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

MaxQuant.Live enables global targeting of more than 25,000 peptides

(2019) Christoph Wichmann et al.   MOLECULAR & CELLULAR PROTEOMICS

Evaluating Chromatographic Approaches for the Quantitative Analysis of a Human Proteome on Orbitrap-Based Mass Spectrometry Systems

(2019) Ying Zhang et al.   JOURNAL OF PROTEOME RESEARCH

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

(2019) Siegfried Gessulat et al.   NATURE METHODS

High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis

(2019) Shivani Tiwary et al.   NATURE METHODS

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

(2018) Christina Ludwig et al.   Molecular Systems Biology

OUP accepted manuscript

(2018)   NUCLEIC ACIDS RESEARCH

Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry

(2018) Ngoc Hieu Tran et al.   NATURE METHODS

Performance Evaluation of the Q Exactive HF-X for Shotgun Proteomics

(2017) Christian D. Kelstrup et al.   JOURNAL OF PROTEOME RESEARCH

Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results

(2017) Roland Bruderer et al.   MOLECULAR & CELLULAR PROTEOMICS

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

(2017) George Rosenberger et al.   NATURE METHODS

An Optimized Shotgun Strategy for the Rapid Generation of Comprehensive Human Proteomes

(2017) Dorte B. Bekker-Jensen et al.   Cell Systems

Peptide retention time prediction

(2016) Luminita Moruz et al.   MASS SPECTROMETRY REVIEWS

A multicenter study benchmarks software tools for label-free proteome quantification

(2016) Pedro Navarro et al.   NATURE BIOTECHNOLOGY

High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation

(2016) Roland Bruderer et al.   PROTEOMICS

Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data

(2015) Ying S. Ting et al.   MOLECULAR & CELLULAR PROTEOMICS

Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues

(2015) Roland Bruderer et al.   MOLECULAR & CELLULAR PROTEOMICS

Deep learning

(2015) Yann LeCun et al.   NATURE

Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files

(2015) Yuanyue Li et al.   NATURE METHODS

MSPLIT-DIA: sensitive peptide identification for data-independent acquisition

(2015) Jian Wang et al.   NATURE METHODS

DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics

(2015) Chih-Chiang Tsou et al.   NATURE METHODS

MS2PIP prediction server: compute and visualize MS2peak intensity predictions for CID and HCD fragmentation

(2015) Sven Degroeve et al.   NUCLEIC ACIDS RESEARCH

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

(2014) Hannes L Röst et al.   NATURE BIOTECHNOLOGY

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

(2014) George Rosenberger et al.   Scientific Data

Application of modern reversed-phase peptide retention prediction algorithms to the Houghten and DeGraw dataset: Peptide helicity and its effect on prediction accuracy

(2012) Janice Reimer et al.   JOURNAL OF CHROMATOGRAPHY A

MS-Simulator: Predicting Y-Ion Intensities for Peptides with Two Charges Based on the Intensity Ratio of Neighboring Ions

(2012) Shiwei Sun et al.   JOURNAL OF PROTEOME RESEARCH

Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis

(2012) Ludovic C. Gillet et al.   MOLECULAR & CELLULAR PROTEOMICS

The development of selected reaction monitoring methods for targeted proteomics via empirical refinement

(2012) Michael S. Bereman et al.   PROTEOMICS

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

(2012) Claudia Escher et al.   PROTEOMICS

A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease

(2011) Terri A Addona et al.   NATURE BIOTECHNOLOGY

On the Accuracy and Limits of Peptide Fragmentation Spectrum Prediction

(2010) Sujun Li et al.   ANALYTICAL CHEMISTRY

Training, Selection, and Robust Calibration of Retention Time Models for Targeted Proteomics

(2010) Luminita Moruz et al.   JOURNAL OF PROTEOME RESEARCH

High-Abundance Polypeptides of the Human Plasma Proteome Comprising the Top 4 Logs of Polypeptide Abundance

(2008) G. L. Hortin et al.   CLINICAL CHEMISTRY