Danakil Metagenomics
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB24651 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Depression is a common and disabling disorder, representing a major social and economic health issue. Moreover, depression is associated with the progression of diseases with an inflammatory etiology including many inflammatory-related disorders. At the molecular level, the mechanisms by which depression might promote the onset of these diseases and associated immune-dysfunction are not well understood. In this study we assessed genome-wide patterns of DNA methylation in whole blood-derived DNA obtained from individuals with a self-reported history of depression (n=100) and individuals without a history of depression (n=100) using the Illumina 450K microarray. Our analysis identified 6 significant (Sidak corrected P < 0.05) depression-associated differentially methylated regions (DMRs); the top-ranked DMR was located in exon 1 of the LTB4R2 gene (Sidak corrected P = 1.27 x 10-14). Polygenic risk scores (PRS) for depression were generated and known biological markers of inflammation, telomere length (TL) and IL-6, were measured in DNA and serum samples respectively. Next, we employed a systems-level approach to identify networks of co-methylated loci associated with a history of depression, in addition to depression PRS, TL and IL-6 levels. Our analysis identified one depression-associated co-methylation module (P = 0.04). Interestingly, the depression-associated module was highly enriched for pathways related to immune function and was also associated with TL and IL-6 cytokine levels. In summary, our genome-wide DNA methylation analysis of individuals with and without a self-reported history of depression identified several candidate DMRs of potential relevance to the pathogenesis of depression and its associated immune-dysfunction phenotype. Overall design: DNA from whole blood from 194 individuals
2018-06-01 | GSE113725 | GEO
Project description:JCVI Metagenomics Reports (METAREP) is a Web 2.0 application designed to help scientists analyze and compare annotated metagenomics datasets. It utilizes Solr/Lucene, a high-performance scalable search engine, to quickly query large data collections. Furthermore, users can use its SQL-like query syntax to filter and refine datasets. METAREP provides graphical summaries for top taxonomic and functional classifications as well as a GO, NCBI Taxonomy and KEGG Pathway Browser. Users can compare absolute and relative counts of multiple datasets at various functional and taxonomic levels. Advanced comparative features comprise statistical tests as well as multidimensional scaling, heatmap and hierarchical clustering plots. Summaries can be exported as tab-delimited files, publication quality plots in PDF format. A data management layer allows collaborative data analysis and result sharing.Web site http://www.jcvi.org/metarep; source code http://github.com/jcvi/METAREP CONTACT: syooseph@jcvi.orgSupplementary data are available at Bioinformatics online.
1000-01-01 | S-EPMC2951084 | BioStudies
Project description:The term metagenomics refers to the use of sequencing methods to simultaneously identify genomic material from all organisms present in a sample, with the advantage of greater taxonomic resolution than culture or other methods. Applications include pathogen detection and discovery, species characterisation, antimicrobial resistance detection, virulence profiling, and study of the microbiome and microecological factors affecting health. However, metagenomics involves complex and multistep processes and there are important technical and methodological challenges that require careful consideration to support valid inference. We co-ordinated a multidisciplinary, international expert group to establish reporting guidelines that address specimen processing, nucleic acid extraction, sequencing platforms, bioinformatics considerations, quality assurance, limits of detection, power and sample size, confirmatory testing, causality criteria, cost, and ethical issues. The guidance recognises that metagenomics research requires pragmatism and caution in interpretation, and that this field is rapidly evolving.
2020-01-01 | S-EPMC7406238 | BioStudies
Project description:Obtaining an unbiased view of the phylogenetic composition and functional diversity within a microbial community is one central objective of metagenomic analysis. New technologies, such as 454 pyrosequencing, have dramatically reduced sequencing costs, to a level where metagenomic analysis may become a viable alternative to more-focused assessments of the phylogenetic (e.g., 16S rRNA genes) and functional diversity of microbial communities. To determine whether the short (approximately 100 to 200 bp) sequence reads obtained from pyrosequencing are appropriate for the phylogenetic and functional characterization of microbial communities, the results of BLAST and COG analyses were compared for long (approximately 750 bp) and randomly derived short reads from each of two microbial and one virioplankton metagenome libraries. Overall, BLASTX searches against the GenBank nr database found far fewer homologs within the short-sequence libraries. This was especially pronounced for a Chesapeake Bay virioplankton metagenome library. Increasing the short-read sampling depth or the length of derived short reads (up to 400 bp) did not completely resolve the discrepancy in BLASTX homolog detection. Only in cases where the long-read sequence had a close homolog (low BLAST E-score) did the derived short-read sequence also find a significant homolog. Thus, more-distant homologs of microbial and viral genes are not detected by short-read sequences. Among COG hits, derived short reads sampled at a depth of two short reads per long read missed up to 72% of the COG hits found using long reads. Noting the current limitation in computational approaches for the analysis of short sequences, the use of short-read-length libraries does not appear to be an appropriate tool for the metagenomic characterization of microbial communities.
1000-01-01 | S-EPMC2258652 | BioStudies
Project description:The microbiome influences prognosis and side effects in early breast cancer
| PRJNA718520 | ENA
Project description:Prenatal exposure to maternal stress and depression has been identified as a risk factor for adverse behavioral and neurodevelopmental outcomes in early childhood. However, the molecular mechanisms through which maternal psychopathology shapes offspring development remain poorly understood. We analyzed transcriptome-wide gene expression profiles of 149 UCB samples from neonates born to mothers with prenatal PTSD (n=20), depression (n=31) and PTSD with comorbid depression (PTSD/Dep; n=13), compared to neonates born to carefully matched trauma exposed controls without meeting PTSD criteria (TE; n=23) and healthy mothers (n=62). We also evaluated physiological and developmental measures in these infants at birth, six months and twenty-four months. A multistep analytic approach was used that specifically sought to: 1) identify dysregulated genes, molecular pathways and discrete groups of co-regulated gene modules in UCB associated with prenatal maternal psychopathologies; and 2) to determine the impact of perinatal PTSD and depression on early childhood development outcomes. Overall design: Transcriptome-wide gene expression assays were applied to umbilical cord blood samples from neonates born to mothers with posttraumatic stress disorder (PTSD; n=20), depression (n=31) and PTSD with comorbid depression (n=13) compared to carefully matched trauma exposed controls (n=23) and healthy mothers (n=62).
2018-05-25 | GSE114852 | GEO