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UPS1

Sigma-Aldrich

Universal Proteomics Standard Set

Protein Mass Spectrometry Calibration Standard

Synonym(s):

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

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

Kit Components Also Available Separately

Product No.
Description
SDS
  • T6567Trypsin from porcine pancreas, Proteomics Grade, BioReagent, Dimethylated 20 μgSDS

Signal Word

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 Code

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

WGK

WGK 3

Certificates of Analysis (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
Chia-Yu Yen et al.
Molecular & cellular proteomics : MCP, 10(7), M111-M111 (2011-05-03)
The unambiguous assignment of tandem mass spectra (MS/MS) to peptide sequences remains a key unsolved problem in proteomics. Spectral library search strategies have emerged as a promising alternative for peptide identification, in which MS/MS spectra are directly compared against a
Christian J Koehler et al.
Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry, 25(1), 61-66 (2019-11-02)
Proteolytic digestion prior to LC-MS analysis is a key step for the identification of proteins. Digestion of proteins is typically performed with trypsin, but certain proteins or important protein sequence regions might be missed using this endoproteinase. Only few alternative
Amanda G Paulovich et al.
Molecular & cellular proteomics : MCP, 9(2), 242-254 (2009-10-28)
Optimal performance of LC-MS/MS platforms is critical to generating high quality proteomics data. Although individual laboratories have developed quality control samples, there is no widely available performance standard of biological complexity (and associated reference data sets) for benchmarking of platform
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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