Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1154303 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR30524900_1.fastq.gz | Fastqsanger.gz | |||
| SRR30524900_2.fastq.gz | Fastqsanger.gz | |||
| SRR30524901_1.fastq.gz | Fastqsanger.gz | |||
| SRR30524901_2.fastq.gz | Fastqsanger.gz | |||
| SRR30524902_1.fastq.gz | Fastqsanger.gz |
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Project description:Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is a widely used approach to study DNA methylation genome-wide. Here, we present a novel MeDIP-Seq protocol compatible with the Ion Torrent semiconductor-based sequencing platform that is scalable and accurately identifies sites of differential DNA methylation. Additionally, we demonstrate that the high-throughput data derived from MeDIP-Seq on the Ion Torrent platform provides adequate coverage of CpG cytosines, the methylation states of which we validated at single-base resolution on the Infinium HumanMethylation450K Beadchip array. We applied this integrative approach to further investigate the role of DNA methylation in alternative splicing and to profile 5-mC and 5-hmC variants of DNA methylation in normal human brain tissue that we observed localize over distinct genomic regions. These applications of MeDIP-Seq on the Ion Torrent platform have broad utility and add to the current methodologies for profiling genome-wide DNA methylation states in normal and disease conditions. MeDIP-Seq on Ion Torrent Platform in HCT116 and Human Brain
2015-06-10 | E-GEOD-61968 | biostudies-arrayexpress
Project description:An updated representation of S. meliloti metabolism that was manually-curated and encompasses information from 240 literature sources, which includes transposon-sequencing (Tn-seq) data and Phenotype MicroArray data for wild-type and mutant strains.
2020-06-01 | MODEL2003240001 | BioModels
Project description:Next-generation sequencing has been widely used for the genome-wide profiling of histone modifications, transcription factor binding and gene expression through chromatin immunoprecipitated DNA sequencing (ChIP-seq) and cDNA sequencing (RNA-seq). Here, we describe a versatile library construction method that can be applied to both ChIP-seq and RNA-seq on the widely used Illumina platforms. Standard methods for ChIP-seq library construction require nanograms of starting DNA, substantially limiting its application to rare cell types or limited clinical samples. By minimizing the DNA purification steps that cause major sample loss, our method achieved a high sensitivity in ChIP-seq library preparation. Using this method, we achieved the following: (1) generated high-quality epigenomic and transcription factor-binding maps using ChIP-seq for murine adipocytes; (2) successfully prepared a ChIP-seq library from as little as 25 pg of starting DNA; (3) achieved paired-end sequencing of the ChIP-seq libraries; (4) systematically profiled gene expression dynamics during murine adipogenesis using RNA-seq; and (5) preserved the strand specificity of the transcripts in RNA-seq. Given its sensitivity and versatility in both double-stranded and single-stranded DNA library construction, this method has wide applications in genomic, epigenomic, transcriptomic and interactomic studies. Pre-adipocytes and mature adipocytes were collected. Their chromatin and RNA were subjected to ChIP and mRNA extraction. Sequencing libraries from ChIP DNA or mRNA were generated following either standard protocols or TELP method. The quality and features of TELP libraries were proved and demonstrated in comparison with standard libraries or other published data.
2015-12-15 | E-GEOD-75966 | biostudies-arrayexpress
Project description:DNase I hypersensitive sites (DHSs) provide important information on the presence of transcriptional regulatory elements and the state of chromatin in mammalian cells1, 2, 3. Conventional DNase sequencing (DNase-seq) for genome-wide DHSs profiling is limited by the requirement of millions of cells4, 5. Here we report an ultrasensitive strategy, called single-cell DNase sequencing (scDNase-seq) for detection of genome-wide DHSs in single cells. We show that DHS patterns at the single-cell level are highly reproducible among individual cells. Among different single cells, highly expressed gene promoters and enhancers associated with multiple active histone modifications display constitutive DHS whereas chromatin regions with fewer histone modifications exhibit high variation of DHS. Furthermore, the single-cell DHSs predict enhancers that regulate cell-specific gene expression programs and the cell-to-cell variations of DHS are predictive of gene expression. Finally, we apply scDNase-seq to pools of tumour cells and pools of normal cells, dissected from formalin-fixed paraffin-embedded tissue slides from patients with thyroid cancer, and detect thousands of tumour-specific DHSs. Many of these DHSs are associated with promoters and enhancers critically involved in cancer development. Analysis of the DHS sequences uncovers one mutation (chr18: 52417839G>C) in the tumour cells of a patient with follicular thyroid carcinoma, which affects the binding of the tumour suppressor protein p53 and correlates with decreased expression of its target gene TXNL1. In conclusion, scDNase-seq can reliably detect DHSs in single cells, greatly extending the range of applications of DHS analysis both for basic and for translational research, and may provide critical information for personalized medicine. Exploring the landscape of chromatin accessibility in single cells and clinical samples
2015-11-25 | E-GEOD-61844 | biostudies-arrayexpress
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Project description:Quantitative analysis of the sequence determinants of transcription and translation regulation is of special relevance for systems and synthetic biology applications. Here, we developed a novel generic approach for the fast and efficient analysis of these determinants in vivo. ELM-seq (expression level monitoring by DNA methylation) uses Dam coupled to high-throughput sequencing) as a reporter that can be detected by DNA-seq. We used the genome-reduced bacterium Mycoplasma pneumoniae to show that it is a quantitative reporter. We showed that the methylase activity correlates with protein expression, does not affect cell viability, and has a large dynamic range (~10,000-fold). We applied ELM-seq to randomized libraries of promoters or 5’ untranslated regions. We found that transcription is greatly influenced by the bases around the +1 of the transcript and the Pribnow box, and we also identified several epistatic interactions (including the +1 and the “extended Pribnow”). Regarding translation initiation, we confirmed that the Shine-Dalgarno motif is not relevant, but instead, that RNA secondary structure is the main governing factor. With this in hand, we developed a predictor to help tailor gene expression in M. pneumoniae. The simple ELM-seq methodology will allow identifying and optimizing key sequence determinants for promoter strength and translation. The ELM-seq methodology allows both researchers and companies to identify and optimize in an easy and comprehensive manner, key sequence determinants for promoter strength and translation.
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Project description:Kilian2024 - Immune cell dynamics in Cue-Induced Extended Human Colitis Model
Single-cell technologies such as scRNA-seq and flow cytometry provide critical insights into immune cell behavior in inflammatory bowel disease (IBD). However, integrating these datasets into computational models for dynamic analysis remains challenging. Here, Kilian et al., (2024) developed a deterministic ODE-based model that incorporates these technologies to study immune cell population changes in murine colitis. The model parameters were optimized to fit experimental data, ensuring an accurate representation of immune cell behavior over time. It was then validated by comparing simulations with experimental data using Pearson’s correlation and further tested on independent datasets to confirm its robustness. Additionally, the model was applied to clinical bulk RNA-seq data from human IBD patients, providing valuable insights into immune system dynamics and potential therapeutic strategies.
Figure 4c, obtained from the simulation of human colitis model is highlighted here.
This model is described in the article:
Kilian, C., Ulrich, H., Zouboulis, V.A. et al. Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease. npj Syst Biol Appl 10, 69 (2024). https://doi.org/10.1038/s41540-024-00395-9
Abstract:
Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.
This model was curated during the Hackathon hosted by BioMed X GmbH in 2024.
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2014-12-01 | E-MTAB-2492 | biostudies-arrayexpress
Project description:Ustilago maydis is an important plant pathogen causing corn-smut disease and an effective biotechnological production host. The lack of a comprehensive metabolic overview hinders a full understanding of the organism’s environmental adaptation and a full use of its metabolic potential. Here, we report the first genome scale metabolic model (GSMM) of Ustilago maydis (iUma22) for the simulation of metabolic activities. iUma22 was reconstructed from sequencing and annotation using PathwayTools, the biomass equation was derived from literature values and from the codon composition. The final model contains over 25% of annotated genes (6,909) in the sequenced genome. Substrate utilization was corrected by Biolog-Phenotype arrays and exponential batch cultivations were used to test growth predictions. The growth data revealed a metabolic phenotype shift at high glucose uptake rates and the model allowed its quantification. A pan-genome of four different U. maydis strains revealed missing metabolic pathways in iUma22. The new model allows studies of metabolic adaptations to different environmental niches as well as for biotechnological applications.
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