https://lentera.uin-alauddin.ac.id/question/gratis-terlengkap/https://old-elearning.uad.ac.id/gampang-menang/https://fk.ilearn.unand.ac.id/demo/https://elearning.uika-bogor.ac.id/tanpa-potongan/https://e-learning.iainponorogo.ac.id/thai/https://organisasi.palembang.go.id/userfiles/images/https://lms.binawan.ac.id/terbaik/https://disperkim.purwakartakab.go.id/storage/https://pakbejo.jatengprov.go.id/assets/https://zonalapor.fis.unp.ac.id/-/slot-terbaik/https://sepasi.tubankab.go.id/2024tte/storage/http://ti.lab.gunadarma.ac.id/jobe/runguard/https://satudata.kemenpora.go.id/uploads/terbaru/
DriverFuse: An R package for analysis of next-generation sequencing datasets to identify cancer driver fusion genes – TRANSLATIONAL BIOINFORMATICS GROUP
  • Sat. Apr 20th, 2024

DriverFuse: An R package for analysis of next-generation sequencing datasets to identify cancer driver fusion genes

Abstract

We developed the DriverFuse package to integrate orthogonal data types such as Structural Variants (SV) and Copy Number Variations (CNV) to characterize fusion genes in cancer datasets. A fusion gene is reported as a driver or passenger fusion gene, based on mapping SV and CNV profiles. DriverFuse generates a fusion plot of fusion genes with their mapping SV, CNV profile, domain architecture and classification of its role in cancer. The analysis facilitates discrimination of driver fusions from passenger fusions. To demonstrate the utility of DriverFuse, we analyzed two datasets, one each for CCLE (Cancer Cell Line Encyclopedia) for lung cancer and HCC1395BL for breast cancer. The analysis validates the driver fusion genes that are already reported for the datasets. Thus, DriverFuse is a valuable tool for studying the driver fusion genes in cancers, enabling the identification of recurrent complex rearrangements that provide intuitive insights into disease driver events.

Click here to know more:-  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0262686