title: INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery.
abstract: While high-throughput sequencing (HTS) has been used successfully to discover tumor-specific mutant peptides (neoantigens) from somatic missense mutations, the field currently lacks a method for identifying which gene fusions may generate neoantigens.
  
  
  
  We demonstrate the application of our gene fusion neoantigen discovery pipeline, called INTEGRATE-Neo, by identifying gene fusions in prostate cancers that may produce neoantigens.
  
  
  
  INTEGRATE-Neo is implemented in C ++ and Python. Full source code and installation instructions are freely available from https://github.com/ChrisMaherLab/INTEGRATE-Neo .
  
  
  
  christophermaher@wustl.edu.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors: McDonnell Genome Institute.|Department of Internal Medicine.|Siteman Cancer Center.
authors: Jin
authors: J
authors: Zhang
authors: McDonnell Genome Institute.|Siteman Cancer Center.|Department of Molecular Microbiology.|Department of Medicine.|Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.
authors: Elaine R
authors: ER
authors: Mardis
authors: McDonnell Genome Institute.|Department of Internal Medicine.|Siteman Cancer Center.|Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63105, USA.
authors: Christopher A
authors: CA
authors: Maher
mesh_terms: D000951:Antigens, Neoplasm / Q000032:analysis* / Q000235:genetics; D050939:Gene Fusion; D023281:Genomics / Q000379:methods*; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000378:metabolism; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software
chemical_list: D000951:Antigens, Neoplasm