FusionCatcher – a tool for finding somatic fusion genes in paired-end RNA-sequencing data

By Daniel Nicorici, Mihaela Satalan, Henrik Edgren, S. Kangaspeska, Astrid Murumägi, Olli-Pekka Kallioniemi, Sami Virtanen, Olavi Kilkku. bioRxiv, 2014

405 citations recorded by the source corpus.

FusionCatcher is a software tool for finding somatic fusion genes in paired-end RNA-sequencing data from human or other vertebrates. FusionCatcher achieves competitive detection rates and real-time PCR validation rates in RNA-sequencing data from tumor cells. Fusion-Catcher is available at http://code.google.com/p/fusioncatcher/.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e039dd47ead98e34d3de3a56bbfecefde79dc813

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Daniel C. Koboldt

In Molecular Biology

1. M. Mahmoud
2. Nastassia Gobet
3. Diana Ivette Cruz-Dávalos
4. Fritz J. Sedlazeck
5. Daniel C. Koboldt
6. Christophe Dessimoz
7. Ninon Mounier

Citations in the corpus

• Accurate and efficient detection of gene fusions from RNA sequencing data (2021, background): “Common strategies use multiple rounds of alignment with iteratively trimmed reads (Jia et al. 2013), alignment with multiple algorithms (Nicorici et al. 2014), or alignment against assemblies generated on the fly (Davidson et al. 2015).”
• Best practices for variant calling in clinical sequencing (2020, methodology): “Gene fusions (RNA-seq) fusionCatcher [56], fusionMap [57], mapSplice [58], SOAPfuse [59], STAR-Fusion [60], TopHat-Fusion [61]”
• Structural variant calling: the long and the short of it (2019, methodology): “For instance, methods such as FusionCatcher [43] and Eric-Script [42] inherently focus on the annotated parts of the genome.”