (UNAFold: http://mfold.rna.albany.edu/), and an E-value >10 when aligned against the NCBI nucleotide collection (nr/nt: http://blast.ncbi.nlm.nih.gov) using BLASTN. From these candidates 19 sequences of 25 nucleotides long were selected for use as external reference for data-normalization (ERDN) spike-ins. For size-range quality control (SRQC) spike-ins different sequences were selected of 10, 16, 19, 22, 25, 28, 34, 40, 50, 60 and 70 nucleotides long. The 10-nucleotide SRQC spike-in was not checked using BLASTN because of its short size. These sequences were obtained as single-stranded oligoribonucleotides with a 5′-phosphate and polyacrylamide gel purified (Eurogentec S.A.). The oligoribonucleotides were dissolved in 100 μM in RNase-free TE (10 mM Tris–HCl pH 8 and 1 mM ethylenediaminetetraacetic acid (EDTA)) and stored in aliquots at −80°C.

Volgorde-volgorde

sRNA libraries were prepared according to the manufacturers’ protocols using the Ion Total RNA-Seq Kit v2 (Life Technologies). Briefly, adapters were ligated to the sRNA and a reverse transcription reaction was performed. The resulting cDNA was amplified and at the same time barcoded with IonXpress RNA-Seq BC01-BC16 (Life Technologies). The yield and the size distribution of the amplified cDNA were assessed using the 2200 Tapestation with the Agilent D1K ScreenTape (Agilent Technologies). Emulsion PCR was performed using the Ion PI Template OT2 200 Kit on an Ion OneTouch 2 Instrument, after which the template-positive Ion PI Ion Sphere Particles were recovered, quantified with a Qubit 2.0 fluorometer, and enriched using an Ion OneTouch ES (Life Technologies). Sequencing was carried out on the Ion Proton system using an Ion PI Chip v2 and Ion PI Sequencing 200 kit (Life Technologies) following the manufacturers’ protocols (Revision 3.0).

Data toegang

All sequencing results are accessible through the European Nucleotide Archive (http://www.ebi.ac.uk/ena/) under the project accession number ERP007147. For detailed info see Supplementary Data 4.

Bioinformatika-analise

Mapping NGS reads to spike-in sets

Before alignment of the reads to the spike-in sets, all reads were trimmed to 40 nucleotides from the 3′-end using Trimmomatic 0.30 ( 21) (option CROP:40). Bowtie2 ( 22) was used for the alignment of the trimmed reads to the synthetic spike-in sequences. The parameters used for alignment were -L 6 -i S,0,0.5 –ignore-quals –norc –score-min L,-1,-0.6 -D 20. This corresponds to ∼10% of mismatches allowed. Samtools ( 23) was used to convert the alignment results to the BAM file format. Reads were selected only if the alignment length was >80% of the target length using the Rsamtools package ( 24).

Mapping NGS reads to sRNA

For miRNA alignment, all reads were trimmed to 30 nucleotides and reads shorter than 15 nucleotides were discarded. Trimmed reads were aligned to the mature zebrafish miRNA sequences from miRBase version 20 ( 25–29) with Bowtie2 using the same settings as used for spike-in sequences alignment. Only reads with perfect alignments were selected.

For piRNA alignment, all reads were trimmed to 40 nucleotides and reads shorter than 12 nucleotides were discarded. Trimmed reads were aligned to both strands of piRNA sequences from piRNABank ( 30) with Bowtie2 using the same alignment score settings as used for spike-in sequences alignment. Only reads with perfect alignments were selected. Finally, Samtools ( 23) was used to convert the alignment results to the BAM file format.

Normalisering

For testing the SRQC spike-in set using different ethanol concentrations (Figure 2A, Supplementary Figure S1.1 and Supplementary Figure S2.3) the number of reads that mapped to each size spike-in was first divided by the total number of zebrafish miRNA reads for that sample and then multiplied by the average number of mapped miRNA reads over all samples.

Design and testing of small-RNA size-range quality control (SRQCs) spike-in set. (A) Technical reproducibility. sRNA-seq analysis over six independent SRQC spike-in experiments. Triangles in the dot plot represent the non-normalized read count of six technical replicates for each SRQC (SS-10 to SS-70). (B) SRQC sequences. Size spike-ins (SS-) of 10 to 70 nucleotides long.

Design and testing of small-RNA size-range quality control (SRQCs) spike-in set. (A) Technical reproducibility. sRNA-seq analysis over six independent SRQC spike-in experiments. Triangles in the dot plot represent the non-normalized read count of six technical replicates for each SRQC (SS-10 to SS-70). (B) SRQC sequences. Size spike-ins (SS-) of 10 to 70 nucleotides long.

Using SRQCs to monitor size range in sRNA-seq. (A) Monitoring size-range biases in sRNA isolation. Fold abundance here is for each size spike-in the ratio of the observed number of reads divided by the number of reads found when using the standard ethanol concentration. (B) An example of biased size range in sRNA-seq. Fold abundance here is for each size spike-in the ratio of the observed number of reads divided by the number of reads in the adult male zebrafish sample.

Using SRQCs to monitor size range in sRNA-seq. (A) Monitoring size-range biases in sRNA isolation. Fold abundance here is for each size spike-in the ratio of the observed number of reads divided by the number of reads found when using the standard ethanol concentration. (B) An example of biased size range in sRNA-seq. Fold abundance here is for each size spike-in the ratio of the observed number of reads divided by the number of reads in the adult male zebrafish sample.

ERDN-based normalization (Figures 2B, 4, 5C–F) was performed by first calculating size factors from the ERDN counts using the DESeq R package ( 31). These size factors were then used to scale the number of reads between the samples. By this approach all ERDN spike-ins contribute in the normalization procedure, irrespective of their abundance. For comparison, this normalization procedure was repeated using the miRNA-mapped reads as input to calculate the size factors in DESeq.

Design and testing of external small-RNA normalization spike-ins (ERDNs). (A) Technical reproducibility. sRNA-seq analysis of six independent ERDN spike-in experiments. The average non-normalized number of reads for each normalization spike-in plotted against the median number of reads of each spike-in over the six technical replicates. Error bars indicate standard deviations. (B) Dynamic range of miRNA expression in zebrafish. Zebrafish miRNAs are shown, sorted by increasing abundance and plotted against their corresponding average number of reads over the six replicates. Error bars are standard deviations (n = 6). (C) ERDN sequences. Oligoribonucleotide sequences of the 25-mer ERDN spike-in set. (D) Quantitative diversity of the zebrafish miRNA pool. Zebrafish miRNAs are shown sorted by decreasing abundance and plotted as cumulative fraction of reads that is consumed by each miRNA. The dashed line indicates the fraction of the reads used up by the 10 most abundant miRNAs.

Design and testing of external small-RNA normalization spike-ins (ERDNs). (A) Technical reproducibility. sRNA-seq analysis of six independent ERDN spike-in experiments. The average non-normalized number of reads for each normalization spike-in plotted against the median number of reads of each spike-in over the six technical replicates. Error bars indicate standard deviations. (B) Dynamic range of miRNA expression in zebrafish. Zebrafish miRNAs are shown, sorted by increasing abundance and plotted against their corresponding average number of reads over the six replicates. Error bars are standard deviations (n = 6). (C) ERDN sequences. Oligoribonucleotide sequences of the 25-mer ERDN spike-in set. (D) Quantitative diversity of the zebrafish miRNA pool. Zebrafish miRNAs are shown sorted by decreasing abundance and plotted as cumulative fraction of reads that is consumed by each miRNA. The dashed line indicates the fraction of the reads used up by the 10 most abundant miRNAs.

Evaluating ERDN-based normalization. (A) Effect of ERDN-based normalization on the dilution data of the fold-change oligoribonucleotides. Linear regression analysis was performed on plots of the dilution factor versus the number of reads obtained for the 2-fold serial dilutions of each fold-change oligonucleotide. The difference in the coefficients of determination (ΔR 2 ) between non-normalized data and DESeq-normalized data is plotted for each fold-change oligo (SS-10 to SS-40). DESeq normalization was performed using as a reference either the number of miRNA-mapped reads (green) or the ERDNs (blue). SS-50, SS-60, SS-70 are not showed as they have <10 reads in more than three runs. (B) Effect of ERDN-based normalization on the variance of replicate miRNA samples. The standard deviation of the number of reads (log2) obtained for each zebrafish miRNA was calculated over the eight replicates used under (A). The results are presented in kernel density plots where the density (vertical axis) signifies the number of miRNAs that have a particular standard deviation (horizontal axis). Red, not normalized green, normalized with total miRNA-mapped reads, and blue, normalized with ERDN. (C) Average raw read distribution of small RNA and SRQC. The average non normalized number of reads of the six different samples were calculated and plotted against the length of sRNAs (blue, left y-axis) and SRQC spike-ins (black, right y-as). The error bars show the standard deviation. (D) Effect of ERDN-based normalization on the variance of sequences of different length. The standard deviation of the number of reads (log2) obtained for each size spike-in was calculated over six female zebrafish samples, for non-normalized data and ERDN-normalized data.

Evaluating ERDN-based normalization. (A) Effect of ERDN-based normalization on the dilution data of the fold-change oligoribonucleotides. Linear regression analysis was performed on plots of the dilution factor versus the number of reads obtained for the 2-fold serial dilutions of each fold-change oligonucleotide. The difference in the coefficients of determination (ΔR 2 ) between non-normalized data and DESeq-normalized data is plotted for each fold-change oligo (SS-10 to SS-40). DESeq normalization was performed using as a reference either the number of miRNA-mapped reads (green) or the ERDNs (blue). SS-50, SS-60, SS-70 are not showed as they have <10 reads in more than three runs. (B) Effect of ERDN-based normalization on the variance of replicate miRNA samples. The standard deviation of the number of reads (log2) obtained for each zebrafish miRNA was calculated over the eight replicates used under (A). The results are presented in kernel density plots where the density (vertical axis) signifies the number of miRNAs that have a particular standard deviation (horizontal axis). Red, not normalized green, normalized with total miRNA-mapped reads, and blue, normalized with ERDN. (C) Average raw read distribution of small RNA and SRQC. The average non normalized number of reads of the six different samples were calculated and plotted against the length of sRNAs (blue, left y-axis) and SRQC spike-ins (black, right y-as). The error bars show the standard deviation. (D) Effect of ERDN-based normalization on the variance of sequences of different length. The standard deviation of the number of reads (log2) obtained for each size spike-in was calculated over six female zebrafish samples, for non-normalized data and ERDN-normalized data.

Using ERDN for normalization of samples with different miRNA content. (A en B) Size distribution of sRNA in female and male adult zebrafish. Histograms of the number of reads versus the read length are shown for a female (A) and a male (B) adult zebrafish. Blou, all reads rooi, miRNA-mapped reads and groen, piRNA-mapped reads. Reads shorter than nine nucleotides are not included. (C en D) Size-selection profile in zebrafish samples. Comparison of the SRQC reads between the female and male adult zebrafish (C) and between male and egg (D). (E en F) ERDN-based normalization preserves the natural miRNA content. Reads were normalized by DESeq using as a reference either the number of mapped reads (E) or the ERDN spike-in set (F). For each miRNA and piRNA, the average number of reads was calculated over four female and over four male zebrafish. Die log2 ratio of male over female miRNAs (red) and piRNAs (green) is displayed in kernel density plots. The vertical dashed line indicates an equal number of reads in female and male zebrafish.

Using ERDN for normalization of samples with different miRNA content. (A en B) Size distribution of sRNA in female and male adult zebrafish. Histograms of the number of reads versus the read length are shown for a female (A) and a male (B) adult zebrafish. Blou, all reads rooi, miRNA-mapped reads and groen, piRNA-mapped reads. Reads shorter than nine nucleotides are not included. (C en D) Size-selection profile in zebrafish samples. Comparison of the SRQC reads between the female and male adult zebrafish (C) and between male and egg (D). (E en F) ERDN-based normalization preserves the natural miRNA content. Reads were normalized by DESeq using as a reference either the number of mapped reads (E) or the ERDN spike-in set (F). For each miRNA and piRNA, the average number of reads was calculated over four female and over four male zebrafish. Die log2 ratio of male over female miRNAs (red) and piRNAs (green) is displayed in kernel density plots. The vertical dashed line indicates an equal number of reads in female and male zebrafish.

R 2 calculation (Figure 4A)

The levels of each fold-change control in all eight dilutions were evaluated by individually plotting their relative input concentration against their normalized number of reads. These data should follow a linear relationship on a log2 scale with a slope that equals the fold-change difference between consecutive dilution mixes. The difference between the observed and expected fold-changes was thus assessed by linear regression with a fixed slope of 1 (log2 of the expected 2-fold changes), returning the coefficient of determination (R 2 ) as a measure of similarity.

Down-sampling raw sequence files

Down-sampled fastq files were generated by randomly sampling 2.5, 3.0, 3.5, 4.0, 4.5 and 5.0 million of reads from the original fastq files (starting respectively from 7 611 054, 6 343 893, 6 990 446, 6 082 600, 6 227 225 and 6 054 478 total reads) using the Seqtk tool (http://github.com/lh3/seqtk).


BESPREKING

In the RNA-Seq approaches employed to date ( 4, 6–9) RNA is first converted into ds cDNA, and then processed into a sequencing library. Two modifications of the ds cDNA synthesis have been suggested so far that allow one to preserve information about the direction of the transcripts ( 12, 13). The first procedure is based on changing all cytidine residues in RNA to uridines by bisulfite treatment prior to cDNA synthesis ( 12). Another approach ( 13) involves first-strand cDNA synthesis from a tagged random hexamer primer, and SSS from a DNA–RNA template-switching primer. Both procedures are laborious. The bisulfite approach requires a non-standard sequencing data analysis scheme and also leads to the loss of ∼30% of uniquely matched sequencing reads because part of the genome complexity is lost during transformation of four bases into a three-base code. Combining a random primer with template switching may result in uneven coverage of the genes.

In other directional transcriptome profiling schemes adapters are ligated directly to single-stranded RNA molecules [21 DGE Small RNA Sample Prep Kit (Illumina) SOLiD Small RNA Expression Kit (Applied Biosystems)]. These schemes are laborious and time consuming, but they are the only choice for analysis of short RNAs. Adaptor-ligation methods are sensitive to ribosomal RNA contamination, so the RNA fraction of interest (mRNA, microRNA or short transcripts) must be pre-selected.

The suggested ssRNA-Seq approach is a modification of standard cDNA synthesis, and compatible with commercially available kits. The principle of the procedure—labeling of one of ds cDNA strands so that it can be removed—does not specifically require dUTP. For example, biotinylated nucleotides could be incorporated and the biotinylated strand then removed using streptavidin-coated magnetic particles. Strand labeling can also be performed during FSS (results not shown). The protocol can be easily adapted for other second generation sequencing platforms: SOLiD/ABI, 454/Roche.

We routinely use ssRNA-Seq for transcriptome analysis with the Illumina second-generation sequencing platform for both single read and paired end sequencing. After using ssRNA-Seq for more than a year for transcriptome analysis in different organisms (mammals, birds, fishes, plants, yeast), the procedure has proven to be convenient, reliable and highly reproducible.


Kyk die video: functies met input zonder return (September 2022).