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791 results for “noncoding”
The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons
<p>This project contains the annotated transcriptomes in GTF format used in the manuscript "The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons" DOI: <a href="https://doi.org/10.1093/molbev/msae123">https://doi.org/10.1093/molbev/msae123</a></p>
FIGURE 5 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 5. Phylogenetic relationships among some cephalopod species (and their orders) based on mitochondrial DNA sequences. Parsimony phylogram is based on COX and ATPase genes for cephalopod species whose mitogenomes have been sequenced; Katharina tunicata was used as an outgroup (not shown). Bootstrap values are shown along branches.
FIGURE 2 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 2. Arrangement of the mitogenome of Allonautilus scrobiculatus; the lengths of the individual genes are drawn approximately to scale. Genes encoding on the same strand as CO1 are shown (in white) on the outer portion of the circular genome and are transcribed in the clockwise direction. Genes on the other strand are transcribed in the counterclockwise direction and are indicated on the inner portion of the genome and shaded in blue; the nine largest noncoding regions (20 bp or greater) are shown in gray.
FIGURE 4 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 4. Base composition of the major genes (excluding tRNAs) in the mitogenome of Allonautilus. Plusstrand (+) defined as the coding strand for CO1.
FIGURE 1 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 1. Allonautilus differs from Nautilus in the size and shape of the umbilicus, type of periostracum, and texture of the hood (e.g., Saunders et al., 1987). A. Allonautilus scrobiculatus, Little Ndrova Island, Papua New Guinea, AMNH 101045. B. Nautilus macromphalus, New Caledonia, AMNH 94104.
FIGURE 3 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 3. Architecture of the large noncoding region of extant nautilid mitogenomes. Features are shown for the strand on which CO1 is coded. Nautilus macromphalus (Boore, 2006) was characterized by a microsatellite, six copies of a 62 bp repeat (R1–R6), and a poly-T monomer; in Allonautilus, the microsatellite and first copy of the repeat were missing in one individual (indel pattern A), and an additional repeat was missing in two other individuals (indel pattern B); all individuals possessed the poly-T monomer (blue bar). The frequency of T and G nucleotides varied considerably through the noncoding region: their frequencies, in a 100 bp sliding window, are shown in the lower panel.
Data from: Major group-B enterovirus populations deleted in the noncoding 5' region of genomic RNA modulate activation of the type I interferon pathway in cardiomyocytes and induce myocarditis
<p>Major 5'-terminally deleted (5'TD) RNA forms of group-B coxsackievirus (CVB-5'TD) has been associated with myocarditis in both mice and humans. Although it is known that interferon-β (IFN-β) signaling is critical for an efficient innate immune response against CVB-induced myocarditis, the link between CVB-5'TD RNA forms and type I IFN signaling in cardiomyocytes remains to be explored. In a mouse model of CVB3/28-induced myocarditis, major early-emerging forms of CVB-5'TD RNA have been characterized as replicative viral populations that impair IFN-β production in the heart. Synthetic CVB3/28 RNA forms mimicking each of these major 5'TD virus populations were transfected in mice and have been shown to modulate innate immune responses in the heart and to induce myocarditis in mice. Remarkably, transfection of synthetic viral RNA with deletions in the secondary structures of the 5'-terminal CVB3 RNA domain I, modifying stem-loops "b", "c" or "d", were found to impair IFN-β production in human cardiomyocytes. In addition, the activation of innate immune response by Poly(I:C), was found to restore IFN-β production and to reduce the burden of CVB-5'TD RNA-forms in cardiac tissues, thereby reducing the mortality rate of infected mice. Overall, our results indicate that major early-emerging CVB3 populations deleted in the domain I of genomic RNA, in the 5' noncoding region, modulate the activation of the type I IFN pathway in cardiomyocytes and induce myocarditis in mice. These findings shed new light on the role of replicative CVB-5'TD RNA forms as key pathophysiological factors in CVB-induced human myocarditis.</p>
DisFull_Lnc: discover full-length long noncoding RNAs without transcriptional initiation profiles
<p>DisFull_Lnc aims to identify full-length lncRNAs directly from raw RNA-seq data without H3K4me3 profiles from the corresponding sample.</p>
Modelling the genetic aetiology of complex disease: human-mouse conservation of noncoding features and disease-associated loci
<p>Understanding the genetic aetiology of loci associated with disease is crucial for developing preventative measures and effective treatments. Mouse models are used extensively to understand human pathobiology and mechanistic functions of disease-associated loci. However, the utility of mouse models is limited by evolutionary divergence in transcription regulation for pathways of interest. Here, we summarise the conservation of genomic (exonic and multi-cell regulatory) features and complex disease associated variant sites between humans and mice. Our results highlight the importance of understanding evolutionary divergence in transcription regulation when interpreting functional studies using mice as models for human disease variants.</p>
CeNGEN Integrated mRNA data and bulk noncoding RNA lookup table
<p>This CeNGEN web tool provides a user interface to datasets described in Barrett et al(1): <a href="http://bulk.cengen.org:3838/Bulk_Integrated_viz/">http://bulk.cengen.org:3838/Bulk_Integrated_viz/</a>. It can also be run as an Rshiny app locally using the attached files.</p> <p>Raw data are being processed at GEO, and this resource will be updated with the accession number when available.</p> <p>(1)Integrating bulk and single cell RNA-seq refines transcriptomic profiles of specific C. elegans neurons. Alec Barrett, Erdem Varol, Alexis Weinreb, Seth R. Taylor, Rebecca McWhirter, CyrilCros, Manasa Basavaraju, Abigail Poff, John A. Tipps, Maryam Majeed, Berta Vidal, Chen Wang, Eviatar Yemini, Emily A. Bayer, HaoSheng Sun, Oliver Hobert, David M.Miller III, Marc Hammarlund. bioRxiv 2022.04.05.487209; doi: https://doi.org/10.1101/2022.04.05.487209</p> <p> </p>
Phenotype-Informed Noncoding Element Scoring
<p>Functional characterization of the noncoding genome is essential for the biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring) which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. A unique feature of PINES is that analyses may be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.</p>
Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data
<p>Flnc is software that can accurately identify full-length long noncoding RNAs (lncRNAs) from human RNA-seq data. lncRNAs are linear transcripts of more than 200 nucleotides that do not encode proteins. The most common approach for identifying lncRNAs from RNA-seq data which examines the coding abilities of assembled transcripts will result in a very high false-positive rate (30%-75%) of lncRNA identification. The falsely discovered lncRNAs lack transcriptional start sites and most of them are RNA fragments or result from transcriptional noise. Unlike the false-positive lncRNAs, true lncRNAs are full-length lncRNA transcripts that include transcriptional start sites (TSSs). To exclude these false lncRNAs, H3K4me3 chromatin immunoprecipitation sequencing (ChIP-seq) data had been used to examine transcriptional start sites of putative lncRNAs, which are transcripts without coding abilities. However, because of cost, time, and the limited availability of sample materials for generating H3K4me3 ChIP-seq data, most samples (especially clinical biospecimens) may have available RNA-seq data but lack matched H3K4me3 ChIP-seq data. This Flnc method solves the problem of lacking transcriptional initiation profiles when identifying lncRNAs.</p> <p>Flnc integrates seven machine-learning algorithms built with four genomic features. Flnc achieves state-of-the-art prediction power with a AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the common approach to over 85% on five independent datasets without requiring matched H3K4me3 ChIP-seq data. In addition to the stranded polyA-selected RNA-seq data, Flnc can also be applied to identify lncRNAs from stranded RNA-seq data of ribosomal RNA depleted samples or unstranded RNA-seq data of polyA-selected samples.</p>
Multimodal learning of noncoding variant effects using genome sequence and chromatin structure
<p>ncVarPred-1D3D:</p> <p>The data used for testing the inconsistency among genome sequence, epigenetic profile, and later, to show its relation to 3D chromatin structure can be found in sanity_check_data.tar.gz.</p> <p>Some trained model for noncoding mutation effect prediction (mapping genome sequence to epigenetic profile) can be found in CNN_MLP, CNN_GCN, CNN_RNN_MLP, CNN_RNN_GCN.tar.gz.</p> <p>The trained model for pathogenic variants prediction can be found in fewshot_pathogenic_model.tar.gz. </p> <p>The training data can be found in training_data.tar.gz.</p> <p>Some noncoding variant effects prediction results, e.g. eQTL and pathogenic variants, can be replicated using the data shared in ncVar_data.tar.gz.</p>
Multimodal learning of noncoding variant effects using genome sequence and chromatin structure
<p>ncVarPred-1D3D: pretrained models of Sei (PMID: 35817977) + our 3D structure embedding models are shared. The models are trained and validated using DeepSEA (PMID: 26301843) selected 200 bp regions (we extended to 4K bp neighboring) to predict the epigenetic profile containing 21907 epigenetic events Sei processed.</p> <p>The pretrained DeepSEA (PMID: 26301843) and reproduced DanQ (PMID: 27084946) can be found in SOTA.tar.gz.</p>
Data from: Convergent evolution of noncoding elements associated with short tarsus length in birds
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Data from: PITAR, a DNA damage-inducible Cancer/Testis long noncoding RNA, inactivates p53 by binding and stabilizing TRIM28 mRNA
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Modelling the genetic aetiology of complex disease: human-mouse conservation of noncoding features and disease-associated loci
Open the record for dataset details and reuse information.
Data from: Major group-B enterovirus populations deleted in the noncoding 5' region of genomic RNA modulate activation of the type I interferon pathway in cardiomyocytes and induce myocarditis
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Test data for running snakePipes : noncoding-RNA-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the noncoding-RNA-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for mouse (<strong>GRCm38</strong>) genome. Repeat Masker file is required for this workflow.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Data from: Genome-wide analysis uncovers regulation of long intergenic noncoding RNAs in Arabidopsis
Long intergenic noncoding RNAs (lincRNAs) transcribed from intergenic regions of yeast and animal genomes play important roles in key biological processes. Yet, plant lincRNAs remain poorly characterized and how lincRNA biogenesis is regulated is unclear. Using a reproducibility-based bioinformatics strategy to analyze 200 Arabidopsis transcriptome datasets, we identified 13,230 intergenic transcripts of which 6,480 can be classified as lincRNAs. Expression of 2,708 lincRNAs was detected by RNA-seq experiments. Transcriptome profiling by custom microarrays revealed that the majority of these lincRNAs are expressed at a level between those of mRNAs and pri-miRNAs. A subset of lincRNA genes show organ-specific expression whereas others are responsive to biotic and/or abiotic stresses. Further analysis of transcriptome data in 11 mutants uncovered SERRATE, CBP20, and CBP80 as regulators of lincRNA expression and biogenesis. RT-PCR experiments confirmed these 3 proteins are also needed for splicing of a small group of intron-containing lincRNAs.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.