Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, as exemplified by the technique SWATH-MS, has emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale data sets. We demonstrate that statistical concepts developed for discovery proteomics based on spectrum-centric scoring can be adapted to large-scale DIA experiments that have been analyzed with peptide-centric scoring strategies, and we provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent the accumulation of false positives across large-scale data sets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for the detected peptide queries, peptides and inferred proteins.

Mass spectrometry with data-independent acquisition (DIA) is a promising method to improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory by systematically measuring all peptide precursors in a biological sample. However, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms (SNPs) and alternative site localizations in phosphoproteomics data. We report Specter (https://github.com/rpeckner-broad/Specter), an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly through comparison to a spectral library, thus circumventing the problems associated with typical fragment-correlation-based approaches. We validate the sensitivity of Specter and its performance relative to that of other methods, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECAN to build a plasma proteome library from DIA data and to query known sequence variants.

In mass spectrometry-based proteomics, data-independent acquisition (DIA) strategies can acquire a single data set useful for both identification and quantification of detectable peptides in a complex mixture. However, DIA data are noisy owing to a typical five- to tenfold reduction in precursor selectivity compared to data obtained with data-dependent acquisition or selected reaction monitoring. We demonstrate a multiplexing strategy, MSX, for DIA analysis that increases precursor selectivity fivefold.

Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data.