We have developed and implemented a sequence identification algorithm (inSeq) that processes tandem mass spectra in real-time using the mass spectrometer's (MS) onboard processors. The inSeq algorithm relies on accurate mass tandem MS data for swift spectral matching with high accuracy. The instant spectral processing technology takes ∼16 ms to execute and provides information to enable autonomous, real-time decision making by the MS system. Using inSeq and its advanced decision tree logic, we demonstrate (i) realtime prediction of peptide elution windows en masse (∼3 min width, 3,000 targets), (ii) significant improvement of quantitative precision and accuracy (~3x boost in detected protein differences), and (iii) boosted rates of posttranslation modification site localization (90% agreement in real-time vs. offline localization rate and an approximate 25% gain in localized sites). The decision tree logic enabled by inSeq promises to circumvent problems with the conventional data-dependent acquisition paradigm and provides a direct route to streamlined and expedient targeted protein analysis.T he shotgun sequencing method has rapidly evolved over the past two decades (1, 2). In this strategy eluting peptide cations have their mass-to-charge (m∕z) values measured in the MS 1 scan. Then precursor m∕z values are selected for a series of sequential tandem MS events (MS 2 ). This succession is cycled for the duration of the analysis. The process, called data-dependent acquisition (DDA), is at the very core of shotgun analysis and has not changed for over 15 y, however, MS hardware has. Major improvements in MS sensitivity, scan rate, mass accuracy, and resolution have been achieved. Orbitrap hybrid systems, for example, routinely achieve low ppm mass accuracy with MS/MS repetition rates of 5-10 Hz (3, 4). Constant operation of such systems generates hundreds of thousands of spectra in hours. These MS 2 spectra are then mapped to sequence using database search algorithms (5-7).The DDA sampling strategy offers an elegant simplicity and has proven highly useful for discovery-driven proteomics. Of recent years, however, emphasis has shifted from identification to quantification-often with certain targets in mind. In this context, faults in the DDA approach have become increasingly evident. There are two primary limitations of the DDA approach: First, is poor run-to-run reproducibility and, second, is the inability to effectively target peptides of interest (8). Hundreds of peptides often coelute so that low-level signals often are selected in one run and not the next, and selecting m∕z peaks to sequence by abundance certainly does not offer the opportunity to inform the system of preselected targets.Several DDA add-ons and alternatives have been examined. Sampling depth, for example, can be increased by preventing selection of an m∕z value identified in a prior technical replicate (PAnDA) (9). Irreproducibility can be somewhat countered by informing the DDA algorithm of the precursor m∕z values of desired targets (inclu...