FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction.

Journal: STAR protocols
Published Date: Mar 18, 2022

Abstract

Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).

Authors

  • Pora Kim
    Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
  • Hua Tan
    Department of Diagnostic Radiology, Wake Forest Medical School, Winston-Salem, NC 27103, USA.
  • Jiajia Liu
    College of Science, North China University of Science and Technology, Tangshan 063210, China.
  • Himansu Kumar
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
  • Xiaobo Zhou
    Department of Diagnostic Radiology, Wake Forest Medical School, Winston-Salem, NC 27103, USA. Electronic address: xizhou@wakehealth.edu.

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