Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1309869 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We have previously demonstrated that the gut microbiota can play a role in the pathogenesis of conditions associated with exposure to environmental pollutants. It is well accepted that diets high in fermentable fibers such as inulin can beneficially modulate the gut microbiota and lessen the severity of pro-inflammatory diseases. Therefore, we aimed to test the hypothesis that hyperlipidemic mice fed a diet enriched with inulin would be protected from the pro-inflammatory toxic effects of PCB 126.
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Project description:Sequence-based deep learning models have become the state of the art for the analysis of the genomic regulatory code. Particularly for transcriptional enhancers, deep learning models excel at deciphering sequence features and grammar that underlie their spatiotemporal activity. To enable end-to-end enhancer modeling and design, we developed a software and modeling package, called CREsted. It combines preprocessing starting from single-cell ATAC-seq data; modeling with a choice of several architectures for training classification and regression models on either topics or pseudobulk peak heights; sequence design using multiple strategies; and downstream analysis through a collection of tools to locate transcription factor (TF) binding sites, infer the effect of a TF (activating or repressing) on enhancer accessibility, decipher enhancer grammar, and score gene loci. We demonstrate CREsted using a mouse cortex model that we validate using the BICCN collection of in vivo validated mouse brain enhancers. Classical enhancers in immune cells, including the IFN-β enhanceosome are revisited using a PBMC model, and we assess the accuracy of TF binding site predictions with ChIP-seq. Additionally, we use CREsted to compare mesenchymal-like cancer cell states between tumor types; and we investigate different fine-tuning strategies of Borzoi within CREsted, comparing their performance and explainability with CREsted models trained from scratch. Finally, we train a CREsted model on a scATAC-seq atlas of zebrafish development, and use this to design and in vivo validate cell type-specific synthetic enhancers in 3 tissues. For varying datasets we demonstrate that CREsted facilitates efficient training and analyses, enabling scrutinization of the enhancer logic and design of synthetic enhancers across tissues and species. CREsted is available at https://crested.readthedocs.io.
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Project description:The aim of this project was to explore the role of gut microbiota in the development of small intestine. The gut microbiota from different groups was used to treat the mice for 1 or 2 weeks. Then the small intestine samples were collected. The RNA was used for the RNA-seq analysis to search the role of gut microbiota in the development of small intestine. Groups: IMA100 mean gut microbiota from Alginate oligosaccharide 100mg/kg treated mice; IMA10 mean gut microbiota from Alginate oligosaccharide 10mg/kg treated mice; IMC mean gut microbiota from control group mice (dosed with water); Sa mean dosed with saline (no gut microbiota). "1" mean dosed for 1 week, "2" means dosed for 2 weeks.
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