4.7 Article

MHCVision: estimation of global and local false discovery rate for MHC class I peptide binding prediction

期刊

BIOINFORMATICS
卷 37, 期 21, 页码 3830-3838

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab479

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资金

  1. Science Achievement Scholarship of Thailand (SAST)
  2. Chulalongkorn Academic Advancement into Its 2nd Century (CUAASC) Project
  3. Thailand Research Fund [MRG6280189]

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MHC-peptide binding prediction is crucial for understanding immune responses and developing immunotherapeutics. Introducing global FDR and PEP estimation provides a more reliable evaluation of predicted results. MHCVision offers a better balance between sensitivity and precision in comparison to using traditional threshold values.
Motivation: MHC-peptide binding prediction has been widely used for understanding the immune response of individuals or populations, each carrying different MHC molecules as well as for the development of immunotherapeutics. The results from MHC-peptide binding prediction tools are mostly reported as a predicted binding affinity (IC50) and the percentile rank score, and global thresholds e.g. IC50 value < 500nM or percentile rank < 2% are generally recommended for distinguishing binding peptides from non-binding peptides. However, it is difficult to evaluate statistically the probability of an individual peptide binding prediction to be true or false solely considering predicted scores. Therefore, statistics describing the overall global false discovery rate (FDR) and local FDR, also called posterior error probability (PEP) are required to give statistical context to the natively produced scores. Result: We have developed an algorithm and code implementation, called MHCVision, for estimation of FDR and PEP values for the predicted results of MHC-peptide binding prediction from the NetMHCpan tool. MHCVision performs parameter estimation using a modified expectation maximization framework for a two-component beta mixture model, representing the distribution of true and false scores of the predicted dataset. We can then estimate the PEP of an individual peptide's predicted score, and conversely the probability that it is true. We demonstrate that the use of global FDR and PEP estimation can provide a better trade-off between sensitivity and precision over using currently recommended thresholds from tools. Availability and implementation: https://github.com/PGB-LIV/MHCVision. Contact: andrew.jones@liverpool.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

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