MHCVision: estimation of global and local false discovery rate for MHC class I peptide binding prediction
Author(s) -
Phorutai Pearngam,
Sira Sriswasdi,
Trairak Pisitkun,
Andrew R. Jones
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab479
Subject(s) - false discovery rate , context (archaeology) , percentile , computer science , mhc class i , rank (graph theory) , major histocompatibility complex , statistics , computational biology , mathematics , biology , genetics , combinatorics , paleontology , antigen , gene
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 < 500 nM 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.
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