Analyses of mass spectrometry-based shotgun proteomics data rely heavily upon computational algorithms for automating peptide identification via database searching. Database search engines assign each tandem mass spectrum to the best-scoring peptide sequence in the database based on scoring functions using spectral features1–7
. Several different search engines are available today, and peptides identified with high confidence often show good consensus across different engines8
. Nevertheless, many high-quality MS/MS spectra remain unassigned to peptide sequences or have scores below chosen confidence thresholds9
. Moreover, some spectra may be assigned to different peptides by different search engines, which vary in their scoring schemes4, 10
. Provided that these issues are properly addressed, pooling peptide identifications from multiple search engines is expected to improve peptide identifications and to leave fewer mass spectra without assignment to peptide sequences.
To date, a few computational approaches have been proposed for integrating database search results. Alves et al.
proposed a calibration of p
-values from multiple search engines into a meta-analytic p
-value for each peptide8
. Searle et al.
proposed a Bayes approach to adjust probability scores computed in individual search engines based on the agreement between search engines, in which the largest adjusted probability is taken as the final score for each peptide11
. Although these methods allow for more efficient use of available data, the integration of search results still has room for further development. First, the number of peptide-spectrum matches (PSMs) identified in some but not all search engines grows at combinatorial rates as more search engines are considered for integration, and the scores must be properly calibrated for the PSMs identified by individual search engines to control the overall identification error rates in a unified manner. Second, since some search engines only report the best matching peptide sequence for each spectrum, potential matches to lower-ranking peptides are ignored in the report even if individual scores for those secondary matches are nearly as good as the best match score and thus are likely true hits. If data are integrated from different search engines, one must include lower-ranking PSMs from every search engine and recalibrate the scores into a unified score as was done in Searle et al11
. The strategy of integrating data after the selection of high confidence PSMs (i.e.
, leaving out lower-ranking scores) may lead to inaccurate estimation of integrative probability scores unless search engines are sufficiently homogeneous12–14
To address these issues, we developed a unified probabilistic approach for the integrative analysis of unique PSMs, termed MSblender (). We use probability mixture models for distinguishing correct and incorrect identifications. The score distributions across search engines are jointly modeled using multivariate distributions up to the number of observed dimensions to accommodate the correlation in raw search scores. Using this model, MSblender computes a unified posterior probability of correct identification for all PSMs identified by search engines. The conversion into posterior probabilities automatically calibrates PSM scores reported by individual search engines in two ways: (1) the likelihood is marginalized to the search engines identifying individual PSMs, and (2) prior probability is adjusted for different combinations of search engines. More importantly, MSblender pools raw search scores for every possible PSM and directly models the distribution for all listed scores from the beginning, so it is not necessary to revisit lower-ranking PSMs to account for the PSMs not agreed upon by all search engines.
Figure 1 Schematic view of MSblender. Each spectrum is listed with all unique peptide assignments across the database search engines considered for integration. MSblender considers all different cases. PSMs may be found by some search engines, but not by others (more ...)
We evaluate the performance of MSblender with respect to peptide identification and protein quantification by spectral counting using three independent datasets. First, we use a yeast dataset (Yeast YPD hereafter) to assess the sensitivity and specificity profile for bona fide identifications, where high-confidence identifications reproducibly reported in multiple published datasets can be used as a benchmark set. Next, we include a (Sigma) UPS2 dataset featuring a simple mixture of 48 human proteins, where concentrations are known for all proteins and thus the accuracy in both identification and quantification can be evaluated. Lastly, we use a dataset (iPRG09) from an Association of Biomolecular Resource Facilities (ABRF) proteome informatics research group (iPRG) 2009 study consisting of two biological samples, in which proteins present in only one sample are known and thus the influence of improved identifications can be evaluated by differential expression analysis. Through these examples, we show that integrative analysis by MSblender increases the number of identifications substantially with accurate estimation of low false discovery rate (FDR), and it improves quantitative analysis of protein concentrations.