Dataset Information

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

ABSTRACT: The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

SUBMITTER: Creason A

PROVIDER: S-EPMC8376800 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

Creason Allison A Haan David D Dang Kristen K Chiotti Kami E KE Inkman Matthew M Lamb Andrew A Yu Thomas T Hu Yin Y Norman Thea C TC Buchanan Alex A van Baren Marijke J MJ Spangler Ryan R Rollins M Rick MR Spellman Paul T PT Rozanov Dmitri D Zhang Jin J Maher Christopher A CA Caloian Cristian C Watson John D JD Uhrig Sebastian S Haas Brian J BJ Jain Miten M Akeson Mark M Ahsen Mehmet Eren ME Stolovitzky Gustavo G Guinney Justin J Boutros Paul C PC Stuart Joshua M JM Ellrott Kyle K

Cell systems 20210618 8

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a compa ...[more]

PMID: 34146471

Similar Datasets

Project description:Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call "joint modeling of multiple RNA-seq samples for accurate isoform quantification" (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ's advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

Dataset Information

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

Publications

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets