Computational protocol: Identification of metastasis-associated microRNAs in serum from rectal cancer patients

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Protocol publication

[…] Quality control of the raw sequence data was performed using fastQC []. Trimming of sequence adapters from the 3’end of the raw sequences was performed using cutadapt-1.2.1 []. The trimmed sequences were collapsed with the fastx collapser tool ( into single unique reads along with their total read count and mapped to the human (hg38) genome using bowtie2 [], allowing for up to 10 alignments per read to account for reads from duplicated miRNA loci (bowtie2 – k10). Reads overlapping with mature miRNA loci were identified using htseq-count from the HTseq python package []. These reads were further filtered to identify those with perfect alignment to the genome, and the total read count for mature miRNAs were then computed by summing the total read count per sequence (isomiR) overlapping each miRNA locus. Mature miRNAs and non-coding RNAs were annotated using miRBase (Release 21, 2014) and RNA Central ( respectively. IsomiR variants were detected using SeqBuster [] combined with a panel of in-house perl and R-scripts, which are available upon request. IsomiRs with mismatches to the genome were discarded from the analysis, as these could not be excluded as sequencing errors. However, isomiRs with non-templated addition at the 3’end were included in the analysis. Differentially expressed miRNAs and isomiRs were identified using the Bioconductor package limma combined with voom transformation [, ]. All miRNA sequence information was retrieved from miRBase []. In order to compare miRNA expression between samples, read counts were normalized using the calibrator RNA normalization factors calculated in limma, followed by counts per million (cpm) normalization. The calibrator RNAs were not filtered prior to normalization and the calcNormFactors in limma were calculated using the full calibrator count matrix. The processed count data is available in -. [...] Internal normalization controls for the qRT-PCR experiment were selected based on the criteria that they should be highly expressed in serum and not differentially expressed between metastatic and non-metastatic patients. By manually inspecting the limma results from the comparisons between metastatic and non-metastatic patients, we selected three miRNA as internal controls: miR-128a-3p, miR-92a-3p and miR-151a-3p. Further, we ran the normalization algorithm Normfinder on the HTS data to confirm that the three miRNA we selected indeed showed low variation and high stability across samples and between the metastatic and non-metastatic groups (see for complete results from Normfinder). The three miRNAs miR-128a-3p, miR-92a-3p and miR-151a-3p showed a group difference value of 0.04, 0.09 and 0.01, respectively, group standard deviation of 0.28, 0.55 and 0.47, and a stability value of 0.09, 0.15 and 0.12. In comparison, the median values for all miRNAs were 0.2 for group difference, 0.73 for group standard deviation and 0.235 for stability. From this we concluded that the three miRNAs were suited as internal normalization controls.CDNA synthesis was performed using Applied Biosystems TaqMan Advanced miRNA cDNA Synthesis Kit from RNA isolated from 200 uL serum. The cDNA was diluted 1:10. The qPCR were performed on the StepOne Real-Time PCR System using TaqMan Advanced miRNA Assays following the manufacturer's Instructions. For miR-320d, the previous TaqMan cDNA and qPCR kit was used, as the advanced kit was not available. The miRNA expression data analysis determined by RT-qPCR was compared between metastasis and non-metastasis patients using unpaired Student's t-tests. ΔCt values were calculated by normalizing to the geometric mean of the internal control miRNAs. Relative quantities (RQ) were calculated as 2–ΔCt and the RQ of the miRNAs of interest in each sample was determined as the mean RQ in the cDNA synthesis duplicates. All qRT-PCR experiments were performed in triplicates. […]

Pipeline specifications

Software tools FastQC, cutadapt, FASTX-Toolkit, Bowtie2, HTSeq, SeqBuster, limma, voom, edgeR, NormFinder
Databases miRBase RNAcentral
Applications sRNA-seq analysis, qPCR
Organisms Homo sapiens