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Abstract
SummaryPhylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization.Availability and implementation phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Shotgun and amplicon sequencing allow previously intractable microbial communities to be characterized and compared, but translating these comparisons into gene-level mechanisms remains difficult
A pipeline allowing researchers without specific expertise in phylogenetic regression to analyze their own data via the web, an R package (R Core Team, 2017), or the popular microbiome workflow tool QIIME2 (Bolyen et al, 2018)
Shotgun data should be mapped to MIDAS species (Nayfach et al, 2016); amplicon data should be denoised to amplicon sequence variants (ASVs) with DADA2 or Deblur. phylogenize uses BURST (AlGhalith and Knights, 2017) to map ASVs to MIDAS species via individual PATRIC genomes (Wattam et al, 2014), using a default cutoff of 98.5% nucleotide identity (Rodriguez-R et al, 2018) and summing reads mapping to the same species
Summary
Shotgun and amplicon sequencing allow previously intractable microbial communities to be characterized and compared, but translating these comparisons into gene-level mechanisms remains difficult. Researchers typically correlate microbial gene abundance with environments using metagenomes, either from shotgun sequencing (Nayfach and Pollard, 2016) or imputed from amplicon sequences (Langille et al, 2013; Aßhauer et al, 2015). A pipeline allowing researchers without specific expertise in phylogenetic regression to analyze their own data via the web, an R package (R Core Team, 2017), or the popular microbiome workflow tool QIIME2 (Bolyen et al, 2018).
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