Here we present the Trinity Cancer Transcriptome Analysis Toolkit (CTAT), a newly developed suite of RNA-Seq targeted fusion detection tools leveraging a combination of reference genome read-mapping and de novo transcriptome assembly, coupled to in silico fusion transcript validation, annotation, and visualization. Although many such fusion detection software tools have recently been made available, they often differ greatly in prediction accuracy, execution times, computational requirements, installation complexity, and in not being readily accessible to non-bioinformatician cancer researchers. Transcriptome sequencing via RNA-Seq coupled with downstream bioinformatics software applications offers an effective method for identifying candidate fusion transcripts, and is more targeted and cost-effective than whole genome sequencing. Chromosomal rearrangements leading to fusion transcripts represent a class of oncogenic aberrations that are of high interest for understanding cancer biology, treating cancer patients, and as targets for the development of new therapies.
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