A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry

[1]  Maohui Luo,et al.  DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins , 2023, bioRxiv.

[2]  Yaoyang Zhang,et al.  Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics , 2023, Nature communications.

[3]  O. Ohara,et al.  Data-Independent Acquisition Mass Spectrometry-Based Deep Proteome Analysis for Hydrophobic Proteins from Dried Blood Spots Enriched by Sodium Carbonate Precipitation. , 2021, Methods in molecular biology.

[4]  Sean J. Humphrey,et al.  MaxDIA enables library-based and library-free data-independent acquisition proteomics , 2021, Nature Biotechnology.

[5]  Chunhui Yuan,et al.  ProteomeExpert: a Docker image-based web server for exploring, modeling, visualizing and mining quantitative proteomic datasets , 2021, Bioinform..

[6]  Ben C. Collins,et al.  diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition , 2020, Nature Methods.

[7]  Arnaud Droit,et al.  Extensive and accurate benchmarking of DIA acquisition methods and software tools using a complex proteomic standard , 2020, bioRxiv.

[8]  Hongwen Zhu,et al.  Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies , 2020, Nature Communications.

[9]  Xue Cai,et al.  Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020 , 2020, Proteomics.

[10]  Ludovic C. Gillet,et al.  Publisher Correction: OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data , 2020, Nature Biotechnology.

[11]  Olga Vitek,et al.  Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition* , 2019, Molecular & Cellular Proteomics.

[12]  Christoph B. Messner,et al.  DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput , 2019, Nature Methods.

[13]  Roland Bruderer,et al.  Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy. , 2019, Molecular omics.

[14]  William Stafford Noble,et al.  Speeding up Percolator. , 2019, Journal of proteome research.

[15]  Mathias Wilhelm,et al.  Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning , 2019, Nature Methods.

[16]  Michael J MacCoss,et al.  Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry , 2018, Nature Communications.

[17]  Michael J MacCoss,et al.  Statistical control of peptide and protein error rates in large-scale targeted DIA analyses , 2017, Nature Methods.

[18]  Alexey I Nesvizhskii,et al.  New targeted approaches for the quantification of data‐independent acquisition mass spectrometry , 2017, Proteomics.

[19]  Yasset Perez-Riverol,et al.  A multi-center study benchmarks software tools for label-free proteome quantification , 2016, Nature Biotechnology.

[20]  Ruedi Aebersold,et al.  Mass-spectrometric exploration of proteome structure and function , 2016, Nature.

[21]  Brett Larsen,et al.  Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry , 2016, bioRxiv.

[22]  Oliver M. Bernhardt,et al.  Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues* , 2015, Molecular & Cellular Proteomics.

[23]  Lars Malmström,et al.  DIANA - algorithmic improvements for analysis of data-independent acquisition MS data , 2015, Bioinform..

[24]  Ludovic C. Gillet,et al.  Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps , 2015, Nature Medicine.

[25]  Chih-Chiang Tsou,et al.  DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics , 2015, Nature Methods.

[26]  Eric W. Deutsch,et al.  A repository of assays to quantify 10,000 human proteins by SWATH-MS , 2014, Scientific Data.

[27]  Ludovic C. Gillet,et al.  Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* , 2012, Molecular & Cellular Proteomics.

[28]  John D. Venable,et al.  Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra , 2004, Nature Methods.

[29]  R. Doolittle,et al.  A simple method for displaying the hydropathic character of a protein. , 1982, Journal of molecular biology.

[30]  Patrick G. A. Pedrioli,et al.  Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses in , 2017 .

[31]  Jin-Hwan Cho.  Software & Tools , 2009 .

[32]  Brendan MacLean,et al.  Bioinformatics Applications Note Gene Expression Skyline: an Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments , 2022 .