In jedick/JMDplots: Plots from Papers by Jeffrey M. Dick

library(knitr)
library(kableExtra)
## Use pngquant to reduce size of PNG images
knit_hooks$set(pngquant = hook_pngquant)
pngquant <- "--speed=1 --quality=0-25"
# In case pngquant isn't available
if (!nzchar(Sys.which("pngquant"))) pngquant <- NULL 

options(width = 80)

nH2O <- "<i>n</i><sub>H<sub>2</sub>O</sub>"
nO2 <- "<i>n</i><sub>O<sub>2</sub></sub>"

This vignette runs the functions to make the plots from the following manuscript:

Dick JM. 2024. Adaptations of microbial genomes to human body chemistry. bioRxiv preprint doi: 10.1101/2023.02.12.528246

Update: In July 2024, the taxonomic classifications and reference database were updated to use GTDB release 220. The plots therefore display minor differences compared to the preprint, which used GTDB release 207. In addition, two 16S rRNA gene sequence datasets have been added for nasopharyngeal and gut communities in COVID-19.

This vignette was compiled on r Sys.Date() with JMDplots r packageDescription("JMDplots")$Version, chem16S r packageDescription("chem16S")$Version, and canprot r packageDescription("canprot")$Version.

library(JMDplots)

Consistency between shotgun metagenomes and community reference proteomes (Figure 1)

microhum_1()

Data source: @HMP12.

Chemical metrics are broadly different among genera and are similar between GTDB and low-contamination genomes from UHGG (Figure 2)

microhum_2()

Data sources: Genome Taxonomy Database (GTDB release 220) [@PCR+22]; Unified Human Gastrointestinal Genome (UHGG v2.0.1) [@ANB+21].

Chemical variation of microbial proteins across body sites (multi-omics comparison) (Figure 3)

microhum_3()

Data sources: (A) @BPB+21; (B) See Table 1 in manuscript; (C) @LLZ+21, @CZH+22, @ZZL+20; (D) @TWC+22, @MLL+17, @JZW+22, @GNT+21.

Differences of chemical metrics between controls and COVID-19 or IBD patients (Figure 4)

microhum_4()

Data sources: (A) See Table 1 in manuscript; (B) MAGs [@KWL22], based on data from @YZL+21 and @ZZL+20; (C) @HZX+21 and @GPM+22; (D) See Table 1 in manuscript; (E) @LAA+19.

Differences of relative abundances of genera between controls and patients (Figure 5)

Figure_5_genera <- microhum_5()

Oxygen tolerance of genera in body sites, COVID-19, and IBD (Figure 6)

microhum_6()

Data sources: (a)--(b) List of Prokaryotes according to their Aerotolerant or Obligate Anaerobic Metabolism [@MR18] with modifications made in this study; (c) See Table 1 in manuscript.

Amount of putative human DNA removed from HMP metagenomes in screening step (Figure S1)

microhum_S1()

Differences of oxygen and water content of proteins between untreated and viral-inactivated samples (Figure S2)

microhum_S2()

Data source: @BPB+21.

Chemical features of reference proteomes for genera with known oxygen tolerance (Figure S3)

microhum_S3()

Data source: List of Prokaryotes according to their Aerotolerant or Obligate Anaerobic Metabolism [@MR18] with modifications made in this study.

Differences of chemical features of proteins between subcommunities of obligate anaerobes and aerotolerant genera from controls and patients (Figure S4)

microhum_S4()

Summary of metagenome sequence processing (part of Supplementary File 1)

datadir <- system.file("extdata/microhum/ARAST", package = "JMDplots")
dataset <- c("HMP12", "HMP12_no_screening", "LLZ+21", "CZH+22", "ZZL+20", "LAA+19")
summary_list <- lapply(dataset, function(this_dataset) {
  file <- file.path(datadir, paste0(this_dataset, "_stats.csv"))
  dat <- read.csv(file)
  runs <- nrow(dat)
  average_input_sequences <- sum(dat$input_sequences) / runs
  protein_prediction_rate <- dat$coding_sequences / dat$input_sequences * 100
  runs_with_low_protein_prediction <- sum(protein_prediction_rate < 40)
  average_protein_prediction_rate <- round(sum(protein_prediction_rate) / runs, 2)
  average_protein_length <- round(sum(dat$protein_length) / runs, 2)
  data.frame(runs, average_input_sequences, average_protein_prediction_rate,
    runs_with_low_protein_prediction, average_protein_length)
})
description <- c("HMP with human DNA screening", "HMP without screening",
  "COVID-19 nasopharyngeal", "COVID-19 oropharyngeal", "COVID-19 gut", "IBD gut")
summary_table <- cbind(description, dataset, do.call(rbind, summary_list))
col.names <- c("Description", "Dataset", "Number of sequencing runs", "Average number of input sequences",
  "Average protein prediction rate (%)", "Runs with protein prediction rate < 40%",
  "Average protein length")
kable(summary_table, col.names = col.names)

Number of genomes and genera in GTDB and UHGG (Unified Human Gastrointestinal Genome from MGnify)

GTDB_taxonomy <- read.csv(system.file("RefDB/GTDB_220/taxonomy.csv.xz", package = "JMDplots"))
print(paste("GTDB:", nrow(GTDB_taxonomy), "genomes and", length(unique(GTDB_taxonomy$genus)), "genera"))
UHGG_taxonomy_full <- read.csv(system.file("RefDB/UHGG_2.0.1/fullset/taxonomy.csv.xz", package = "JMDplots"))
print(paste("UHGG (contamination < 5% and completeness > 50%):", nrow(UHGG_taxonomy_full), "species-level clusters and", length(unique(UHGG_taxonomy_full$genus)), "genera"))
UHGG_taxonomy <- read.csv(system.file("RefDB/UHGG_2.0.1/taxonomy.csv.xz", package = "JMDplots"))
print(paste("UHGG (contamination < 2% and completeness > 95%):", nrow(UHGG_taxonomy), "species-level clusters and", length(unique(UHGG_taxonomy$genus)), "genera"))

List genera identified in Figure 5 that are not present in UHGG

not_in_UHGG <- paste(Figure_5_genera[!Figure_5_genera %in% UHGG_taxonomy_full$genus], collapse = " ")
paste("Figure 5 shows", length(Figure_5_genera), "genera; of these,", not_in_UHGG, "is not in UHGG")

References

jedick/JMDplots documentation built on April 12, 2025, 1:35 p.m.