SEPATH - Simulated Cancer Metagenomes
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB31019 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:In this study, we analyzed the microbial communities from a methane-based bio-reactor with selenate as an electron accepter. Four biological replicates were analyzed by metagenomics, of which data can be found in the SRA database (Accession number: SRP136677, SRP136696, SRP136790 and SRP136859). Based on the metagenomic data, we detected the expressed proteins using metaproteomics. This data is also included in this submission.
2022-03-01 | PXD011889 | Pride
Project description:In this study we developed metaproteomics based methods for quantifying taxonomic composition of microbiomes (microbial communities). We also compared metaproteomics based quantification to other quantification methods, namely metagenomics and 16S rRNA gene amplicon sequencing. The metagenomic and 16S rRNA data can be found in the European Nucleotide Archive (Study number: PRJEB19901). For the method development and comparison of the methods we analyzed three types of mock communities with all three methods. The communities contain between 28 to 32 species and strains of bacteria, archaea, eukaryotes and bacteriophage. For each community type 4 biological replicate communities were generated. All four replicates were analyzed by 16S rRNA sequencing and metaproteomics. Three replicates of each community type were analyzed with metagenomics. The "C" type communities have same cell/phage particle number for all community members (C1 to C4). The "P" type communities have the same protein content for all community members (P1 to P4). The "U" (UNEVEN) type communities cover a large range of protein amounts and cell numbers (U1 to U4). We also generated proteomic data for four pure cultures to test the specificity of the protein inference method. This data is also included in this submission.
2017-05-12 | PXD006118 | Pride
Project description:Microbial communities developed in cassette-electrode microbial fuel cells
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Project description:Altered chromatin structure is a hallmark of cancer, and inappropriate regulation of chromatin structure may represent the origin of transformation. Several important studies have mapped human nucleosome distributions genome wide, but the genome-wide role of chromatin structure in cancer progression has not been addressed. We developed a MNase-Sequence Capture method, mTSS-seq, to map genome-wide nucleosome distribution in primary human lung and colon adenocarcinoma tissue. Here, we confirm that nucleosome redistribution is an early, widespread event in lung (LAC) and colon (CRC) adenocarcinoma. These altered nucleosome architectures are consistent between LAC and CRC patient samples indicating that they may serve as important early adenocarcinoma markers. We demonstrate that the nucleosome alterations are driven by the underlying DNA sequence and potentiate transcription factor binding. We conclude that DNA-directed nucleosome redistributions are widespread early in cancer progression. We have proposed an entirely new hierarchical model for chromatin-mediated genome regulation. â Nucleosome distribution mapping in primary patient tissue at all transcription start sites in the human genome Please note that two processed data files '4137N_ALLcombined.bed' and '4137T_ALLcombined.bed' (linked as Series supplementary file) are processed bed files combined from three 4137N_*_hiseq samples (total 6 raw data files) and three 4137T_*_hiseq samples (total 6 raw data files), respectively.
2015-10-27 | E-GEOD-74340 | biostudies-arrayexpress
Project description:Blood collected from infants at week 6 and week 7 of life to profile how the transcriptome is changed following routine early life vaccination. Paired faecal metagenomic data is available for these infants under BioProject PRJNA807448 at the Sequence Read Archive.
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Project description:Gene expression of developed cancer stem cells
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Project description:Recent advancements in metabolic flux estimations in vivo are limited to preclinical models, primarily due to challenges in tissue sampling, tumor microenvironment heterogeneity, and non-steady state conditions. To address these limitations and enable flux estimation in human patients, we developed two machine learning-based frameworks. First, the digital twin framework integrates first-principles stoichiometric and isotopic simulations with convolutional neural networks to estimate fluxes in patient bulk samples. Second, the 13C-scMFA framework combines patient scRNA-seq data with 13C-isotope tracing, allowing single-cell-level flux quantification. These studies allow quantification of metabolic activity in neoplastic glioma cells, revealing frequently elevated purine synthesis and serine uptake compared to non-malignant cells. Our models also identify metabolic heterogeneity among patients and mice with brain cancer, in turn predicting treatment responses to metabolic inhibitors. Our frameworks advance in vivo metabolic flux analysis, may lead to novel metabolic therapies, and identify biomarkers for metabolism-directed therapies in patients.
2025-11-28 | GSE311151 | GEO
Project description:Recent advancements in metabolic flux estimations in vivo are limited to preclinical models, primarily due to challenges in tissue sampling, tumor microenvironment heterogeneity, and non-steady state conditions. To address these limitations and enable flux estimation in human patients, we developed two machine learning-based frameworks. First, the digital twin framework integrates first-principles stoichiometric and isotopic simulations with convolutional neural networks to estimate fluxes in patient bulk samples. Second, the 13C-scMFA framework combines patient scRNA-seq data with 13C-isotope tracing, allowing single-cell-level flux quantification. These studies allow quantification of metabolic activity in neoplastic glioma cells, revealing frequently elevated purine synthesis and serine uptake compared to non-malignant cells. Our models also identify metabolic heterogeneity among patients and mice with brain cancer, in turn predicting treatment responses to metabolic inhibitors. Our frameworks advance in vivo metabolic flux analysis, may lead to novel metabolic therapies, and identify biomarkers for metabolism-directed therapies in patients.
2025-11-29 | GSE311464 | GEO
Project description:Here we developed a new approach to sepsis diagnosis that integrates host transcriptional profiling with metagenomic broad-range pathogen detection from cell-free plasma RNA and DNA.
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