Paper - Review

DOI: 10.1186/s13059-019-1842-9

Abstract

Background

Accurate fusion transcript detection
→ is essential
→ for comprehensive characterization ← of cancer transcriptomes

Multiple bioinformatic tools
→ have been developed → to predict fusions ← from RNA-seq
← based on 1⃣ read mapping 2⃣ de novo fusion transcript assembly

Results

Benchmark → 23 different methods
← including 1⃣ STAR-fusion 2⃣ TrinityFusion
→ leveraging → both 1⃣ simulated 2⃣ real RNA-seq

1⃣ STAR-Fusion 2⃣ Arriba 3⃣ STAR-SEQR
→ are the most accurate & fastest
→ for fusion detection ← on cancer transcriptomes

Conclusion

❗: the lower accuracy ← of de novo assembly-based methods
→ notwithstanding
❗: they are useful
→ for reconstructing 1⃣ fusion isoforms 2⃣ tumor viruses

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