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sequencing index kits  (New England Biolabs)


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    New England Biolabs sequencing index kits
    Sequencing Index Kits, supplied by New England Biolabs, used in various techniques. Bioz Stars score: 96/100, based on 1791 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/sequencing index kits/product/New England Biolabs
    Average 96 stars, based on 1791 article reviews
    sequencing index kits - by Bioz Stars, 2026-05
    96/100 stars

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    A. Schematic of building in silico microbial communities (see Methods). Briefly, we used MICOM to create site- and patient- specific in silico microbiome communities from previously published 16s rRNA <t>sequencing</t> datasets. We then performed flux-balance analysis to investigate the differences between modeled microbial growth and metabolic activity in tumor vs normal tissue associated samples. B. Counts of bacterial metabolite exchange fluxes (fluxpaths) active in all samples in each of the three datasets examined. C. Counts of bacterial taxa growing in at least half of the samples in each of the three datasets. D-F. Growth rate of F. nucleatum across all three datasets (Burns, Hale, and Niccolai, respectively). P and q values from paired Wilcoxon signed rank tests. F. nucleatum was the only taxon with significantly different growth in tumor vs. normal samples; no metabolite fluxpath had significantly different growth in tumor vs. normal samples across all three datasets.
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    A. Schematic of building in silico microbial communities (see Methods). Briefly, we used MICOM to create site- and patient- specific in silico microbiome communities from previously published 16s rRNA sequencing datasets. We then performed flux-balance analysis to investigate the differences between modeled microbial growth and metabolic activity in tumor vs normal tissue associated samples. B. Counts of bacterial metabolite exchange fluxes (fluxpaths) active in all samples in each of the three datasets examined. C. Counts of bacterial taxa growing in at least half of the samples in each of the three datasets. D-F. Growth rate of F. nucleatum across all three datasets (Burns, Hale, and Niccolai, respectively). P and q values from paired Wilcoxon signed rank tests. F. nucleatum was the only taxon with significantly different growth in tumor vs. normal samples; no metabolite fluxpath had significantly different growth in tumor vs. normal samples across all three datasets.

    Journal: bioRxiv

    Article Title: Metabolic modeling and functional genomics reveal taxa and host gene interactions in colorectal cancer

    doi: 10.64898/2026.01.26.700635

    Figure Lengend Snippet: A. Schematic of building in silico microbial communities (see Methods). Briefly, we used MICOM to create site- and patient- specific in silico microbiome communities from previously published 16s rRNA sequencing datasets. We then performed flux-balance analysis to investigate the differences between modeled microbial growth and metabolic activity in tumor vs normal tissue associated samples. B. Counts of bacterial metabolite exchange fluxes (fluxpaths) active in all samples in each of the three datasets examined. C. Counts of bacterial taxa growing in at least half of the samples in each of the three datasets. D-F. Growth rate of F. nucleatum across all three datasets (Burns, Hale, and Niccolai, respectively). P and q values from paired Wilcoxon signed rank tests. F. nucleatum was the only taxon with significantly different growth in tumor vs. normal samples; no metabolite fluxpath had significantly different growth in tumor vs. normal samples across all three datasets.

    Article Snippet: DNA samples were quantified using a fluorimetric PicoGreen assay. gDNA samples were converted to Illumina sequencing libraries using Illumina’s NexteraXT DNA Sample Preparation Kit (Cat. # FC-130-1005).

    Techniques: In Silico, Sequencing, Activity Assay