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metagenomic sequencing data  (Biotechnology Information)

 
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    Biotechnology Information metagenomic sequencing data
    Metagenomic Sequencing Data, supplied by Biotechnology Information, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/metagenomic+sequencing+data/data+rna+seq+%E2%80%A2/pm42242027-325-1-12
    Average 86 stars, based on 1 article reviews
    metagenomic sequencing data - by Bioz Stars, 2026-11
    86/100 stars

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    Article Title: Multi-omics and network pharmacology reveal the mechanisms of Scutellaria barbata D.Don and Scleromitrion diffusum (Willd.) R.J.Wang against pancreatic cancer.
    Article Snippet: .. The datasets generated and analyzed during the current study are available in the following repositories: the metagenomic sequencing data are deposited in the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA) under accession number PRJNA1357588; the metabolomics data can be accessed at the China National Center for Bioinformation (CNCB) under accession number OMIX013085; and the proteomic sequencing data are available in the Integrated Proteome Resources (iProX) database under accession number IPX0014205000. ..

    Article Title: Multi-omics and network pharmacology reveal the mechanisms of Scutellaria barbata D.Don and Scleromitrion diffusum (Willd.) R.J.Wang against pancreatic cancer
    Article Snippet: .. The datasets generated and analyzed during the current study are available in the following repositories: the metagenomic sequencing data are deposited in the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA) under accession number PRJNA1357588; the metabolomics data can be accessed at the China National Center for Bioinformation (CNCB) under accession number OMIX013085; and the proteomic sequencing data are available in the Integrated Proteome Resources (iProX) database under accession number IPX0014205000. ..

    Sequencing:

    Article Title: Multi-omics and network pharmacology reveal the mechanisms of Scutellaria barbata D.Don and Scleromitrion diffusum (Willd.) R.J.Wang against pancreatic cancer.
    Article Snippet: .. The datasets generated and analyzed during the current study are available in the following repositories: the metagenomic sequencing data are deposited in the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA) under accession number PRJNA1357588; the metabolomics data can be accessed at the China National Center for Bioinformation (CNCB) under accession number OMIX013085; and the proteomic sequencing data are available in the Integrated Proteome Resources (iProX) database under accession number IPX0014205000. ..

    Article Title: Bifidobacterium longum alleviation of metabolic dysfunction-associated steatotic liver disease: A multi-omics landscape of microbiota and metabolome reconfiguration.
    Article Snippet: The gut microbiome-host metabolism axis plays a critical role in the pathogenesis of metabolic dysfunctionassociated steatotic liver disease (MASLD).. Although the probiotic Bifidobacterium longum (B. longum) shows promise in ameliorating metabolic disorders, its functional impact on the microbiome-metabolome interplay in MASLD remains elusive.. Herein, we established a MASLD mouse model using a high-fat, high-fructose (HFHF) diet and conducted integrated multi-omics analyses, including liver transcriptomics, gut metagenomics, and serum metabolomics, following B. longum intervention.

    Article Title: Large-scale metagenomic assembly provide new insights into the genetic evolution of gut microbiomes in plateau ungulates
    Article Snippet: .. Additionally, metagenomic sequencing data for seven species ( Equus caballus , n = 72; Cervus nippon , n = 74; Capreolus pygargus , n = 40; Bos grunniens , n = 55; Bos taurus , n = 198; Capra hircus , n = 86; and O vis aries , n = 83) were downloaded from the National Center for Biotechnology Information (NCBI) ( www.ncbi.nlm.nih.gov ). ..

    Article Title: Impact of NH 4 + -N on Organic Micropollutant Removal and Antibiotic Resistance Gene Occurrence during Simulated Riverbank Filtration.
    Article Snippet: Organic micropollutants (OMPs) facilitate the spread of antibiotic resistance genes (ARGs).. Ammonia-oxidizing microorganisms (AOMs) are crucial for OMP degradation during riverbank filtration (RBF) and significantly influenced by NH4-N concentrations.. However, the effect of NH4-N on OMP removal and ARG occurrence in RBF remains unclear.

    Article Title: Deciphering the mobility, pathogenic hosts, and co-selection of antibiotic resistance genes in untreated wastewater from three different hospitals.
    Article Snippet: .. The metagenomic sequencing data were downloaded from the National Center for Biotechnology Information under project identifier PRJNA723368[19]. ..

    Article Title: Metagenomic analysis reveals global landscape of viruses in biogeochemical cycles and microbial resistance in paddy soils and wetlands.
    Article Snippet: • Lysogeny is dominant viral lifestyle in natural & artificial wetlands worldwide.. • Viral AMGs are involved in 30 metabolic pathways for elemental cycling.. • Diverse viral ARGs & MRGs can create antibiotic/metal(loid)-resistant

    Article Title: Toxoplasma gondii disrupts vitamin B and K2 biosynthetic pathways in feline gut microbiota: microbial adaptation
    Article Snippet: .. Metagenomic sequencing data for this study have been deposited in the National Center for Biotechnology Information (NCBI) under the BioProject PRJNA1203944. ..

    Article Title: Multi-omics and network pharmacology reveal the mechanisms of Scutellaria barbata D.Don and Scleromitrion diffusum (Willd.) R.J.Wang against pancreatic cancer
    Article Snippet: .. The datasets generated and analyzed during the current study are available in the following repositories: the metagenomic sequencing data are deposited in the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA) under accession number PRJNA1357588; the metabolomics data can be accessed at the China National Center for Bioinformation (CNCB) under accession number OMIX013085; and the proteomic sequencing data are available in the Integrated Proteome Resources (iProX) database under accession number IPX0014205000. ..



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    Integrated omics analysis based on <t>metagenomics</t> and metabolomics. A Multiple correlation analysis of rumen, serum, and milk metabolites. Edges between nodes represent Spearman correlations: green lines indicate positive correlations and gray lines indicate negative correlations (| r |> 0.50, P < 0.05). B Multiple correlation analysis of rectum, serum, and milk metabolites. Edges between nodes represent Spearman correlations: green lines indicate positive correlations and gray lines indicate negative correlations (| r |> 0.50, P < 0.05). C The PLS-PM module provided an integrated analysis of the relationships among rumen microbial module 6, rumen GPEtn(12:0/18:3), serum EPA, milk Pe(20:5/0:0), and MY. Numbers above arrows represent standardized effect coefficients. R . 2 = coefficient of determination; GoF = goodness-of-fit index. EPA = eicosapentaenoic acid; MY = milk yield. * P < 0.05; ** P < 0.01
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    Integrated omics analysis based on metagenomics and metabolomics. A Multiple correlation analysis of rumen, serum, and milk metabolites. Edges between nodes represent Spearman correlations: green lines indicate positive correlations and gray lines indicate negative correlations (| r |> 0.50, P < 0.05). B Multiple correlation analysis of rectum, serum, and milk metabolites. Edges between nodes represent Spearman correlations: green lines indicate positive correlations and gray lines indicate negative correlations (| r |> 0.50, P < 0.05). C The PLS-PM module provided an integrated analysis of the relationships among rumen microbial module 6, rumen GPEtn(12:0/18:3), serum EPA, milk Pe(20:5/0:0), and MY. Numbers above arrows represent standardized effect coefficients. R . 2 = coefficient of determination; GoF = goodness-of-fit index. EPA = eicosapentaenoic acid; MY = milk yield. * P < 0.05; ** P < 0.01

    Journal: Microbiome

    Article Title: The impact of rumen and hindgut microbiomes on the persistent productivity of long-lived dairy cows

    doi: 10.1186/s40168-025-02309-1

    Figure Lengend Snippet: Integrated omics analysis based on metagenomics and metabolomics. A Multiple correlation analysis of rumen, serum, and milk metabolites. Edges between nodes represent Spearman correlations: green lines indicate positive correlations and gray lines indicate negative correlations (| r |> 0.50, P < 0.05). B Multiple correlation analysis of rectum, serum, and milk metabolites. Edges between nodes represent Spearman correlations: green lines indicate positive correlations and gray lines indicate negative correlations (| r |> 0.50, P < 0.05). C The PLS-PM module provided an integrated analysis of the relationships among rumen microbial module 6, rumen GPEtn(12:0/18:3), serum EPA, milk Pe(20:5/0:0), and MY. Numbers above arrows represent standardized effect coefficients. R . 2 = coefficient of determination; GoF = goodness-of-fit index. EPA = eicosapentaenoic acid; MY = milk yield. * P < 0.05; ** P < 0.01

    Article Snippet: The metagenomic raw sequencing data have been deposited in the National Center for Biotechnology Information (NCBI) under the BioProject accession number PRJNA1288150.

    Techniques: Metagenomics