STAR-SEQR: Accurate detection and quantification of RNA fusions using Next Generation Sequencing data

Genomic structural variation and associated RNA fusions are a common clinical feature known to be involved in the initiation and pathogenesis of cancer. This complex class of variants also has significant implications on therapeutic decisions and has emerging roles in evidence-based clinical applications.

Knowledge of disease-specific fusions have improved and several databases now exist to aid in clinical interpretation and annotation. From which, we now understand that fusions are tissue-specific and widely vary in prevalence. It is estimated that fusions drive 90% of lymphoma cases, over half of leukemias, and one third of soft tissue tumors.

Consequently, fusion detection is an emerging aspect of precision medicine and has clinical utility both as a biomarker and as therapeutically relevant target with several approved drugs and others in clinical development.

Despite the recent advancements, there is a crucial need for software to accurately and precisely identify fusions which also adheres to rigorous software and analytical standards to accompany diagnostic applications.

This scientific poster highlights STAR-SEQR, an algorithm used to detect and quantify fusions from RNA-Seq data. STAR-SEQR is a fast and accurate tool that goes beyond fusion detection and also provides rich annotation and useful reporting features to aid in the adoption of fusions in clinical diagnostics.

Authors:
Jeff Jasper
Jason Powers
Victor Weigman, Ph.D, Director, Translational Genomics

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