NBPMF

Abstract

Mass spectrometry (MS) is an analytical technique for determining the composition of a sample. Conventionally, peptide mass fingerprinting is widely used to identify proteins from MS dataset. Here, the authors developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, they designed new peptide protein matching score functions. They present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input data as dependent peptides. Moreover, they use linear regression to adjust the matching score according to the masses of proteins. In addition, they consider the order of retention time to further correct the score function. In the post processing, they design two algorithms: assignment of peaks and protein filtration. Finally, they propose two strategies to estimate the false discovery rate. The experiments on simulated, authentic, and simulated authentic dataset demonstrate that their NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.