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A computational workflow for predicting cancer neo-antigens


Sandeep Kasaragod1, Chinmaya Narayana Kotimoole1, Sumrati Gurtoo1, Thottethodi Subrahmanya Keshava Prasad1, Harsha Gowda1, 2,* & Prashant Kumar Modi1,*



1Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, India; 2Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India; *Corresponding authors



Sandeep Kasaragod - E-mail: sandeepk@yenepoya.edu.in

Chinmaya Narayana Kotimoole - E-mail: chinmaya_k@yenepoya.edu.in

Sumrati Gurtoo - E-mail: sumrati@yenepoya.edu.in

Thottethodi Subrahmanya Keshava Prasad - E-mail: keshav@yenepoya.edu.in

Harsha Gowda - E-mail: harshahc@gmail.com

Prashant Kumar Modi - E-mail: prashantmodi@yenepoya.edu.in


Article Type

Research Article



Received February 25, 2022; Revised March 24, 2022; Accepted March 31, 2022, Published March 31, 2022



Neo-antigens presented on cell surface play a pivotal role in the success of immunotherapies. Peptides derived from mutant proteins are thought to be the primary source of neo-antigens presented on the surface of cancer cells. Mutation data from cancer genome sequencing is often used to predict cancer neo-antigens. However, this strategy is associated with significant false positives as many coding mutations may not be expressed at the protein level. Hence, we describe a computational workflow to integrate genomic and proteomic data to predictpotential neo-antigens.



Neoantigens, proteogenomics, cancer proteogenomics, multi-omics



Kasaragod et al. Bioinformation 18(3): 214-218 (2022)


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.