iMir: An integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq

Abstract

BackgroundQualitative and quantitative analysis of small non-coding RNAs by next generation sequencing (smallRNA-Seq) represents a novel technology increasingly used to investigate with high sensitivity and specificity RNA population comprising microRNAs and other regulatory small transcripts. Analysis of smallRNA-Seq data to gather biologically relevant information, i.e. detection and differential expression analysis of known and novel non-coding RNAs, target prediction, etc., requires implementation of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. As a consequence, the analytical workflow is slowed down by the need for continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once.ResultsWe designed a novel modular pipeline (iMir) for comprehensive analysis of smallRNA-Seq data, comprising specific tools for adapter trimming, quality filtering, differential expression analysis, biological target prediction and other useful options by integrating multiple open source modules and resources in an automated workflow. As statistics is crucial in deep-sequencing data analysis, we devised and integrated in iMir tools based on different statistical approaches to allow the operator to analyze data rigorously. The pipeline created here proved to be efficient and time-saving than currently available methods and, in addition, flexible enough to allow the user to select the preferred combination of analytical steps. We present here the results obtained by applying this pipeline to analyze simultaneously 6 smallRNA-Seq datasets from either exponentially growing or growth-arrested human breast cancer MCF-7 cells, that led to the rapid and accurate identification, quantitation and differential expression analysis of ~450 miRNAs, including several novel miRNAs and isomiRs, as well as identification of the putative mRNA targets of differentially expressed miRNAs. In addition, iMir allowed also the identification of ~70 piRNAs (piwi-interacting RNAs), some of which differentially expressed in proliferating vs growth arrested cells.ConclusionThe integrated data analysis pipeline described here is based on a reliable, flexible and fully automated workflow, useful to rapidly and efficiently analyze high-throughput smallRNA-Seq data, such as those produced by the most recent high-performance next generation sequencers. iMir is available at http://www.labmedmolge.unisa.it/inglese/research/imir.

Small RNA profiling reveals deregulated phosphatase and tensin homolog (PTEN)/phosphoinositide 3-kinase (PI3K)/Akt pathway in bronchial smooth muscle cells from asthmatic patients

“…Interestingly, around 0.5% of Rfam reads aligned to known piRNA sequences annotated in piRNA Bank, 33 which is in line with recent findings demonstrating piRNA expression and activity in other somatic tissues. [20][21][22][23] Sequencing revealed significant differences in miRNA expression patterns between BSM cells of asthmatic patients and healthy subjects and demonstrated for the first time that piRNAs are also expressed in airway BSM cells, showing differential expression between the 2 conditions. In total, we identified that the expression of 32 sncRNAs (26 known miRNAs, 5 piRNAs, and 1 snoRNA) demonstrates statistically significant differential expression (jFCj > _ 1.3, P < .05) in BSM cells of asthmatic patients with respect to those from healthy subjects (Table I).…”

“…Using iMir software, 23 we identified 11 novel miRNAs: 7 by using miRanalyzer and 4 by using miRDeep2 algorithms (see Tables E3 and E4 in this article's Online Repository at www.jacionline.org). Only miRNA candidates expressed with 30 or more read counts in at least 3 samples were considered true novel miRNAs (see Table E5 in this article's Online Repository at www.jacionline.org).…”

Small non-coding RNA deregulation in endometrial carcinogenesis

“…The data obtained from the sequencer were analyzed using iMir, a modular pipeline for comprehensive analysis of small RNA-Seq data, comprising specific tools for adapter trimming, quality filtering, identification of known sncRNAs, novel miRNA prediction, differential expression analysis and target prediction [72, 73]. …”

Post-transcriptional Regulation of Human Breast Cancer Cell Proteome by Unliganded Estrogen Receptor β via microRNAs

“…Sequence reads were then filtered for quality and clustered to unique sequences to remove redundancy, retaining their individual read count information. Unique sequences 18 nucleotides or more in length were mapped, without any mismatch, on miRNA annotation according to miRBase version 18 using iMir, a modular pipeline for comprehensive analysis of small RNA sequencing data, comprising specific tools for adapter trimming, quality filtering, identification of known small noncoding RNAs, novel miRNA prediction, differential expression analysis, and target prediction (37). The pipeline created has proven to be efficient and flexible enough to allow a user to select the preferred combination of analytical steps.…”