Analysis Of Small RNA-seq Data Research Articles

Evaluation of sensitivity and specificity in RNA-Seq-based detection of grapevine viral pathogens

Virus detection is a crucial step for the implementation of clean stock programs that preserve healthy crop species. Viral infections in grapevine, a vegetatively propagated perennial crop, cannot be eradicated from the vineyards by the application of agrochemicals and must be curtailed at the stage of nursery production during the propagation of planting material. Viral detection is routinely performed using enzyme-linked immunosorbent assays (ELISA) or Reverse Transcription-quantitative Polymerase Chain Reactions (RT-qPCR). High throughput sequencing (HTS) approaches have the potential to detect all viral pathogens in a plant specimen. However, to date, no published HTS-based study has used threshold selection based on ROC curves for discriminating positive from negative samples. To fill this gap, we assessed the specificity and sensitivity of different sequencing and bioinformatics approaches for nine common viruses, which were tested in the same specimens using ELISA and/or RT-qPCR. The normalized detection thresholds giving the best results were 19.28 Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for alignment-based total RNA-Seq approaches, 386 Reads Per Million mapped reads (RPM) for metagenomics-based total RNA-Seq, 1572 FPKM for alignment-based small RNA-Seq analysis and 0.97 % of contigs for de novo analysis of small RNA-Seq data. Validation of the proposed thresholds using independent specimens collected over time from the same stocks and other specimens collected from nearby stocks that had derived from the same propagating material showed that HTS approaches are accurate, with RNA-Seq approaches showing better performance than small RNA-Seq.

WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data.

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND ( Workflow for p IRNAs a Nd beyon D), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

Transcriptomic analyses of rice (Oryza sativa) genes and non-coding RNAs under nitrogen starvation using multiple omics technologies

BackgroundNitrogen (N) is a key macronutrient essential for plant growth, and its availability has a strong influence on crop development. The application of synthetic N fertilizers on crops has increased substantially in recent decades; however, the applied N is not fully utilized due to the low N use efficiency of crops. To overcome this limitation, it is important to understand the genome-wide responses and functions of key genes and potential regulatory factors in N metabolism.ResultsHere, we characterized changes in the rice (Oryza sativa) transcriptome, including genes, newly identified putative long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) and their target mRNAs in response to N starvation using four different transcriptome approaches. Analysis of rice genes involved in N metabolism and/or transport using strand-specific RNA-Seq identified 2588 novel putative lncRNA encoding loci. Analysis of previously published RNA-Seq datasets revealed a group of N starvation-responsive lncRNAs showing differential expression under other abiotic stress conditions. Poly A-primed sequencing (2P-Seq) revealed alternatively polyadenylated isoforms of N starvation-responsive lncRNAs and provided precise 3′ end information on the transcript models of these lncRNAs. Analysis of small RNA-Seq data identified N starvation-responsive miRNAs and down-regulation of miR169 family members, causing de-repression of NF-YA, as confirmed by strand-specific RNA-Seq and qRT-PCR. Moreover, we profiled the N starvation-responsive down-regulation of root-specific miRNA, osa-miR444a.4-3p, and Degradome sequencing confirmed MADS25 as a novel target gene.ConclusionsIn this study, we used a combination of multiple RNA-Seq analyses to extensively profile the expression of genes, newly identified lncRNAs, and microRNAs in N-starved rice roots and shoots. Data generated in this study provide an in-depth understanding of the regulatory pathways modulated by N starvation-responsive miRNAs. The results of comprehensive, large-scale data analysis provide valuable information on multiple aspects of the rice transcriptome, which may be useful in understanding the responses of rice plants to changes in the N supply status of soil.

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.

Abstract 4447: Analysis of small RNA-seq data for differential expression of small noncoding RNAs in human colorectal cancer

Abstract Background: Cancer is a major cause of deaths worldwide, despite improved healthcare and technologies. Recent advancements in genome sequencing have led to the rapid study of the whole transcriptome and small RNAs with their biological functions. Non-coding RNAs (ncRNAs) play an important role in biological processes that greatly impact biomarker development for diagnosis, prognosis, and therapy. Non-coding RNAs such as microRNAs (miRNA), Piwi-interacting-RNAs (piRNAs), small nuclear/nucleolar RNAs (sn/snoRNAs) have recently been studied to understand their biology and pathology. Results: In the present study, we used eight matched colorectal patient tissue samples (benign, tumor, and metastasis) small RNA sequencing data remapped for various small RNA annotation. We identified aberrant expression of 13 miRs in tumor and metastasis specimens [tumor vs benign group (19 miRs) and metastasis vs benign group (38 miRs)] of which five were upregulated, and eight were downregulated, during disease progression. We also investigated pathway analysis on abbarent expression of miRNAs, which showed majority of miRs involved in the colon other types of cancers. Further analysis of piRNAs revealed that six piRNAs in tumor vs benign and 24 in metastasis vs benign samples (commonly in both groups, only two piRNAs). Additionally, we examined other types of small RNAs [sn/snoRNAs, mt_rRNA, misc_RNA, nonsense mediated decay (NMD) and rRNA], we identified 15 in tumor vs benign and 104 metastasis vs benign and only four in commonly expressed. Conclusion: In summary, our results identified multiple sncRNAs during colorectal cancer progression which needs to be further validated and can be used for prognosis, diagnosis, and therapeutic potentials. Citation Format: Srinivas V. Koduru, Amit K. Tiwari, Sprague W. Hazard, Milind K. Mahajan, Dino J. Ravnic. Analysis of small RNA-seq data for differential expression of small noncoding RNAs in human colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4447. doi:10.1158/1538-7445.AM2017-4447

Modeling bias and variation in the stochastic processes of small RNA sequencing.

The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data.

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

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. 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). aweisz@unisa.it or ggiurato@unisa.it. Supplementary data are available at Bioinformatics online.

RNAi-mediated transgenic rice resistance to Rice stripe virus

Rice stripe virus (RSV) often causes severe rice yield loss in temperate regions of East Asia. Although the correlation of small interfering RNAs (siRNAs) with transgenic virus resistance of plants using RNA interference (RNAi) is known for decades, no systematical research has been done on the profiling of siRNAs from a genomic scale. Our research is aiming to systematically study the RNAi impact in RSV-resistant transgenic rice, which was generated by introducing an inverted repeat construct that targets RSV nucleocapsid protein (NCP) gene. In this paper, three independent RSV-re-sistant transgenic rice lines were generated, their stable integration of the T-DNA fragment and the expression of siRNAs were confirmed by Southern blotting and Northern blotting analyses, and the majority of siRNAs were in lengths of 21, 22, and 24 nucleotides (nt), which have validated a connection between the presence of the RSV NCP homologous siRNAs and the RSV resistance in those transgenic rice lines. In one of these transgenic lines (T4-B1), the T-DNA fragment was found to have been inserted at chromosome 1 of the rice genome, substituting the rice genome fragment from 32 158 773 to 32 158 787 nt. Bioinformatics analysis of small RNA-Seq data on the T4-B1 line also confirmed the large population of NCP-derived siRNAs in transgenic plants, and the RSV-infected library (T4-B1-V) possessed more siRNAs than its mock inoculated libraries (T4-B1-VF), these results indicating the inverted repeat construct and RSV could introduce abundance of siRNAs in transgenic rice. Moreover, a varied expression level of specific siRNAs was found among different segments of the NCP gene template, about 47% of NCP-derived siRNAs reads aligned with the fragment from 594 to 832 nt (239 nt in length) in NCP gene (969 nt in length) in the T4-B1-V, indicating a potential usage of hotspot regions for RNAi silencing in future research. In conclusion, as the first study to address the siRNA profile in RSV-resistant transgenic plant using next generation sequencing (NGS) technique, we confirmed that the massive abundance of siRNA derived from the inverted repeat of NCP is the major reason for RSV-resistance.

Chimira: analysis of small RNA sequencing data and microRNA modifications.

Summary: Chimira is a web-based system for microRNA (miRNA) analysis from small RNA-Seq data. Sequences are automatically cleaned, trimmed, size selected and mapped directly to miRNA hairpin sequences. This generates count-based miRNA expression data for subsequent statistical analysis. Moreover, it is capable of identifying epi-transcriptomic modifications in the input sequences. Supported modification types include multiple types of 3′-modifications (e.g. uridylation, adenylation), 5′-modifications and also internal modifications or variation (ADAR editing or single nucleotide polymorphisms). Besides cleaning and mapping of input sequences to miRNAs, Chimira provides a simple and intuitive set of tools for the analysis and interpretation of the results (see also Supplementary Material). These allow the visual study of the differential expression between two specific samples or sets of samples, the identification of the most highly expressed miRNAs within sample pairs (or sets of samples) and also the projection of the modification profile for specific miRNAs across all samples. Other tools have already been published in the past for various types of small RNA-Seq analysis, such as UEA workbench, seqBuster, MAGI, OASIS and CAP-miRSeq, CPSS for modifications identification. A comprehensive comparison of Chimira with each of these tools is provided in the Supplementary Material. Chimira outperforms all of these tools in total execution speed and aims to facilitate simple, fast and reliable analysis of small RNA-Seq data allowing also, for the first time, identification of global microRNA modification profiles in a simple intuitive interface.Availability and implementation: Chimira has been developed as a web application and it is accessible here: http://www.ebi.ac.uk/research/enright/software/chimira.Contact: aje@ebi.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

PlantDARIO: web based quantitative and qualitative analysis of small RNA-seq data in plants.

High-throughput sequencing techniques have made it possible to assay an organism's entire repertoire of small non-coding RNAs (ncRNAs) in an efficient and cost-effective manner. The moderate size of small RNA-seq datasets makes it feasible to provide free web services to the research community that provide many basic features of a small RNA-seq analysis, including quality control, read normalization, ncRNA quantification, and the prediction of putative novel ncRNAs. DARIO is one such system that so far has been focussed on animals. Here we introduce an extension of this system to plant short non-coding RNAs (sncRNAs). It includes major modifications to cope with plant-specific sncRNA processing. The current version of plantDARIO covers analyses of mapping files, small RNA-seq quality control, expression analyses of annotated sncRNAs, including the prediction of novel miRNAs and snoRNAs from unknown expressed loci and expression analyses of user-defined loci. At present Arabidopsis thaliana, Beta vulgaris, and Solanum lycopersicum are covered. The web tool links to a plant specific visualization browser to display the read distribution of the analyzed sample. The easy-to-use platform of plantDARIO quantifies RNA expression of annotated sncRNAs from different sncRNA databases together with new sncRNAs, annotated by our group. The plantDARIO website can be accessed at http://plantdario.bioinf.uni-leipzig.de/.

Comprehensive analysis of small RNA-seq data reveals that combination of miRNA with its isomiRs increase the accuracy of target prediction in Arabidopsis thaliana

Along with the canonical miRNA, distinct miRNA-like sequences called sibling miRNAs (sib-miRs) are generated from the same pre-miRNA. Among them, isomeric sequences featuring slight variations at the terminals, relative to the canonical miRNA, constitute a pool of isomeric sibling miRNAs (isomiRs). Despite the high prevalence of isomiRs in eukaryotes, their features and relevance remain elusive. In this study, we performed a comprehensive analysis of mature precursor miRNA (pre-miRNA) sequences from Arabidopsis to understand their features and regulatory targets. The influence of isomiR terminal heterogeneity in target binding was examined comprehensively. Our comprehensive analyses suggested a novel computational strategy that utilizes miRNA and its isomiRs to enhance the accuracy of their regulatory target prediction in Arabidopsis. A few targets are shared by several members of isomiRs; however, this phenomenon was not typical. Gene Ontology (GO) enrichment analysis showed that commonly targeted mRNAs were enriched for certain GO terms. Moreover, comparison of these commonly targeted genes with validated targets from published data demonstrated that the validated targets are bound by most isomiRs and not only the canonical miRNA. Furthermore, the biological role of isomiRs in target cleavage was supported by degradome data. Incorporating this finding, we predicted potential target genes of several miRNAs and confirmed them by experimental assays. This study proposes a novel strategy to improve the accuracy of predicting miRNA targets through combined use of miRNA with its isomiRs.