Data analysis strategy for maximizing high-confidence protein identifications in complex proteomes such as human tumor secretomes and human serum.

摘要: Detection of biologically interesting, low-abundance proteins in complex proteomes such as serum typically requires extensive fractionation and high-performance mass spectrometers. Processing the resulting large data sets involves trade-offs between confidence identification depth protein coverage; that is, higher stringency filters preferentially reduce number identified. In current study, an alternative database search results filtering strategies were evaluated using test samples ranging from purified to ovarian tumor secretomes human maximize peptide coverage. Full partial tryptic searches compared because substantial numbers peptides observed all samples, proportion was particularly high for serum. When yielded similar false discovery rates (FDR) used, full detected far fewer than s...