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TSNAD v2.0: A one-stop software solution for tumor-specific neoantigen detection.
Zhan Zhou1,2, Jingcheng Wu1,3, Jianan Ren1
1Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Computational and Structural Biotechnology Journal
|September 2, 2021
Summary
TSNAD v2.0 is a new software for predicting neoantigens from tumor sequencing data. It enhances neoantigen prediction by integrating RNA-Seq analysis and a novel deep learning model for peptide-MHC binding and immunogenicity.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Accurate neoantigen prediction is crucial for developing personalized cancer immunotherapies.
- Existing tools often lack comprehensive analysis capabilities or rely on less sophisticated prediction algorithms.
Purpose of the Study:
- To introduce TSNAD v2.0, an upgraded software solution for predicting neoantigens.
- To enhance neoantigen prediction accuracy and broaden analytical functionalities.
Main Methods:
- Incorporation of RNA-Seq analysis, including gene expression and fusion detection.
- Replacement of NetMHCpan with the developed DeepHLApan model for improved peptide-MHC binding and immunogenicity prediction.
- Support for multiple reference genome versions.
Main Results:
- TSNAD v2.0 demonstrates strong performance on standard datasets for neoantigen prediction.
- The new version offers enhanced capabilities for analyzing tumor-normal sequencing data.
Conclusions:
- TSNAD v2.0 provides a robust and versatile platform for neoantigen discovery.
- The software is accessible via a Docker version, web service, and open-source code.
Keywords:
Major histocompatibility complexNeoantigensOne-stop softwareSomatic mutationsTumor immunotherapy
