Open AccessDecoy-free protein-level false discovery rate estimation
Author(s) -
Ben Teng,
Ting Huang,
Zengyou He
Publication year - 2013
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/btt431
Subject(s) - false discovery rate , computer science , inference , resampling , bipartite graph , decoy , permutation (music) , statistical inference , random permutation , identification (biology) , statistical hypothesis testing , algorithm , data mining , artificial intelligence , graph , statistics , mathematics , theoretical computer science , biology , botany , receptor , geometry , block (permutation group theory) , gene , physics , biochemistry , acoustics
Statistical validation of protein identifications is an important issue in shotgun proteomics. The false discovery rate (FDR) is a powerful statistical tool for evaluating the protein identification result. Several research efforts have been made for FDR estimation at the protein level. However, there are still certain drawbacks in the existing FDR estimation methods based on the target-decoy strategy.
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