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Long non-coding RNAs (lncRNAs) were found to widely exist in eukaryotes and play important roles in most biological processes. Trees belonging to Populus spp. are economically important because of their fast-growing characteristics and wide use for wood, pulp, paper, and fuel. However, the prevalence of leaf rust disease caused by Melampsora spp. on some species of Populus severely affects their growth and decreases wood production. Therefore, the identification and characterization of lncRNAs involved in the defense of Melampsora spp. would not only help us to understand plant-pathogen interactions but also provide genetic elements for producing disease-resistant poplars. In the present study, an RNA-Seq transcriptome analysis was performed for poplar leaves in response to early infection by M. larici-populina. The hybrid variety “NL895” (P. × euramericana) was used as plant material. A total of 3994 lncRNAs were identified by mining the RNA-Seq data. The identified lncRNAs had lower abundance, fewer exons, and shorter lengths when compared to protein-coding genes. In addition, 53 lncRNAs were differentially expressed between treatments and controls. Two of these 53 lncRNAs were predicted to compete to be the target of miRNAs. Moreover, the differentially expressed lncRNAs were found to be preferentially located in close proximity to the protein-coding genes they co-expressed. In this study, we were able to show that lncRNAs may play important roles in plant-pathogen interactions in the poplar. This study also improves our understanding of how plants deploy their defense system when encountering biotic stresses.

The onion maggot Delia antiqua is a major insect pest of cultivated vegetables, especially the onion, and a good model to investigate the molecular mechanisms of diapause. To better understand the biology and diapause mechanism of the insect pest species, D. antiqua, the transcriptome was sequenced using Illumina paired-end sequencing technology. Approximately 54 million reads were obtained, trimmed, and assembled into 29,659 unigenes, with an average length of 607 bp and an N50 of 818 bp. Among these unigenes, 21,605 (72.8%) were annotated in the public databases. All unigenes were then compared against Drosophila melanogaster and Anopheles gambiae. Codon usage bias was analyzed and 332 simple sequence repeats (SSRs) were detected in this organism. These data represent the most comprehensive transcriptomic resource currently available for D. antiqua and will facilitate the study of genetics, genomics, diapause, and further pest control of D. antiqua.

Introduction Juvenile myelomonocytic leukemia (JMML) is a rare pediatric hematological disease, characterized by aberrant proliferation of myeloid precursors and hypersensitivity to GM-CSF stimulation. Mutations in PTPN11, N/K-RAS, CBL, or NF1 genes are found in ~90% of the cases. Allogeneic hematopoietic stem cell transplantation (HSCT) is the only curative therapy for those patients harboring somatic PTPN11 mutations, while in patients with somatic NRAS mutations, transplantation need a careful evaluation, as these patients may have spontaneous resolution of the disease. Here, we show data from a deep-sequencing analysis enlightening typical molecular features of CD33+/34+ and CD14+ primary cells from NRAS and PTPN11 mutated patients compared with healthy donors (HDs). Patients and Methods Four Cord Blood samples from healthy donors (HDs) and 16 Bone Marrow (BM) samples from PTPN11 (N=8) and NRAS (n=8) mutated JMML patients were collected after approvals from International Review Boards of each Institution. CD33+/34+ and CD14+ cells were magnetically sorted following MACS protocols (Miltenyi). TruSeq and NextSeq kits (Illumina) were used for libraries preparation in order to perform the global transcriptomic and smallRNAs sequencing analyses on Illumina platform, according to the company protocols. StringTie and DESeq2 were used for differential expression analysis and REDItools python scripts for RNA editing analysis. Results In our first analysis, we screened ~60000 gene transcripts, finding different expression signatures between HDs, PTPN11 and NRAS samples. We built Expression Heat-Maps reporting the top 100 most statistically significant (Fig. 1, 2; adjusted p < 0.001) deregulated sequences in all the possible pairwise comparisons. Among the top 100 transcripts, we identified differential expression of 5 different genes: ZNF185, MRLPL30 resulted upregulated in PTPN11 vs NRAS CD14+ cells while CD36, RAG2 and CAMK2A resulted downregulated. Interestingly, comparison of PTPN11 and NRAS cohorts in CD33+/34+ subset, these 5 transcripts showed the opposite expression trend. Moreover, among the upregulated genes, we identified a subset of PRAME paralog transcripts in both NRAS and PTPN11 patients when compared to HDs. Seven transcripts (PRAMEF4, 5, 6, 9, 11, 15, 26) were shared between CD33+/34+ and CD14+ populations, while PRAMEF7, 8, 20, 21 resulted upregulated in CD33+/34+ cells and PRAME, PRAMEF22, 23, 25 were upregulated in CD14+ cells, as compared to HDs. In addition, we found aberrant upregulation of Hemoglobin γ chains 1 and 2 (HBG1, 2, i.e. those needed for fetal hemoglobin) in all JMML vs HDs in CD14+ cells, while in CD33+/34+ a downregulation of Hemoglobin α and β chains (HBA1,2 and HBB) in PTPN11 samples vs NRAS was detected. We also report an enhanced LIN28B expression in JMML samples vs HDs but only in CD33+/34+ cells. A global ADAR-dependent RNA editing analysis revealed differences in CD33+/34+ cells showing an enhanced Adenine to Inosine conversion in NRAS patients vs PTPN11 and HDs. Finally, microRNAs sequencing reveals a significant upregulation of miR-144-3p in all JMML samples and cell lineages compared to HDs, while the miR-146a-5p was upregulated only in CD14+ cells. Also the miR-22 showed decreased expression in PTPN11 vs HDs in CD33+/34+ subset, while the miR-29a-3p was downregulated in NRAS vs HDs in CD14+ cells. Discussion We report different expression and editing signatures among samples cohorts and cellular lineages, showing novel insights in JMML molecular biology. Upregulation of several PRAME paralog genes depicts an interesting array of possible targets for immunotherapies (e.g., using genetically modified T cells with a PRAME-specific T-cell receptor), although their expression have to be further validated. Differences in the expression of microRNAs subsets, LIN28B and subunits of the HbA and HbF already described in JMML, but here related to specific mutations and cell lineages, could help to better understand the pathogenic processes and peculiar clinical behaviors. Finally, the novel evidence of differential RNA editing in JMML opens new perspectives for further studies regarding differential protein isoforms expression and regulation as well as microRNA targeting. Disclosures Niemeyer: Celgene: Consultancy. Locatelli:Bellicum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Miltenyi: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; bluebird bio: Consultancy. Merli:Novartis: Honoraria; Sobi: Consultancy; Amgen: Honoraria; Bellicum: Consultancy.

High throughput RNA sequencing technologies have provided invaluable research opportunities across distinct scientific domains by producing quantitative readouts of the transcriptional activity of both entire cellular populations and single cells. The majority of RNA-Seq analyses begin by mapping each experimentally produced sequence (i.e., read) to a set of annotated reference sequences for the organism of interest. For both biological and technical reasons, a significant fraction of reads remains unmapped. In this work, we develop Read Origin Protocol (ROP) to discover the source of all reads originating from complex RNA molecules, recombinant T and B cell receptors, and microbial communities. We applied ROP to 8,641 samples across 630 individuals from 54 tissues. A fraction of RNA-Seq data (n=86) was obtained in-house; the remaining data was obtained from the Genotype-Tissue Expression (GTEx v6) project. To generalize the reported number of accounted reads, we also performed ROP analysis on thousands of different, randomly selected, and publicly available RNA-Seq samples in the Sequence Read Archive (SRA). Our approach can account for 99.9% of 1 trillion reads of various read length across the merged dataset (n=10641). Using in-house RNA-Seq data, we show that immune profiles of asthmatic individuals are significantly different from the profiles of control individuals, with decreased average per sample T and B cell receptor diversity. We also show that immune diversity is inversely correlated with microbial load. Our results demonstrate the potential of ROP to exploit unmapped reads in order to better understand the functional mechanisms underlying connections between the immune system, microbiome, human gene expression, and disease etiology. ROP is freely available athttps://github.com/smangul1/ropand currently supports human and mouse RNA-Seq reads.

Plant-fungal symbioses are a common feature in nature. They vary from pathogenic interactions, where fungi subvert plant resources for their own use, to mutualistic associations, where both fungus and host benefit from the interaction. Although the ecological importance of plant-fungal symbioses has long been recognized and the biology of several key associations are now well studied, new technologies have the potential to allow fresh insight into the molecular basis of plant-fungal interactions. One such technique - high throughput RNA sequencing - has recently been used to explore the molecular basis of cross-species communications. Here, we give a brief overview of this emerging technology, and present a general guide for employing the methodology to dissect plant-fungal symbiosis.

The study of gene regulation in cells has recently begun to shift from a period dominated by the study of transcription factor-DNA interactions to a new focus on RNA regulation. This was sparked by the still-emerging recognition of the central role for RNA in cellular complexity emanating from the RNA World hypothesis, and has been facilitated by technologic advances, in particular high throughput RNA sequencing and crosslinking methods (RNA-Seq, CLIP, and HITS-CLIP). This study will place these advances in context, and, focusing on CLIP, will explain the method, what it can be used for, and how to approach using it. Examples of the successes, limitations, and future of the technique will be discussed. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website

The recent advances in high throughput RNA sequencing (RNA-Seq) have generated huge amounts of data in a very short span of time for a single sample. These data have required the parallel advancement of computing tools to organize and interpret them meaningfully in terms of biological implications, at the same time using minimum computing resources to reduce computation costs. Here we describe the method of analyzing RNA-seq data using the set of open source software programs of the Tuxedo suite: TopHat and Cufflinks. TopHat is designed to align RNA-seq reads to a reference genome, while Cufflinks assembles these mapped reads into possible transcripts and then generates a final transcriptome assembly. Cufflinks also includes Cuffdiff, which accepts the reads assembled from two or more biological conditions and analyzes their differential expression of genes and transcripts, thus aiding in the investigation of their transcriptional and post transcriptional regulation under different conditions. We also describe the use of an accessory tool called CummeRbund, which processes the output files of Cuffdiff and gives an output of publication quality plots and figures of the user's choice. We demonstrate the effectiveness of the Tuxedo suite by analyzing RNA-Seq datasets of Arabidopsis thaliana root subjected to two different conditions.

As RNA-Seq and other high-throughput sequencing grow in use and remain critical for gene expression studies, technical variability in counts data impedes studies of differential expression studies, data across samples and experiments, or reproducing results. Studies like Dillies et al. (2013) compare several between-lane normalization methods involving scaling factors, while Hansen et al. (2012) and Risso et al. (2014) propose methods that correct for sample-specific bias or use sets of control genes to isolate and remove technical variability. This paper evaluates four normalization methods in terms of reducing intra-group, technical variability and facilitating differential expression analysis or other research where the biological, inter-group variability is of interest. To this end, the four methods were evaluated in differential expression analysis between data from Pickrell et al. (2010) and Montgomery et al. (2010) and between simulated data modeled on these two datasets. Though the between-lane scaling factor methods perform worse on real data sets, they are much stronger for simulated data. We cannot reject the recommendation of Dillies et al. to use TMM and DESeq normalization, but further study of power to detect effects of different size under each normalization method is merited.

Though powerful, short-read high throughput RNA sequencing is limited in its ability to directly measure exon connectivity in mRNAs containing multiple alternative exons located farther apart than the maximum read lengths. Here, we use the Oxford Nanopore MinION™ sequencer to identify 7,899 ‘full-length’ isoforms expressed from four Drosophila genes, Dscam1, MRP, Mhc, and Rdl. These results demonstrate that nanopore sequencing can be used to deconvolute individual isoforms and that it has the potential to be an important method for comprehensive transcriptome characterization.

The emergence of next-generation high-throughput RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate identification of alternative splicing events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies. Identifying exon skipping (ES) events is an essential part in genome-wide alternative splicing event identification. In this paper, we propose a novel method ESFinder, a random forest classifier to identify ES events from RNA-Seq data. ESFinder conducts thorough studies on predicting features and figures out proper features according to their relevance for ES event identification. Experimental results on real human skeletal muscle and brain RNA-Seq data show that ESFinder could effectively predict ES events with high predictive accuracy. The codes of ESFinder are available at http://mlg.hit.edu.cn/ybai/ES/ESFinder.html.

Wool is an important material in textile manufacturing. In order to investigate the intrinsic factors that regulate wool follicle cycling and wool fiber properties, Illumina sequencing was performed on wool follicle bulb samples from the middle anagen, catagen and late telogen/early anagen phases. In total, 13,898 genes were identified. KRTs and KRTAPs are the most highly expressed gene families in wool follicle bulb. In addition, 438 and 203 genes were identified to be differentially expressed in wool follicle bulb samples from the middle anagen phase compared to the catagen phase and the samples from the catagen phase compared to the late telogen/early anagen phase, respectively. Finally, our data revealed that two groups of genes presenting distinct expression patterns during the phase transformation may have important roles for wool follicle bulb regression and regeneration. In conclusion, our results demonstrated the gene expression patterns in the wool follicle bulb and add new data towards an understanding of the mechanisms involved in wool fiber growth in sheep.

In this study, we utilized high throughput RNA sequencing to obtain a comprehensive gene expression profile of muscle-derived satellite cells (MDSCs) upon induction of differentiation. MDSCs were cultured in vitro and RNA was extracted for sequencing prior to differentiation (MDSC-P), and again during the early and late differentiation (MDSC-D1, and MDSC-D3, respectively) stages. Sequence tags were assembled and analyzed by digital gene expression profile to screen for differentially expressed genes, Gene Ontology annotation, and pathway enrichment analysis. Quantitative real-time PCR was used to confirm the results of RNA sequencing. Our results indicate that certain of genes were changed during skeletal muscle cell development, cell cycle progression, and cell metabolism during differentiation of bovine MDSCs. Furthermore, we identified certain genes that could be used as novel candidates for future research of muscle development. Additionally, the sequencing results indicated that lipid metabolism might be the predominant cellular process that occurs during MDSC differentiation.

We provide a detailed protocol for preparing cDNA libraries suitable for high throughput sequencing that are derived specifically from the 5′ ends of RNA (5′ specific RNA-seq). The protocol describes how cDNA libraries for 5′ specific RNA-seq can be tailored to analyze specific classes of RNAs based upon the phosphorylation status of the 5′ end. Thus, the analysis of cDNA libraries generated by these methods provides information regarding both the sequence and phosphorylation status of the 5′ ends of RNAs. 5′ specific RNA-seq can be used to analyze transcription initiation and post-transcriptional processing of RNAs with single base pair resolution on a genome-wide level.

Status epilepticus (SE) is a life-threatening condition that can give rise to a number of neurological disorders, including learning deficits, depression, and epilepsy. Many of the effects of SE appear to be mediated by alterations in gene expression. To gain deeper insight into how SE affects the transcriptome, we employed the pilocarpine SE model in mice and Illumina-based high-throughput sequencing to characterize alterations in gene expression from the induction of SE, to the development of spontaneous seizure activity. While some genes were upregulated over the entire course of the pathological progression, each of the three sequenced time points (12-hour, 10-days and 6-weeks post-SE) had a largely unique transcriptional profile. Hence, genes that regulate synaptic physiology and transcription were most prominently altered at 12-hours post-SE; at 10-days post-SE, marked changes in metabolic and homeostatic gene expression were detected; at 6-weeks, substantial changes in the expression of cell excitability and morphogenesis genes were detected. At the level of cell signaling, KEGG analysis revealed dynamic changes within the MAPK pathways, as well as in CREB-associated gene expression. Notably, the inducible expression of several noncoding transcripts was also detected. These findings offer potential new insights into the cellular events that shape SE-evoked pathology.

The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data, which could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.

The French StatOmique Consortium gathers more than 40 statisticians and bioinformaticians involved in high-throughput transcriptome data analysis in France. The objective of this group, created in 2008, is to share among researchers and practitioners knowledge of the statistical analysis of high-throughput data. Chantier qualité GA; International audience; During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.

In this study, we report the first use of RNA-sequencing to gain insight into the wide range of transcriptional events that are associated with leafy head development in Chinese cabbage. We generated 53.5 million sequence reads (90 bp in length) from the rosette and heading leaves. The sequence reads were aligned to the recently sequenced Chiifu genome and were analyzed to measure the gene expression levels, to detect alternative splicing events and novel transcripts, to determine the expression of single nucleotide polymorphisms, and to refine the annotated gene structures. The analysis of the global gene expression pattern suggests two important concepts, which govern leafy head formation. Firstly, some stimuli, such as carbohydrate levels, light intensity and endogenous hormones might play a critical role in regulating the leafy head formation. Secondly, the regulation of transcription factors, protein kinases and calcium may also be involved in this developmental process.

High-throughput RNA sequencing (RNA-seq) continues to provide unparalleled insight into transcriptome complexity. Now the “gold standard” for assessing global transcript levels, RNA-seq is poised to revolutionize our understanding of transcription and posttranscriptional regulation of RNA. Despite significant advantages over prior experimental strategies, RNA-seq is not without pitfalls. We have identified a number of confounding factors that significantly affect sequencing coverage. These include regional GC content, preferential sites of fragmentation, and read “pile-up” due to primer affinity and transcript end effects. Independent of cell type and laboratory, when ignored, these factors can bias analyses. Understanding the underlying principles responsible for producing these artifacts is key to recognizing both their presence and how their effects may be controlled. Here we outline the causes of and strategies to avoid several previously unreported complicating factors common to RNA-seq experiments.

RNA is a polymeric molecule implicated in various biological processes, such as the coding, decoding, regulation, and expression of genes. Numerous studies have examined RNA features using whole transcriptome sequencing (RNA-seq) approaches. RNA-seq is a powerful technique for characterizing and quantifying the transcriptome and accelerates the development of bioinformatics software. In this review, we introduce routine RNA-seq workflow together with related software, focusing particularly on transcriptome reconstruction and expression quantification.

Long intergenic non-coding RNAs (lincRNAs) have historically been ignored in cancer biology. However, thousands of lincRNAs have been identified in mammals using recently developed genomic tools, including microarray and high-throughput RNA sequencing (RNA-seq). Several of the lincRNAs identified have been well characterized for their functions in carcinogenesis. Here we performed RNA-seq experiments comparing gastric cancer with normal tissues to find differentially expressed transcripts in intergenic regions. By analyzing our own RNA-seq and public microarray data, we identified 31 transcripts, including a known expressed sequence tag, BM742401. BM742401 was downregulated in cancer, and its downregulation was associated with poor survival in gastric cancer patients. Ectopic overexpression of BM742401 inhibited metastasis-related phenotypes and decreased the concentration of extracellular MMP9. These results suggest that BM742401 is a potential lincRNA marker and therapeutic target.

The rapid expansion in the quantity and quality of RNA-Seq data requires the development of sophisticated high-performance bioinformatics tools capable of rapidly transforming this data into meaningful information that is easily interpretable by biologists. Currently available analysis tools are often not easily installed by the general biologist and most of them lack inherent parallel processing capabilities widely recognized as an essential feature of next-generation bioinformatics tools. We present here a user-friendly and fully automated RNA-Seq analysis pipeline (R-SAP) with built-in multi-threading capability to analyze and quantitate high-throughput RNA-Seq datasets. R-SAP follows a hierarchical decision making procedure to accurately characterize various classes of transcripts and achieves a near linear decrease in data processing time as a result of increased multi-threading. In addition, RNA expression level estimates obtained using R-SAP display high concordance with levels measured by microarrays.