Small RNA-seq Datasets Research Articles

Non-gonadal expression of piRNAs is widespread across Arthropoda.

PIWI-interacting RNAs (piRNAs) were discovered in the early 2000s and became known for their role in protecting the germline genome against mobile genetic elements. Successively, piRNAs were also detected in the somatic cells of gonads in multiple animal species. In recent years, piRNAs have been reported in non-gonadal tissues in various arthropods, contrary to the initial assumptions of piRNAs being exclusive to gonads. Here, we performed an extensive literature review, which revealed that reports on non-gonadal somatic piRNA expression are not limited to a few specific species. Instead, when multiple studies are considered collectively, it appears to be a widespread phenomenon across arthropods. Furthermore, we systematically analyzed 168 publicly available small RNA-seq datasets from diverse tissues in 17 species, which further supported the bibliographic reports that piRNAs are expressed across tissues and species in Arthropoda.

Detection of a 7SL RNA-derived small non-coding RNA using Molecular Beacons invitro and in cells.

Small non-coding RNAs (sncRNA) are involved in many steps of the gene expression cascade and regulate processing and expression of mRNAs by the formation ofribonucleoprotein complexes (RNP) such as the RNA-induced silencing complex (RISC). By analyzing small RNA Seq data sets, we identified a sncRNA annotated as piR-hsa-1254, which is likely derived from the 3'-end of 7SL RNA2 (RN7SL2), herein referred to as snc7SL RNA. The 7SL RNA is an abundant long non-coding RNA polymerase III transcript and serves as structural component of the cytoplasmic signal recognition particle (SRP). To evaluate a potential functional role of snc7SL RNA, we aimed to define its cellular localization by live cell imaging. Therefore, a Molecular Beacon (MB)-based method was established to compare the subcellular localization of snc7SL RNA with its precursor 7SL RNA. We designed and characterized several MBs invitro and tested those by live cell fluorescence microscopy. Using a multiplex approach, we show that 7SL RNA localizes mainly to the endoplasmic reticulum (ER), as expected for the SRP, whereas snc7SL RNA predominately localizes to the nucleus. This finding suggests a fundamentally different function of 7SL RNA and its derivate snc7SL RNA.

Systematic Assessment of Small RNA Profiling in Human Extracellular Vesicles.

Extracellular vesicles (EVs) are produced and released by most cells and are now recognized to play a role in intercellular communication through the delivery of molecular cargo, including proteins, lipids, and RNA. Small RNA sequencing (small RNA-seq) has been widely used to characterize the small RNA content in EVs. However, there is a lack of a systematic assessment of the quality, technical biases, RNA composition, and RNA biotypes enrichment for small RNA profiling of EVs across cell types, biofluids, and conditions. We collected and reanalyzed small RNA-seq datasets for 2756 samples from 83 studies involving 55 with EVs only and 28 with both EVs and matched donor cells. We assessed their quality by the total number of reads after adapter trimming, the overall alignment rate to the host and non-host genomes, and the proportional abundance of total small RNA and specific biotypes, such as miRNA, tRNA, rRNA, and Y RNA. We found that EV extraction methods varied in their reproducibility in isolating small RNAs, with effects on small RNA composition. Comparing proportional abundances of RNA biotypes between EVs and matched donor cells, we discovered that rRNA and tRNA fragments were relatively enriched, but miRNAs and snoRNA were depleted in EVs. Except for the export of eight miRNAs being context-independent, the selective release of most miRNAs into EVs was study-specific. This work guides quality control and the selection of EV isolation methods and enhances the interpretation of small RNA contents and preferential loading in EVs.

Smalldisco, a pipeline for siRNA discovery and 3' tail identification.

Capturing and sequencing small RNAs is standard practice; however, identification of a group of these small RNAs-small interfering RNAs (siRNAs)-has been more difficult. We present smalldisco, a command-line tool for small interfering RNA discovery and annotation from small RNA-seq datasets. smalldisco can distinguish short reads that map antisense to an annotated genomic feature (e.g. exons or mRNAs), annotate these siRNAs, and quantify their abundance. smalldisco also uses the program Tailor to quantify 3' nontemplated nucleotides of siRNAs or any small RNA species. smalldisco and supporting documentation are available for download from GitHub (https://github.com/ianvcaldas/smalldisco) and archived in Zenodo (https://doi.org/10.5281/zenodo.7799621).

Long non-coding RNA and microRNA landscape of two major domesticated cotton species

Allotetraploid cotton plants Gossypium hirsutum and Gossypium barbadense have been widely cultivated for their natural, renewable textile fibres. Even though ncRNAs in domesticated cotton species have been extensively studied, systematic identification and annotation of lncRNAs and miRNAs expressed in various tissues and developmental stages under various biological contexts are limited. This influences the comprehension of their functions and future research on these cotton species. Here, we report high confidence lncRNAs and miRNA collection from G. hirsutum accession and G. barbadense accession using large-scale RNA-seq and small RNA-seq datasets incorporated into a user-friendly database, CoNCRAtlas. This database provides a wide range and depth of lncRNA and miRNA annotation based on the systematic integration of extensive annotations such as expression patterns derived from transcriptome data analysis in thousands of samples, as well as multi-omics annotations. We assume this comprehensive resource will accelerate evolutionary and functional studies in ncRNAs and inform future breeding programs for cotton improvement. CoNCRAtlas is accessible at http://www.nipgr.ac.in/CoNCRAtlas/.

First Identification and Investigation of piRNAs in the Larval Gut of the Asian Honeybee, Apis cerana.

Piwi-interacting RNAs (piRNAs), a class of small non-coding RNAs (ncRNAs), play pivotal roles in maintaining the genomic stability and modulating biological processes such as growth and development via the regulation of gene expression. However, the piRNAs in the Asian honeybee (Apis cerana) are still largely unknown at present. In this current work, on the basis of previously gained high-quality small RNA-seq datasets, piRNAs in the larval gut of Apis cerana cerana, the nominated species of A. cerana, were identified for the first time, followed by an in-depth investigation of the regulatory roles of differentially expressed piRNAs (DEpiRNAs) in the developmental process of the A. c. cerana. Here, a total of 621 piRNAs were identified in A. c. cerana larval guts, among which 499 piRNAs were shared by 4-(Ac4 group), 5-(Ac5 group), and 6-day-old (Ac6 group) larval guts, while the numbers of unique ones equaled 79, 37, and 11, respectively. The piRNAs in each group ranged from 24 nucleotides (nt) to 33 nt in length, and the first base of the piRNAs had a cytosine (C) bias. Additionally, five up-regulated and five down-regulated piRNAs were identified in the Ac4 vs. Ac5 comparison group, nine of which could target 9011 mRNAs; these targets were involved in 41 GO terms and 137 pathways. Comparatively, 22 up-regulated piRNAs were detected in the Ac5 vs. Ac6 comparison group, 21 of which could target 28,969 mRNAs; these targets were engaged in 46 functional terms and 164 pathways. The results suggested an overall alteration of the expression pattern of piRNAs during the developmental process of A. c. cerana larvae. The regulatory network analysis showed that piR-ace-748815 and piR-ace-512574 in the Ac4 vs. Ac5 comparison group as well as piR-ace-716466 and piR-ace-828146 in the Ac5 vs. Ac6 comparison group were linked to the highest number of targets. Further investigation indicated that targets of DEpiRNAs in the abovementioned two comparison groups could be annotated to several growth and development-associated pathways, such as the Jak/STAT, TGF-β, and Wnt signaling pathways, indicating the involvement of DEpiRNAs in modulating larval gut development via these crucial pathways. Moreover, the expression trends of six randomly selected DEpiRNAs were verified using a combination of stem-loop RT-PCR and RT-qPCR. These results not only provide a novel insight into the development of the A. c. cerana larval gut, but also lay a foundation for uncovering the epigenetic mechanism underlying larval gut development.

Cerebrospinal fluid and blood profiles of transfer RNA fragments show age, sex, and Parkinson's disease-related changes.

Transfer RNA fragments (tRFs) have recently been shown to be an important family of small regulatory RNAs with diverse functions. Recent reports have revealed modified tRF blood levels in a number of nervous system conditions including epilepsy, ischemic stroke, and neurodegenerative diseases, but little is known about tRF levels in the cerebrospinal fluid (CSF). To address this issue, we studied age, sex, and Parkinson's disease (PD) effects on the distributions of tRFs in the CSF and blood data of healthy controls and PD patients from the NIH and the Parkinson's Progression Markers Initiative (PPMI) small RNA-seq datasets. We discovered that long tRFs are expressed in higher levels in the CSF than in the blood. Furthermore, the CSF showed a pronounced age-associated decline in the level of tRFs cleaved from the 3'-end and anti-codon loop of the parental tRNA (3'-tRFs, i-tRFs), and more pronounced profile differences than the blood profiles between the sexes. In comparison, we observed moderate age-related elevation of blood 3'-tRF levels. In addition, distinct sets of tRFs in the CSF and in the blood segregated PD patients from controls. Finally, we found enrichment of tRFs predicted to target cholinergic mRNAs (Cholino-tRFs) among mitochondrial-originated tRFs, raising the possibility that the neurodegeneration-related mitochondrial impairment in PD patients may lead to deregulation of their cholinergic tone. Our findings demonstrate that the CSF and blood tRF profiles are distinct and that the CSF tRF profiles are modified in a sex-, age-, and disease-related manner, suggesting that they reflect the inter-individual cerebral differences and calling for incorporating this important subset of small RNA regulators into future studies.

Abstract 2291: microRNA/mRNA integrated analysis of multiple myeloma transcriptomics

Abstract Myeloma is plasma cell disorder, mostly effects adults over 60 years. Non-coding RNAs are emerging field and play vital role in development of disease. Major non-coding RNAs are miRNAs, lncRNAs, circRNAs and sn/snoRNAs. We analyzed restricted and publically available RNA-seq and small RNA-seq data sets for biomarkers identification and their involvement in myeloma. We obtained restricted data from “BluePrint”, which contains 11 myeloma plasma cells and 4 normal tonsil plasma cells (EGAS00001001110). We identified 1534 genes are differentially regulated (2-fold cut-off, >10-FC 218 genes). Top 10 upregulated genes were: EDNRB (1246-FC), SCUBE1 (737-FC), MC4R (691-FC), NDNF (601-FC), PTGS2 (567-FC), GPRC5D (531-FC), MFAP3L (467-FC), CCND1 (381-FC), CXCL12 (344-FC) and BTBD3 (326-FC) ; top 10 downregulated were: EBF1 (-111-FC), HLA-DRB1(-133-FC), CPXM1 (-171-FC), LOC642131 (-171-FC), IGHV1OR15-3 (-171-FC), HLA-DRB5 (-204-FC), PRAMENP (-210-FC), DTX1 (-220-FC), CD22 (-337-FC) and RFTN1 (-356-FC). Publically available small RNA-seq data downloaded and analyzed for miRNAs, lncRNAs, circRNAs and sn/snoRNAs which contains 3 healthy donor’s plasma cells and 3 newly diagnosed myeloma patient plasma cells (PRJNA377345). We used mirDIP portal to analyze miRNA and mRNA differentially expressed data, predicated from more than 13 databases showed major role of miR-152-3p (targets 28 mRNAs), miR-93-5p (targets 19 mRNAs), miR-301a-3p (targets 13 mRNAs), miR-29c-3p (targets 12 mRNAs), and miR-144-3p (targets 9 mRNAs). Integrated analysis can provide valuable information from the transcriptomics data and effect of miRNAs on mRNAs. Citation Format: Srinivas V. Koduru. microRNA/mRNA integrated analysis of multiple myeloma transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2291.

DIANA-miTED: a microRNA tissue expression database.

microRNAs (miRNAs) are short (∼23nt) single-stranded non-coding RNAs that act as potent post-transcriptional gene expression regulators. Information about miRNA expression and distribution across cell types and tissues is crucial to the understanding of their function and for their translational use as biomarkers or therapeutic targets. DIANA-miTED is the most comprehensive and systematic collection of miRNA expression values derived from the analysis of 15 183 raw human small RNA-Seq (sRNA-Seq) datasets from the Sequence Read Archive (SRA) and The Cancer Genome Atlas (TCGA). Metadata quality maximizes the utility of expression atlases, therefore we manually curated SRA and TCGA-derived information to deliver a comprehensive and standardized set, incorporating in total 199 tissues, 82 anatomical sublocations, 267 cell lines and 261 diseases. miTED offers rich instant visualizations of the expression and sample distributions of requested data across variables, as well as study-wide diagrams and graphs enabling efficient content exploration. Queries also generate links towards state-of-the-art miRNA functional resources, deeming miTED an ideal starting point for expression retrieval, exploration, comparison, and downstream analysis, without requiring bioinformatics support or expertise. DIANA-miTED is freely available at http://www.microrna.gr/mited.

EVAtlas: a comprehensive database for ncRNA expression in human extracellular vesicles.

Extracellular vesicles (EVs) packing various molecules play vital roles in intercellular communication. Non-coding RNAs (ncRNAs) are important functional molecules and biomarkers in EVs. A comprehensive investigation of ncRNAs expression in EVs under different conditions is a fundamental step for functional discovery and application of EVs. Here, we curated 2030 small RNA-seq datasets for human EVs (1506 sEV and 524 lEV) in 24 conditions and over 40 diseases. We performed a unified reads dynamic assignment algorithm (RDAA) considering mismatch and multi-mapping reads to quantify the expression profiles of seven ncRNA types (miRNA, snoRNA, piRNA, snRNA, rRNA, tRNA and Y RNA). We constructed EVAtlas (http://bioinfo.life.hust.edu.cn/EVAtlas), a comprehensive database for ncRNA expression in EVs with four functional modules: (i) browse and compare the distribution of ncRNAs in EVs from 24 conditions and eight sources (plasma, serum, saliva, urine, sperm, breast milk, primary cell and cell line); (ii) prioritize candidate ncRNAs in condition related tissues based on their expression; (iii) explore the specifically expressed ncRNAs in EVs from 24 conditions; (iv) investigate ncRNA functions, related drugs, target genes and EVs isolation methods. EVAtlas contains the most comprehensive ncRNA expression in EVs and will be a key resource in this field.

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.

IsomiR_Window: a system for analyzing small-RNA-seq data in an integrative and user-friendly manner

BackgroundIsomiRs are miRNA variants that vary in length and/or sequence when compared to their canonical forms. These variants display differences in length and/or sequence, including additions or deletions of one or more nucleotides (nts) at the 5′ and/or 3′ end, internal editings or untemplated 3′ end additions. Most available tools for small RNA-seq data analysis do not allow the identification of isomiRs and often require advanced knowledge of bioinformatics. To overcome this, we have developed IsomiR Window, a platform that supports the systematic identification, quantification and functional exploration of isomiR expression in small RNA-seq datasets, accessible to users with no computational skills.MethodsIsomiR Window enables the discovery of isomiRs and identification of all annotated non-coding RNAs in RNA-seq datasets from animals and plants. It comprises two main components: the IsomiR Window pipeline for data processing; and the IsomiR Window Browser interface. It integrates over ten third-party softwares for the analysis of small-RNA-seq data and holds a new algorithm that allows the detection of all possible types of isomiRs. These include 3′ and 5′end isomiRs, 3′ end tailings, isomiRs with single nucleotide polymorphisms (SNPs) or potential RNA editings, as well as all possible fuzzy combinations. IsomiR Window includes all required databases for analysis and annotation, and is freely distributed as a Linux virtual machine, including all required software.ResultsIsomiR Window processes several datasets in an automated manner, without restrictions of input file size. It generates high quality interactive figures and tables which can be exported into different formats. The performance of isomiR detection and quantification was assessed using simulated small-RNA-seq data. For correctly mapped reads, it identified different types of isomiRs with high confidence and 100% accuracy. The analysis of a small RNA-seq data from Basal Cell Carcinomas (BCCs) using isomiR Window confirmed that miR-183-5p is up-regulated in Nodular BCCs, but revealed that this effect was predominantly due to a novel 5′end variant. This variant displays a different seed region motif and 1756 isoform-exclusive mRNA targets that are significantly associated with disease pathways, underscoring the biological relevance of isomiR-focused analysis. IsomiR Window is available at https://isomir.fc.ul.pt/.

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.

Multi-branch Convolutional Neural Network for Identification of Small Non-coding RNA genomic loci

Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra- and inter- species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at gitlab.com/RBP_Bioinformatics/mustard.

Serum-free media supplements carry miRNAs that co-purify with extracellular vesicles

ABSTRACTRecent studies on extracellular RNA raised awareness that extracellular vesicles (EVs) isolated from cultured cells may co-purify RNAs derived from media supplements such as fetal bovine serum (FBS) confounding EV-associated RNA. Defined culture media supplemented with a range of nutrient components provide an alternative to FBS addition and allow EV-collection under full medium conditions avoiding starvation and cell stress during the collection period. However, the potential contribution of serum-free media supplements to EV-RNA contamination has remained elusive and has never been assessed. Here, we report that RNA isolated from EVs harvested from cells under serum-replacement conditions includes miRNA contaminants carried into the sample by defined media components. Subjecting unconditioned, EV-free medium to differential centrifugation followed by reverse transcription quantitative PCR (RT-qPCR) on RNA isolated from the pellet resulted in detection of miRNAs that had been classified as EV-enriched by RNA-seq or RT-qPCR of an isolated EV-fraction. Ribonuclease (RNase-A) and detergent treatment removed most but not all of the contaminating miRNAs. Further analysis of the defined media constituents identified Catalase as a main source of miRNAs co-isolating together with EVs. Hence, miRNA contaminants can be carried into EV-samples even under serum-free harvesting conditions using culture media that are expected to be chemically defined. Formulation of miRNA-free media supplements may provide a solution to collect EVs clean from confounding miRNAs, which however still remains a challenging task. Differential analysis of EVs collected under full medium and supplement-deprived conditions appears to provide a strategy to discriminate confounding and EV-associated RNA. In conclusion, we recommend careful re-evaluation and validation of EV small RNA-seq and RT-qPCR datasets by determining potential medium background.

Identification of microRNAs and their target transcripts in the migratory locust adult brain revealed their roles in the epigenetic regulation of polyphenisms

To investigate the unknown epigenetic regulation mechanisms underlying polyphenisms by microRNAs (miRNAs) in migratory locusts, we performed small RNA-sequencing (RNA-seq) analysis using 12 different groups according to habitat, sex, and phase. We identified a total of 175 miRNA clusters from 12 small RNA-seq data sets using a bioinformatics tool designed for this study. We found differences among expressed mature miRNAs among groups. The numbers of miRNAs detected from each group ranged from 35 to 115. In addition, 8–35 habitat-, sex- and phase-specific miRNAs were identified. To investigate the in vivo functions of the miRNAs in polyphenisms, target mRNAs for sex- and phase-specific miRNAs were predicted. Gene Ontology analysis results for target mRNAs suggested that sex- and phase-specific miRNAs in the locust brains may be involved with the polyphenisms by regulating the expressions of target mRNAs in various physiological functions. Thus, further studies using the sex- and phase-specific miRNAs identified in this study may allow the development of migratory locust-specific control tools (e.g., male and female sterilization techniques) or management methods to prevent locust transition from solitary to gregarious phases.

Removing bias against short sequences enables northern blotting to better complement RNA-seq for the study of small RNAs

Changes in small non-coding RNAs such as micro RNAs (miRNAs) can serve as indicators of disease and can be measured using next-generation sequencing of RNA (RNA-seq). Here, we highlight the need for approaches that complement RNA-seq, discover that northern blotting of small RNAs is biased against short sequences and develop a protocol that removes this bias. We found that multiple small RNA-seq datasets from the worm Caenorhabditis elegans had shorter forms of miRNAs that appear to be degradation products that arose during the preparatory steps required for RNA-seq. When using northern blotting during these studies, we discovered that miRNA-length probes can have ∼1000-fold bias against detecting even synthetic sequences that are 8 nt shorter. By using shorter probes and by performing hybridization and washes at low temperatures, we greatly reduced this bias to enable nearly equivalent detection of 24 to 14 nt RNAs. Our protocol can discriminate RNAs that differ by a single nucleotide and can detect specific miRNAs present in total RNA from C. elegans and pRNAs in total RNA from bacteria. This improved northern blotting is particularly useful to analyze products of RNA processing or turnover, and functional RNAs that are shorter than typical miRNAs.

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/.

Detection of PIWI and piRNAs in the mitochondria of mammalian cancer cells

Piwi-interacting RNAs (piRNAs) are 26–31 nt small noncoding RNAs that are processed from their longer precursor transcripts by Piwi proteins. Localization of Piwi and piRNA has been reported mostly in nucleus and cytoplasm of higher eukaryotes germ-line cells, where it is believed that known piRNA sequences are located in repeat regions of nuclear genome in germ-line cells. However, localization of PIWI and piRNA in mammalian somatic cell mitochondria yet remains largely unknown. We identified 29 piRNA sequence alignments from various regions of the human mitochondrial genome. Twelve out 29 piRNA sequences matched stem-loop fragment sequences of seven distinct tRNAs. We observed their actual expression in mitochondria subcellular fractions by inspecting mitochondrial-specific small RNA-Seq datasets. Of interest, the majority of the 29 piRNAs overlapped with multiple longer transcripts (expressed sequence tags) that are unique to the human mitochondrial genome. The presence of mature piRNAs in mitochondria was detected by qRT-PCR of mitochondrial subcellular RNAs. Further validation showed detection of Piwi by colocalization using anti-Piwil1 and mitochondria organelle-specific protein antibodies.