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840 results for “RNA modeling”
Evolution towards increasing complexity through functional diversification in a protocell model of the RNA world
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Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency
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Structural insights into distinct mechanisms of RNA polymerase II and III recruitment to snRNA promoters - segmented EM density used in integrative modeling
<p>segmented cryoEM map and derived gaussian mixture model used in the integrative modeling of the human SNAPc complex </p>
Chronic nicotine exposure alters sperm small RNA content in a C57BL/6J mouse model: Implications for epigenetic inheritance
<p>Raw small RNA sequencing data files to accompany manuscript</p>
Development of a multivariable risk model integrating urinary peptide metabolites and Extracellular Vesicle RNA data to detect significant prostate cancer
<p>The aim of this study was to investigate whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples were analyzed by mass spectrometry and NanoString gene-expression analysis. As a result, four classifiers were generated: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, ‘SoC’ (standard of care) and ‘ExoSpec’. The best prediction for Gs³3+4 at initial biopsy (AUC=0.83, 95% CI:0.77-0.88) was achieved by applying ‘ExoSpec’ classifier and he outperformed other predictive classifiers. In addition, the results showed that the performance of ‘ExoSpec’ could reduce unnecessary biopsies by 30%.</p>
EMRNA: Accurate RNA structure determination from cryo-EM maps by deep learning and integrated modeling
<p>EMRNA: Accurate RNA structure determination from cryo-EM maps by deep learning and integrated modeling.</p><p>Here stores the input files and output structures of EMRNA and the reproduction result of auto-DRRAFTER.</p>
RNA 3D structural models used to train, test and validate lociPARSE
<p>This repository contains all the training, validation and test decoy sets to train and evalaute lociPARSE. It also contains training and benchmarks set-2 decoys from ARES.</p>
Deep Learning Based Models for Preimplantation Mouse and Human Embryos Based on Single Cell RNA Sequencing
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RNA large language models embeddings on benchmark datasets
<p>This repository contains pre-computed embeddings for several RNA sequences, using most recent Large Language Models (LLM) pre-trained on RNA sequences. </p> <p>compressed files for each combination of RNA-LLM models and benchmarking RNA datasets. </p>
Structural 3D domain reconstruction of the RNA genome from viruses from a secondary structure model
<p>Fragments and final models of reconstructed STMV genome from in virio and in vitro secondary structures reported in Larman et al. (2017).</p> <p>Simulation scripts for simulations of genome and fragments.</p> <p>Full code of SPQR package for performing simulations.</p>
The dataset used in "A max-margin model for predicting residue-base contacts in protein-RNA interactions"
<p>The dataset used in "A max-margin model for predicting residue-base contacts in protein-RNA interactions"</p>
Tailored machine learning models for functional RNA detection in genome-wide screens
<p>The prediction of noncoding RNA and protein coding genetic loci has received<br> considerable attention in comparative genomics aiming in particular<br> at the identification of properties of nucleotide sequences that are<br> informative of their biological role in the cell. We present here a<br> software framework for the alignment-based training, evaluation and<br> application of machine learning models with user-defined<br> parameters. Instead of focusing on the one-size-fits-all approach of<br> pervasive \is annotation pipelines, we offer a framework for the<br> structured generation and evaluation of models based on arbitrary<br> features and input data, focusing on stable and explainable results.<br> Furthermore, we showcase the usage of our software package in a<br> full-genome screen of Drosophila melanogaster and evaluate<br> our results against the well-known but much less flexible program<br> RNAz.</p> <p> </p>
Data from: Assessing models of speciation under different biogeographic scenarios; an empirical study using multi-locus and RNA-seq analyses
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Analytical workflow for "Grad-seq shines light on unrecognized RNA and protein complexes in the model bacterium Escherichia coli", Hör et al. 2020
<p>Analytical workflow including scripts, data and Singularity image for "Grad-seq shines light on unrecognized RNA and protein complexes in the model bacterium Escherichia coli", Hör et al. 2020, (<a href="https://doi.org/10.1101/2020.06.29.177014">https://doi.org/10.1101/2020.06.29.177014</a>)</p>
RNA m6A methylation in unilateral ureteral ligations model
<p>Chronic kidney disease (CKD) is a high incidence disease. Renal fibrosis is the ultimate pathological pathway for chronic kidney diseases leading to end-stage renal failure. There is currently no effective treatment to prevent the renal fibrosis. Renal EMT is a key mechanism of renal fibrosis. Recent evidence suggests m<sup>6</sup>A modification in RNA can regulate EMT in tumor cells. However, a role of m<sup>6</sup>A RNA modification in renal fibrosis has not been reported. Therefore, we investigate whether m<sup>6</sup>A RNA methylation plays an important role in the epithelial-to-mesenchymal transition (EMT) process of renal fibrosis. Unilateral ureteral ligations were performed in C57BL/6 mice to establish a mouse model of renal fibrosis and TGF-b intervention was used in HK2 cells to establish a cellular model of renal fibrosis. The effects of m<sup>6</sup>A and the role of <i>Mettl14 </i>on kidney disease and HK2 cells were studied. </p>
Single cell RNA-seq transcriptomic profile of circulating immune and progenitor cells in a mouse model of neonatal hypoxic/ischemic (HI) brain damage.
<p>Hematopoietic cells play a pivotal role in regulating the inflammatory and reparative immune responses triggered after ischemic tissue damage. The response initiated within the injured tissue leads to compositional and transcriptional changes in circulating hematopoietic and progenitor cells, which have been utilized as biomarkers. While the importance of different immune and progenitor cell subtypes in the development of ischemic damage has been extensively researched in adult tissue injuries, there has been limited investigation in neonates. This is a critical developmental stage where ischemic damage can result in severe and irreversible health consequences if not promptly treated. To determine how ischemic damage could affect circulating cells in neonates, we have induced hypoxic-ischemic (HI) brain damage in seven-day-old mice, characterized by focal white and gray-matter injury (Rice-Vannucci model). Brain damage and circulating cells were analyzed at 48h post-HI, the intermediate reparative/inflammatory response phase post-injury. We applied scRNAseq to dissect the transcriptional and cellular composition changes in the peripheral blood of HI-treated and SHAM control neonates. This study provides the first scRNAseq dataset for immune and progenitor circulating cells in newborns with cerebral HI damage. It may help to identify biomarkers and selective therapeutic approaches aimed at modulating inflammatory and reparative pathways. </p><p>CD1 postnatal day 7 (P7) mice were subjected to hypoxic/ischemic (HI) brain injury by permanent ligation of the left common carotid artery followed, after 2h recover, by relocation to a hypoxia chamber for 90 minutes. Sham control mice (SHAM) underwent a skin incision and wound closure followed by hypoxia exposure. Circulating blood cells were collected from SHAM and HI mice at P9. After red blood cells (RBC) lysis, 7AAD-Ter119- cells were FACS sorted and analysed using sc RNAseq. Other samples were FACS sorted for CD45+CD11+ and CD45-CD31+ cells and mixed.</p>
Orthrus: Towards Evolutionary and Functional RNA Foundation Models
<p>Orthrus is a mature RNA model for RNA property prediction. It uses a Mamba encoder backbone, a variant of state-space models specifically designed for long-sequence data, such as RNA.</p> <p> </p> <p>Two versions of Orthrus are available:</p> <ul> <li>4-track base version: Encodes the mRNA sequence with a simplified one-hot approach.</li> <li>6-track large version: Adds biological context by including splice site indicators and coding sequence markers, which is crucial for accurate mRNA property prediction such as RNA half-life, ribosome load, and exon junction detection.</li> </ul> <p>This repository contains the annotations used to train Orthrus, as well as processed datasets used to evaluate Orthrus's ability to perform RNA property prediction. The datasets are taken from the following sources:</p> <ul> <li>Protein Subcellular Localization: Thul, P. J. et al. A subcellular map of the human proteome. Science 356 (2017).</li> <li>Mean Ribosome Load: Sugimoto, Y. & Ratcliffe, P. J. Isoform-resolved mRNA profiling of ribosome load defines interplay of HIF and mTOR dysregulation in kidney cancer. Nature Structural Molecular Biology 29, 871–880 (2022).</li> <li>RNA Halflife: Agarwal, V. & Kelley, D. R. The genetic and biochemical determinants of mRNA degradation rates in mammals. Genome Biol 23, 245 (2022).</li> <li>GO Molecular Function: Consortium, T. G. O. et al. The Gene Ontology knowledgebase in 2023. Genetics 224, iyad031 (2023).</li> </ul> <p>Dataset files encode sequence using a one-hot encoding using the vocabulary: [A, C, G, T].</p>
Original data for "Multiple co-existing structures of an RNA four-way junction resolved by FRET, SAXS, and integrative modeling"
<p>Experimental single-molecule FRET data (Intensity ratio histograms) and starting structures used for rigid body docking for an RNA four-way junction related to the hairpin ribozyme.</p> <p> </p>
in vitro analysis of a competitive inhibition model for T7 RNA polymerase biosensors
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Data from: Challenges and solutions for analyzing dual RNA-seq data for non-model host/pathogen systems
1. Dual RNA-seq simultaneously profiles the transcriptomes of a host and pathogen during infection and may reveal the mechanisms underlying host-pathogen interactions. Dual RNA-seq is inherently a mixture of transcripts from at least two species (host and pathogen), so this mixture must be computationally sorted into host and pathogen components. Sorting relies on aligning reads to respective reference genomes, which may be unavailable for both species in non-model host-pathogen pairs. This lack of genomic resources may present challenges to applying dual RNA-seq to non-model systems. 2. We assessed the accuracy of alignments of dual RNA-seq when using the genomic resources of a closely-related species to the species of interest by simulating datasets of mixed transcripts from a host and pathogen. Specifically, we compared how different aligners performed across different proportions of pathogen to host transcripts and across variation in the genetic distance between the pathogen genome and reference genome. We performed extensive analyses for a host plant with fungal pathogen, and then we extended the plant-fungus results by repeating key analyses in vertebrate (human)-fungus and vertebrate-bacterium systems. 3. Aligners that were able to map pathogen transcripts to the reference genome of a species closely related to the pathogen (a "related reference genome") also mismapped transcripts originating from the host to the pathogen's related reference genome, which results in regions where this occurred being quantified as overexpressed. If a host reference genome was available, we show that to minimize host transcript mismapping while retaining the ability to map pathogen transcripts, one could concatenate it with the pathogen's related reference genome, then map transcripts to the concatenated genomes. If a host genome was unavailable, assembling reads de novo prior to aligning substantially decreased host read mismapping, while retaining the ability to map pathogen transcripts to a related reference genome. 4. The application of dual RNA-seq to organisms without reference genomes is currently limited. We propose an analytical workflow that leverages the genomic resources of species closely related to species of interest to facilitate application of dual RNA-seq to reveal the mechanisms of host-pathogen interactions across a wider array of systems.
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.