Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Finding non-coding RNAs through genome-scale clustering.

ABSTRACT: Non-coding RNAs (ncRNAs) are transcripts that do not code for proteins. Recent findings have shown that RNA-mediated regulatory mechanisms influence a substantial portion of typical microbial genomes. We present an efficient method for finding potential ncRNAs in bacteria by clustering genomic sequences based on homology inferred from both primary sequence and secondary structure. We evaluate our approach using a set of predominantly Firmicutes sequences. Our results showed that, though primary sequence based-homology search was inaccurate for diverged ncRNA sequences, through our clustering method, we were able to infer motifs that recovered nearly all members of most known ncRNA families. Hence, our method shows promise for discovering new families of ncRNA.

SUBMITTER: Tseng HH

PROVIDER: S-EPMC3417115 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Finding non-coding RNAs through genome-scale clustering.

Tseng Huei-Hun HH Weinberg Zasha Z Gore Jeremy J Breaker Ronald R RR Ruzzo Walter L WL

Journal of bioinformatics and computational biology 20090401 2

Non-coding RNAs (ncRNAs) are transcripts that do not code for proteins. Recent findings have shown that RNA-mediated regulatory mechanisms influence a substantial portion of typical microbial genomes. We present an efficient method for finding potential ncRNAs in bacteria by clustering genomic sequences based on homology inferred from both primary sequence and secondary structure. We evaluate our approach using a set of predominantly Firmicutes sequences. Our results showed that, though primary ...[more]

PMID: 19340921

Similar Datasets

LeARN: a platform for detecting, clustering and annotating non-coding RNAs.

Project description:BackgroundIn the last decade, sequencing projects have led to the development of a number of annotation systems dedicated to the structural and functional annotation of protein-coding genes. These annotation systems manage the annotation of the non-protein coding genes (ncRNAs) in a very crude way, allowing neither the edition of the secondary structures nor the clustering of ncRNA genes into families which are crucial for appropriate annotation of these molecules.ResultsLeARN is a flexible software package which handles the complete process of ncRNA annotation by integrating the layers of automatic detection and human curation.ConclusionThis software provides the infrastructure to deal properly with ncRNAs in the framework of any annotation project. It fills the gap between existing prediction software, that detect independent ncRNA occurrences, and public ncRNA repositories, that do not offer the flexibility and interactivity required for annotation projects. The software is freely available from the download section of the website http://bioinfo.genopole-toulouse.prd.fr/LeARN.

| S-EPMC2241582 | biostudies-literature

Whole-genome-scale identification of novel non-protein-coding RNAs controlling cell proliferation and survival through a functional forward genetics strategy.

Project description:Identification of cell fate-controlling lncRNAs is essential to our understanding of molecular cell biology. Here we present a human genome-scale forward-genetics approach for the identification of lncRNAs based on gene function. This approach can identify genes that play a causal role, and immediately distinguish them from those that are differentially expressed but do not affect cell function. Our genome-scale library plus next-generation-sequencing and bioinformatic approach, radically upscales the breadth and rate of functional ncRNA discovery. Human gDNA was digested to produce a lentiviral expression library containing inserts in both sense and anti-sense orientation. The library was used to transduce human Jurkat T-leukaemic cells. Cell populations were selected using continuous culture ± anti-FAS IgM, and sequencing used to identify sequences controlling cell proliferation. This strategy resulted in the identification of thousands of new sequences based solely on their function including many ncRNAs previously identified as being able to modulate cell survival or to act as key cancer regulators such as AC084816.1*, AC097103.2, AC087473.1, CASC15*, DLEU1*, ENTPD1-AS1*, HULC*, MIRLET7BHG*, PCAT-1, SChLAP1, and TP53TG1. Independent validation confirmed 4 out of 5 sequences that were identified by this strategy, conferred a striking resistance to anti-FAS IgM-induced apoptosis.

| S-EPMC8741825 | biostudies-literature

Capturing functional long non-coding RNAs through integrating large-scale causal relations from gene perturbation experiments.

Project description:Characterizing functions of long noncoding RNAs (lncRNAs) remains a major challenge, mostly due to the lack of lncRNA-involved regulatory relationships. A wide array of genome-wide expression profiles generated by gene perturbation have been widely used to capture causal links between perturbed genes and response genes. Through annotating >600 gene perturbation profiles, over 354,000 causal relationships between perturbed genes and lncRNAs were identified. This large-scale resource of causal relations inspired us to develop a novel computational approach LnCAR for inferring lncRNAs' functions, which showed a higher accuracy than the co-expression based approach. By application of LnCAR to the cancer hallmark processes, we identified 38 lncRNAs involved in distinct carcinogenic processes. The "activating invasion & metastasis" related lncRNAs were strongly associated with metastatic progression in various cancer types and could act as a predictor of cancer metastasis. Meanwhile, the "evading immune destruction" related lncRNAs showed significant associations with immune infiltration of various immune cells and, importantly, can predict response to anti-PD-1 immunotherapy, suggesting their potential roles as biomarkers for immune therapy. Taken together, our approach provides a novel way to systematically reveal functions of lncRNAs, which will be helpful for further experimental exploration and clinical translational research of lncRNAs.

| S-EPMC6156711 | biostudies-literature

Distinguishing protein-coding from non-coding RNAs through support vector machines.

Project description:RIKEN's FANTOM project has revealed many previously unknown coding sequences, as well as an unexpected degree of variation in transcripts resulting from alternative promoter usage and splicing. Ever more transcripts that do not code for proteins have been identified by transcriptome studies, in general. Increasing evidence points to the important cellular roles of such non-coding RNAs (ncRNAs). The distinction of protein-coding RNA transcripts from ncRNA transcripts is therefore an important problem in understanding the transcriptome and carrying out its annotation. Very few in silico methods have specifically addressed this problem. Here, we introduce CONC (for "coding or non-coding"), a novel method based on support vector machines that classifies transcripts according to features they would have if they were coding for proteins. These features include peptide length, amino acid composition, predicted secondary structure content, predicted percentage of exposed residues, compositional entropy, number of homologs from database searches, and alignment entropy. Nucleotide frequencies are also incorporated into the method. Confirmed coding cDNAs for eukaryotic proteins from the Swiss-Prot database constituted the set of true positives, ncRNAs from RNAdb and NONCODE the true negatives. Ten-fold cross-validation suggested that CONC distinguished coding RNAs from ncRNAs at about 97% specificity and 98% sensitivity. Applied to 102,801 mouse cDNAs from the FANTOM3 dataset, our method reliably identified over 14,000 ncRNAs and estimated the total number of ncRNAs to be about 28,000.

| S-EPMC1449884 | biostudies-literature

Genome-Scale Expression Pattern of Long Non-Coding RNAs in Chinese Uyghur Patients with Parkinson's Disease.

Project description:BACKGROUND Long non-coding RNAs (lncRNAs) are transcripts thought to regulate gene expression at the post-transcriptional level. Some lncRNAs are associated with Parkinson's disease (PD) and participate in pathological processes of PD. The incidence of PD is relatively high in members of the Uyghur minority living in Xingjiang province of China. This study measured the expression of lncRNAs in the peripheral blood cells of Chinese Uyghur individuals with and without PD and analyzed the possible function of these lncRNAs in the development of PD. MATERIAL AND METHODS Peripheral blood samples were collected from 55 Uyghur patients with PD and 55 healthy volunteers. Total RNA was extracted, and the levels of expression of whole-genome lncRNAs and mRNAs in 10 samples (5 PD and 5 controls) were determined by microarray method. The expression levels of lncRNAs in all 100 subjects were determined by qRT-PCR. The lncRNA expression profiles of PD patients were determined based on lncRNA microarray chip analysis, and differentially expressed lncRNAs were identified. The results of chip analysis were confirmed in a large clinical cohort. RESULTS Comparison of subjects with and without PD identified 32 significantly up-regulated and 18 significantly down-regulated lncRNAs in the PD group. GO analysis showed that mRNAs encoding proteins involved in the regulation of biological processes were differentially expressed, with the inflammatory immune response being the most significantly related pathway. CONCLUSIONS The expression of lncRNAs in peripheral blood differed significantly in PD patients and controls. These differentially expressed lncRNAs may play a role in the development of PD.

| S-EPMC7552881 | biostudies-literature

Finding new proteins encoded by long non-coding RNAs in human cells

Project description:We performed ribosome profiling to assess the translation elongation on translating RNA in HBE, A549, and H1299. We detected the new proteins in the cells.

2019-07-20 | GSE79539 | GEO

Genome-wide identification of non-coding RNAs interacted with microRNAs in soybean.

Project description:A wide range of RNA species interacting with microRNAs (miRNAs) form a complex gene regulation network and play vital roles in diverse biological processes. In this study, we performed a genome-wide identification of endogenous target mimics (eTMs) for miRNAs and phased-siRNA-producing loci (PHAS) in soybean with a focus on those involved in lipid metabolism. The results showed that a large number of eTMs and PHAS genes could be found in soybean. Additionally, we found that lipid metabolism related genes were potentially regulated by 28 miRNAs, and nine of them were potentially further regulated by a number of eTMs with expression evidence. Thirty-three miRNAs were found to trigger production of phasiRNAs from 49 PHAS genes, which were able to target lipid metabolism related genes. Degradome data supported miRNA- and/or phasiRNA-mediated cleavage of genes involved in lipid metabolism. Most eTMs for miRNAs involved in lipid metabolism and phasiRNAs targeting lipid metabolism related genes showed a tissue-specific expression pattern. Our bioinformatical evidences suggested that lipid metabolism in soybean is potentially regulated by a complex non-coding network, including miRNAs, eTMs, and phasiRNAs, and the results extended our knowledge on functions of non-coding RNAs.

| S-EPMC4274897 | biostudies-other

Genome-scale deletion screening of human long non-coding RNAs using a paired-guide RNA CRISPR-Cas9 library.

Project description:CRISPR-Cas9 screens have been widely adopted to analyze coding-gene functions, but high-throughput screening of non-coding elements using this method is more challenging because indels caused by a single cut in non-coding regions are unlikely to produce a functional knockout. A high-throughput method to produce deletions of non-coding DNA is needed. We report a high-throughput genomic deletion strategy to screen for functional long non-coding RNAs (lncRNAs) that is based on a lentiviral paired-guide RNA (pgRNA) library. Applying our screening method, we identified 51 lncRNAs that can positively or negatively regulate human cancer cell growth. We validated 9 of 51 lncRNA hits using CRISPR-Cas9-mediated genomic deletion, functional rescue, CRISPR activation or inhibition and gene-expression profiling. Our high-throughput pgRNA genome deletion method will enable rapid identification of functional mammalian non-coding elements.

| S-EPMC5592164 | biostudies-literature

Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease.

Project description:Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

| S-EPMC8651477 | biostudies-literature

Identification of cyanobacterial non-coding RNAs by comparative genome analysis.

Project description:BACKGROUND: Whole genome sequencing of marine cyanobacteria has revealed an unprecedented degree of genomic variation and streamlining. With a size of 1.66 megabase-pairs, Prochlorococcus sp. MED4 has the most compact of these genomes and it is enigmatic how the few identified regulatory proteins efficiently sustain the lifestyle of an ecologically successful marine microorganism. Small non-coding RNAs (ncRNAs) control a plethora of processes in eukaryotes as well as in bacteria; however, systematic searches for ncRNAs are still lacking for most eubacterial phyla outside the enterobacteria. RESULTS: Based on a computational prediction we show the presence of several ncRNAs (cyanobacterial functional RNA or Yfr) in several different cyanobacteria of the Prochlorococcus-Synechococcus lineage. Some ncRNA genes are present only in two or three of the four strains investigated, whereas the RNAs Yfr2 through Yfr5 are structurally highly related and are encoded by a rapidly evolving gene family as their genes exist in different copy numbers and at different sites in the four investigated genomes. One ncRNA, Yfr7, is present in at least seven other cyanobacteria. In addition, control elements for several ribosomal operons were predicted as well as riboswitches for thiamine pyrophosphate and cobalamin. CONCLUSION: This is the first genome-wide and systematic screen for ncRNAs in cyanobacteria. Several ncRNAs were both computationally predicted and their presence was biochemically verified. These RNAs may have regulatory functions and each shows a distinct phylogenetic distribution. Our approach can be applied to any group of microorganisms for which more than one total genome sequence is available for comparative analysis.

| S-EPMC1242208 | 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