pr8b00019_si_001.pdf (219.43 kB)
Target-Decoy-Based False Discovery Rate Estimation for Large-Scale Metabolite Identification
journal contribution
posted on 2018-05-23, 00:00 authored by Xusheng Wang, Drew R. Jones, Timothy I. Shaw, Ji-Hoon Cho, Yuanyuan Wang, Haiyan Tan, Boer Xie, Suiping Zhou, Yuxin Li, Junmin PengMetabolite identification is a crucial
step in mass spectrometry
(MS)-based metabolomics. However, it is still challenging to assess
the confidence of assigned metabolites. We report a novel method for
estimating the false discovery rate (FDR) of metabolite assignment
with a target-decoy strategy, in which the decoys are generated through
violating the octet rule of chemistry by adding small odd numbers
of hydrogen atoms. The target-decoy strategy was integrated into JUMPm,
an automated metabolite identification pipeline for large-scale MS
analysis and was also evaluated with two other metabolomics tools,
mzMatch and MZmine 2. The reliability of FDR calculation was examined
by false data sets, which were simulated by altering MS1 or MS2 spectra.
Finally, we used the JUMPm pipeline coupled to the target-decoy strategy
to process unlabeled and stable-isotope-labeled metabolomic data sets.
The results demonstrate that the target-decoy strategy is a simple
and effective method for evaluating the confidence of high-throughput
metabolite identification.