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

• DOI: 10.1016/j.cels.2021.05.021
• Publication Date: 2021-06-18T14:31:35Z
• Authors: Allison Creason; David Haan; Kristen Dang; Kami E. Chiotti; Matthew Inkman; Andrew Lamb; Thomas Yu; Yin Hu; Thea C. Norman; Alex Buchanan; Marijke J. van Baren; Ryan Spangler; M. Rick Rollins; Paul T. Spellman; Dmitri Rozanov; Jin Zhang; Christopher A. Maher; Cristian Caloian; John D. Watson; Sebastian Uhrig; Brian J. Haas; Miten Jain; Mark Akeson; Mehmet Eren Ahsen; Gustavo Stolovitzky; Justin Guinney; Paul C. Boutros; Joshua M. Stuart; Kyle Ellrott; Hongjiu Zhang; Yifan Wang; Yuanfang Guan; Cu Nguyen; Christopher Sugai; Alokkumar Jha; Jing Woei Li; Alexander Dobin
• Keywords: isoform quantification; 0301 basic medicine; 570; Bioinformatics and Computational Biology; RNA fusion; 03 medical and health sciences; Cancer Genomics; benchmark; Neoplasms; Genetics; Humans; Protein Isoforms; RNA-Seq; Cancer; evaluation; Sequence Analysis, RNA; Human Genome; Biological Sciences; DREAM Challenge; 004; 3. Good health; SMC-RNA Challenge Participants; Biochemistry and cell biology; RNA; Generic health relevance; Biochemistry and Cell Biology; RNA-seq; Sequence Analysis; Cloud compute; crowd-sourced

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.