CICERO：一种使用癌症 RNA 测序数据检测复杂多样的驱动融合的通用方法。
CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data.
机构信息
Department of Computational Biology, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
出版信息
Genome Biol. 2020 May 28;21(1):126. doi: 10.1186/s13059-020-02043-x.
To discover driver fusions beyond canonical exon-to-exon chimeric transcripts, we develop CICERO, a local assembly-based algorithm that integrates RNA-seq read support with extensive annotation for candidate ranking. CICERO outperforms commonly used methods, achieving a 95% detection rate for 184 independently validated driver fusions including internal tandem duplications and other non-canonical events in 170 pediatric cancer transcriptomes. Re-analysis of TCGA glioblastoma RNA-seq unveils previously unreported kinase fusions (KLHL7-BRAF) and a 13% prevalence of EGFR C-terminal truncation. Accessible via standard or cloud-based implementation, CICERO enhances driver fusion detection for research and precision oncology. The CICERO source code is available at https://github.com/stjude/Cicero.
为了发现超出经典exon-to-exon 嵌合转录本的驱动融合，我们开发了 CICERO，这是一种基于局部组装的算法，它将 RNA-seq 读支持与候选排名的广泛注释相结合。CICERO 优于常用方法，在 170 个儿科癌症转录组中，对 184 个独立验证的驱动融合（包括内部串联重复和其他非规范事件）的检测率达到 95%。对 TCGA 胶质母细胞瘤 RNA-seq 的重新分析揭示了以前未报告的激酶融合（KLHL7-BRAF）和 13%的 EGFR C 末端截断的发生率。通过标准或基于云的实现方式均可访问，CICERO 增强了研究和精准肿瘤学的驱动融合检测。CICERO 的源代码可在 https://github.com/stjude/Cicero 上获得。
Zotero 插件