CDKN3 ELISA Kit

Name: CDKN3 ELISA Kit
SKU: DEIA-XYA397
Rating: 5 (1 reviews)

Regulatory status: For research use only, not for use in diagnostic procedures.

COA

SDS

Datasheet

Summary

Sample

cultured cells

Species Reactivity

Human; Mouse

Detection Method

Indirect ELISA

Intended Use

The CDKN3 Cell-Based ELISA Kit is a convenient, lysate-free, high throughput and sensitive assay kit that can monitor CDKN3 protein expression profile in cells. The kit can be used for measuring the relative amounts of CDKN3 in cultured cells as well as screening for the effects that various treatments, inhibitors (ie. siRNA or chemicals), or activators have on CDKN3.

Contents of Kit

1. 96-Well Cell Culture Clear-Bottom Microplate: 1 plate
2. 10x TBS: 24 mL (10x), Clear
3. Quenching Buffer: 24 mL (1x), Clear
4. Blocking Buffer: 50 mL (1x), Clear
5. 10x Wash Buffer: 50 mL (10x), Clear
6. 100x Anti-CDKN3 Antibody (Rabbit Polyclonal): 60 μL (100x), Purple
7. 100x Anti-GAPDH Antibody (Mouse Monoclonal): 60 μL (100x), Green
8. HRP-Conjugated Anti-Rabbit IgG Antibody: 6 mL (1x), Glass
9. HRP-Conjugated Anti-Mouse IgG Antibody: 6 mL (1x), Glass
10. Primary Antibody Diluent: 12 mL (1x), Clear
11. Ready-to-Use Substrate: 12 mL (1x), Brown
12. Stop Solution: 12 mL (1x), Clear
13. Crystal Violet Solution: 6 mL (1x), Glass
14. SDS Solution: 24 mL (1x), Clear
15. Adhesive Plate Seals: 4 seals

Storage

4°C/6 Months

Citations

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Documents

pdf COA

pdf SDS

pdf Datasheet

QA & Reviews

Customer Reviews

References

Controlling mixed directional false discovery rate in multidimensional decisions with applications to microarray studies

TEST

Authors: Zhao, Haibing; Fung, Wing Kam

Abstract

Time-course microarray experiments harvested samples at several time points. To reveal the dynamic gene expression changes over time, we need to identify the significant genes and detect the patterns of gene expressions, which may bring directional errors. Guo et al. (Biometrics 66(2):485-492, 2010) introduced a mixed directional false discovery rate (mdFDR) controlled procedure, which controls the sum of expected proportions of Type I and Type III errors among all rejections. In this paper, we develop weighted p value procedures for mdFDR control and give out some sufficient conditions to assure the (asymptotic) mdFDR control. Some weights and their estimators are illustrated to satisfy the sufficient conditions. The proposed weighted p value procedures are compared with the existing method by extensive simulations. Based on the proposed weighted p values procedure, we provide multiple CIs which control the false coverage-statement rate (FCR). We use the proposed methods to analyze the time-course microarray data studied in Lobenhofer et al. (Mol Endocrinol 16:1215-1229, 2002). Most of our findings are the same as those obtained by the existing method. In addition, we identify some other important genes, such as CDKN3 and NQO1.

Structural protein interactions predict kinase-inhibitor interactions in upregulated pancreas tumour genes expression data

COMPUTATIONAL LIFE SCIENCES, PROCEEDINGS

Authors: Dawelbait, G; Pilarsky, C; Zhang, YJ; Grutzmann, R; Schroeder, M

Abstract

Micro-arrays can identify co-expressed genes at large scale. The gene expression analysis does however not show functional relationships between co-expressed genes. To address this problem, we link gene expression data to protein interaction data. For the gene products of coexpressed genes, we identify structural domains by sequence alignment and threading. Next, we use the protein structure interaction PSIMAP to find structurally interacting domains. Finally, we generate structural and sequence alignments of the original gene products and the identified structures and check conservation of the relevant interaction interfaces. From this analysis, we derive potentially relevant protein interactions for the gene expression data. We applied this method to co-expressed genes in pancreatic ductal carcinoma. Our method reveals among others a number of functional clusters related to the proteasome, signalling, ubiquitinisation, serine proteases, immunoglobulin and kinases. We investigate the kinase cluster in detail and reveal an interaction between the cell division control protein CDC2 and the cyclin-dependent kinase inhibitor CDKN3, which is also confirmed by literature. Furthermore, our method reveals new interactions between CDKN3 and the cell division protein kinase CDK7 and between CDKN3 and the serine/threonine-protein kinase CDC2L1.