iSRAP - a one-touch research tool for rapid profiling of small RNA-seq data

Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking

Expression analysis of small noncoding RNA (sRNA), including microRNA, piwi-interacting RNA, small rRNA-derived RNA, and tRNA-derived small RNA, is a novel and quickly developing field. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for the analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = Read length - 40% Adapter length; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; (4) analyzing differential expression with DESeq2 with adjusted p-value < 0.05 or limma with p-value < 0.05 if there is very little signal and few transcripts.

RNAdetector: a free user-friendly stand-alone and cloud-based system for RNA-Seq data analysis

Background

RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement.

Results

Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species.

Conclusions

RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04211-7.

A benchmarking of pipelines for detecting ncRNAs from RNA-Seq data

Next-Generation Sequencing (NGS) is a high-throughput technology widely applied to genome sequencing and transcriptome profiling. RNA-Seq uses NGS to reveal RNA identities and quantities in a given sample. However, it produces a huge amount of raw data that need to be preprocessed with fast and effective computational methods. RNA-Seq can look at different populations of RNAs, including ncRNAs. Indeed, in the last few years, several ncRNAs pipelines have been developed for ncRNAs analysis from RNA-Seq experiments. In this paper, we analyze eight recent pipelines (iSmaRT, iSRAP, miARma-Seq, Oasis 2, SPORTS1.0, sRNAnalyzer, sRNApipe, sRNA workbench) which allows the analysis not only of single specific classes of ncRNAs but also of more than one ncRNA classes. Our systematic performance evaluation aims at guiding users to select the appropriate pipeline for processing each ncRNA class, focusing on three key points: (i) accuracy in ncRNAs identification, (ii) accuracy in read count estimation and (iii) deployment and ease of use.

Visualization of the small RNA transcriptome using seqclusterViz

The study of small RNAs provides us with a deeper understanding of the complexity of gene regulation within cells. Of the different types of small RNAs, the most important in mammals are miRNA, tRNA fragments and piRNAs. Using small RNA-seq analysis, we can study all small RNA types simultaneously, with the potential to detect novel small RNA types. We describe SeqclusterViz, an interactive HTML-javascript webpage for visualizing small noncoding RNAs (small RNAs) detected by Seqcluster. The SeqclusterViz tool allows users to visualize known and novel small RNA types in model or non-model organisms, and to select small RNA candidates for further validation. SeqclusterViz is divided into three panels: i) query-ready tables showing detected small RNA clusters and their genomic locations, ii) the expression profile over the precursor for all the samples together with RNA secondary structures, and iii) the mostly highly expressed sequences. Here, we show the capabilities of the visualization tool and its validation using human brain samples from patients with Parkinson’s disease.

Automated analysis of small RNA datasets with RAPID

Understanding the role of short-interfering RNA (siRNA) in diverse biological processes is of current interest and often approached through small RNA sequencing. However, analysis of these datasets is difficult due to the complexity of biological RNA processing pathways, which differ between species. Several properties like strand specificity, length distribution, and distribution of soft-clipped bases are few parameters known to guide researchers in understanding the role of siRNAs. We present RAPID, a generic eukaryotic siRNA analysis pipeline, which captures information inherent in the datasets and automatically produces numerous visualizations as user-friendly HTML reports, covering multiple categories required for siRNA analysis. RAPID also facilitates an automated comparison of multiple datasets, with one of the normalization techniques dedicated for siRNA knockdown analysis, and integrates differential expression analysis using DESeq2.

miR-MaGiC improves quantification accuracy for small RNA-seq

Objective

Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small size of miRNAs, the small number of unique miRNAs, the fact that similar miRNAs can be transcribed from multiple loci, and the presence of miRNA isoforms known as isomiRs. Methods failing to address these issues can return misleading information. We propose a novel quantification method designed to address these concerns.

Results

We present miR-MaGiC, a novel miRNA quantification method, implemented as a cross-platform tool in Java. miR-MaGiC performs stringent mapping to a core region of each miRNA and defines a meaningful set of target miRNA sequences by collapsing the miRNA space to “functional groups”. We hypothesize that these two features, mapping stringency and collapsing, provide more optimal quantification to a more meaningful unit (i.e., miRNA family). We test miR-MaGiC and several published methods on 210 small RNA-seq libraries, evaluating each method’s ability to accurately reflect global miRNA expression profiles. We define accuracy as total counts close to the total number of input reads originating from miRNAs. We find that miR-MaGiC, which incorporates both stringency and collapsing, provides the most accurate counts.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3418-2) contains supplementary material, which is available to authorized users.

iSmaRT: a toolkit for a comprehensive analysis of small RNA-Seq data

Summary

The interest in investigating the biological roles of small non-coding RNAs (sncRNAs) is increasing, due to the pleiotropic effects of these molecules exert in many biological contexts. While several methods and tools are available to study microRNAs (miRNAs), only few focus on novel classes of sncRNAs, in particular PIWI-interacting RNAs (piRNAs). To overcome these limitations, we implemented iSmaRT ( i ntegrative Sm all R NA T ool-kit), an automated pipeline to analyze smallRNA-Seq data.

Availability and Implementation

iSmaRT is a collection of bioinformatics tools and own algorithms, interconnected through a Graphical User Interface (GUI). In addition to performing comprehensive analyses on miRNAs, it implements specific computational modules to analyze piRNAs, predicting novel ones and identifying their RNA targets. A smallRNA-Seq dataset generated from brain samples of Huntington's Disease patients was used here to illustrate iSmaRT performances, demonstrating how the pipeline can provide, in a rapid and user friendly way, a comprehensive analysis of different classes of sncRNAs. iSmaRT is freely available on the web at ftp://labmedmolge-1.unisa.it (User: iSmart - Password: password).

Contact

aweisz@unisa.it or ggiurato@unisa.it.

Supplementary information

Supplementary data are available at Bioinformatics online.

Technical Advances to Study Extracellular Vesicles

Extracellular vesicles are a heterogeneous and dynamic group of lipid bilayer membrane nanoparticles that can be classified into three different groups depending on their cellular origin: exosomes, microvesicles, and apoptotic bodies. They are produced by different cell types and can be isolated from almost all body fluids. EVs contain a variety of proteins, lipids, nucleic acids, and metabolites which regulate a number of biological and pathological scenarios both locally and systemically. Different techniques have been described in order to determine EV isolation, release, uptake, and cargo. Although standard techniques such as immunoblotting, fluorescent microscopy, and electron microscopy are still being used to characterize and visualize EVs, in the last years, more fine-tuned techniques are emerging. For example, EV uptake can be specifically determined at a single cell level using the Cre reporter methodology and bioluminescence based-methods reports have been employed to determine both EV release and uptake. In addition, techniques for cargo identification have also enormously evolved during these years. Classical mass spectrometry and next generation sequencing have been used in the past, but nowadays, advances in these tools have facilitated a more in depth characterization of the EV content. In this review, we aim to assess the standard and latest technical advances for studying EV biology in different biological systems.

Placental trophoblast debris mediated feto-maternal signalling via small RNA delivery: implications for preeclampsia

To profile the small RNA cargo carried by trophoblast debris derived from the placenta during normal and preeclamptic pregnancies and to determine whether trophoblast debris can deliver its small RNAs to endothelial cells with functional consequences. We confirmed that trophoblast debris can deliver its small RNAs contents to recipient endothelial cells during the co-culture. Next generation sequencing was employed to profile the small RNA contents in both normotensive and preeclamptic trophoblast debris. We identified 1278 mature miRNAs and 2646 non-miRNA small RNA fragments contained. Differential expression analysis identified 16 miRNAs (including miR-145), 5 tRNA fragments from 3 different tRNAs, 13 snRNA fragments and 85 rRNA fragments that were present in different levels between preeclamptic and normotensive trophoblast debris. We loaded a miR-145 mimic into normotensive trophoblast debris via transfection of placental explants from which the debris was derived and found the miR-145 loaded debris induced transcriptomic changes in endothelial cells similar to those induced by preeclamptic trophoblast debris. Trophoblast debris deported into maternal circulation can deliver its small RNA contents to maternal cells thereby contributing to feto-maternal communication. Small RNAs that are dysregulated in preeclamptic trophoblast debris might contribute to the endothelial cell activation which is a hallmark of preeclampsia.

QuickMIRSeq: a pipeline for quick and accurate quantification of both known miRNAs and isomiRs by jointly processing multiple samples from microRNA sequencing

BACKGROUND

Genome-wide miRNA expression data can be used to study miRNA dysregulation comprehensively. Although many open-source tools for microRNA (miRNA)-seq data analyses are available, challenges remain in accurate miRNA quantification from large-scale miRNA-seq dataset. We implemented a pipeline called QuickMIRSeq for accurate quantification of known miRNAs and miRNA isoforms (isomiRs) from multiple samples simultaneously.

RESULTS

QuickMIRSeq considers the unique nature of miRNAs and combines many important features into its implementation. First, it takes advantage of high redundancy of miRNA reads and introduces joint mapping of multiple samples to reduce computational time. Second, it incorporates the strand information in the alignment step for more accurate quantification. Third, reads potentially arising from background noise are filtered out to improve the reliability of miRNA detection. Fourth, sequences aligned to miRNAs with mismatches are remapped to a reference genome to further reduce false positives. Finally, QuickMIRSeq generates a rich set of QC metrics and publication-ready plots.

CONCLUSIONS

The rich visualization features implemented allow end users to interactively explore the results and gain more insights into miRNA-seq data analyses. The high degree of automation and interactivity in QuickMIRSeq leads to a substantial reduction in the time and effort required for miRNA-seq data analysis.

Defining the purity of exosomes required for diagnostic profiling of small RNA suitable for biomarker discovery

Small non-coding RNAs (ncRNA), including microRNAs (miRNA), enclosed in exosomes are being utilised for biomarker discovery in disease. Two common exosome isolation methods involve differential ultracentrifugation or differential ultracentrifugation coupled with Optiprep gradient fractionation. Generally, the incorporation of an Optiprep gradient provides better separation and increased purity of exosomes. The question of whether increased purity of exosomes is required for small ncRNA profiling, particularly in diagnostic and biomarker purposes, has not been addressed and highly debated. Utilizing an established neuronal cell system, we used next-generation sequencing to comprehensively profile ncRNA in cells and exosomes isolated by these 2 isolation methods. By comparing ncRNA content in exosomes from these two methods, we found that exosomes from both isolation methods were enriched with miRNAs and contained a diverse range of rRNA, small nuclear RNA, small nucleolar RNA and piwi-interacting RNA as compared with their cellular counterparts. Additionally, tRNA fragments (30-55 nucleotides in length) were identified in exosomes and may act as potential modulators for repressing protein translation. Overall, the outcome of this study confirms that ultracentrifugation-based method as a feasible approach to identify ncRNA biomarkers in exosomes.