Restructured project: Moved files into case_studies/CARD, removed red…

case_studies/CARD/Bug-Drug Code.R

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+# config.R
+url <- "https://card.mcmaster.ca/download/0/broadstreet-v3.3.0.tar.bz2"
+destfile <- "broadstreet-v3.3.0.tar.bz2"
+
+# Download the file
+download.file(url, destfile)
+
+#Extract the file
+if (!require("R.utils")) {
+  install.packages("R.utils")
+  library(R.utils)
+}
+
+
+# Extract the tar file
+untar("broadstreet-v3.3.0.tar.bz2", exdir = "CARD_data")
+
+
+# Map CARD Short Name
+
+# Parse the required files using readr::read_delim
+aro_index <- read_delim("CARD_data/aro_index.tsv", delim = "\t", col_names = TRUE)
+antibiotics_data <- read_delim("CARD_data/shortname_antibiotics.tsv", delim = "\t", col_names = TRUE)
+pathogens_data <- read_delim("CARD_data/shortname_pathogens.tsv", delim = "\t", col_names = TRUE)
+
+
+
+# Extract pathogen, gene, drug, and include Protein.Accession from 'CARD Short Name'
+extract_card_info <- function(card_short_name, drug_class, `Protein Accession`, `DNA Accession`) {
+  # Split the CARD Short Name by underscores
+  split_names <- unlist(strsplit(card_short_name, "_"))
+
+  # Initialize variables with defaults
+  pathogen <- NA
+  gene <- NA
+  drug <- drug_class  # Default to Drug Class column
+
+  # Determine the information based on the split names and patterns
+  if (length(split_names) == 1) {
+    # Gene only (single part entry)
+    gene <- split_names[1]
+    pathogen <- "MULTI"  # Assign MULTI as default for pathogen
+  } else if (all(toupper(split_names) == split_names)) {
+    # Gene complex (all uppercase entries)
+    gene <- card_short_name  # Entire entry as gene
+    pathogen <- "MULTI"
+  } else if (length(split_names) == 2) {
+    # Pathogen-Gene scenario
+    pathogen <- split_names[1]
+    gene <- split_names[2]
+  } else if (length(split_names) == 3) {
+    # Pathogen-Gene-Drug scenario
+    pathogen <- split_names[1]
+    gene <- split_names[2]
+    drug <- split_names[3]  # Assign drug from the split entry
+  }
+
+  # If both pathogen and gene are NA, classify as complex gene
+  if (is.na(pathogen) && is.na(gene)) {
+    gene <- card_short_name  # Assign entire CARD Short Name as gene
+    pathogen <- "MULTI"      # Default to MULTI for pathogen
+  }
+
+  # Handle Protein Accession
+  if (is.na(`Protein Accession`) || `Protein Accession` == "") {
+    `Protein Accession` <- `DNA Accession`  # Use DNA Accession if Protein Accession is NA
+  }
+
+  return(list(Pathogen = pathogen, Gene = gene, Drug = drug, Protein_Accession = `Protein Accession`))
+}
+
+# Apply the function to the data frame
+resistance_profile_data <- aro_index %>%
+  mutate(extracted_info = pmap(list(`CARD Short Name`, `Drug Class`, `Protein Accession`, `DNA Accession`),
+                               extract_card_info)) %>%
+  unnest_wider(extracted_info)
+
+# View the resulting data frame
+print(resistance_profile_data)
+
+# Define a relative path for saving the data
+output_path <- file.path("CARD_data", "resistance_profile_data.tsv")
+
+# Save resistance_profile_data to the specified path
+write_delim(resistance_profile_data, output_path, delim = "\t")
+
+# Load data
+resistance_profile_data <- read_delim(output_path, delim = "\t", col_names = TRUE)
+antibiotics_data <- read_delim("CARD_data/shortname_antibiotics.tsv", delim = "\t", col_names = TRUE)
+pathogens_data <- read_delim("CARD_data/shortname_pathogens.tsv", delim = "\t", col_names = TRUE)
+
+
+# Merge the extracted resistance profile data with antibiotics_data on Drug
+merged_data_antibiotics <- left_join(
+  resistance_profile_data,
+  antibiotics_data,
+  by = c("Drug" = "AAC Abbreviation"), # Adjusting for abbreviations between datasets
+  relationship = "many-to-many"
+)
+
+# Merge the result with pathogens_data on Pathogen, renaming Pathogen.y to Pathogen_Full_Name
+merged_data_pathogens <- left_join(
+  merged_data_antibiotics,
+  pathogens_data,
+  by = c("Pathogen" = "Abbreviation")
+) %>%
+  rename(Pathogen_Full_Name = Pathogen.y)
+
+# Assign "Multi-species" to Pathogen_Full_Name where Pathogen values are "MULTI"
+merged_data_pathogens <- merged_data_pathogens %>%
+  mutate(Pathogen_Full_Name = if_else(Pathogen == "MULTI", "Multi-species", Pathogen_Full_Name))
+
+
+# Assign "Multi-class" to Molecule where Drug values are full names (not abbreviations)
+merged_data_pathogens <- merged_data_pathogens %>%
+  mutate(Molecule = if_else(grepl(" ", Drug) | grepl("-", Drug), "Multi-class", Molecule))
+
+
+#FASTA sequences
+#Install and Load required packages
+if (!requireNamespace("rentrez", quietly = TRUE)) {
+  install.packages("rentrez")
+}
+if (!requireNamespace("XML", quietly = TRUE)) {
+  install.packages("XML")
+}
+if (!requireNamespace("stringr", quietly = TRUE)) {
+  install.packages("stringr")
+}
+
+
+library(rentrez)
+library(XML)
+library(stringr)
+
+# Filter for the target drug (DAP) and pathogen (Staphylococcus aureus)
+filter_resistance_mechanisms <- function(data, drug, bug, exclude_multiclass = FALSE, species_restricted = TRUE) {
+
+  # Filter by drug using partial match to include multiclass entries containing the target drug
+  filtered_data <- data %>%
+    filter(grepl(drug, Drug, ignore.case = TRUE))
+
+  # Filter by pathogen, using partial match
+  filtered_data <- filtered_data %>%
+    filter(grepl(bug, Pathogen_Full_Name, ignore.case = TRUE))
+
+  # Optionally exclude multiclass resistance if exclude_multiclass = TRUE
+  if (exclude_multiclass) {
+    filtered_data <- filtered_data %>%
+      filter(!grepl(";", Drug))  # Only include entries with single drug classes
+  }
+
+  # Optionally restrict to species-specific mechanisms if species_restricted = TRUE
+  if (species_restricted) {
+    filtered_data <- filtered_data %>%
+      filter(Pathogen_Full_Name == bug)  # Include only entries with exact match to the bug of interest
+  }
+
+  return(filtered_data)
+}
+
+# Usage example for Staphylococcus aureus resistant to DAP, including multispecies and multiclass resistance
+filtered_data_saurdap <- filter_resistance_mechanisms(
+  data = merged_data_pathogens,
+  drug = "DAP",
+  bug = "Staphylococcus aureus",
+  exclude_multiclass = FALSE,
+  species_restricted = FALSE
+)
+
+# View the filtered results
+View(filtered_data_saurdap)
+
+
+# Fetch FASTA sequence from Entrez
+fetch_fasta_sequence <- function(protein_accession) {
+  tryCatch({
+    # Fetch the FASTA sequence using Entrez
+    fasta_seq <- rentrez::entrez_fetch(db = "protein",
+                                       id = protein_accession,
+                                       rettype = "fasta",
+                                       retmode = "text")
+
+    if (!is.null(fasta_seq)) {
+      # Ensure the first line starts with ">"
+      if (!grepl("^>", fasta_seq[1])) {
+        fasta_seq[1] <- paste0(">", fasta_seq[1])
+      }
+
+      # Split the sequence into lines
+      lines <- str_split(fasta_seq, "\n")[[1]]
+
+      # Join the lines back together
+      fasta_seq <- paste(lines, collapse = "\n")
+
+      return(fasta_seq)
+    } else {
+      warning(paste("Failed to retrieve FASTA sequence for protein accession:", protein_accession))
+      return(NULL)
+    }
+  }, error = function(e) {
+    warning(paste("Error fetching FASTA sequence for protein accession:", protein_accession, ":", e$message))
+    return(NULL)
+  })
+}
+
+
+# Define the output file for the FASTA sequences
+output_fasta_file <- "Staph_aureus_Daptomycin_sequences.fasta"
+
+# Initialize an empty character vector to store the sequences
+combined_sequences <- character()
+
+# Loop through each Protein Accession in the filtered data to fetch sequences
+for (i in 1:nrow(filtered_data_saurdap)) {
+  # Get the Protein Accession ID
+  Protein_accession <- filtered_data_saurdap$Protein_Accession[i]
+
+  cat("Fetching sequence for Protein Accession:", protein_accession, "\n")  # Debugging message
+
+  # Fetch the FASTA sequence
+  fasta_sequence <- fetch_fasta_sequence(protein_accession)
+
+  # If the sequence was fetched successfully, add it to the combined_sequences vector
+  if (!is.null(fasta_sequence)) {
+    combined_sequences <- c(combined_sequences, fasta_sequence)
+    cat("Successfully fetched sequence for:", protein_accession, "\n")
+  } else {
+    cat("Failed to fetch sequence for:", protein_accession, "\n")
+  }
+}
+
+# Check if there are any fetched sequences
+if (length(combined_sequences) > 0) {
+  # Save all fetched sequences to a FASTA file
+  writeLines(combined_sequences, output_fasta_file)
+  cat("Sequences saved to", output_fasta_file, "\n")
+} else {
+  cat("No sequences were fetched, so no FASTA file was created.\n")
+}
+
+# Read the contents of the file
+fasta_contents <- readLines(output_fasta_file)
+
+# Print the contents
+cat(fasta_contents, sep = "\n")
+
+
+
+
+
+
+
+
+
+
+

case_studies/CARD/CARD_data/CARD-Download-README.txt

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+# CARD README
+
+## Source:
+This dataset was downloaded from the Comprehensive Antibiotic Resistance Database (CARD) in 2024-10 at https://card.mcmaster.ca/download/0/broadstreet-v3.3.0.tar.bz2
+
+
+CITATION:
+
+Alcock et al. 2023. "CARD 2023: expanded curation, support for machine learning, and resistome 
+prediction at the Comprehensive Antibiotic Resistance Database" Nucleic Acids Research, 
+51, D690-D699. https://pubmed.ncbi.nlm.nih.gov/36263822/
+
+## CARD SHORT NAMES
+
+The CARD database uses standardized abbreviations, known as CARD Short Names, for AMR gene names associated with Antibiotic Resistance Ontology terms. These names are created for compatibility across data files and outputs from the Resistance Gene Identifier (RGI). Short Names for genes with 15 or fewer characters retain the original gene name, while longer names are abbreviated to uniquely represent each gene or protein. All CARD Short Names replace whitespace with underscores. For pathogen names, CARD follows the convention of capitalizing the first letter of the genus followed by the first three letters of the species in lowercase. Where applicable, CARD Short Names adopt formats such as “pathogen_gene,” “pathogen_gene_drug,” or “gene_drug.” Full lists of these abbreviations are available in the provided files:
+
+shortname_antibiotics.tsv
+shortname_pathogens.tsv"
+
+
+## FASTA
+
+The FASTA files included here contain retrieved sequences of antimicrobial resistance genes.
+
+## Data Files Downloaded
+aro_index.tsv
+This file contains an index of ARO (Antibiotic Resistance Ontology) identifiers with associated GenBank accessions. Each entry includes information used to link antibiotic resistance genes to GenBank sequences.
+shortname_antibiotics.tsv
+Contains standardized abbreviations for antibiotics used in CARD’s short names. These abbreviations, which follow conventions from the American Society for Microbiology (ASM) and additional custom terms, provide a uniform naming system for antibiotics referenced within CARD data.
+
+shortname_pathogens.tsv
+Lists standardized abbreviations for pathogens used in CARD. Each abbreviation represents pathogen names in a condensed format, commonly the first letter of the genus followed by the first three letters of the species. This abbreviation system simplifies pathogen referencing in CARD outputs.