SUMMARY: Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel-derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanoma patients. AVAILABILITY AND IMPLEMENTATION: ScanNeo is implemented in Python. It is freely accessible at the GitHub repository (https://github.com/ylab-hi/ScanNeo). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel-derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanomapatients. AVAILABILITY AND IMPLEMENTATION: ScanNeo is implemented in Python. It is freely accessible at the GitHub repository (https://github.com/ylab-hi/ScanNeo). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Ting-You Wang; Qi Liu; Yanan Ren; Sk Kayum Alam; Li Wang; Zhu Zhu; Luke H Hoeppner; Scott M Dehm; Qi Cao; Rendong Yang Journal: Mol Cell Date: 2021-04-15 Impact factor: 17.970
Authors: Megan M Richters; Huiming Xia; Katie M Campbell; William E Gillanders; Obi L Griffith; Malachi Griffith Journal: Genome Med Date: 2019-08-28 Impact factor: 11.117