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Systematic assessment of long-read RNA-seq methods for transcript identification and quantification.

Francisco J Pardo-PalaciosDingjie WangFairlie ReeseMark DiekhansSílvia Carbonell-SalaBrian WilliamsJane E LovelandMaite De MaríaMatthew S AdamsGabriela Balderrama-GutierrezAmit K BeheraJose M GonzalezToby HuntJulien LagardeCindy E LiangHaoran LiMarcus Jerryd MeadeDavid A Moraga AmadorAndrey D PrjibelskiInanc BirolHamed BostanAshley M BrooksMuhammed Hasan ÇelikYing ChenMei R M DuColette FeltonJonathan GokeSaber HafezqoraniRalf HerwigHideya KawajiJoseph LeeJian-Liang LiMatthias LienhardAlla MikheenkoDennis MulliganKa Ming NipMihaela PerteaMatthew E RitchieAndre D SimAlison D TangYuk Kei WanChangqing WangBrandon Y WongChen YangIf BarnesAndrew BerrySalvador CapellaAngela N BrooksJose M Fernandez-GonzalezLuis Ferrández-PeralNatàlia Garcia-ReyeroStefan GoetzCarles Hernández-FerrerLiudmyla KondratovaTianyuan LiuAlessandra Martinez-MartinCarlos MenorJorge Mestre-TomásJonathan M MudgeNedka G PanayotovaAlejandro PaniaguaDmitry RepchevskyEric C RouchkaEnrique SapenaLeon SheynkmanMelissa Laird SmithMarie-Marthe SunerHazuki TakahashiIngrid Ashley YoungworthPiero CarniciNancy D DenslowRoderic GuigóMargaret E HunterHagen U TilgnerBarbara J WoldChristopher VollmersAdam FrankishKin Fai AuGloria M SheynkmanAli MortazaviAna Conesa
Published in: bioRxiv : the preprint server for biology (2023)
The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.
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