SEPATH - Simulated Cancer Metagenomes
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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
| PRJDB1126 | ENA
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.
2025-01-14 | GSE198276 | GEO
Project description:Gene expression of developed cancer stem cells
| PRJNA327245 | ENA
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.
2022-08-03 | GSE189401 | 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.
2022-08-03 | GSE189400 | GEO
Project description:16S rRNA amplicon sequences are predominantly used to identify the taxonomic composition of a microbiome, but they can also be used to generate simulated metagenomes to circumvent costly empirical shotgun sequencing. The effectiveness of using "simulated metagenomes" (shotgun metagenomes simulated from 16S rRNA amplicons using a database of full genomes closely related to the amplicons) in nonmodel systems is poorly known. We sought to determine the accuracy of simulated metagenomes in a nonmodel organism, the Canada goose (Branta canadensis), by comparing metagenomes and metatranscriptomes to simulated metagenomes derived from 16S amplicon sequencing. We found significant differences between the metagenomes, metatranscriptomes, and simulated metagenomes when comparing enzymes, KEGG orthologies (KO), and metabolic pathways. The simulated metagenomes accurately identified the majority (>70%) of the total enzymes, KOs, and pathways. The simulated metagenomes accurately identified the majority of the short-chain fatty acid metabolic pathways crucial to folivores. When narrowed in scope to specific genes of interest, the simulated metagenomes overestimated the number of antimicrobial resistance genes and underestimated the number of genes related to the breakdown of plant matter. Our results suggest that simulated metagenomes should not be used in lieu of empirical sequencing when studying the functional potential of a nonmodel organism's microbiome. Regarding the function of the Canada goose microbiome, we found unexpected amounts of fermentation pathways, and we found that a few taxa are responsible for large portions of the functional potential of the microbiome. IMPORTANCE The taxonomic composition of a microbiome is predominately identified using amplicon sequencing of 16S rRNA genes, but as a single marker, it cannot identify functions (genes). Metagenome and metatranscriptome sequencing can determine microbiome function but can be cost prohibitive. Therefore, computational methods have been developed to generate simulated metagenomes derived from 16S rRNA sequences and databases of full-length genomes. Simulated metagenomes can be an effective alternative to empirical sequencing, but accuracy depends on the genomic database used and whether the database contains organisms closely related to the 16S sequences. These tools are effective in well-studied systems, but the accuracy of these predictions in a nonmodel system is less known. Using a nonmodel bird species, we characterized the function of the microbiome and compared the accuracy of 16S-derived simulated metagenomes to sequenced metagenomes. We found that the simulated metagenomes reflect most but not all functions of empirical metagenome sequencing.
| S-EPMC9769641 | biostudies-literature