4.7 Article

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

期刊

CELL SYSTEMS
卷 12, 期 8, 页码 827-+

出版社

CELL PRESS
DOI: 10.1016/j.cels.2021.05.021

关键词

-

资金

  1. National Cancer Institute
  2. ITCR [R01CA180778]
  3. Oregon Health AMP
  4. Science University [U24CA210957, U24CA143799, HHSN261200800001E]
  5. UCLA [P30CA016042]
  6. Sage Bionetworks [5U24CA209923]

向作者/读者索取更多资源

The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge aimed to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing data, which is crucial for analyzing the cancer transcriptome. The evaluation compared 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据