Conserved noncoding RNA genes were annotated using the Infernal pipeline95 (link). To gain insights into the evolution of conserved miRNA families along the bat lineages, we performed two analyses that investigate (i) expansion or contraction of members with miRNA gene families, and (ii) gain or loss of miRNA gene families. To explore variation in miRNA sequence unique to bats, we aligned and investigated single-copy miRNA genes across these 48 taxa. We developed a pipeline to predict the gene targets of candidate miRNAs and the biological processes in which they are potentially engaged. To identify novel miRNAs evolved in bats, we sequenced small RNA libraries from brain, kidney and liver for all six bat species using Illumina miRNA-seq. We carried out a comprehensive pipeline to identify novel miRNA commonly shared by the ancestral bat lineage. We further used luciferase assays96 (link),97 (link) to test the functionality of candidate miRNAs in vitro. A full description is provided in Supplementary Note 5 .
Mirnaseq
Manufactured by Illumina
The MiRNASeq is a laboratory equipment designed for the analysis of microRNA (miRNA) expression. It is a high-throughput sequencing-based platform that enables the detection and quantification of miRNA molecules within a sample. The core function of the MiRNASeq is to provide a comprehensive and accurate assessment of miRNA profiles, which are important in various biological processes and disease states.
Automatically generated - may contain errors
9 protocols using mirnaseq
1
Conserved and Novel miRNA Annotation in Bats
2
Comprehensive Profiling of OC Transcriptome
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RNA expression profiling data of 359 cases of OC were downloaded from the TCGA database (https://portal.gdc.cancer.gov/ ) and then normalized; the data had been generated using the Illumina HiSeq RNASeq and miRNASeq platforms. Clinical data including survival time were also obtained from the TCGA database. Data on 180 healthy control samples from the GTEx v8 release (https://gtexportal.org/home/datasets ) were manually downloaded. The complete characteristics of the healthy controls were obtained from the official GTEx annotation. lncRNAs and mRNAs were identified and annotated using Ensembl (Homo_sapiens. GRCh38.97) (18 (link)), and RNAs that were absent from the database were excluded. No ethical approval was required for this study, as the data were obtained from TCGA and GTEx and TCGA and GTEx publication guidelines were strictly followed.
3
TCGA mRNA, miRNA, and TP53 Mutation Analysis
4
Colorectal Cancer Transcriptome Profiling
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The mRNA, lncRNA, and miRNA expression profiles of the CRC patients were downloaded from TCGA database (https://cancergenome.nih.gov/ ). We collected RNA-Seq data for 612 samples (568 CRC and 44 normal samples) in HTSeq-Counts files and miRNA-Seq data for 548 samples (539 CRC and nine normal samples). The sequence data were derived from Illumina HiSeq RNASeq and miRNASeq platforms, which were publicly available. GENCODE (https://www.gencodegenes.org/ ) was used to convert the RNA-Seq data into lncRNAs (sense_overlapping, lincRNA, 3prime_overlapping_ncRNA, processed_transcript, antisense, and sense_intronic) and mRNAs (protein-coding).
5
Prostate Cancer miRNA-mRNA Correlation
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Expression levels of selected miRNAs and target genes were obtained from TCGA-PRAD patient cohort data available in UCSC Xena. Expression data of miRNA mature strand [miRNA-Seq (IlluminaHiSeq_miRNASeq)] and genes [RNAseq (IlluminaHiSeq)] were downloaded as log2 (RPM+1) values. PCa samples from 495 patients were included in the present analysis. We generated a correlation matrix between hsa-miR-133a-3p, hsa-miR-1-3p and the selected target genes, applying the Spearman correlation coefficient using the Hmisc R package. For the selected miRNAs, target genes with a negative correlation coefficient rho plus p-Value <0.05 were selected for further analysis. Also, for correlation matrix graphical representation, the R function “chart.Correlation” from PerformanceAnalytics package (version 4.0.2) was used.
6
Transcriptome Profiling of HER2-Positive Breast Cancer
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The transcriptome expression profiles of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://cancergenome.nih.gov ). In this study, the transcriptome profiles of 887 cases and 101 HER2-positive breast tumors were included in the coexpression analysis. Level 3 Illumina miRNASeq was used to analyze miRNA expression. For the miRNASeq data, “reads_per_million_miRNA_mapped” values were used to calculate miRNAs.
7
TCGA Gastric Cancer Omics Data
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The research data, including high throughput RNA sequencing (RNA-Seq), miRNA-Seq, Illumina Infinium Human Methylation 450, and clinical follow-up information data, which provided all mRNA and miRNA expression and methylation profiles for followed GC patients, were downloaded from the Cancer Genome Atlas (TCGA)[17 (link)] on October 31, 2018. Samples with more than 30 d of follow-up were screened from clinical follow-up data to further match the RNAseq expression profile, miRNA expression profile, and methylation profile. We obtained 387 samples and 2173 miRNAs, 335 samples and 19754 mRNAs, and 364 samples and 358418 methylation sites, respectively. The study was approved by the Clinical Research Ethics Committee of College of Medicine, Zhejiang University.
8
Survival Analysis of HG-SOC Transcriptome
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Normalized miRNA and gene expression were obtained from TCGA of HG-SOC. Data from different platforms were interrogated (Agilent, RNAseqv2 and Illumina miRNA-Seq). All the expression value differences between subgroups of samples were evaluated applying unpaired t-test and permutation test. A false discovery rate procedure [44 ] was also included for multiple comparisons. Pearson's correlation coefficient and Spearman coefficient were calculated between miRNA and gene target expression. Survival and progression free survival were evaluated by Kaplan-Meier method [45 ] and a log-rank test used to establish the statistical significance of the distance between curves [46 (link)]. The impact of clinical variables on the survival curves was investigated by a multivariate Cox proportional hazard regression model [47 ]. Subgroup of samples in survival analyses were individuated basing on the z-scores of the signal, and considering as the most separate subgroups of samples those with absolute z-score higher than 1.
9
miR-330 Expression and Survival Analysis
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For miR-330 expression and survival analysis, miRNA data (Illumina miRNASeq) and clinical data were downloaded from The Cancer Genome Atlas (TCGA) (Nature, 2012) . Data were processed and analyzed essentially as previously described (Vaidyanathan, Cato, et al., 2016; Vaidyanathan, Thangavelu, & Duijf, 2016) , except that the ratio between the low and high expression groups was 1:3. In addition, for statistical analyses, aside from Mantel-Cox (log-rank) tests and Cox proportional hazards regression analyses, Gehan-Breslow-Wilcoxon (GBW) tests were performed on all patients, as well as separately for patients with stage 1, 2, 3 or 4 tumors (Thangavelu, Krenács, Dray, & Duijf, 2016; Thangavelu et al., 2017) .
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