| Title: | Chemical Metrics for Microbial Communities |
|---|---|
| Description: | Combines taxonomic classifications of high-throughput 16S rRNA gene sequences with reference proteomes of archaeal and bacterial taxa to generate amino acid compositions of community reference proteomes. Calculates chemical metrics including carbon oxidation state ('Zc'), stoichiometric oxidation and hydration state ('nO2' and 'nH2O'), H/C, N/C, O/C, and S/C ratios, grand average of hydropathicity ('GRAVY'), isoelectric point ('pI'), protein length, and average molecular weight of amino acid residues. Uses precomputed reference proteomes for archaea and bacteria derived from the Genome Taxonomy Database ('GTDB'). Also includes reference proteomes derived from the NCBI Reference Sequence ('RefSeq') database and manual mapping from the 'RDP Classifier' training set to 'RefSeq' taxonomy as described by Dick and Tan (2023) <doi:10.1007/s00248-022-01988-9>. Processes taxonomic classifications in 'RDP Classifier' format or OTU tables in 'phyloseq-class' objects from the Bioconductor package 'phyloseq'. |
| Authors: | Jeffrey Dick [aut, cre]
|
| Maintainer: | Jeffrey Dick <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.0 |
| Built: | 2024-09-30 06:42:01 UTC |
| Source: | CRAN |
Functions and data to calculate chemical metrics for reference proteomes for microbial (archaeal and bacterial) communities. Amino acid compositions of community reference proteomes are generated by combining reference proteomes of taxa (derived from GTDB or RefSeq) with taxonomic classifications of 16S rRNA gene sequences.
read_RDP - Read and filter an RDP Classifier table
map_taxa - Map RDP Classifier assignments to the NCBI taxonomy
get_metrics - Get chemical metrics for community reference proteomes
get_metadata - Example function for retrieving sample metadata
plot_metrics - Plot metrics with symbols and colors based on metadata
physeq - Functions designed to analyze phyloseq-class objects
History:
Work begun in 2021. The combination of RefSeq reference proteomes with taxonomic abundances to compute community-level chemical metrics was described by Dick and Tan (2023). chem16S originated as code in the JMDplots package (https://github.com/jedick/JMDplots).
Development in 2022. Dick and Meng (2023) compared community ZC with redox potential measurements from local to global scales. The term “community reference proteomes” was first applied, and chem16S was split into a separate package.
Late 2022. GTDB r207 was added as a reference database.
June–July 2023. Integration with phyloseq and addition of vignettes: Chemical metrics of reference proteomes, Integration of chem16S with phyloseq, and Plotting two chemical metrics. Default reference database changed to GTDB r207.
April 2024. Updated to GTDB r214.
chem16S sets an option using the global options mechanism in R.
This option will be set when package chem16S (or its namespace) is loaded if not already set.
manual_mappingsA data frame of mappings between RDP and NCBI (RefSeq) taxonomies, which is read from ‘extdata/manual_mappings.csv’.
The columns include RDP.rank, RDP.name, NCBI.rank, NCBI.name, and notes.
This option is made available so the user can modify the manual mappings used by map_taxa at runtime.
NOTE: None of the ‘*.R’ files in the ‘extdata’ directories are included in the package submitted to CRAN; see GitHub or Zenodo for these files.
This directory contains two sets of files: 1) scripts to process source RefSeq sequence files to generate amino acid compositions of species-level reference proteomes and taxonomic names; 2) script and output for amino acid compositions of higher-level taxa. The files are based on RefSeq release 206 of 2021-05-21 (O'Leary et al., 2016).
Description of steps to generate reference proteomes of species-level taxa (including downloads and shell commands).
Helper script to extract microbial protein records from the RefSeq catalog.
R code to sum the amino acid compositions of all proteins for each bacterial, archaeal, and viral species in the NCBI Reference Sequence database.
NOTE: To save space in this package, the output file (‘genome_AA.csv’) is stored in the RefDB/RefSeq_206 directory of the JMDplots package on GitHub (https://github.com/jedick/JMDplots.
The first five columns are: protein (“refseq”), organism (taxonomic id), ref (organism name), abbrv (empty), chains (number of protein sequences for this organism).
Columns 6 to 25 have the counts of amino acids.
R code for processing taxonomic IDs; the output file is ‘taxonomy.csv’. The columns are NCBI taxonomic ID (taxid), and names at different taxonomic rank (species, genus, family, order, class, phylum, superkingdom).
Functions to create the files listed below:
Average amino acid composition of reference proteomes for all species in each genus, family, order, class, phylum, and superkingdom.
Functions to process GTDB source files (Parks et al., 2022) and produce the following output file:
Average amino acid composition of reference proteomes for all species in each genus, family, order, class, phylum, and domain.
In both this file and the corresponding file for RefSeq (see above), the protein, organism, ref, and abbrv columns contain the rank, taxon name, number of species used to generate the amino acid composition of this taxon, and parent taxon.
chains is 1, denoting a single polypeptide chain, so the amino acid composition represents the average per-protein amino acid composition in this taxon, and the sum of amino acid counts is the average protein length.
Metadata for Heart Lake Geyser Basin, Yellowstone (Bowen De León et al., 2012).
Metadata for the Baltic Sea (Herlemann et al., 2016).
Metadata for Bison Pool, Yellowstone (Swingley et al., 2012).
Output of RDP Classifier with the default training set.
Pipeline for sequence data processing (uses external programs fastq-dump, vsearch, seqtk, RDP Classifier).
This was used to make the files in both ‘RDP’ and ‘RDP-GTDB_207’ (the latter with GTDB = TRUE in the script).
Heart Lake Geyser Basin.
Baltic Sea.
Bison Pool.
Output of RDP Classifer using the GTDB r207 SSU training set (doi:10.5281/zenodo.7633100).
Heart Lake Geyser Basin.
Baltic Sea.
Bison Pool.
Identification and taxonomic classification of sequences using DADA2 with GTDB r214.
Analysis of data from Fonseca et al. (2022) for marine sediment from the Humboldt Sulfuretum.
‘pipeline.R’ has the commands used to process the 16S rRNA gene sequence data and was adapted by Jeffrey Dick from the DADA2 pipeline tutorial (Callahan, 2020).
‘SraRunInfo.csv’ was obtained from the NCBI Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA251688).
‘sample_data.csv’ has data obtained from NCBI BioSample records for BioProject PRJNA251688.
‘*.png’ are several plots created while running the DADA2 pipeline.
‘ps_FEN+22.rds’ contains the phyloseq object with (including otu_table, sample_data, and refseq objects) created at the end of the DADA2 pipeline.
Analysis of data from Zhang et al. (2023) for hot springs in the Qinghai-Tibet Plateau.
‘pipeline.R’ has the commands used to process the 16S rRNA gene sequence data and was adapted by Jeffrey Dick from the DADA2 pipeline tutorial (Callahan, 2020).
‘SraRunInfo.csv’ was obtained from the NCBI Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA860942).
‘sample_data.csv’ has data obtained from NCBI BioSample records for BioProject PRJNA860942.
‘*.png’ are several plots created while running the DADA2 pipeline.
‘ps_ZFZ+23.rds’ contains the phyloseq object with (including otu_table, sample_data, and refseq objects) created at the end of the DADA2 pipeline.
Bowen De León K, Gerlach R, Peyton BM, Fields MW. 2013. Archaeal and bacterial communities in three alkaline hot springs in Heart Lake Geyser Basin, Yellowstone National Park. Frontiers in Microbiology 4: 330. doi:10.3389/fmicb.2013.00330
Callahan B. 2020. DADA2 Pipeline Tutorial (1.16). https://benjjneb.github.io/dada2/tutorial.html, accessed on 2023-06-14.
Dick JM, Meng D. 2023. Community- and genome-based evidence for a shaping influence of redox potential on bacterial protein evolution. mSystems 8(3): e00014-23. doi:10.1128/msystems.00014-23
Dick JM, Tan J. 2023. Chemical links between redox conditions and estimated community proteomes from 16S rRNA and reference protein sequences. Microbial Ecology 85: 1338–1355. doi:10.1007/s00248-022-01988-9
Fonseca A, Espinoza C, Nielsen LP, Marshall IPG, Gallardo VA. 2022. Bacterial community of sediments under the Eastern Boundary Current System shows high microdiversity and a latitudinal spatial pattern. Frontiers in Microbiology 13: 1016418. doi:10.3389/fmicb.2022.1016418
Herlemann DPR, Lundin D, Andersson AF, Labrenz M, Jürgens K. 2016. Phylogenetic signals of salinity and season in bacterial community composition across the salinity gradient of the Baltic Sea. Frontiers in Microbiology 7: 1883. doi:10.3389/fmicb.2016.01883
O'Leary NA et al. 2016. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Research 44: D733-D745. doi:10.1093/nar/gkv1189
Parks DH, Chuvochina M, Rinke C, Mussig AJ, Chaumeil P-A, Hugenholtz P. 2022. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Research 50: D785–D794. doi:10.1093/nar/gkab776
Swingley WD, Meyer-Dombard DR, Shock EL, Alsop EB, Falenski HD, Havig JR, Raymond J. 2012. Coordinating environmental genomics and geochemistry reveals metabolic transitions in a hot spring ecosystem. PLOS One 7(6): e38108. doi:10.1371/journal.pone.0038108
Zhang H-S, Feng Q-D, Zhang D-Y, Zhu G-L, Yang L. 2023. Bacterial community structure in geothermal springs on the northern edge of Qinghai-Tibet plateau. Frontiers in Microbiology 13: 994179. doi:10.3389/fmicb.2022.994179
Objects in phyloseq-class format created by following the DADA2 Pipeline Tutorial (Callahan, 2020).
The example dataset for gut communities in a single mouse was taken from the mothur MiSeq SOP. It is an extract of the complete dataset reported by Schloss et al. (2012). The output files were generated by using three different training sets to assign taxonomy to genus level:
silva_nr99_v138.1: Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2
rdp_train_set_18: RDP taxonomic training data formatted for DADA2 (RDP trainset 18/release 11.5)
GTDB_bac120_arc53_ssu_r214: GTDB: DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea
Jeffrey M. Dick, following the DADA2 Pipeline Tutorial by Callahan (2020).
Callahan B (2020). DADA2 Pipeline Tutorial (1.16). https://benjjneb.github.io/dada2/tutorial.html, accessed on 2023-06-14. Publication year taken from https://code.bioconductor.org/browse/dada2/blob/RELEASE_3_11/DESCRIPTION.
Schloss PD, Schubert AM, Zackular JP, Iverson KD, Young VB, Petrosino JF. 2012. Stabilization of the murine gut microbiome following weaning. Gut Microbes 3(4): 383–393. doi:10.4161/gmic.21008
# Load one data set data(mouse.silva) # Summarize the object (phyloseq-class) mouse.silva# Load one data set data(mouse.silva) # Summarize the object (phyloseq-class) mouse.silva
Formatted labels for indicating chemical metrics on plots.
chemlab(varname)chemlab(varname)
varname |
character, the name of a variable |
This function provides formatted labels for chemical metrics and related variables.
varname can be any one of the chemical metrics described in calc_metrics.
For most values of varname, a language object suitable for plotting (see plotmath).
For labels where no special mathematical notation is required, a character object.
If varname is not among the chemical metrics described in calc_metrics, the value of varname itself.
chemlab("Zc")chemlab("Zc")
Simple function that processes metadata files.
get_metadata(file, metrics)get_metadata(file, metrics)
file |
character, metadata file (CSV format) |
metrics |
data frame, output of |
This function is provided as a simple example of reading metadata about 16S rRNA datasets. The basic metadata for all datasets include ‘name’ (study name), ‘Run’ (SRA or other run ID), ‘BioSample’ (SRA BioSample), and ‘Sample’ (sample description). Each data set contains environmental data, but the specific variables differ between datasets. Therefore, this function processes the environmental data for different datasets and appends ‘pch’ and ‘col’ columns that can be used for visualization and identifying trends between groups of samples.
Samples are identified by the Run column (case-sensitive) in both file and metrics.
It is possible that metrics for one or more samples are not available (for example, if the number of RDP classifications is below mincount in get_metrics).
The function therefore removes metadata for unavailable samples.
Then, the samples in metrics are placed in the same order as they appear in metadata.
Ordering the samples is important because the columns in RDP Classifier output are not necessarily in the same order as the metadata.
If metrics is NULL, a data frame read from file with columns ‘pch’ and ‘col’ appended.
Otherwise, a list with two components, metadata and metrics.
In this list, metrics is the value from the metrics argument and metadata is a data frame read from file with columns ‘pch’ and ‘col’ appended and excluding any rows that correspond to unavailable samples in metrics.
# Get metrics for the Bison Pool dataset (Swingley et al., 2012) RDPfile <- system.file("extdata/RDP/SMS+12.tab.xz", package = "chem16S") RDP <- read_RDP(RDPfile) map <- map_taxa(RDP, refdb = "RefSeq_206") metrics <- get_metrics(RDP, map, refdb = "RefSeq_206") # Read the metadata file mdatfile <- system.file("extdata/metadata/SMS+12.csv", package = "chem16S") mdat <- get_metadata(mdatfile, metrics) # Print sample names mdat$metadata$Sample # 1 2 3 4 5 # Plot Zc of community reference proteomes at sites 1-5 Site <- mdat$metadata$Sample Zc <- mdat$metrics$Zc plot(Site, Zc, ylab = "Carbon oxidation state", type = "b") # Find the columns of the RDP file for samples 1 to 5 match(mdat$metadata$Run, colnames(RDP)) # 6 5 7 8 9 # NOTE: for this dataset, samples 1 and 2 are in columns 6 and 5, respectively# Get metrics for the Bison Pool dataset (Swingley et al., 2012) RDPfile <- system.file("extdata/RDP/SMS+12.tab.xz", package = "chem16S") RDP <- read_RDP(RDPfile) map <- map_taxa(RDP, refdb = "RefSeq_206") metrics <- get_metrics(RDP, map, refdb = "RefSeq_206") # Read the metadata file mdatfile <- system.file("extdata/metadata/SMS+12.csv", package = "chem16S") mdat <- get_metadata(mdatfile, metrics) # Print sample names mdat$metadata$Sample # 1 2 3 4 5 # Plot Zc of community reference proteomes at sites 1-5 Site <- mdat$metadata$Sample Zc <- mdat$metrics$Zc plot(Site, Zc, ylab = "Carbon oxidation state", type = "b") # Find the columns of the RDP file for samples 1 to 5 match(mdat$metadata$Run, colnames(RDP)) # 6 5 7 8 9 # NOTE: for this dataset, samples 1 and 2 are in columns 6 and 5, respectively
Combines taxonomic classifications with reference proteomes for taxa to get amino acid compositions of community reference proteomes. Amino acid compositions are used to calculate chemical metrics.
get_metrics(RDP, map, refdb = "GTDB_214", taxon_AA = NULL, groups = NULL, return_AA = FALSE, zero_AA = NULL, metrics = c("Zc", "nO2", "nH2O"))get_metrics(RDP, map, refdb = "GTDB_214", taxon_AA = NULL, groups = NULL, return_AA = FALSE, zero_AA = NULL, metrics = c("Zc", "nO2", "nH2O"))
RDP |
data frame, taxonomic abundances produced by |
map |
data frame, taxonomic mapping produced by |
refdb |
character, name of reference database (‘GTDB_214’ or ‘RefSeq_206’) |
taxon_AA |
data frame, amino acid compositions of taxa, used to bypass |
groups |
list of indexing vectors, samples to be aggregated into groups |
return_AA |
logical, return the amino acid composition for each sample instead of the chemical metrics? |
zero_AA |
character, three-letter abbreviation(s) of amino acid(s) to assign zero counts for calculating chemical metrics |
metrics |
character, the chemical metrics to calculate |
get_metrics calculates selected chemical metrics for the community reference proteome in each sample.
The community reference proteome is computed from the amino acid compositions of reference proteomes for taxa (obtained from the reference database in refdb), multiplied by taxonomic abundances given in RDP.
RDP may include results from the RDP Classifier (read using read_RDP) or derived from the OTU table of a phyloseq-class object (see ps_taxacounts).
map defines the taxonomic mapping between RDP and refdb.
Then, chemical metrics are calculated from the amino acid composition of the community reference proteome.
The default chemical metrics are carbon oxidation state (ZC), stoichiometric oxidation state (nO2), and stoichiometric hydration state (nH2O).
See calc_metrics for other available metrics.
groups, if given, is a list of one or more indexing vectors (with logical or numeric values) corresponding to samples whose taxonomic classifications are aggregated into groups before calculating amino acid compositions and chemical metrics.
A data frame with one row for each sample, corresponding to columns 5 and above of RDP.
The sample names are in the first column, which is named Run by default, or group if the groups argument is provided.
The remaining columns have numeric values and are named for each of the calculated metrics.
## First two examples are for RDP Classifier with default training set ## and mapping to NCBI taxonomy with RefSeq reference proteomes # Get chemical metrics for all samples in a dataset RDPfile <- system.file("extdata/RDP/BGPF13.tab.xz", package = "chem16S") RDP <- read_RDP(RDPfile) map <- map_taxa(RDP, refdb = "RefSeq_206") # This is a data frame with 14 rows and Run, Zc, nO2, and nH2O columns (metrics <- get_metrics(RDP, map, refdb = "RefSeq_206")) # Read the metadata file mdatfile <- system.file("extdata/metadata/BGPF13.csv", package = "chem16S") # Create list with metadata and metrics in same sample order mdat <- get_metadata(mdatfile, metrics) # Calculate metrics for aggregated samples of Archaea and Bacteria groups <- list(A = mdat$metadata$domain == "Archaea", B = mdat$metadata$domain == "Bacteria") # This is a data frame with 2 rows and group, Zc, nO2, and nH2O columns get_metrics(RDP, map, refdb = "RefSeq_206", groups = groups) ## For the next example, classifications were made using the RDP Classifer ## with 16S rRNA sequences from GTDB r207, but ## reference proteomes are based on GTDB r214 ## (this uses the default option of refdb = "GTDB_214"). RDPfile.GTDB <- system.file("extdata/RDP-GTDB_207/BGPF13.tab.xz", package = "chem16S") RDP.GTDB <- read_RDP(RDPfile.GTDB) map.GTDB <- map_taxa(RDP.GTDB) metrics.GTDB <- get_metrics(RDP.GTDB, map.GTDB) # Plot Zc from GTDB vs RefSeq xylim <- range(metrics$Zc, metrics.GTDB$Zc) plot(metrics$Zc, metrics.GTDB$Zc, xlim = xylim, ylim = xylim, type = "n") lines(xylim, xylim, lty = 2, col = 8) points(metrics$Zc, metrics.GTDB$Zc, pch = mdat$metadata$pch, bg = mdat$metadata$col) md.leg <- mdat$metadata[1:2, ] legend("bottomright", md.leg$domain, pch = md.leg$pch, pt.bg = md.leg$col) title(quote(italic(Z)[C]~"from GTDB vs RefSeq")) # To exclude tryptophan, tyrosine, and phenylalanine # from the calculation of chemical metrics get_metrics(RDP.GTDB, map.GTDB, zero_AA = c("Trp", "Tyr", "Phe"))## First two examples are for RDP Classifier with default training set ## and mapping to NCBI taxonomy with RefSeq reference proteomes # Get chemical metrics for all samples in a dataset RDPfile <- system.file("extdata/RDP/BGPF13.tab.xz", package = "chem16S") RDP <- read_RDP(RDPfile) map <- map_taxa(RDP, refdb = "RefSeq_206") # This is a data frame with 14 rows and Run, Zc, nO2, and nH2O columns (metrics <- get_metrics(RDP, map, refdb = "RefSeq_206")) # Read the metadata file mdatfile <- system.file("extdata/metadata/BGPF13.csv", package = "chem16S") # Create list with metadata and metrics in same sample order mdat <- get_metadata(mdatfile, metrics) # Calculate metrics for aggregated samples of Archaea and Bacteria groups <- list(A = mdat$metadata$domain == "Archaea", B = mdat$metadata$domain == "Bacteria") # This is a data frame with 2 rows and group, Zc, nO2, and nH2O columns get_metrics(RDP, map, refdb = "RefSeq_206", groups = groups) ## For the next example, classifications were made using the RDP Classifer ## with 16S rRNA sequences from GTDB r207, but ## reference proteomes are based on GTDB r214 ## (this uses the default option of refdb = "GTDB_214"). RDPfile.GTDB <- system.file("extdata/RDP-GTDB_207/BGPF13.tab.xz", package = "chem16S") RDP.GTDB <- read_RDP(RDPfile.GTDB) map.GTDB <- map_taxa(RDP.GTDB) metrics.GTDB <- get_metrics(RDP.GTDB, map.GTDB) # Plot Zc from GTDB vs RefSeq xylim <- range(metrics$Zc, metrics.GTDB$Zc) plot(metrics$Zc, metrics.GTDB$Zc, xlim = xylim, ylim = xylim, type = "n") lines(xylim, xylim, lty = 2, col = 8) points(metrics$Zc, metrics.GTDB$Zc, pch = mdat$metadata$pch, bg = mdat$metadata$col) md.leg <- mdat$metadata[1:2, ] legend("bottomright", md.leg$domain, pch = md.leg$pch, pt.bg = md.leg$col) title(quote(italic(Z)[C]~"from GTDB vs RefSeq")) # To exclude tryptophan, tyrosine, and phenylalanine # from the calculation of chemical metrics get_metrics(RDP.GTDB, map.GTDB, zero_AA = c("Trp", "Tyr", "Phe"))
Maps taxonomic names to NCBI (RefSeq) or GTDB taxonomy by automatic matching of taxonomic names, with manual mappings for some groups.
map_taxa(taxacounts = NULL, refdb = "GTDB_214", taxon_AA = NULL, quiet = FALSE)map_taxa(taxacounts = NULL, refdb = "GTDB_214", taxon_AA = NULL, quiet = FALSE)
taxacounts |
data frame with taxonomic name and abundances |
refdb |
character, name of reference database (‘GTDB_214’ or ‘RefSeq_206’) |
taxon_AA |
data frame, amino acid compositions of taxa, used to bypass |
quiet |
logical, suppress printed messages? |
This function maps taxonomic names to the NCBI (RefSeq) or GTDB taxonomy.
taxacounts should be a data frame generated by either read_RDP or ps_taxacounts.
Input names are made by combining the taxonomic rank and name with an underscore separator (e.g. ‘genus_ Escherichia/Shigella’).
Input names are then matched to the taxa listed in ‘taxon_AA.csv.xz’ found under ‘RefDB/RefSeq_206’ or ‘RefDB/GTDB_214’.
The protein and organism columns in these files hold the rank and taxon name extracted from the RefSeq or GTDB database.
Only exactly matching names are automatically mapped.
For mapping to the NCBI (RefSeq) taxonomy, some group names are manually mapped as follows (see Dick and Tan, 2023):
| RDP training set | NCBI |
| genus_Escherichia/Shigella | genus_Escherichia |
| phylum_Cyanobacteria/Chloroplast | phylum_Cyanobacteria |
| genus_Marinimicrobia_genera_incertae_sedis | species_Candidatus Marinimicrobia bacterium |
| class_Cyanobacteria | phylum_Cyanobacteria |
| genus_Spartobacteria_genera_incertae_sedis | species_Spartobacteria bacterium LR76 |
| class_Planctomycetacia | class_Planctomycetia |
| class_Actinobacteria | phylum_Actinobacteria |
| order_Rhizobiales | order_Hyphomicrobiales |
| genus_Gp1 | genus_Acidobacterium |
| genus_Gp6 | genus_Luteitalea |
| genus_GpI | genus_Nostoc |
| genus_GpIIa | genus_Synechococcus |
| genus_GpVI | genus_Pseudanabaena |
| family_Family II | family_Synechococcaceae |
| genus_Subdivision3_genera_incertae_sedis | family_Verrucomicrobia subdivision 3 |
| order_Clostridiales | order_Eubacteriales |
| family_Ruminococcaceae | family_Oscillospiraceae |
To avoid manual mapping, GTDB can be used for both taxonomic assignemnts and reference proteomes.
Taxonomic assignments based on 16S rRNA sequences from GTDB can be made using training files for the RDP Classifier (doi:10.5281/zenodo.7633099) or dada2 (doi:10.5281/zenodo.2541238) (make sure to choose the appropriate GTDB version).
Example files created using the RDP Classifier are provided under ‘extdata/RDP-GTDB_207’.
An example dataset created with DADA2 is data(mouse.GTDB_214); this is a phyloseq-class object that can be processed with functions described at physeq.
Change quiet to TRUE to suppress printing of messages about manual mappings, most abundant unmapped groups, and overall percentage of mapped names.
Integer vector with length equal to number of rows of taxacounts.
Values are rownumbers in the data frame generated by reading taxon_AA.csv.xz, or NA for no matching taxon.
Attributes unmapped_groups and unmapped_percent have the input names of unmapped groups and their percentage of the total classification count.
Dick JM, Tan J. 2023. Chemical links between redox conditions and estimated community proteomes from 16S rRNA and reference protein sequences. Microbial Ecology 85: 1338–1355. doi:10.1007/s00248-022-01988-9
# Mapping taxonomic classifications from RDP training set to NCBI taxonomy file <- system.file("extdata/RDP/SMS+12.tab.xz", package = "chem16S") RDP <- read_RDP(file) map <- map_taxa(RDP, refdb = "RefSeq_206") # About 24% of classifications are unmapped sum(attributes(map)$unmapped_percent) # Mapping from GTDB training set to GTDB taxonomy file <- system.file("extdata/RDP-GTDB_207/SMS+12.tab.xz", package = "chem16S") RDP.GTDB <- read_RDP(file) map.GTDB <- map_taxa(RDP.GTDB) # The classifications were made with GTDB r207, but the reference database # in chem16S has been updated to GTDB r214, so the mapping rate is less than 100 % sum(attributes(map.GTDB)$unmapped_percent)# Mapping taxonomic classifications from RDP training set to NCBI taxonomy file <- system.file("extdata/RDP/SMS+12.tab.xz", package = "chem16S") RDP <- read_RDP(file) map <- map_taxa(RDP, refdb = "RefSeq_206") # About 24% of classifications are unmapped sum(attributes(map)$unmapped_percent) # Mapping from GTDB training set to GTDB taxonomy file <- system.file("extdata/RDP-GTDB_207/SMS+12.tab.xz", package = "chem16S") RDP.GTDB <- read_RDP(file) map.GTDB <- map_taxa(RDP.GTDB) # The classifications were made with GTDB r207, but the reference database # in chem16S has been updated to GTDB r214, so the mapping rate is less than 100 % sum(attributes(map.GTDB)$unmapped_percent)
Calculate chemical metrics of community reference proteomes from taxonomic abundances in phyloseq-class objects and make plots of individual metrics or two metrics against each other.
ps_taxacounts(physeq, split = TRUE) ps_metrics(physeq, split = TRUE, refdb = "GTDB_214", quiet = FALSE, ...) plot_ps_metrics(physeq, metrics = c("Zc", "nO2", "nH2O"), x = "samples", color = NULL, shape = NULL, title = NULL, scales = "free_y", nrow = 1, sortby = NULL, ...) plot_ps_metrics2(physeq, metrics = c("Zc", "nH2O"), color = NULL, shape = NULL, title = NULL, refdb = "GTDB_214", quiet = FALSE)ps_taxacounts(physeq, split = TRUE) ps_metrics(physeq, split = TRUE, refdb = "GTDB_214", quiet = FALSE, ...) plot_ps_metrics(physeq, metrics = c("Zc", "nO2", "nH2O"), x = "samples", color = NULL, shape = NULL, title = NULL, scales = "free_y", nrow = 1, sortby = NULL, ...) plot_ps_metrics2(physeq, metrics = c("Zc", "nH2O"), color = NULL, shape = NULL, title = NULL, refdb = "GTDB_214", quiet = FALSE)
physeq |
'phyloseq-class', a phyloseq object containing an 'otu_table' and 'tax_table' |
split |
logical, calculate metrics for each sample (TRUE) or the entire set (FALSE) |
refdb |
character, name of reference database (passed to |
quiet |
logical, suppress printed messages? (passed to |
... |
additional arguments passed to |
metrics |
character, chemical metrics to calculate |
x |
map this variable to the horizontal axis |
color |
map this variable to colors |
shape |
map this variable to shapes |
title |
main title for the plot |
scales |
‘free_y’ or ‘fixed’ for different vertical scales or same scale in each panel |
nrow |
number of rows of panels (passed to |
sortby |
subset of |
ps_taxacounts returns a data frame with lowest-level classifications (from genus to phylum) and abundances for each OTU in the physeq object.
ps_metrics calculates chemical metrics of community reference proteomes from the lowest-level classifications after taxonomic mapping with map_taxa.
plot_ps_metrics plots individual chemical metrics or multiple chemical metrics, each in their own panel.
This function is modified from plot_richness; see that help page for further details about graphics-related arguments.
plot_ps_metrics2 plot two chemical metrics against each other.
The metrics argument is passed to get_metrics and selects the metrics to calculate.
split, refdb, and quiet can also be used as additional arguments in plot_ps_metrics and plot_ps_metrics2.
For ps_taxacounts, a data frame with samples in rows, and columns taxid (sample name taken from the OTU table obtained from otu_table(physeq)), lineage (all taxonomic names from the taxonomy table obtained from tax_table(physeq), separated by semicolons), name (lowest-level taxonomic name), rank (rank of lowest-level taxon), followed by the abundances from the OTU table.
For ps_metrics, a data frame with samples in rows, and columns corresponding to each of the metrics.
For plot_ps_metrics and plot_ps_metrics2, ggplot objects.
The author of this Rd file and the ps_taxacounts and ps_metrics functions is Jeffrey Dick.
plot_ps_metrics is based on the plot_richness function by Paul J. McMurdie and was modified from that function by Jeffrey Dick.
# Example 1: Humboldt Sulfuretum (Fonseca et al., 2022) file <- system.file("extdata/DADA2-GTDB_214/FEN+22/ps_FEN+22.rds", package = "chem16S") physeq <- readRDS(file) # Get lowest-level (to genus) classification for each OTU taxacounts <- ps_taxacounts(physeq) # Show numbers of assignments at each taxonomic level table(taxacounts$rank) # Map taxonomic names map <- map_taxa(taxacounts, refdb = "GTDB_214") # Taxonomy and reference proteomes are both from GTDB, # so there are no unmapped classifications sum(attr(map, "unmapped_percent")) # == 0 # Calculate chemical metrics ps_metrics(physeq, refdb = "GTDB_214") # Example 2: GlobalPatterns dataset from phyloseq data(GlobalPatterns, package = "phyloseq") # Plot metrics grouped by sample type and sorted by mean Zc; # refdb = "RefSeq" uses manual mappings from the RDP to NCBI taxonomy p <- plot_ps_metrics(GlobalPatterns, x = "SampleType", sortby = "Zc", refdb = "RefSeq_206") # Change orientation of x-axis labels p + ggplot2::theme(axis.text.x = ggplot2::element_text( angle = 45, vjust = 1, hjust = 1))# Example 1: Humboldt Sulfuretum (Fonseca et al., 2022) file <- system.file("extdata/DADA2-GTDB_214/FEN+22/ps_FEN+22.rds", package = "chem16S") physeq <- readRDS(file) # Get lowest-level (to genus) classification for each OTU taxacounts <- ps_taxacounts(physeq) # Show numbers of assignments at each taxonomic level table(taxacounts$rank) # Map taxonomic names map <- map_taxa(taxacounts, refdb = "GTDB_214") # Taxonomy and reference proteomes are both from GTDB, # so there are no unmapped classifications sum(attr(map, "unmapped_percent")) # == 0 # Calculate chemical metrics ps_metrics(physeq, refdb = "GTDB_214") # Example 2: GlobalPatterns dataset from phyloseq data(GlobalPatterns, package = "phyloseq") # Plot metrics grouped by sample type and sorted by mean Zc; # refdb = "RefSeq" uses manual mappings from the RDP to NCBI taxonomy p <- plot_ps_metrics(GlobalPatterns, x = "SampleType", sortby = "Zc", refdb = "RefSeq_206") # Change orientation of x-axis labels p + ggplot2::theme(axis.text.x = ggplot2::element_text( angle = 45, vjust = 1, hjust = 1))
Functions to plot chemical metrics of community reference proteomes.
plot_metrics(mdat, xvar = "Zc", yvar = "nH2O", xlim = NULL, ylim = NULL, xlab = chemlab(xvar), ylab = chemlab(yvar), plot.it = TRUE, add = FALSE, identify = FALSE, points = TRUE, lines = FALSE, title = TRUE, cex = 1, pt.open.col = 1, pch1 = 1, pch2 = 21, return = "data", extracolumn = NULL )plot_metrics(mdat, xvar = "Zc", yvar = "nH2O", xlim = NULL, ylim = NULL, xlab = chemlab(xvar), ylab = chemlab(yvar), plot.it = TRUE, add = FALSE, identify = FALSE, points = TRUE, lines = FALSE, title = TRUE, cex = 1, pt.open.col = 1, pch1 = 1, pch2 = 21, return = "data", extracolumn = NULL )
mdat |
list, output by |
xvar |
character, name of x variable |
yvar |
character, name of y variable |
xlim |
numeric, x axis limits |
ylim |
numeric, y axis limits |
xlab |
x axis label |
ylab |
y axis label |
plot.it |
logical, make a plot? |
add |
logical, add to existing plot? |
identify |
logical, run |
points |
logical, plot points? |
lines |
logical, plot lines? |
title |
character, plot title |
cex |
numeric, point size |
pt.open.col |
color of border for open point symbols ( |
pch1 |
numeric, symbol for samples in group 1 |
pch2 |
numeric, symbol for samples in group 2 |
return |
character, indicates whether to return ‘data’ values or group ‘means’ |
extracolumn |
character, the name of one or more extra columns (from |
plot_metrics plots the values of ZC and nH2O (or other variables indicated by xvar and yvar) provided in mdat$metrics.
Symbol shape and color (pch and col) are taken from mdat$metadata.
If pch1 and pch2 are provided, then samples are classified into two groups according to the value of mdat$metadata$pch.
Mean values of the chemical metrics for each group are plotted with star-shaped symbols.
For plot_metrics, a data frame with columns for study name and Run IDs (‘name’, ‘Run’), chemical metrics (taken from mdat$metrics), and symbols and colors for plotting points (‘pch’, ‘col’).
Herlemann, D. P. R., Lundin, D., Andersson, A. F., Labrenz, M. and Jürgens, K. (2016) Phylogenetic signals of salinity and season in bacterial community composition across the salinity gradient of the Baltic Sea. Front. Microbiol. 7, 1883. doi:10.3389/fmicb.2016.01883
# Make a plot for the Baltic Sea salinity gradient # (data from Herlemann et al., 2016) RDPfile <- system.file("extdata/RDP/HLA+16.tab.xz", package = "chem16S") RDP <- read_RDP(RDPfile) map <- map_taxa(RDP, refdb = "RefSeq_206") metrics <- get_metrics(RDP, map, refdb = "RefSeq_206") mdatfile <- system.file("extdata/metadata/HLA+16.csv", package = "chem16S") mdat <- get_metadata(mdatfile, metrics) pm <- plot_metrics(mdat) # Add a legend legend <- c("< 6 PSU", "6-20 PSU", "> 20 PSU") pch <- c(24, 20, 21) pt.bg <- c(3, NA, 4) legend("bottomright", legend, pch = pch, col = 1, pt.bg = pt.bg, bg = "white") # Classify samples with low and high salinity ilo <- mdat$metadata$salinity < 6 ihi <- mdat$metadata$salinity > 20 # Add convex hulls canprot::add_hull(pm$Zc[ilo], pm$nH2O[ilo], col = adjustcolor("green3", alpha.f = 0.3), border = NA) canprot::add_hull(pm$Zc[ihi], pm$nH2O[ihi], col = adjustcolor("blue", alpha.f = 0.3), border = NA) # Show points for all samples and larger star-shaped points # for mean values of high- and low-salinity samples plot_metrics(mdat, pch1 = 21, pch2 = 24) # Plot nO2 instead of Zc plot_metrics(mdat, xvar = "nO2") # Make a plot for only Proteobacteria RDP <- read_RDP(RDPfile, lineage = "Proteobacteria") map <- map_taxa(RDP, refdb = "RefSeq_206") metrics <- get_metrics(RDP, map, refdb = "RefSeq_206") mdatfile <- system.file("extdata/metadata/HLA+16.csv", package = "chem16S") mdat <- get_metadata(mdatfile, metrics) mdat$metadata$name <- paste(mdat$metadata$name, "(Proteobacteria)") plot_metrics(mdat)# Make a plot for the Baltic Sea salinity gradient # (data from Herlemann et al., 2016) RDPfile <- system.file("extdata/RDP/HLA+16.tab.xz", package = "chem16S") RDP <- read_RDP(RDPfile) map <- map_taxa(RDP, refdb = "RefSeq_206") metrics <- get_metrics(RDP, map, refdb = "RefSeq_206") mdatfile <- system.file("extdata/metadata/HLA+16.csv", package = "chem16S") mdat <- get_metadata(mdatfile, metrics) pm <- plot_metrics(mdat) # Add a legend legend <- c("< 6 PSU", "6-20 PSU", "> 20 PSU") pch <- c(24, 20, 21) pt.bg <- c(3, NA, 4) legend("bottomright", legend, pch = pch, col = 1, pt.bg = pt.bg, bg = "white") # Classify samples with low and high salinity ilo <- mdat$metadata$salinity < 6 ihi <- mdat$metadata$salinity > 20 # Add convex hulls canprot::add_hull(pm$Zc[ilo], pm$nH2O[ilo], col = adjustcolor("green3", alpha.f = 0.3), border = NA) canprot::add_hull(pm$Zc[ihi], pm$nH2O[ihi], col = adjustcolor("blue", alpha.f = 0.3), border = NA) # Show points for all samples and larger star-shaped points # for mean values of high- and low-salinity samples plot_metrics(mdat, pch1 = 21, pch2 = 24) # Plot nO2 instead of Zc plot_metrics(mdat, xvar = "nO2") # Make a plot for only Proteobacteria RDP <- read_RDP(RDPfile, lineage = "Proteobacteria") map <- map_taxa(RDP, refdb = "RefSeq_206") metrics <- get_metrics(RDP, map, refdb = "RefSeq_206") mdatfile <- system.file("extdata/metadata/HLA+16.csv", package = "chem16S") mdat <- get_metadata(mdatfile, metrics) mdat$metadata$name <- paste(mdat$metadata$name, "(Proteobacteria)") plot_metrics(mdat)
Reads RDP Classifier output and optionally extracts lineages, removes classifications below a certain count, or truncates classifications to lowest taxonomic level.
read_RDP(file, lineage = NULL, mincount = 200, lowest.level = NULL, quiet = FALSE)read_RDP(file, lineage = NULL, mincount = 200, lowest.level = NULL, quiet = FALSE)
file |
character, file name |
lineage |
character, regular expression for filtering lineages |
mincount |
integer, samples with less than this number of RDP classifications are excluded |
lowest.level |
character, truncate classifications at this taxonomic level |
quiet |
logical, suppress printed messages? |
read_RDP reads and filters RDP results for all samples in a study.
The input file should be created by RDP Classifer using the -h option to create a hierarchical listing.
Classifications for multiple samples can be combined into a single file using the merge-count command of RDP Classifier.
Only rows (lineages) with count > 0 for at least one sample are retained.
Sequences with classification results at only the root or domain level (Root, Archaea, or Bacteria) are omitted because they provide poor taxonomic resolution.
Sequences classified to the class Chloroplast or genera Chlorophyta or Bacillariophyta are also omitted because they have little correspondence with the NCBI taxonomy.
Then, only columns (samples) with classification count >= mincount are retained.
All remaining sequences (those classified to genus or higher levels) can be used for mapping to the NCBI taxonomy.
The lineage text of the RDP Classifier looks like “Root;rootrank;Archaea;domain;Diapherotrites; phylum;Diapherotrites Incertae Sedis AR10;genus;”, so you can use lineage = "Archaea" to select the archaeal classifications or lineage = "genus" to select genus-level classifications.
Use the lowest.level argument to truncate the classifications to a level higher than genus.
This argument does not reduce the number of classifications, but only trims the RDP lineages to the specified level.
This may create duplicate lineages, for which the classification counts are summed, and only unique lineages are present in the returned data frame.
Change quiet to TRUE to suppress printing of messages about percentage classification to genus level, omitted sequences, and final range of total counts among all samples.
Data frame with columns inherited from file (from RDP output files: taxid, rank, lineage, name, and one column of classification counts for each sample).
If any sample has less than mincount total counts, that column is omitted from the result.
The rows in the result are subset from those in file by filtering operations.
NB: taxids in RDP files are not NCBI taxids.
# An example file file <- system.file("extdata/RDP/SMS+12.tab.xz", package = "chem16S") # Default settings r0 <- read_RDP(file) # Suppress messages r1 <- read_RDP(file, quiet = TRUE) # Increase minimum count threshold r2 <- read_RDP(file, mincount = 500) # This should be 3 (i.e., 3 samples have less than 500 counts) ncol(r1) - ncol(r2) # Keep only Archaea r3 <- read_RDP(file, lineage = "Archaea") # This should be TRUE all(grepl("Archaea", r3$lineage)) # Truncate taxonomy at phylum r4 <- read_RDP(file, lowest.level = "phylum") # This should be TRUE all(sapply(strsplit(r4$lineage, ";"), tail, 1) == "phylum")# An example file file <- system.file("extdata/RDP/SMS+12.tab.xz", package = "chem16S") # Default settings r0 <- read_RDP(file) # Suppress messages r1 <- read_RDP(file, quiet = TRUE) # Increase minimum count threshold r2 <- read_RDP(file, mincount = 500) # This should be 3 (i.e., 3 samples have less than 500 counts) ncol(r1) - ncol(r2) # Keep only Archaea r3 <- read_RDP(file, lineage = "Archaea") # This should be TRUE all(grepl("Archaea", r3$lineage)) # Truncate taxonomy at phylum r4 <- read_RDP(file, lowest.level = "phylum") # This should be TRUE all(sapply(strsplit(r4$lineage, ";"), tail, 1) == "phylum")