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      Towards standards for human fecal sample processing in metagenomic studies

      , , , , , , , , , , , ,   , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
      Nature Biotechnology
      Springer Nature

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          Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies.

          Amplicon-based marker gene surveys form the basis of most microbiome and other microbial community studies. Such PCR-based methods have multiple steps, each of which is susceptible to error and bias. Variance in results has also arisen through the use of multiple methods of next-generation sequencing (NGS) amplicon library preparation. Here we formally characterized errors and biases by comparing different methods of amplicon-based NGS library preparation. Using mock community standards, we analyzed the amplification process to reveal insights into sources of experimental error and bias in amplicon-based microbial community and microbiome experiments. We present a method that improves on the current best practices and enables the detection of taxonomic groups that often go undetected with existing methods.
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            Relating the metatranscriptome and metagenome of the human gut.

            Although the composition of the human microbiome is now well-studied, the microbiota's >8 million genes and their regulation remain largely uncharacterized. This knowledge gap is in part because of the difficulty of acquiring large numbers of samples amenable to functional studies of the microbiota. We conducted what is, to our knowledge, one of the first human microbiome studies in a well-phenotyped prospective cohort incorporating taxonomic, metagenomic, and metatranscriptomic profiling at multiple body sites using self-collected samples. Stool and saliva were provided by eight healthy subjects, with the former preserved by three different methods (freezing, ethanol, and RNAlater) to validate self-collection. Within-subject microbial species, gene, and transcript abundances were highly concordant across sampling methods, with only a small fraction of transcripts (<5%) displaying between-method variation. Next, we investigated relationships between the oral and gut microbial communities, identifying a subset of abundant oral microbes that routinely survive transit to the gut, but with minimal transcriptional activity there. Finally, systematic comparison of the gut metagenome and metatranscriptome revealed that a substantial fraction (41%) of microbial transcripts were not differentially regulated relative to their genomic abundances. Of the remainder, consistently underexpressed pathways included sporulation and amino acid biosynthesis, whereas up-regulated pathways included ribosome biogenesis and methanogenesis. Across subjects, metatranscriptional profiles were significantly more individualized than DNA-level functional profiles, but less variable than microbial composition, indicative of subject-specific whole-community regulation. The results thus detail relationships between community genomic potential and gene expression in the gut, and establish the feasibility of metatranscriptomic investigations in subject-collected and shipped samples.
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              eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges

              Orthologous relationships form the basis of most comparative genomic and metagenomic studies and are essential for proper phylogenetic and functional analyses. The third version of the eggNOG database (http://eggnog.embl.de) contains non-supervised orthologous groups constructed from 1133 organisms, doubling the number of genes with orthology assignment compared to eggNOG v2. The new release is the result of a number of improvements and expansions: (i) the underlying homology searches are now based on the SIMAP database; (ii) the orthologous groups have been extended to 41 levels of selected taxonomic ranges enabling much more fine-grained orthology assignments; and (iii) the newly designed web page is considerably faster with more functionality. In total, eggNOG v3 contains 721 801 orthologous groups, encompassing a total of 4 396 591 genes. Additionally, we updated 4873 and 4850 original COGs and KOGs, respectively, to include all 1133 organisms. At the universal level, covering all three domains of life, 101 208 orthologous groups are available, while the others are applicable at 40 more limited taxonomic ranges. Each group is amended by multiple sequence alignments and maximum-likelihood trees and broad functional descriptions are provided for 450 904 orthologous groups (62.5%).
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                Author and article information

                Journal
                Nature Biotechnology
                Nat Biotechnol
                Springer Nature
                1087-0156
                1546-1696
                October 2 2017
                October 2 2017
                :
                :
                Article
                10.1038/nbt.3960
                28967887
                f3080da4-2493-44a3-9302-238bfb0f8829
                © 2017
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