title: UB Mus musculus 22K v1
status: Public on Dec 01 2010
contact.city: Barcelona
contact.first: Felix
contact.last: Junyent
contact.email: felixjunyent@ub.edu
contact.address: Avda/Diagonal
contact.country: Spain
contact.institute: Universitat de Barcelona
organism: Mus musculus
manufacture_protocol: Printing of arrays:
  A mus musculus 22 thousand 63-mer oligo library from SigmaGenosys sets was resuspended to 50 mM in the Micro Spotting solution (Arraylt Brand Products). SuperAmine-coated slides 25 x 75 mm (TeleChem International, Inc.) were printed in duplicate, and fixed at 80ºC for 4 fours. For pre-hybridization, the slides were re-hydrated with water vapor at 60ºC and fixed two cycles of UV light (1,200J). After boiling for 2 min at 92ºC, the slides were washed with 95% ethanol for 1 min and prehybridized in 5 x SCC, 0.1% SDS, and 1% BSA for 1 h at 42ºC. The slides were the washed and dried for further hybridization.
  Probe Preparation and Hybridization to Arrays:
  For cDNA synthesis, 10 g of total RNA was used as the template, incorporating dUTP-Cy3 or dUTP-Cy5 with the CyScribe First-Strand cDNA labeling kit (Amersham). Incorporation of the fluorophore was analysed by evaluating the absorbance at 555 nm for Cy3 and 655 nm for Cy5. Equal quantities of labeled cDNA were hybridized for 14 hours at 42ºC to the 22 thousand oligo mice arrays in UniHyb hybridization solution (TeleChem Interbational, Inc).
  Data Acquisition and Analysis of Array Images:
  The acquisition and quantification of the array images were performed in a ScanArray 4000 using the accompanying software (Packard BioChips). All images were captured at a 50% scan rate using 65% PMT gain, 70 to 75% lase power, and 10 m resolution. For each spot, the Cy3 and Cy5 mean density value and the Cy3 and Cy5 mean background value were calculated with ArrayPro Analyzer software (Media Cibernetics). In all cases, the fluorescence signal was between seven to ten times more intense than the background signal, and the background was evaluated just beside the labeled spot.
  Data analysis:
  Microarray data analysis was performed with the GenArise software, developed by the Computing Unit at the Cellular Physiology Institute of the UNAM. GenArise carries out a number of transformations: background correction, lowest normalization, intensity filter, replicate analysis, and selection of differentially expressed genes. The goal of GenArise is to identify good evidence that the genes are differentially expressed, and it has been successfully used in recent studies. The software identifies differentially expressed genes by calculating an intensity-dependent z-score. It uses a sliding window algorithm to calculate the mean and standard deviation within a window surrounding each data point, and it defines a z-score that measures the number of standard deviations a data point is from the mean: zi = (Ri - mean(R)) / sd(R)
  Where zii is the z-score for each element, Ri is the log-ratio for each element, and sd(R) is the standard deviation of the log-ratio. Applying these criteria, the elements with a z-score > 2 standard deviations are genes likely to be differentially expressed.