| microhum {JMDplots} | R Documentation |
Plots from the manuscript by Dick (2024).
microhum_1(pdf = FALSE)
microhum_2(pdf = FALSE)
microhum_3(pdf = FALSE)
microhum_4(pdf = FALSE)
microhum_5(pdf = FALSE)
microhum_6(pdf = FALSE)
microhum_1_1(pdf = FALSE)
microhum_3_1(pdf = FALSE)
microhum_5_1(pdf = FALSE)
microhum_6_1(pdf = FALSE)
dataset_metrics()
pdf |
logical, make a PDF file? |
This table briefly describes each plotting function.
microhum_1 | Consistency between shotgun metagenomes and community reference proteomes |
microhum_2 | Chemical metrics are broadly different among genera and are similar between GTDB and low-contamination genomes from UHGG |
microhum_3 | Chemical variation of microbial proteins across body sites (multi-omics comparison) |
microhum_4 | Differences of chemical metrics between controls and COVID-19 or IBD patients |
microhum_5 | Differences of relative abundances of genera between controls and patients |
microhum_6 | Oxygen tolerance of genera in body sites, COVID-19, and IBD |
microhum_1_1 | Amount of putative human DNA removed from HMP metagenomes in screening step |
microhum_3_1 | Differences of nO2 and nH2O between untreated and viral-inactivated samples |
microhum_5_1 | Chemical metrics of reference proteomes for genera with known oxygen tolerance |
microhum_5_1 | Differences of nO2 and nH2O between subcommunities of obligate anaerobes and aerotolerant genera in controls and patients |
dataset_metrics is used to precompute mean values of chemical metrics for controls and COVID-19/IBD patients in each study (chemical metrics are for community reference proteomes; source data are 16S rRNA sequences).
The data files listed below are stored in ‘extdata/microhum’:
Pipeline for sequence data processing (uses external programs fastq-dump, vsearch, seqtk, RDP Classifier, and GNU Parallel (Tange, 2023))
RDP Classifier results combined into a single CSV file for each study, created with the classify and mkRDP functions in ‘pipeline.R’.
GTDB 16S SSU rRNA training files for RDP Classifier are available at https://doi.org/10.5281/zenodo.7633100.
Sample metadata for each study
Mean values of chemical metrics for samples in 16S studies, created with dataset_metrics
Directory with scripts and output for metagenomes
Processing pipeline (modified from Dick et al. (2019) to implement screening for human sequences)
Helper scripts for ARAST.R
Script to run pipeline with specific settings for each dataset
Summed amino acid compositions of protein sequences inferred from each metagenomic sequencing run (produced by runARAST.R)
Metagenome sequence processing statistics (produced by runARAST.R)
Scripts to process metaproteomic data (5 directories). See the comments in each ‘mkaa.R’ for the required files that contain source data. Required files are available from databases or SI tables and are not included here.
Output of mkaa.R with amino acid composition of proteins
Directory with scripts and data for analyzing MAGs from Ke et al. (2022)
BioSample metadata obtained from NCBI BioProjects PRJNA624223 and PRJNA650244
Script to get amino acid compositions for proteins predicted by Prodigal
Output of mkaa.R with amino acid compositions of proteins for 5403 MAGs
List of strictly anaerobic and aerotolerant genera modified from Table S1 of Million and Raoult (2018)
List of genera in Figure 5, created from the value invisibly returned by microhum_5
Dick JM, Yu M, Tan J and Lu A (2019) Changes in carbon oxidation state of metagenomes along geochemical redox gradients. Front. Microbiol. 10, 120. doi:10.3389/fmicb.2019.00120
Dick JM (2024) Adaptations of microbial genomes to human body chemistry. bioRxiv. doi:10.1101/2023.02.12.528246
Ke S, Weiss ST and Liu Y-Y (2022) Dissecting the role of the human microbiome in COVID-19 via metagenome-assembled genomes. Nat. Commun. 13, 5253. doi:10.1038/s41467-022-32991-w
Million M and Raoult D (2018) Linking gut redox to human microbiome. Human Microbiome Journal 10, 27–32. doi:10.1016/j.humic.2018.07.002
Tange O (2023) GNU Parallel 20230222 ('Gaziantep'). doi:10.5281/zenodo.7668338
# Figure 1
microhum_1()