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
+install.packages("R.utils")
+library(R.utils)
+
+# Decompress the file
+bunzip2("broadstreet-v3.3.0.tar.bz2", destname = "broadstreet-v3.3.0.tar", remove = FALSE)
+file.rename("broadstreet-v3.3.0.tar", "broadstreet-v3.3.0_old.tar")
+
+# Extract the tar file
+untar("broadstreet-v3.3.0_old.tar", exdir = "CARD_data")
+
+# List the contents of the extraction directory
+list.files("CARD_data")
+
+# Parse the ARO_index.tsv file using read.delim
+aro_index <- read.delim("CARD_data/ARO_index.tsv", header = TRUE, sep = "\t")
+
+
+# Map CARD Short Name
+# Load necessary library
+library(dplyr)
+
+#  Read the files
+aro_index <- read.delim("CARD_data/aro_index.tsv", sep = "\t", header = TRUE)
+antibiotics_data <- read.delim("CARD_data/shortname_antibiotics.tsv", sep = "\t", header = TRUE)
+pathogens_data <- read.delim("CARD_data/shortname_pathogens.tsv", sep = "\t", header = TRUE)
+
+
+# Mutate data
+aro_index <- aro_index %>%
+  mutate(
+    pathogen = sapply(strsplit(CARD.Short.Name, "_"), `[`, 1),   # First part: Pathogen
+    gene = sapply(strsplit(CARD.Short.Name, "_"), `[`, 2),       # Second part: Gene
+    drug = ifelse(sapply(strsplit(CARD.Short.Name, "_"), length) == 3,   # Third part: Drug
+                  sapply(strsplit(CARD.Short.Name, "_"), `[`, 3), NA),
+    Protein.Accession = Protein.Accession   # Include existing Protein.Accession column
+  )
+
+# View the mutated data
+head(aro_index)
+
+
+# Extract pathogen, gene, drug, and include Protein.Accession from 'CARD.Short.Name'
+aro_index_clean <- aro_index %>%
+  mutate(
+    pathogen = sapply(strsplit(CARD.Short.Name, "_"), `[`, 1),  # Extract pathogen
+    gene = sapply(strsplit(CARD.Short.Name, "_"), `[`, 2),      # Extract gene
+    drug = ifelse(sapply(strsplit(CARD.Short.Name, "_"), length) == 3,   # Extract drug
+                  sapply(strsplit(CARD.Short.Name, "_"), `[`, 3), NA),
+    Protein.Accession = Protein.Accession  # Include the Protein.Accession column
+  )
+
+# Merge aro_index_clean with the antibiotics_data and pathogens_data
+# For merging with antibiotics_data
+merged_data_antibiotics <- left_join(aro_index_clean, antibiotics_data,
+                                     by = c("drug" = "AAC.Abbreviation"))
+
+# For merging with pathogens_data
+merged_data_pathogens <- left_join(merged_data_antibiotics, pathogens_data,
+                                   by = c("pathogen" = "Abbreviation"))
+
+# View the resulting merged data
+head(merged_data_pathogens)
+
+
+#filter out rows where pathogen is empty
+cleaned_data <- merged_data_pathogens %>%
+  distinct() %>%
+  filter(!is.na(Pathogen)) # Use 'Pathogen' instead of 'pathogen'
+View(cleaned_data)
+
+# Group by Pathogen, Gene, Drug, and Protein.Accession, then summarize Antibiotic information
+summarized_data <- cleaned_data %>%
+  group_by(Pathogen = Pathogen, Gene = gene, Drug = drug, Protein_Accession = Protein.Accession) %>%
+  summarize(Antibiotic_Info = paste(unique(Molecule), collapse = ", ")) %>%
+  arrange(Pathogen, Gene, Drug, Protein_Accession)
+
+# Filter for Staphylococcus aureus and DAP (Bug-Drug of Interest)
+staph_aureus_dap_combinations <- summarized_data %>%
+  filter(Pathogen == "Staphylococcus aureus", Drug == "DAP")
+
+# View the filtered data
+head(staph_aureus_dap_combinations)
+
+
+#Fetch FASTA sequences from Entrez using protein accession
+#Load required packages
+library(rentrez)
+library(XML)
+library(stringr)
+
+
+# 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$message))
+    return(NULL)
+  })
+}
+
+# Loop through staph_aureus_dap_combinations to fetch and save FASTA sequences
+combined_sequences <- character()
+
+for (i in 1:nrow(staph_aureus_dap_combinations)) {
+  # Fetch FASTA sequence for each protein accession
+  protein_accession <- staph_aureus_dap_combinations$Protein_Accession[i]
+  fasta_sequence <- fetch_fasta_sequence(protein_accession)
+
+  if (!is.null(fasta_sequence)) {
+    combined_sequences <- c(combined_sequences, fasta_sequence)
+  }
+}
+
+# Save the combined FASTA sequences
+filename <- "Staph_aureus_Daptomycin_sequences5.fasta"
+
+writeLines(combined_sequences, filename)
+
+# Read the FASTA file
+fasta_content <- readLines(filename)
+
+# Display the contents
+cat(fasta_content, sep = "\n")

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

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+CARD Download README
+
+Use or reproduction of these materials, in whole or in part, by any commercial 
+organization whether or not for non-commercial (including research) or commercial purposes
+is prohibited, except with written permission of McMaster University. Commercial uses are
+offered only pursuant to a written license and user fee. To obtain permission and begin 
+the licensing process, see http://card.mcmaster.ca/about.
+
+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:
+
+A CARD-specific abbreviation for AMR gene names associated with Antibiotic Resistance
+Ontology terms, often not based on the literature. This is used for programmatic and 
+compatibility purposes and is not ontologically relevant. Each ontology term with an 
+associated AMR detection model has a CARD Short Name that appears in CARD data files 
+and output generated by RGI. If the original gene name is less than 15 characters, the 
+CARD short name is identical; if the gene name is greater than 15 characters, the CARD 
+Short Name has been abbreviated by CARD curators specifically to identify the proper 
+gene or protein name. All CARD Short Names are unique and have whitespace characters 
+replaced by underscore characters. The convention for pathogen names is capitalized 
+first letter of the genus followed by the lowercase first three letters of the species 
+name. The antibiotic abbreviations are from https://journals.asm.org/journal/aac/abbreviations
+plus some custom abbreviations by the CARD curators. Simple CARD Short Names often do not
+involve either, e.g. CTX-M-15, but where applicable the CARD Short Names follow pathogen_gene
+or pathogen_gene_drug. The full lists of abbreviations can be found in the enclosed files: 
+
+"shortname_antibiotics.tsv"
+"shortname_pathogens.tsv"
+
+FASTA:
+
+Nucleotide and corresponding protein FASTA downloads are available as separate files for 
+each model type.  For example, the "protein homolog" model type contains sequences of
+antimicrobial resistance genes that do not include mutation as a determinant of resistance
+- these data are appropriate for BLAST analysis of metagenomic data or searches excluding 
+secondary screening for resistance mutations. In contrast, the "protein variant" model 
+includes reference wild type sequences used for mapping SNPs conferring antimicrobial 
+resistance - without secondary mutation screening, analyses using these data will include 
+false positives for antibiotic resistant gene variants or mutants.
+
+MODELS:
+
+The file "card.json" contains the complete data for all of CARD's AMR detection models, 
+including reference sequences, SNP mapping data, model parameters, and ARO classification.
+"card.json" is used by the Resistance Gene Identifier software. 
+
+Values for "High Confidence TB", "Moderate Confidence TB", "Minimal Confidence TB", and
+"Indeterminate Confidence TB" were obtained from https://platform.reseqtb.org.
+
+INDEX FILES:
+
+The file "aro_index.tsv" contains a list of ARO tagging of GenBank accessions stored in 
+CARD.
+
+The file "aro_categories.tsv" contains a list of ARO terms used to categorize all entries
+in CARD and results via the RGI. These categories reflect AMR gene family, target drug 
+class, and mechanism of resistance.
+
+The file "aro_categories_index.tsv" contains a list a GenBank accessions stored 
+in CARD cross-referenced with the major categories within the ARO. These categories 
+reflect AMR gene family, target drug class, and mechanism of resistance, so GenBank 
+accessions may have more than one cross-reference. For more complex categorization of 
+the data, use the full ARO available at http://card.mcmaster.ca/download.
+
+The file "snps.txt" lists the SNPs associated with specific detection models.