Literature DB >> 18514480

Comparison of normalization methods with microRNA microarray.

You-Jia Hua1, Kang Tu, Zhong-Yi Tang, Yi-Xue Li, Hua-Sheng Xiao.   

Abstract

MicroRNAs (miRNAs) are a group of RNAs that play important roles in regulating gene expression and protein translation. In a previous study, we established an oligonucleotide microarray platform to detect miRNA expression. Because it contained only hundreds of probes, data normalization was difficult. In this study, the microarray data for eight miRNAs extracted from inflamed rat dorsal root ganglion (DRG) tissue were normalized using 15 methods and compared with the results of real-time polymerase chain reaction. It was found that the miRNA microarray data normalized by the print-tip loess method were the most consistent with results from real-time polymerase chain reaction. Moreover, the same pattern was also observed in 14 different types of rat tissue. This study compares a variety of normalization methods and will be helpful in the preprocessing of miRNA microarray data.

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Year:  2008        PMID: 18514480     DOI: 10.1016/j.ygeno.2008.04.002

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  26 in total

1.  Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression.

Authors:  Anna Git; Heidi Dvinge; Mali Salmon-Divon; Michelle Osborne; Claudia Kutter; James Hadfield; Paul Bertone; Carlos Caldas
Journal:  RNA       Date:  2010-04-01       Impact factor: 4.942

2.  Modified least-variant set normalization for miRNA microarray.

Authors:  Chen Suo; Agus Salim; Kee-Seng Chia; Yudi Pawitan; Stefano Calza
Journal:  RNA       Date:  2010-10-27       Impact factor: 4.942

3.  Impact of normalization on miRNA microarray expression profiling.

Authors:  Sylvain Pradervand; Johann Weber; Jérôme Thomas; Manuel Bueno; Pratyaksha Wirapati; Karine Lefort; G Paolo Dotto; Keith Harshman
Journal:  RNA       Date:  2009-01-28       Impact factor: 4.942

4.  The role of miR-146a in dorsal root ganglia neurons of experimental diabetic peripheral neuropathy.

Authors:  L Wang; M Chopp; A Szalad; Y Zhang; X Wang; R L Zhang; X S Liu; L Jia; Z G Zhang
Journal:  Neuroscience       Date:  2013-12-06       Impact factor: 3.590

5.  Processing of Agilent microRNA array data.

Authors:  Pedro López-Romero; Manuel A González; Sergio Callejas; Ana Dopazo; Rafael A Irizarry
Journal:  BMC Res Notes       Date:  2010-01-22

6.  Evaluation of normalization methods for two-channel microRNA microarrays.

Authors:  Yingdong Zhao; Ena Wang; Hui Liu; Melissa Rotunno; Jill Koshiol; Francesco M Marincola; Maria Teresa Landi; Lisa M McShane
Journal:  J Transl Med       Date:  2010-07-21       Impact factor: 5.531

7.  MicroRNA modulate alveolar epithelial response to cyclic stretch.

Authors:  Nadir Yehya; Adi Yerrapureddy; John Tobias; Susan S Margulies
Journal:  BMC Genomics       Date:  2012-04-26       Impact factor: 3.969

8.  Quality assessment and data analysis for microRNA expression arrays.

Authors:  D Sarkar; R Parkin; S Wyman; A Bendoraite; C Sather; J Delrow; A K Godwin; C Drescher; W Huber; R Gentleman; M Tewari
Journal:  Nucleic Acids Res       Date:  2008-12-22       Impact factor: 16.971

9.  Evaluation of a new high-dimensional miRNA profiling platform.

Authors:  Julie M Cunningham; Ann L Oberg; Pedro M Borralho; Betsy T Kren; Amy J French; Liang Wang; Brian M Bot; Bruce W Morlan; Kevin A T Silverstein; Rod Staggs; Yan Zeng; Anne-Francoise Lamblin; Christopher A Hilker; Jian-Bing Fan; Clifford J Steer; Stephen N Thibodeau
Journal:  BMC Med Genomics       Date:  2009-08-27       Impact factor: 3.063

10.  MicroRNAs show mutually exclusive expression patterns in the brain of adult male rats.

Authors:  Line Olsen; Mikkel Klausen; Lone Helboe; Finn Cilius Nielsen; Thomas Werge
Journal:  PLoS One       Date:  2009-10-06       Impact factor: 3.240

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