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Article

ProGeo-Neo v2.0: A One-Stop Software for Neoantigen Prediction and Filtering Based on the Proteogenomics Strategy

by
Chunyu Liu
1,2,†,
Yu Zhang
2,3,†,
Xingxing Jian
2,
Xiaoxiu Tan
2,4,
Manman Lu
1,2,
Jian Ouyang
2,5,
Zhenhao Liu
2,
Yuyu Li
1,
Linfeng Xu
2,3,
Lanming Chen
1,
Yong Lin
3 and
Lu Xie
1,2,*
1
College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
2
Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, 779 Old Humin Road, Shanghai 200237, China
3
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
4
Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
5
The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, The Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2022, 13(5), 783; https://doi.org/10.3390/genes13050783
Submission received: 7 April 2022 / Revised: 25 April 2022 / Accepted: 26 April 2022 / Published: 28 April 2022
(This article belongs to the Special Issue Bioinformatics and Genetics of Human Diseases)
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Abstract

A proteogenomics-based neoantigen prediction pipeline, namely ProGeo-neo, was previously developed by our team to predict neoantigens, allowing the identification of class-I major histocompatibility complex (MHC) binding peptides based on single-nucleotide variation (SNV) mutations. To improve it, we here present an updated pipeline, i.e., ProGeo-neo v2.0, in which a one-stop software solution was proposed to identify neoantigens based on the paired tumor-normal whole genome sequencing (WGS)/whole exome sequencing (WES) data in FASTQ format. Preferably, in ProGeo-neo v2.0, several new features are provided. In addition to the identification of MHC-I neoantigens, the new version supports the prediction of MHC class II-restricted neoantigens, i.e., peptides up to 30-mer in length. Moreover, the source of neoantigens has been expanded, allowing more candidate neoantigens to be identified, such as in-frame insertion-deletion (indels) mutations, frameshift mutations, and gene fusion analysis. In addition, we propose two more efficient screening approaches, including an in-group authentic neoantigen peptides database and two more stringent thresholds. The range of candidate peptides was effectively narrowed down to those that are more likely to elicit an immune response, providing a more meaningful reference for subsequent experimental validation. Compared to ProGeo-neo, the ProGeo-neo v2.0 performed well based on the same dataset, including updated functionality and improved accuracy.
Keywords: bioinformatics; neoantigen; proteogenomic; tumor immunotherapy bioinformatics; neoantigen; proteogenomic; tumor immunotherapy

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MDPI and ACS Style

Liu, C.; Zhang, Y.; Jian, X.; Tan, X.; Lu, M.; Ouyang, J.; Liu, Z.; Li, Y.; Xu, L.; Chen, L.; et al. ProGeo-Neo v2.0: A One-Stop Software for Neoantigen Prediction and Filtering Based on the Proteogenomics Strategy. Genes 2022, 13, 783. https://doi.org/10.3390/genes13050783

AMA Style

Liu C, Zhang Y, Jian X, Tan X, Lu M, Ouyang J, Liu Z, Li Y, Xu L, Chen L, et al. ProGeo-Neo v2.0: A One-Stop Software for Neoantigen Prediction and Filtering Based on the Proteogenomics Strategy. Genes. 2022; 13(5):783. https://doi.org/10.3390/genes13050783

Chicago/Turabian Style

Liu, Chunyu, Yu Zhang, Xingxing Jian, Xiaoxiu Tan, Manman Lu, Jian Ouyang, Zhenhao Liu, Yuyu Li, Linfeng Xu, Lanming Chen, and et al. 2022. "ProGeo-Neo v2.0: A One-Stop Software for Neoantigen Prediction and Filtering Based on the Proteogenomics Strategy" Genes 13, no. 5: 783. https://doi.org/10.3390/genes13050783

APA Style

Liu, C., Zhang, Y., Jian, X., Tan, X., Lu, M., Ouyang, J., Liu, Z., Li, Y., Xu, L., Chen, L., Lin, Y., & Xie, L. (2022). ProGeo-Neo v2.0: A One-Stop Software for Neoantigen Prediction and Filtering Based on the Proteogenomics Strategy. Genes, 13(5), 783. https://doi.org/10.3390/genes13050783

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