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UPS1

Sigma-Aldrich

Universal Proteomics Standard Set

Protein Mass Spectrometry Calibration Standard

Sinónimos:

Standard Set

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About This Item

EC Number:
232-650-8
UNSPSC Code:
12161503
NACRES:
NA.24

form

ready-to-use solution

Quality Level

200

quality

Protein Mass Spectrometry Calibration Standard

technique(s)

mass spectrometry (MS): suitable

storage temp.

−20°C

Categorías relacionadas

General description

The Universal Proteomics Standard (UPS) Set was developed in collaboration with the Association of Biomolecular Resource Facilities (ABRF) Proteomics Standards Research Group (sPRG). This protein mixture was extensively evaluated and reported under the direction of ABRF′s sPRG during a comprehensive 2005/2006 study. The findings of the study were presented at the ABRF 2006 and US HUPO 2006 conferences.

Application

The Universal Proteomics Standard (UPS) Set is intended to standardize and/or evaluate mass spectrometric (e.g., LC-MS/MS, MALDI-TOF-MS, etc.) and electrophoretic analysis conditions prior to the analysis of complex protein samples. Potential uses include:
  • Bracketing critical experimental datasets for confirming the robustness of analysis methods
  • Comparison of MS or other proteomic data that are generated in different labs using a variety of analytical strategies and instruments
  • Identifying limitations of proteomics analysis systems and search algorithms
  • An external reference to assist with the evaluation of data derived from poorly defined samples

Features and Benefits

Discover the Benefits for Yourself!
  • Test the power of your analytical strategy
  • Troubleshoot and optimize your analytical protocol
  • Confirm system suitability before analyzing critical samples
  • Normalize analytical results day to day or lab to lab

Los componentes del kit también están disponibles por separado

Referencia del producto
Descripción
SDS
  • T6567Trypsin from porcine pancreas, Proteomics Grade, BioReagent, Dimethylated 20 μgSDS

related product

Referencia del producto
Descripción
Precios

signalword

Danger

Hazard Classifications

Acute Tox. 4 Oral - Eye Dam. 1 - Repr. 1B - Resp. Sens. 1 - Skin Irrit. 2 - STOT SE 3

target_organs

Respiratory system

Storage Class

6.1C - Combustible acute toxic Cat.3 / toxic compounds or compounds which causing chronic effects

wgk_germany

WGK 3

Certificados de análisis (COA)

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Min-Sik Kim et al.
Journal of the American Society for Mass Spectrometry, 21(9), 1606-1611 (2010-05-21)
Shotgun proteomics has been used extensively for characterization of a number of proteomes. High-resolution Fourier transform mass spectrometry (FTMS) has emerged as a powerful tool owing to its high mass accuracy and resolving power. One of its major limitations, however
Henrik Molina et al.
Analytical chemistry, 80(13), 4825-4835 (2008-06-11)
Electron transfer dissociation (ETD) is a recently introduced mass spectrometric technique which has proven to be an excellent tool for the elucidation of labile post-translational modifications such as phosphorylation and O-GlcNAcylation of serine and threonine residues. However, unlike collision induced
Leslie Muller et al.
Proteomics, 16(23), 2953-2961 (2016-10-18)
Sample preparation, typically by in-solution or in-gel approaches, has a strong influence on the accuracy and robustness of quantitative proteomics workflows. The major benefit of in-gel procedures is their compatibility with detergents (such as SDS) for protein solubilization. However, SDS-PAGE
Mark V Ivanov et al.
Journal of proteome research, 13(4), 1911-1920 (2014-02-28)
Data-dependent tandem mass spectrometry (MS/MS) is one of the main techniques for protein identification in shotgun proteomics. In a typical LC-MS/MS workflow, peptide product ion mass spectra (MS/MS spectra) are compared with those derived theoretically from a protein sequence database.
Matthew The et al.
Nature communications, 11(1), 3234-3234 (2020-06-28)
In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis. Here, we propose a quantification-first approach
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