How are sno/sca and hairpin probe sets selected and how does that impact my signal summarization?
Probes for snoRNA, scaRNA, and hairpins are selected to maximize probe response to target concentration in the sample while minimizing cross-hybridization to other potential targets in the sample. In order to minimize cross-hybridization, potential targets are inferred from the landscape of known sequences from the input dataset and used in a filtering process called pruning which penalizes probe candidates that may cross-hybridize to unwanted targets. As the landscape of known sequences improves, we can improve our pruning set to avoid probe candidates previously thought to be unique. An improved pruning set will reduce the chances a probe will cross-hybridize to unwanted target, improving the probe set representation of the intended target. As a result of the improved probe selection, few probe sets are required to represent the same number of sequences.
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What is the orientation of the probes on the Thermo Fisher Scientific miRNA arrays?
Probes on the array detect sense target.
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I would like to check my miRNA Array data with qRT-PCR. In addition to my miRs of interest, what other qRT-PCR controls should I run?
At least 5 miRs or SnoRNAs should be used to normalize:
RU44, RU48, and/or U6 microRNAs that are not changed among your samples, and are at least 5X over background, according to your microarrays. These miRs might include miR 15,16, 17, or let 7a, let 7b, let7c
All 5 (or more) of these RNAs should not show any change among the samples. Average some or all of these to get the normalization factor, and apply to your qRT-PCR data.
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Can you explain the correlation of miRNA Array data to miRNA qPCR data?
qPCR is not yet the gold standard for miRNA validation. Unlike mRNA validation, in which the amplicon is already present in the sample, miRNA qPCR requires the amplicon to be synthesized by combining the sample with either a specially designed hairpin molecule, adding a 3' polyA tail, or some other manipulation of the microRNA sample. The amplicon-building process will be different from sample to sample and will result in variability in the PCR results. However, it is still very important to validate the array results with another method, like PCR. The trends in up and down regulation should match in direction, even if they do not match in magnitude.
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Are there any articles that describe miRNA Array data analysis?
F Sato. Intra-Platform Repeatability and Inter-Platform Comparibility of microRNA microarray technology. PLoS ONE May 2009, volume 4, issue 5, e5540.
D Sarkar. Quality Assessment and data analysis for microRNA expression arrays. Nucleic Acids Research 2009, vol. 37, no. 2, e17 (doi: 10.1093/ner/gkn932).
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