ArticleMethods in molecular biology (Clifton, N.J.)2026
Integrative Chimeric RNA Prediction with FASfuse.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
Chimeric RNAs are composed of sequences from different genomic loci caused by various chromosomal rearrangements and splicing events. They are recognized as both biomarkers present in cancer as well as a source of transcriptomic diversity in normal tissues. Numerous computational prediction tools have been developed and aim to analyze and predict chimeric RNAs. However, the performance of these tools vary in accuracy and depend on the sequencing context, necessitating a combination of multiple existing tools to produce the most comprehensive and accurate results. First, this study reviews several major chimeric RNA prediction tools: STAR-Fusion, Arriba, and FuSeq. It highlights the advantages of each program, as demonstrated by benchmarking studies. Second, it presents an integrated pipeline that combines all three top-ranking programs to produce a single output file including detailed annotations, such as chimeric RNA class, breakpoint types, and protein coding potential. The final computational product is a unified framework that supports results for high-confidence fusion transcript predictions for both research and clinical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.