Abstract:
: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.
journal_name
Genome Bioljournal_title
Genome biologyauthors
Tian L,Li Y,Edmonson MN,Zhou X,Newman S,McLeod C,Thrasher A,Liu Y,Tang B,Rusch MC,Easton J,Ma J,Davis E,Trull A,Michael JR,Szlachta K,Mullighan C,Baker SJ,Downing JR,Ellison DW,Zhang Jdoi
10.1186/s13059-020-02043-xsubject
Has Abstractpub_date
2020-05-28 00:00:00pages
126issue
1eissn
1474-7596issn
1474-760Xpii
10.1186/s13059-020-02043-xjournal_volume
21pub_type
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