Chemical features of proteins in microbial genomes (2025)

This vignette was compiled on 2025-05-29 with JMDplots 1.2.22-1, chem16S 1.2.0, and canprot 2.0.0.

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

microhum_1()

Data source: The Human Microbiome Project Consortium (2012).

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) (Parks et al., 2022); Unified Human Gastrointestinal Genome (UHGG v2.0.1) (Almeida et al., 2021).

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

microhum_3()

Data sources: (A) Boix-Amorós et al. (2021); (B) See Table 1 in manuscript; (C) Liu et al. (2021), de Castilhos et al. (2022), Zuo et al. (2020); (D) Thuy-Boun et al. (2022), Maier et al. (2017), Jiang et al. (2022), Granato et al. (2021).

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 (Ke et al., 2022), based on data from Yeoh et al. (2021) and Zuo et al. (2020); (C) He et al. (2021) and Grenga et al. (2022); (D) See Table 1 in manuscript; (E) Lloyd-Price et al. (2019).

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 (Million and Raoult, 2018) 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: Boix-Amorós et al. (2021).

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

microhum_S3()

## [1] "Reference proteomes are available for 201 of 235 obligately anaerobic genera in the list"
## [1] "Reference proteomes are available for 363 of 399 aerotolerant genera in the list"
## [1] "Oxygen tolerance is listed for 1034 of 24959 genera in GTDB"

Data source: List of Prokaryotes according to their Aerotolerant or Obligate Anaerobic Metabolism (Million and Raoult, 2018) 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)

Description	Dataset	Number of sequencing runs	Average number of input sequences	Average protein prediction rate (%)	Runs with protein prediction rate < 40%	Average protein length
HMP with human DNA screening	HMP12	101	13928960	44.70	40	30.74
HMP without screening	HMP12_no_screening	101	13928960	50.04	31	30.69
COVID-19 nasopharyngeal	LLZ+21	9	40593115	26.70	7	47.32
COVID-19 oropharyngeal	CZH+22	122	13884977	59.15	15	31.81
COVID-19 gut	ZZL+20	73	17245724	59.12	12	46.30
IBD gut	LAA+19	139	9722610	56.03	4	31.48

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"))

## [1] "GTDB: 113104 genomes and 24959 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"))

## [1] "UHGG (contamination < 5% and completeness > 50%): 4744 species-level clusters and 1031 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"))

## [1] "UHGG (contamination < 2% and completeness > 95%): 2350 species-level clusters and 643 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")

## [1] "Figure 5 shows 27 genera; of these, Vescimonas is not in UHGG"

References

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Boix-Amorós A, Piras E, Bu K, Wallach D, Stapylton M, Fernández-Sesma A, Malaspina D, Clemente JC. 2021. Viral inactivation impacts microbiome estimates in a tissue-specific manner. mSystems 6(5): e00674–21. doi: 10.1128/mSystems.00674-21

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The Human Microbiome Project Consortium. 2012. Structure, function and diversity of the healthy human microbiome. Nature 486(7402): 207–214. doi: 10.1038/nature11234

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