Abstract
Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses.
Highlights
Deep sequencing of mRNA represents a powerful tool in cancer genome research as well as in cell biology [1,2,3]
To align long reads to a reference genome (FASTA file), the genome data obtained from a database (e.g., Ensembl) must be indexed first (Figure 1, orange frame)
Since the alignment of sequencing reads against a reference genome is a rate-limiting step in the process requiring extensive computing power, genomes should be compressed into an index file (Figure 2)
Summary
Deep sequencing of mRNA represents a powerful tool in cancer genome research as well as in cell biology [1,2,3]. While several solutions for analyzing RNA sequencing data using short reads In contrast to conventional sequencing approaches, nanopore sequencing records conductivity changes when a DNA or RNA strand passes through a biological pore [14]. These signals can be converted to DNA or RNA sequences and can be stored as FASTQ files containing long (>2 kbp) RNA reads. For ONT technology, this process is guided by MinKNOW, an operating software that drives the nanopore sequencing device, performs raw data acquisition, and stores the fluctuating electrical signals in FAST5 files. STihstiisntgooolf, wlohnigchRNA readswferotemrmaesdeAlf-liegxnpaltaonr,aitsopryrinucsiepralilnytseurfitaecde.to quantitatively describe, filter, and analyze any data set consisting of long RNA reads from a self-explanatory user interface 2 of 8 2 of 8 are ssteoqrueednciningFAdSeTviQce,fiplesrfo[1rm2]s, wrahwicdhatcaanacqbueisiimtiomne, daniadtesltyorpesrotcheessfleudctubaytitnhgeeulescetricaalloswiginnagls ailnmost real-tFiAmSeTs5efqiluees.nMciinngK.NTOheWclaolsuoda-lbloawsesdfoOrNloTcapl lbaatsfeorcmalliEnPgI,2aMpEroicsesrseawdhiliychatprapnlisclaatbelsetthoearnawalysizgenhalusman and binatcotebraisaelsDeqNuAencreesadthsa.t Haroewsteovreedr,insoFlAutSiToQnsfifloesr [m12R],NwAhicahnacalynsbise, iemsmpeecdiiaaltleylyfoprroncoesns-ehdubmyatnhedata, are musisesrinalglo. wTinhgeraelfmoroes,t wreeald-teimveelosepqeudenacninega. sTyh-teo-culosuedt-oboaslefdorOrNapTidplaatnfoarlymsiEs PoIf2MloEngisRrNeaAdilryeads, implaeespmppeelcinciaatilbnllyegftotohr eannosanel-eyhdzue-mahnaudnm-dvaaonttaea, naadrlegbomarciisttsehirnmiagl.oDTfhNtehAreefroSeruaedb, srw.eeaHddoewpvaeevcloekpra,egdseoalfunotrieoaRnssy[-1ftoo5r-]umasenRdtNooAml afaoknrianrlaygpsiiusd,se of our panreavlyisoius solfylodnegvReNloAperdeadhsig, ihm-tphleromuegnhtipngutthteooselsed[-1a6n,d1-7v]o. teTahlgisortiothoml, owfhthicehSuwberetaedrmpaecdkaAgelifgonr ator, is priRnc[1ip5]alalnydsumiatekdintgouqsueaonftoituartipvreelvyioduesslcyridbeev,efiloltpeerd, ahnigdha-tnharloyuzgehapnuyt tdoaotlas [s1e6t,1c7o]n. sTihstiisntgooolf, wlohnigchRNA readswferotemrmaesdeAlf-liegxnpaltaonr,aitsopryrinucsiepralilnytseurfitaecde.to quantitatively describe, filter, and analyze any data set consisting of long RNA reads from a self-explanatory user interface
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