Abstract
Advances in high-throughput sequencing have enabled profiling of microRNAs (miRNAs), however, a consensus pipeline for sequencing of small RNAs has not been established. We built and optimized an analysis pipeline using Partek Flow, circumventing the need for analyzing data via scripting languages. Our analysis assessed the effect of alignment reference, normalization method, and statistical model choice on biological data. The pipeline was evaluated using sequencing data from HaCaT cells transfected with either a non-silencing control or siRNA against ΔNp63α, a p53 family member protein which is highly expressed in non-melanoma skin cancer and shown to regulate a number of miRNAs. We posit that 1) alignment and quantification to the miRBase reference provides the most robust quantitation of miRNAs, 2) normalizing sample reads via Trimmed Mean of M-values is the most robust method for accurate downstream analyses, and 3) use of the lognormal with shrinkage statistical model effectively identifies differentially expressed miRNAs. Using our pipeline, we identified previously unrecognized regulation of miRs-149-5p, 18a-5p, 19b-1-5p, 20a-5p, 590-5p, 744-5p and 93-5p by ΔNp63α. Regulation of these miRNAs was validated by RT-qPCR, substantiating our small RNA-Seq pipeline. Further analysis of these miRNAs may provide insight into ΔNp63α’s role in cancer progression. By defining the optimal alignment reference, normalization method, and statistical model for analysis of miRNA sequencing data, we have established an analysis pipeline that may be carried out in Partek Flow or at the command line. In this manner, our pipeline circumvents some of the major hurdles encountered during small RNA-Seq analysis.
Highlights
MiRNAs are small non-coding RNAs of approximately 18–22 nucleotides in length that bind to the 3′ UTR regions of target mRNA to translationally repress or degrade them[1]
All three biological replicates of HaCaT cells transfected with siRNA against p63 showed 80% or greater reduction in p63 transcript levels by ReverseTranscriptionQuantitative PolymeraseChain Reaction (RT-qPCR) (Fig. 1A) and no detectable p63 protein by immunoblot (Fig. 1B) relative to non-silencing control (NSC), confirming p63 knockdown
Bioanalyzer measurements showed that our samples had an average of 7–11% RNA of 10–40 nucleotides in length, which we considered to be miRNAs per the manufacturer’s guideline
Summary
MiRNAs are small non-coding RNAs of approximately 18–22 nucleotides in length that bind to the 3′ UTR regions of target mRNA to translationally repress or degrade them[1]. Given the uniform length of miRNAs, it is possible that normalization methods such as RPKM, which correct for differences in read length, may negatively impact analysis of miRNA datasets Despite these limitations, the choice of alignment index, quantitation reference, and normalization method to identify differentially expressed miRNAs from small RNA-Seq data have not been fully evaluated. Validating a standard pipeline for small RNA-Seq data is critical since each step of processing impacts downstream analysis and identification of statistically significant differentially expressed (DE) miRNAs. In this study, small RNA-Seq was used to identify novel ΔNp63α-regulated miRNAs in keratinocytes by comparing those in which ΔNp63α was silenced relative to non-silencing controls (NSC). To identify miRNAs that are differentially expressed between keratinocytes lacking ΔNp63α versus expressing ΔNp63α, we optimized key parameters in the analysis to develop a standard pipeline for analyzing small RNA-Seq data. The pipeline parameters chosen (e.g. alignment and quantitation to the miRBase reference, TMM normalization and use of an LNS model for identification of differentially expressed miRNAs) may be implemented at the command line with the use of open-source tools for small RNA-Seq analysis
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