about
OpenMS: a flexible open-source software platform for mass spectrometry data analysisFast and Efficient XML Data Access for Next-Generation Mass SpectrometryxTract: software for characterizing conformational changes of protein complexes by quantitative cross-linking mass spectrometryNumerical compression schemes for proteomics mass spectrometry dataaLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data.DIANA--algorithmic improvements for analysis of data-independent acquisition MS data.Identification of a Set of Conserved Eukaryotic Internal Retention Time Standards for Data-independent Acquisition Mass SpectrometryReproducible quantitative proteotype data matrices for systems biology.Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms.Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics.A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis.The Mtb proteome library: a resource of assays to quantify the complete proteome of Mycobacterium tuberculosis.Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system.A repository of assays to quantify 10,000 human proteins by SWATH-MS.Efficient visualization of high-throughput targeted proteomics experiments: TAPIR.A computational tool to detect and avoid redundancy in selected reaction monitoring.BioContainers: An open-source and community-driven framework for software standardization.Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS.TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomicsA multicenter study benchmarks software tools for label-free proteome quantification.pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library.Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.Quantitative proteomics: challenges and opportunities in basic and applied research.Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps.Systems-level Proteomics of Two Ubiquitous Leaf Commensals Reveals Complementary Adaptive Traits for Phyllosphere Colonization.Absolute Proteome Composition and Dynamics during Dormancy and Resuscitation of Mycobacterium tuberculosis.OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.Integrative Personal Omics Profiles during Periods of Weight Gain and Loss.Heterogeneous Ribosomes Preferentially Translate Distinct Subpools of mRNAs Genome-wide.From hype to reality: data science enabling personalized medicineStatistical elimination of spectral features with large between-run variation enhances quantitative protein-level conclusions in experiments with data-independent spectral acquisitionAutomated SWATH Data Analysis Using Targeted Extraction of Ion ChromatogramsHigh-frequency actionable pathogenic exome variants in an average-risk cohortDIAlignR Provides Precise Retention Time Alignment Across Distant Runs in DIA and Targeted ProteomicsMachine Learning in Mass Spectrometric Analysis of DIA DataLongitudinal multi-omics of host-microbe dynamics in prediabetesDeep learning adds an extra dimension to peptide fragmentationAmino acid and lipid metabolism in post-gestational diabetes and progression to type 2 diabetes: A metabolic profiling study
P50
Q28278187-0821AB48-2100-4A59-B666-B7B53193D0C5Q28648175-02AEAB37-D1B7-4CF6-83D5-26D6A7BBA4A3Q28828370-4374B42D-B754-4413-BB26-1A63F6F89601Q30787624-1090A22A-F823-49A4-A9BB-A2C4EE968EDBQ30806107-A382D926-8AE8-4C6B-8602-2B5DDFD416AFQ30864255-5A53E022-D1EF-4DFF-8526-EFC44C87B933Q30982025-6B2DE724-8ED8-4FBF-83D3-CCC6D4CCAD07Q31018998-BAEC8643-AE72-4D3E-821D-376C7B87B77EQ31161210-BCEE815B-DA86-4482-8EFB-C6D7D1C6EF01Q34133645-08D24AC0-442D-4577-B9C8-B7CF1D3CBCA4Q34333925-777D68E7-4170-49B7-ADA0-924BF2C7FF9AQ34555110-0BC0A013-B24E-4462-A3A7-6AD9E804D5B9Q34727765-4E87CB24-5A8D-4217-87CE-FDEDF28F8588Q35026886-A5A7408C-11F5-42CC-A5E7-D24C0EC7CC6BQ35066504-1AC4DB7B-496B-4EB1-A0E7-3A84652BF841Q35581032-3C8320D0-39FF-48B8-AEEB-CF275F9A797FQ36144204-A7E0A0C1-7026-4A82-AB2E-E90C04CAFDEBQ36335520-D31AD31A-AB20-4739-AE51-83891B3416C1Q36399003-14A1C423-EA04-4E35-8078-03E9E5C57F68Q37225838-E6715E66-08A0-426B-9C0C-364ADF130B19Q37432423-6D69FF35-572F-4BE9-9A7B-BCAC5058FE3EQ38451051-A7C85DE8-2136-4709-901B-556AB13B81F8Q38616270-0A8C3DBD-173B-4050-B8C6-47D03D8F8957Q38676266-3821E7C9-79E7-41B5-87F6-252065164CF8Q39404529-F551F1E3-18F5-4956-9139-F28136B17636Q39560421-D3FFE0CF-F719-49B1-8353-7F79BB8FFFA6Q40816049-1330F75B-4196-4420-B899-3A0D94AA9D1AQ48317444-DACDB665-8390-4F38-8717-977F71FDDF49Q50110810-C669D572-87B4-486B-BA16-6538EFE4B18BQ50434557-33E99B57-E365-402F-9F17-6E8451795E65Q57167337-14B679A3-30D5-463B-AA32-A6A94C339D6CQ59639165-4FFE3F9B-EDD0-402C-951E-5E8510D29A4CQ60548401-793D2398-D24B-47DA-BD3D-E7EBFE622CE7Q60922248-FB3DDCB8-05AC-4B10-8567-D95F03C1E4B2Q63493258-E90C4CAC-28E0-4487-ACCA-14B130D5B27BQ89726822-B4CD88EE-1392-4E58-B4CB-15D621BC8F0EQ92383029-1942CB38-7222-4285-BF42-6F60751A42E3Q92419751-1EAC0199-EA2C-4BDC-9509-44437700BA29Q95322052-098BFCC6-D982-41AD-A065-4369B494C966
P50
description
hulumtues
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onderzoeker
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հետազոտող
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name
Hannes L Röst
@nl
Hannes L Röst
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Hannes L. Röst
@en
Hannes L. Röst
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Hannes L Röst
@nl
Hannes L Röst
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Hannes L. Röst
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Hannes Roest Steiner
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Hannes Rost Steiner
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Hannes Rost
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Hannes L Röst
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Hannes L Röst
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Hannes L. Röst
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Hannes L. Röst
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P1053
E-9564-2013
P106
P1153
55253396000
57194558774
P21
P31
P3829
P496
0000-0003-0990-7488