Anti-Ubiquitin monoclonal antibody (DCABY-4229)

Mouse Anti-Human Ubiquitin monoclonal antibody for WB, ELISA(Cap)


Host Species
Antibody Isotype
Species Reactivity
E. coli-derived recombinant human Ubiquitin+1. Met1-Gly75, YADLREDPDRQDHHPGSGAQ (C-terminal sequence generated by a frameshift) Accession Number CAA44911


Application Notes
Western Blot: 1 μg/mL; ELISA Capture: 2-8 μg/mL; ELISA Detection: 0.5-2.0 μg/mL
We recommend the following for sandwich ELISA (Capture - Detection):
DCABY-4229 - DCABY-4303
*Suggested working dilutions are given as a guide only. It is recommended that the user titrates the product for use in their own experiment using appropriate negative and positive controls.


Alternative Names
UBB; ubiquitin B; polyubiquitin-B; polyubiquitin B
Entrez Gene ID
UniProt ID

Product Background

APC/C-mediated degradation of cell cycle proteins; APC/C:Cdc20 mediated degradation of Cyclin B; APC/C:Cdc20 mediated degradation of Securin; APC/C:Cdc20 mediated degradation of mitotic proteins; APC/C:Cdh1 mediated degradation of Cdc20 and other APC/C:Cd


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Custom Antibody Labeling

We offer labeled antibodies using our catalogue antibody products and a broad range of intensely fluorescent dyes and labels including HRP, biotin, ALP, Alexa Fluor® dyes, DyLight® Fluor dyes, R-phycoerythrin (R-PE), at scales from less than 100 μg up to 1 g of IgG antibody. Learn More

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Set membership identification using SLLE and NMC


Authors: Chai, Wei; Sun, Xianfang; Qiao, JunFei

A set membership identification method by pattern classification is proposed for nonlinear-in-parameter regression models with unknown but bounded(UBB) noises. Suppose that the points in the parameter space can be divided into two classes according to whether they are in the feasible solution set or not, the problem of set membership identification is to construct a pattern classifier to decide which class a point belongs to. The method has three steps. Firstly, the training data are selected uniformly in the parameter space and are decided by equation error whether they are in the feasible solution set. Secondly, supervised locally linear embedding(SLLE) is used to map the training data into low-dimensional space. Thirdly, nearest mean classifier(NMC) is trained on the mapped training data. This method not only can describe the feasible solution set approximately in the high-dimensional parameter space, but also can characterize it in the low-dimensional feature space. Simulation results show the effectiveness of the proposed method.

Identification of reference genes for normalization of gene expression in thoroughbred and Jeju native horse(Jeju pony) tissues


Authors: Ahn, Kung; Bae, Jin-Han; Nam, Kyu-Hwi; Lee, Chong-Eon; Park, Kyung-Do; Lee, Hak-Kyo; Cho, Byung-Wook; Kim, Heui-Soo

Quantitative analysis of horse gene expression profiles under diverse experimental conditions is limited by the lack of reliable reference genes for normalization of mRNA levels. Therefore, in this study, the expression of potential reference genes was compared between thoroughbred and Jeju native horse (Jeju pony). We compared the expression of nine genes by quantitative real-time RT-PCR in fourteen tissues between the two horse breeds and analyzed their stability using the geNorm and NormFinder programs. The data obtained in this study suggest that the UBB gene could serve as a reference gene in gene expression analysis of thoroughbred and Jeju native horses.

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