TY - JOUR

T1 - Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

AU - Brooks, Angela

AU - Pardo-Palacios, Francisco

AU - Reese, Fairlie

AU - Carbonell-Sala, Silvia

AU - Diekhans, Mark

AU - Liang, Cindy

AU - Wang, Dingjie

AU - Williams, Brian

AU - Adams, Matthew

AU - Behera, Amit

AU - Lagarde, Julien

AU - Li, Haoran

AU - Пржибельский, Андрей

AU - Balderrama-Gutierrez, Gabriela

AU - Çelik, Muhammed Hasan

AU - María, Maite De

AU - Denslow, Nancy

AU - Garcia-Reyero, Natàlia

AU - Goetz, Stefan

AU - Hunter, Margaret

AU - Loveland, Jane

AU - Menor, Carlos

AU - Moraga, David

AU - Mudge, Jonathan

AU - Takahashi, Hazuki

AU - Tang, Alison

AU - Youngworth, Ingrid

AU - Carninci, Piero

AU - Guigó, Roderic

AU - Tilgner, Hagen U.

AU - Wold, Barbara

AU - Vollmers, Christopher

AU - Sheynkman, Gloria

AU - Frankish, Adam

AU - Au, Kin Fai

AU - Conesa, Ana

AU - Mortazavi, Ali

PY - 2021/8/3

Y1 - 2021/8/3

N2 - With increased usage of long-read sequencing technologies to perform transcriptome analyses, there becomes a greater need to evaluate different methodologies including library preparation, sequencing platform, and computational analysis tools. Here, we report the study design of a community effort called the Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium, whose goals are characterizing the strengths and remaining challenges in using long-read approaches to identify and quantify the transcriptomes of both model and non-model organisms. The LRGASP organizers have generated cDNA and direct RNA datasets in human, mouse, and manatee samples using different protocols followed by sequencing on Illumina, Pacific Biosciences, and Oxford Nanopore Technologies platforms. Participants will use the provided data to submit predictions for three challenges: transcript isoform detection with a high-quality genome, transcript isoform quantification, and de novo transcript isoform identification. Evaluators from different institutions will determine which pipelines have the highest accuracy for a variety of metrics using benchmarks that include spike-in synthetic transcripts, simulated data, and a set of undisclosed, manually curated transcripts by GENCODE. We also describe plans for experimental validation of predictions that are platform-specific and computational tool-specific. We believe that a community effort to evaluate long-read RNA-seq methods will help move the field toward a better consensus on the best approaches to use for transcriptome analyses.

AB - With increased usage of long-read sequencing technologies to perform transcriptome analyses, there becomes a greater need to evaluate different methodologies including library preparation, sequencing platform, and computational analysis tools. Here, we report the study design of a community effort called the Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium, whose goals are characterizing the strengths and remaining challenges in using long-read approaches to identify and quantify the transcriptomes of both model and non-model organisms. The LRGASP organizers have generated cDNA and direct RNA datasets in human, mouse, and manatee samples using different protocols followed by sequencing on Illumina, Pacific Biosciences, and Oxford Nanopore Technologies platforms. Participants will use the provided data to submit predictions for three challenges: transcript isoform detection with a high-quality genome, transcript isoform quantification, and de novo transcript isoform identification. Evaluators from different institutions will determine which pipelines have the highest accuracy for a variety of metrics using benchmarks that include spike-in synthetic transcripts, simulated data, and a set of undisclosed, manually curated transcripts by GENCODE. We also describe plans for experimental validation of predictions that are platform-specific and computational tool-specific. We believe that a community effort to evaluate long-read RNA-seq methods will help move the field toward a better consensus on the best approaches to use for transcriptome analyses.

U2 - 10.21203/rs.3.rs-777702/v1

DO - 10.21203/rs.3.rs-777702/v1

M3 - Article

JO - Nature Methods

JF - Nature Methods

SN - 1548-7091

ER -