Browse
Submit Data
Databases
API
Help

Dataset Information

24 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Long non-coding RNA deep sequencing reveals the role of macrophage in liver disorders.

ABSTRACT: Liver disorders such as hepatitis, cirrhosis and hepatocellular carcinoma are a series of the most life threatening diseases along with extensive inflammatory cellular infiltrations. Macrophage has been proved to be key regulators and initiators of inflammation, and long non-coding RNAs (lncRNAs) are recommended to play critical roles in the occurrence and development of a variety of diseases. To uncover the role of macrophage in liver disorders via lncRNA sequencing method, we first applied a lncRNA classification pipeline to identify 1247 lncRNAs represented on the Affymetrix Mouse Genome 430/430A 2.0 array. We then analyzed the lncRNA expression patterns in a set of previously published gene expression profiles of silica particle exposed macrophages and liver respectively, and identified and validated sets of differentially expressed lncRNAs shared by macrophages and liver. The functional enrichment analysis of these lncRNAs was processed on the basis of their expression signatures, three aspects including cis, trans and co-acting proteins were proposed. This is the first time to correlate macrophage with liver disorders via co-expressed lncRNAs. Our findings indicated that roles of macrophage in liver disorders were double-edged, the differentially expressed lncRNAs and their corresponding regulatory genes or proteins may serve as potential diagnostic biomarkers and therapeutic targets.

SUBMITTER: Lin Z

PROVIDER: S-EPMC5777746 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Long Non-coding RNA in Neuronal Development and Neurological Disorders.

Project description:Long non-coding RNAs (lncRNAs) are transcripts which are usually more than 200 nt in length, and which do not have the protein-coding capacity. LncRNAs can be categorized based on their generation from distinct DNA elements, or derived from specific RNA processing pathways. During the past several decades, dramatic progress has been made in understanding the regulatory functions of lncRNAs in diverse biological processes, including RNA processing and editing, cell fate determination, dosage compensation, genomic imprinting and development etc. Dysregulation of lncRNAs is involved in multiple human diseases, especially neurological disorders. In this review, we summarize the recent progress made with regards to the function of lncRNAs and associated molecular mechanisms, focusing on neuronal development and neurological disorders.

| S-EPMC6351443 | biostudies-literature

Hybridization-based reconstruction of small non-coding RNA transcripts from deep sequencing data.

Project description:Recent advances in RNA sequencing technology (RNA-Seq) enables comprehensive profiling of RNAs by producing millions of short sequence reads from size-fractionated RNA libraries. Although conventional tools for detecting and distinguishing non-coding RNAs (ncRNAs) from reference-genome data can be applied to sequence data, ncRNA detection can be improved by harnessing the full information content provided by this new technology. Here we present NorahDesk, the first unbiased and universally applicable method for small ncRNAs detection from RNA-Seq data. NorahDesk utilizes the coverage-distribution of small RNA sequence data as well as thermodynamic assessments of secondary structure to reliably predict and annotate ncRNA classes. Using publicly available mouse sequence data from brain, skeletal muscle, testis and ovary, we evaluated our method with an emphasis on the performance for microRNAs (miRNAs) and piwi-interacting small RNA (piRNA). We compared our method with Dario and mirDeep2 and found that NorahDesk produces longer transcripts with higher read coverage. This feature makes it the first method particularly suitable for the prediction of both known and novel piRNAs.

| S-EPMC3439898 | biostudies-other

RNA Deep Sequencing Reveals Expression Patterns of Long Non-coding RNAs during Swine Ovary Development

Project description:In this study, we obtained the expression profiles of lncRNAs during the development of swine ovaries with RNA-seq technology. The results showed 103,233 and 94,251 lncRNAs were identified from the libraries of ovaries from 30-day-old and 90-day-old swine, respectively, and 37,323 were significantly differentially expressed between the two libraries. The target genes of 903 lncRNAs were acquired through prediction, and KEGG pathway analysis showed the differential lncRNAs were involved in 268 pathways, including ovarian follicular development processes and pathways, such as the GnRH signaling pathway and estrogen signaling pathway. As swine is an ideal biological model for human genetics, the work will help further understand human reproduction.

2015-08-18 | GSE72115 | GEO

Characterization of long non-coding RNA transcriptome in clear-cell renal cell carcinoma by next-generation deep sequencing.

Project description:IntroductionLong non-coding RNA (lncRNA) have proven to play key roles in cell physiology from nuclear organization and epigenetic remodeling to post-transcriptional regulation. Last decade, gene expression based-classifications have been developed in clear-cell renal cell carcinoma (ccRCC) to identify distinct subtypes of disease and predict patient's outcome. However, there are no current lncRNA comprehensive characterizations in ccRCC.Patients and methodsRNA-sequencing profiles of 475 primary ccRCC samples from the Cancer Genome Atlas (TCGA) were used to assess expressed lncRNA and identify lncRNA-based classification. In addition, integrative analysis was performed to correlate tumor subtypes with copy-number alterations and somatic mutations.ResultsUsing stringent criteria, we identified 1934 expressed lncRNA and assessed their chromatin marks. Unsupervised clustering unravels four lncRNA subclasses in ccRCC associated with distinct clinicopathological and genomic features of this disease. Cluster C2 (23.4%) defines the most aggressive tumours, with the highest Fuhrman grade and stage and the worst overall survival time. Furthermore, cluster C2 is enriched for 9p deletion and chromatin remodeler BAP1 somatic mutations. Interestingly, cluster C4 (7.8%) is related to a tumor subtype arising from the distal tubules of the nephron. Consistent with its distinct ontogeny, cluster C4 is devoid of classical alterations seen in ccRCC, bears frequent 1p deletion and 17q gain, and is enriched for MiTF/TFE translocations. In addition, reexaminations of copy-number data from one side and tumor histology by pathologists from the other side reveal misclassified tumors within C4 cluster including chromophobe RCC and clear cell papillary RCC.ConclusionThis study establishes a foundation for categorizing lncRNA subclasses, which may contribute to understand tumor ontogeny and help predicting patients' outcome in ccRCC.

| S-EPMC4516270 | biostudies-literature

circDeep: deep learning approach for circular RNA classification from other long non-coding RNA.

Project description:MOTIVATION:Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is vital to understanding their biogenesis and purpose. Prediction of circular RNA can be achieved in three steps: distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs and predicting circular RNAs from other long non-coding RNAs (lncRNAs). However, the available tools are less than 80 percent accurate for distinguishing circular RNAs from other lncRNAs due to difficulty of classification. Therefore, the availability of a more accurate and fast machine learning method for the identification of circular RNAs, which considers the specific features of circular RNA, is essential to the development of systematic annotation. RESULTS:Here we present an End-to-End deep learning framework, circDeep, to classify circular RNA from other lncRNA. circDeep fuses an RCM descriptor, ACNN-BLSTM sequence descriptor and a conservation descriptor into high level abstraction descriptors, where the shared representations across different modalities are integrated. The experiments show that circDeep is not only faster than existing tools but also performs at an unprecedented level of accuracy by achieving a 12 percent increase in accuracy over the other tools. AVAILABILITY AND IMPLEMENTATION:https://github.com/UofLBioinformatics/circDeep. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

| S-EPMC6956777 | biostudies-literature

Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations.

Project description:The liver is the largest solid organ in the body and is critical for metabolic and immune functions. However, little is known about the cells that make up the human liver and its immune microenvironment. Here we report a map of the cellular landscape of the human liver using single-cell RNA sequencing. We provide the transcriptional profiles of 8444 parenchymal and non-parenchymal cells obtained from the fractionation of fresh hepatic tissue from five human livers. Using gene expression patterns, flow cytometry, and immunohistochemical examinations, we identify 20 discrete cell populations of hepatocytes, endothelial cells, cholangiocytes, hepatic stellate cells, B cells, conventional and non-conventional T cells, NK-like cells, and distinct intrahepatic monocyte/macrophage populations. Together, our study presents a comprehensive view of the human liver at single-cell resolution that outlines the characteristics of resident cells in the liver, and in particular provides a map of the human hepatic immune microenvironment.

| S-EPMC6197289 | biostudies-literature

Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples.

Project description:BACKGROUND:Long non-coding RNAs (lncRNAs) are emerging as important regulators of various biological processes. While many studies have exploited public resources such as RNA sequencing (RNA-Seq) data in The Cancer Genome Atlas to study lncRNAs in cancer, it is crucial to choose the optimal method for accurate expression quantification. RESULTS:In this study, we compared the performance of pseudoalignment methods Kallisto and Salmon, alignment-based transcript quantification method RSEM, and alignment-based gene quantification methods HTSeq and featureCounts, in combination with read aligners STAR, Subread, and HISAT2, in lncRNA quantification, by applying them to both un-stranded and stranded RNA-Seq datasets. Full transcriptome annotation, including protein-coding and non-coding RNAs, greatly improves the specificity of lncRNA expression quantification. Pseudoalignment methods and RSEM outperform HTSeq and featureCounts for lncRNA quantification at both sample- and gene-level comparison, regardless of RNA-Seq protocol type, choice of aligners, and transcriptome annotation. Pseudoalignment methods and RSEM detect more lncRNAs and correlate highly with simulated ground truth. On the contrary, HTSeq and featureCounts often underestimate lncRNA expression. Antisense lncRNAs are poorly quantified by alignment-based gene quantification methods, which can be improved using stranded protocols and pseudoalignment methods. CONCLUSIONS:Considering the consistency with ground truth and computational resources, pseudoalignment methods Kallisto or Salmon in combination with full transcriptome annotation is our recommended strategy for RNA-Seq analysis for lncRNAs.

| S-EPMC6897288 | biostudies-literature

RNA-sequencing reveals genome-wide long non-coding RNAs profiling associated with early development of diabetic nephropathy.

Project description:BackgroundDiabetic nephropathy (DN) seriously threatens the lives of patients, and the mechanism of DN remains largely unknown because of the complex regulation between long non-coding RNA (lncRNA) and protein-coding genes. In early development of diabetic nephropathy (DN), pathogenesis remains largely unknown.ResultsWe used RNA-sequencing to profile protein-coding and lncRNA gene transcriptome of mouse kidney proximal tubular cells during early stage of DN at various time points. Over 7000 protein-coding and lncRNA genes were differentially expressed, and most of them were time-specific. Nearly 40% of lncRNA genes overlapped with functional element signals using CHIP-Seq data from ENCODE database. Disease progression was characterized by lncRNA expression patterns, rather than protein-coding genes, indicating that the lncRNA genes are potential biomarkers for DN. For gene ontologies related to kidney, enrichment was observed in protein-coding genes co-expressed with neighboring lncRNA genes. Based on protein-coding and lncRNA gene profiles, clustering analysis reveals dynamic expression patterns for kidney, suggesting that they are highly correlated during disease progression. To evaluate translation of mouse model to human conditions, we experimentally validated orthologous genes in human cells in vitro diabetic model. In mouse model, most gene expression patterns were repeated in human cell lines.ConclusionsThese results define dynamic transcriptome and novel functional roles for lncRNAs in diabetic kidney cells; these roles may result in lncRNA-based diagnosis and therapies for DN.

| S-EPMC5739683 | biostudies-literature

Deep learning tools are top performers in long non-coding RNA prediction.

Project description:The increasing amount of transcriptomic data has brought to light vast numbers of potential novel RNA transcripts. Accurately distinguishing novel long non-coding RNAs (lncRNAs) from protein-coding messenger RNAs (mRNAs) has challenged bioinformatic tool developers. Most recently, tools implementing deep learning architectures have been developed for this task, with the potential of discovering sequence features and their interactions still not surfaced in current knowledge. We compared the performance of deep learning tools with other predictive tools that are currently used in lncRNA coding potential prediction. A total of 15 tools representing the variety of available methods were investigated. In addition to known annotated transcripts, we also evaluated the use of the tools in actual studies with real-life data. The robustness and scalability of the tools' performance was tested with varying sized test sets and test sets with different proportions of lncRNAs and mRNAs. In addition, the ease-of-use for each tested tool was scored. Deep learning tools were top performers in most metrics and labelled transcripts similarly with each other in the real-life dataset. However, the proportion of lncRNAs and mRNAs in the test sets affected the performance of all tools. Computational resources were utilized differently between the top-ranking tools, thus the nature of the study may affect the decision of choosing one well-performing tool over another. Nonetheless, the results suggest favouring the novel deep learning tools over other tools currently in broad use.

| S-EPMC9123429 | biostudies-literature

Deep sequencing of small non-coding RNA highlights brain-specific expression patterns and RNA cleavage.

Project description:With the advance of high-throughput sequencing technology numerous new regulatory small RNAs have been identified, that broaden the variety of processing mechanisms and functions of non-coding RNA. Here we explore small non-coding RNA (sncRNA) expression in central parts of the physiological stress and anxiety response system. Therefore, we characterize the sncRNA profile of tissue samples from Amygdala, Hippocampus, Hypothalamus and Adrenal Gland, obtained from 20 pigs. Our analysis reveals that all tissues but Amygdala and Hippocampus possess distinct, tissue-specific expression pattern of miRNA that are associated with Hypoxia, stress responses as well as memory and fear conditioning. In particular, we observe marked differences in the expression profile of limbic tissues compared to those associated to the HPA/stress axis, with a surprisingly high aggregation of 3´-tRNA halves in Amygdala and Hippocampus. Since regulation of sncRNA and RNA cleavage plays a pivotal role in the central nervous system, our work provides seminal insights in the role/involvement of sncRNA in the transcriptional and post-transcriptional regulation of negative emotion, stress and coping behaviour in pigs, and mammals in general.

| S-EPMC6844574 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data