Research ExplorerPublicationsData Normalization Strategies for MicroRNA Quantification

Data Normalization Strategies for MicroRNA Quantification

Standard

Data Normalization Strategies for MicroRNA Quantification. / Schwarzenbach, Heidi; Machado da Silva, Andreia; Calin, George; Pantel, Klaus.

In: CLIN CHEM, Vol. 61, No. 11, 11.2015, p. 1333-42.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Schwarzenbach, H, Machado da Silva, A, Calin, G & Pantel, K 2015, 'Data Normalization Strategies for MicroRNA Quantification', CLIN CHEM, vol. 61, no. 11, pp. 1333-42. https://doi.org/10.1373/clinchem.2015.239459

APA

Schwarzenbach, H., Machado da Silva, A., Calin, G., & Pantel, K. (2015). Data Normalization Strategies for MicroRNA Quantification. CLIN CHEM, 61(11), 1333-42. https://doi.org/10.1373/clinchem.2015.239459

Vancouver

Schwarzenbach H, Machado da Silva A, Calin G, Pantel K. Data Normalization Strategies for MicroRNA Quantification. CLIN CHEM. 2015 Nov;61(11):1333-42. https://doi.org/10.1373/clinchem.2015.239459

Bibtex

@article{31e2d36597cd4967850f5e08c0bc095e,
title = "Data Normalization Strategies for MicroRNA Quantification",
abstract = "BACKGROUND: Different technologies, such as quantitative real-time PCR or microarrays, have been developed to measure microRNA (miRNA) expression levels. Quantification of miRNA transcripts implicates data normalization using endogenous and exogenous reference genes for data correction. However, there is no consensus about an optimal normalization strategy. The choice of a reference gene remains problematic and can have a serious impact on the actual available transcript levels and, consequently, on the biological interpretation of data.CONTENT: In this review article we discuss the reliability of the use of small RNAs, commonly reported in the literature as miRNA expression normalizers, and compare different strategies used for data normalization.SUMMARY: A workflow strategy is proposed for normalization of miRNA expression data in an attempt to provide a basis for the establishment of a global standard procedure that will allow comparison across studies.",
author = "Heidi Schwarzenbach and {Machado da Silva}, Andreia and George Calin and Klaus Pantel",
note = "{\textcopyright} 2015 American Association for Clinical Chemistry.",
year = "2015",
month = nov,
doi = "10.1373/clinchem.2015.239459",
language = "English",
volume = "61",
pages = "1333--42",
journal = "CLIN CHEM",
issn = "0009-9147",
publisher = "American Association for Clinical Chemistry Inc.",
number = "11",

}

RIS

TY - JOUR

T1 - Data Normalization Strategies for MicroRNA Quantification

AU - Schwarzenbach, Heidi

AU - Machado da Silva, Andreia

AU - Calin, George

AU - Pantel, Klaus

N1 - © 2015 American Association for Clinical Chemistry.

PY - 2015/11

Y1 - 2015/11

N2 - BACKGROUND: Different technologies, such as quantitative real-time PCR or microarrays, have been developed to measure microRNA (miRNA) expression levels. Quantification of miRNA transcripts implicates data normalization using endogenous and exogenous reference genes for data correction. However, there is no consensus about an optimal normalization strategy. The choice of a reference gene remains problematic and can have a serious impact on the actual available transcript levels and, consequently, on the biological interpretation of data.CONTENT: In this review article we discuss the reliability of the use of small RNAs, commonly reported in the literature as miRNA expression normalizers, and compare different strategies used for data normalization.SUMMARY: A workflow strategy is proposed for normalization of miRNA expression data in an attempt to provide a basis for the establishment of a global standard procedure that will allow comparison across studies.

AB - BACKGROUND: Different technologies, such as quantitative real-time PCR or microarrays, have been developed to measure microRNA (miRNA) expression levels. Quantification of miRNA transcripts implicates data normalization using endogenous and exogenous reference genes for data correction. However, there is no consensus about an optimal normalization strategy. The choice of a reference gene remains problematic and can have a serious impact on the actual available transcript levels and, consequently, on the biological interpretation of data.CONTENT: In this review article we discuss the reliability of the use of small RNAs, commonly reported in the literature as miRNA expression normalizers, and compare different strategies used for data normalization.SUMMARY: A workflow strategy is proposed for normalization of miRNA expression data in an attempt to provide a basis for the establishment of a global standard procedure that will allow comparison across studies.

U2 - 10.1373/clinchem.2015.239459

DO - 10.1373/clinchem.2015.239459

M3 - SCORING: Journal article

C2 - 26408530

VL - 61

SP - 1333

EP - 1342

JO - CLIN CHEM

JF - CLIN CHEM

SN - 0009-9147

IS - 11

ER -