key: cord-0296482-s0z61ujg
authors: Yang, Yi; Cao, Weiqian; Yan, Guoquan; Kong, Siyuan; Wu, Mengxi; Yang, Pengyuan; Qiao, Liang
title: GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control
date: 2021-03-20
journal: bioRxiv
DOI: 10.1101/2021.03.20.436117
sha: eb2fdaa198edaf0fccf61bf0b506eb2ce5af5417
doc_id: 296482
cord_uid: s0z61ujg

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further adapt a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data dependent acquisition (DDA) based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.

is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further adapt a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data dependent acquisition (DDA) based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies. 3 

Protein glycosylation is one of the most abundant and heterogeneous post-translational modifications (PTMs) that provides great proteomic diversity and plays a key role in various biological processes [1] [2] [3] , even the host-pathogen interaction of the ongoing coronavirus disease 2019 pandemic 4 . Precise characterization of protein glycosylation is critical for understanding mechanism of diseases 5, 6 , discovery of biomarkers for diagnosis 7 , and development of drugs and vaccines 8 . The high heterogeneity of glycans across glycosites results in an increased number of glycoproteoforms. Profiling of intact glycopeptide provides the opportunity of simultaneous analysis of glycans, glycosite occupancy and site-specific glycosylation on a proteome-wide scale 9 , and is an imperative but still challenging component to modern glycoproteomic studies 10 .

Currently, liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is the method of choice widely used in proteomics and glycoproteomics 11, 12 .

Novel MS/MS fragmentation methods derived from higher-energy collisional dissociation (HCD) and electron transfer dissociation (ETD), such as stepped collision energy HCD (SCE-HCD) 13 and ETD with supplemental HCD (EThcD) 14 , have been proven powerful for intact glycopeptides profiling. The most common strategy for peptide identification uses the data-dependent acquisition (DDA) approach, in which MS/MS (MS2) fragmentation is triggered by precursor ions observed in a full mass range survey scan (MS1). Various software tools 15 , such as pGlyco 2.0 16 , MSFragger-Glyco 17 and O-Pair Search 18 , have been developed for the interpretation of DDA data of intact glycopeptides. However, a major bottleneck of the DDA approach is that the precursor selection constitutes a stochastic element 19 , resulting in the "missing value" problem.

To overcome this limitation, data-independent acquisition (DIA) methods have been proposed [20] [21] [22] , including a representative variant named sequential window acquisition of all theoretical mass spectra (SWATH-MS) 23 , where the instrument acquires fragmentation information of all precursor ions within defined isolation windows in a systematic manner. DIA has been reported to achieve deep proteome coverage capabilities with quantitative consistency and accuracy for large-scale proteomic 4 studies 24 , and is now starting to be applied to the field of glycoproteomics 25 Error rate control for glycopeptide identification is essential but particularly complicated in DIA analyses due to the increased complexity of DIA MS/MS spectra originated from multiple co-eluted precursors, especially when using wide isolation windows. In the case of HCD MS/MS, the same set of fragment ions could be generated for glycopeptides common in peptide sequence but different in glycan, resulting in a high level of misinterpretations of DIA data 25 . Although a few studies have elucidated error rate estimation for DDA-based glycopeptide analyses 16, [32] [33] [34] , statistical control of DIA-based proteome-wide glycopeptides analyses, to the best of our knowledge, has not been properly addressed.

Herein, we propose GproDIA, a pipeline that applies the concept of peptide-centric DIA analysis to proteome-wide characterization of intact glycopeptides. GproDIA provides comprehensive statistical control by a 2-dimentional false discovery rate (FDR) approach and a glycoform inference algorithm, enabling accurate glycopeptide identification using wide isolation windows. We further adapt a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries for glycopeptides. We benchmark GproDIA for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms DDA in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. 5 

GproDIA was developed based on the principle of peptide-centric analysis, which has been commonly used for detection of peptides from DIA data 24 . The workflow is presented in Fig. 1 . First, a spectral library of glycopeptides is built by DDA with prefractionation or using a long LC gradient. As LC conditions are different from those used for non-glycosylated peptides, instead of using iRT 35 including peptide fragments (b/y, with or without one HexNAc residue and its crossring fragment) and intact peptide with glycan fragments (Y) (Fig. 1a) . Next, three types of decoy libraries are generated by adding random mass shifts to glycan fragment peaks 36 (glycan decoy), reversing the peptide sequences (peptide decoy), and performing the two operations successively (both decoy) (Fig. 1b) .

Then OpenSWATH 37 is used to extract chromatogram data of the target glycopeptides and the decoys from the DIA data (Fig. 1c) , and the extracted peak group features are scored using a semi-supervised learning approach implemented by PyProphet 38 . A 2-dimentional FDR approach is used to estimate error rates of identification (Fig. 1d) . In brief, the distributions of discriminant scores (D-scores) of the targets and decoys are fitted using a bivariate four-groups mixture model. Local FDR (namely posterior error probability, PEP) of each target peak group is computed from D-score density (Supplementary Fig. 2) . Global FDR (q-value) is then derived from PEPs. Finally, TRIC 39 is used for multi-run alignment to reduce missing values ( Fig. 1e ). values. (c-e) Green color indicates target peak groups, yellow indicates peptide decoy peak groups, blue indicates glycan decoy peak groups, and red indicates both decoy peak groups.

For benchmark purposes, we performed DDA and DIA experiments using an 1 h LC gradient with 4 technical replicates, as well as a DDA using a 6 h LC gradient with 3 technical replicates, on a sample of fission yeast (Schizosaccharomyces pombe) glycopeptides. pGlyco 2.0 16 was used for database searching of the DDA data, and 1% GPSM-level FDR cutoff was used. A sample-specific spectral library (fission yeast SSL,

GproDIA. For DIA, results with q-value < 5% in each run and q-value < 1% in at least one run at peak group level, as well as q-value < 1% at glycopeptide level in the global context 38 , were reported. Statistics of the results are shown in Fig. 2 and Supplementary Fig. 3 -5. In average, 418 ± 2 (mean ± standard deviation, sic passim) glycopeptide precursors corresponding to 348 ± 2 site-specific glycans were detected per replicate run from the DIA data ( Fig. 2a and Supplementary Fig. 5a ), more than those identified from the 1 h DDA (357 ± 16 precursors and 293 ± 9 site-specific glycans, Supplementary Fig. 3a ) and the 6 h DDA (351 ± 12 precursors and 289 ± 9 site-specific glycans, Supplementary Fig. 4a) . Notably, we use the term "site-specific glycan" referring glycans on specific glycosylation sites in a group of glycoproteins that are not distinguishable by protein inference 16 , rather than positions of glycans on peptide sequences. Indeed, it is not very common that an N-glycopeptide has more than one potential glycosylation sites. Considering identifications shared in >50% replicate runs, DIA detected 21% more (418/346) glycopeptide precursors, 20% more (346/288) sitespecific glycans, and 19% more (139/117) protein glycosites than 6 h DDA, as well as 84% more (418/227) precursors, 66% more (346/208) site-specific glycans, and 14% more (139/122) protein glycosites than 1 h DDA ( Fig. 2c and Supplementary Fig. 5c ).

It should be noticed that a less strict error rate control was applied on DDA results (only a GPSM-level FDR cutoff) than that on DIA results (peak group q-value and global glycopeptide q-value). Coefficients of variation (CVs) of glycopeptide precursor, sitespecific glycan and protein glycosite quantification results were calculated among the technical replicates as shown in Fig. 2d and Supplementary Fig. 3c, 4c and 5d . The median CVs were ~11% using DIA, much smaller than those using 1 h DIA (~17% at precursor level, ~23% at site-specific glycan level, and ~37% at protein glycosite level) and 6 h DDA (>32%). We also present the DIA results without multi-run alignment (1% peak group q-value and 1% global glycopeptide q-value) in Supplementary Fig. 6 , wherein glycopeptides identification and quantification results close to the ones with multi-run alignment were obtained. The results indicate that the DIA workflow using GproDIA outperforms DDA not due to the multi-run alignment, but originated from the inherent feature of systematic and panoramic MS/MS recording in DIA that provides broadly informative data. 10 As an alternative to sample-specific libraries, community spectral libraries such as Pan-Human 40 can be effectively used for peptide-centric DIA data analysis 41 . We tested the feasibility of using a lab repository-scale spectral library (fission yeast LRL, Table 1 ) generated by combining the SSL library and fission yeast data of previous projects in our labs. The results are presented in Supplementary Fig.   7 . Using LRL, 18% more (495/418) glycopeptide precursors, 14% more (394/346) sitespecific glycans, and 3% more (143/139) protein glycosites were detected in >50% replicate runs than using SSL, while the CVs (~11%) were very close to those using SSL. DIA analysis was also performed on a budding yeast (Saccharomyces cerevisiae) sample with 3 technical replicates in an 1 h LC gradient, using a spectral library built from budding yeast DDA data (Supplementary Table 1 ). Low levels of missing values and CVs were also observed ( Supplementary Fig. 8 ). All the results suggest that, within the tested condition, DIA with GproDIA improves the number of identifications and reproducibility of glycopeptide characterization compared to DDA-based workflows.

We further compared DDA and DIA on a human serum sample acquired with 3 technical replicates using the 1 h LC gradient (Supplementary Fig. 9 and 10) . The DIA data were analyzed using a sample-specific spectral library built by DDA with prefractionation (serum SSL, Supplementary Table 1) . Unlike high-mannose-type glycans of yeast, glycans in human serum have more complex compositions, presenting greater challenges for DIA analysis. For yeast samples, glycopeptides with the same peptide sequence and different glycans (HexNAc2Hexn) have mass differences of at least one hexose residue (162 Da), and are hardly co-fragmented in an isolation window (25 m/z in this study). For human serum samples, however, DIA analysis suffers from potential interference of glycopeptides with the same peptide sequence but different glycans (referred to as "glycoforms") in the same isolation window. Therefore, although a large number of glycopeptides were reported by DIA (Supplementary Fig. 10) , the results can have high error rates of identification in the glycan part. The performance of the glycoform-level FDR control was tested on the fission yeast data using an entrapment strategy by adding glycopeptides with peptide sequences (peptide entrapment) or glycans (glycan entrapment) or both (both entrapment) from human serum SSL to the fission yeast SSL library. In all the analyses, we kept the number of entrapment glycopeptide precursors similar to that of the yeast library (Supplementary Table 1 ). Since we focus on investigating the performance of the glycoform-level error rate control, multi-run alignment was not performed, and no global glycopeptide-level q-value filter was applied. The DIA analyses results are presented in Fig. 3d . Using the entrapment library containing human glycopeptides 13 (both entrapment), fewer identifications were observed compared to those using the fission yeast library, because the entrapment library contains a large fraction of "false targets" that are not detectable in the sample (referred to as π0 38 ), compromising the detection sensitivity. Nevertheless, no entrapment identifications were observed at 1% peak group-level q-value. Similar results were obtained using the entrapment library containing glycopeptides with peptide sequences from human and glycans from yeast (peptide entrapment), suggesting satisfactory performance of error rate control in the peptide part. Using the entrapment library containing glycopeptides with peptide sequences from yeast and glycans from human (glycan entrapment), although 1% peak group-level q-value filter was applied, there were in average 14% of entrapment identifications (to all of those identified) remaining per run without glycoform inference. After applying 1% glycoform-level q-value filter, despite a loss of 15% yeast identifications due to poor signals of precursors and/or glycoform-specific fragments, the entrapment percentage declined to ~1%.

GproDIA was then tested on the human serum data with glycoform inference enabled.

In addition to the sample-specific library (serum SSL), a lab repository-scale spectral library (serum LRL, Supplementary Table 1 ) was also used for DIA data analysis.

Global glycopeptide-level q-value cutoff was 1%. After multi-run alignment, results with glycoform-level q-value < 5% in each run and < 1% in at least one run were finally reported (Supplementary Fig. 12 and 13) . Comparison between DDA and DIA results is illustrated in Fig. 4a- changes of the organisms (1:0.9:0.8 (S10:S12:S15) for human and 1:1.1:1.2 (S10:S12:S15) for yeast).

The performance of GproDIA was further evaluated on data of mixed-organism samples containing glycopeptides from budding yeast and human serum with different abundance (S10, S12, and S15). DIA analysis was performed using a combined library of the budding yeast library and the serum SSL library, as well as a combined library of the budding yeast library and the serum LRL library, respectively (Supplementary Table 1 and Supplementary Fig. 14-16) . Based on the mean quantities in three replicates of each sample, fold changes of detected site-specific glycans of samples S12/S10 and S15/S10 were calculated and visualized in Fig. 4e . Fold changes of human and yeast glycopeptide abundance were closer to the theoretical values using DIAbased quantification at both MS1 and MS2 level with the yeast + serum SSL library than those using DDA. Using the yeast + serum LRL library, fold changes of human glycopeptide abundance were overestimated, while fold changes of yeast glycopeptides were measured accurately. In addition, the distribution of fold changes was less dispersed using DIA than that using DDA. All the results underline the high quality of DIA-based glycopeptide characterization using GproDIA with glycoform inference that outperforms DDA-based workflows in terms of numbers of identifications, as well as accuracy and precision of quantification.

In peptide-centric DIA data analysis, the capability of detection is limited due to the incomplete coverage of spectral libraries. For this reason, we propose a computational approach to expand the coverage of spectral libraries of glycopeptides semi-empirically, 16 wherein the MS2 spectra of predicted glycopeptides are generated by swapping and combining the peptide and glycan fragment peaks in experimental spectra of different glycopeptides using a k-nearest neighbor (KNN) strategy (Fig. 5a) . The RT of the predicted glycopeptides are the weighted mean RT of experimentally identified glycopeptides with the same peptide sequences and close monosaccharide compositions by the KNN strategy.

Cross validations of the prediction were conducted using the fission yeast LRL library and the budding yeast library, wherein the library entry of each glycopeptide precursor was pulled out from the library, and meanwhile the rest of the library entries were used to generate the predicted library entry of the glycopeptide precursor.

Different numbers of nearest neighbors (k) were tested. Dot products (DPs) were computed between the predicted and experimental MS/MS intensities ( Supplementary   Fig. 17a and 18a) . With k = 3, the median DP was 0.820 for the fission yeast library and 0.922 for the budding yeast library, higher than those without KNN and with k = 2 or 4. Increasing k can lead to higher prediction accuracy, but fewer glycopeptides can be predicted because at least k neighboring entries for generating the peptide part and the glycan part are required in the experimental library. Therefore, we chose k = 3 to achieve the trade-off between prediction accuracy and library coverage. With k = 3, the interquartile ranges (IQRs) of the differences between predicted and experimental RTs were <0.6 min (Supplementary Fig. 17b and 18b) . Pearson correlation coefficients (r) of predicted and experimental RTs were >0.99 (Fig. 5d, Supplementary Fig. 17c and   18c ). The serum SSL library was also used for the cross validation with k = 3. The median DP was 0.865 (Fig. 5b) , the IQR of RT differences was 1.1 min (Fig. 5c) , and the r of RTs was >0.98 (Fig. 5d) . The prediction on human serum glycopeptides is less accurate compared to yeast due to the high complexity of human serum glycopeptides.

DPs, RT differences and r were also computed among replicate experimental spectra, which can be considered as possible upper limit of prediction accuracy (Supplementary Fig. 19) , showing that there are still rooms for improvement. Nevertheless, due to the limit of current available data size of glycopeptides and the highly complex isomeric glycan structures, more accurate prediction by machine 17 learning and deep learning strategies can hardly be applied at this stage.

Next, we built a semi-empirical library based on the fission yeast SSL library, which was then merged with the SSL to form an extended library (fission yeast EXL, Table 1) , containing 331 extra precursors and 288 site-specific glycans that are not present in the SSL (Supplementary Fig. 20a) . From the fission yeast DIA data, 5% more (365/346) site-specific glycans were detected in >50% runs using EXL than those using SSL, while the CVs stayed unchanged approximately ( Fig. 5e and   Supplementary Fig. 21) . Performance of error rate control when using extended libraries was also evaluated using the entrapment strategy (Supplementary Fig. 22 ).

An extended library (serum EXL, Supplementary Table 1 Fig. 20b) . Combining all the 3 replicates, 118 protein glycosites were detected in total using both the SSL and EXL libraries. Among them, on 39 glycosites, more glycoforms were detected using EXL than using SSL, while on 23 glycosites, more glycoforms were detected using SSL (Fig. 5f) . The increase in the detected glycoforms can be attributed to the greater proportion of fucosylated glycans (145 site-specific glycans with fucosylation and without sialylation, as well as 208 with both fucosylation and sialylation) detected using EXL than using SSL (124 with fucosylation and without sialylation, as well as 177 with both fucosylation and sialylation). It should be noted that the semi-empirical approach does not increase the protein glycosites. The CVs stayed unchanged approximately using the human serum EXL (Supplementary Fig.   23 ). 

With more efficient usage of ions, DIA can provide a significant increase in the identification efficiency of glycopeptides compared to DDA 25, 44 . We have benchmarked our DIA-based workflow against the DDA-based glycoproteomics, demonstrating that short-gradient DIA can outperform DDA under the same conditions or even with a much longer gradient in terms of detectable glycopeptides as well as measurement reproducibility. It has been reported that DIA copes well with shortening of the LC gradient length because the deterministic nature of the MS2 sampling in DIA attenuates the attrition in number of identifications for shorter separation gradients, while the number of acquired MS/MS spectra and identifications decrease proportionally with the gradient length in DDA mode 24 . As a less time-consuming approach for intact glycopeptides profiling, short-gradient DIA is favorable for large-scale quantitative glycoproteomic analyses.

Interference from other co-eluted and co-fragmented glycopeptides is the main challenge for DIA data analysis that may result in a high level of misinterpretations. In GproDIA, we adapt a 2-dimensional FDR approach and a glycoform inference strategy, providing comprehensive statistical control for glycopeptide identification. Glycoform inference can be used as an option to perform more strict assessment by utilizing signals 20 of precursors and glycan-specific fragments to resolve interference from potential glycoforms. In this study, glycoform inference was enabled when multiple glycopeptide precursors with the same peptide sequence and different glycans are arranged to be fragmented in one isolation window. Despite the herein proposed strategies to control error rates when using wide isolation windows, we still recommend to design the isolation windows properly according to the mass distribution of glycopeptides 30 , if possible, for improving the detection sensitivity. Recent advances in ion mobility spectrometry (IMS) including high field asymmetric waveform ion mobility spectrometry (FAIMS) 45 and parallel accumulation-serial fragmentation (diaPASEF) 46 have achieved rapid improvements in the sensitivity of DIA analysis. We expect that DIA-based glycopeptides profiling can benefit from the enhanced separation of glycoforms by IMS.

Spectral library coverage determines the upper limit of the identification capacity by peptide-centric DIA analysis. To date, the majority of DIA studies have used DDAbased sample-specific spectral libraries, frequently with pre-fractionation (for the serum sample in this study), or sometimes by repeated DDA analysis of nonfractionated samples (for the fission yeast sample) 24 . Other sources can also be considered as a supplement for library completeness. We achieve the best coverage by using repository-scale libraries integrating data from multiple previous projects in our lab. We envision that more "off-the-shelf" glycopeptide libraries will be built and published by the community, just like those for proteomic studies 40 , which can then be used for glycopeptide DIA data analysis.

Since repository-scale libraries may not always be available, we have built semiempirical libraries as an attempt to extend the library coverage. In such predicted libraries, it is common that a significant fraction of glycopeptides are actually not present in the samples at a detectable level, and the large query space may increase the multiple testing burden and compromise detection sensitivity 24 . Instead of enumerating all the peptide-glycan combinations in a spectral library, we suggest researchers focus on a subset of glycoproteins/glycoproteoforms of interest for their specific biological questions. The current proposed strategy facilitates detection of more glycoforms by 21 DIA on protein glycosites that are observed by DDA, but cannot increase the coverage at glycosite level. However, deep learning-based tools such as Prosit 47 Although it is demonstrated here in the context of N-glycoproteomics, the principle of comprehensive statistical control in GproDIA is also applicable to O-glycopeptides for peptide sequence and glycan identification. O-glycopeptides generally have multiple serine and/or threonine residues that serve as potential glycosites.

Unfortunately, HCD is not sufficient for glycosite localization for O-glycopeptides 10 , and to date, HCD is the only method of choice for DIA. We hope that further instrumental development implements ETD-based DIA methods for site-specific Oglycopeptide analyses.

A test version of GproDIA is available on request to the correspondence author at liang_qiao@fudan.edu.cn.

Glycomics: an integrated systems approach to structure-function relationships of glycans

Biological roles of glycans

Site-specific glycan analysis of the SARS-CoV-2 spike

Glycosylation in cellular mechanisms of health and disease

The role of protein glycosylation in Alzheimer disease

Evaluation of the serum N-linked glycome for the diagnosis of cancer and chronic inflammation

Why glycosylation matters in building a better flu vaccine

Comprehensive analysis of protein glycosylation by solid-phase extraction of Nlinked glycans and glycosite-containing peptides

Optimal dissociation methods differ for N-and O-glycopeptides

Glycopeptide analysis, recent developments and applications

Site-specific glycan-peptide analysis for determination of N-glycoproteome heterogeneity

Characterization of glycopeptides using a stepped higher-energy C-trap dissociation approach on a hybrid quadrupole orbitrap

Electron-transfer/higher-energy collision dissociation (EThcD)-enabled intact glycopeptide/glycoproteome characterization

Recent advances in software tools for more generic and precise intact glycopeptide analysis

pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification

Fast and comprehensive N-and Oglycoproteomics analysis with MSFragger-Glyco

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

Repeatability and reproducibility in proteomic identifications by liquid chromatography−tandem mass spectrometry

Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra

Multiplexed MS/MS for improved data-independent acquisition

Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics

Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis

Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial

The role of data-independent acquisition for glycoproteomics

SWATH-MS glycoproteomics reveals consequences of defects in the glycosylation machinery

Data independent analysis of IgG glycoforms in samples of unfractionated human plasma

Site-specific analysis of changes in the 23 glycosylation of proteins in liver cirrhosis using data-independent workflow with soft fragmentation

Adapting data-independent acquisition for mass spectrometry-based protein site-specific N-glycosylation analysis

Glycopeptide variable window SWATH for improved data independent acquisition glycoprotein analysis

Glyco-DIA: A method for quantitative Oglycoproteomics with in silico-boosted glycopeptide libraries

Computational framework for identification of intact glycopeptides in complex samples

A comprehensive, open-source platform for mass spectrometry-based glycoproteomics data analysis

Toward automated N-glycopeptide identification in glycoproteomics

Using iRT, a normalized retention time for more targeted measurement of peptides

pGlyco: A pipeline for the identification of intact N-glycopeptides by using HCD-and CID-MS/MS and MS3

OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data

Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses

TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics

A repository of assays to quantify 10,000 human proteins by SWATH-MS

Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry

Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS

Large-scale identification of N-linked intact glycopeptides in human serum using HILIC enrichment and spectral library search

Rapid and site-specific deep phosphoproteome profiling by dataindependent acquisition without the need for spectral libraries

A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients

diaPASEF: parallel accumulation-serial fragmentation combined with dataindependent acquisition

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics