DOI: 10.5281/zenodo.15597022
title: Classification Using Restriction Enzyme Diagnostics (CURED): Additional Files

description: RdJ_Main_output.txt. The output from running CURED_Main.py with the RdJ S. aureus dataset.RdJ_RE_output.txt. The output from running CURED_FindREs.py with the RdJ S. aureus datasets.SAE_Main_output.txt. The output from running CURED_Main.py with the USA300 SAE S. aureus dataset.SAE_RE_output.txt. The output from running CURED_FindREs.py with the USA300 SAE S. aureus dataset.NAE_Main_output.txt. The output from running CURED_Main.py with the USA300 NAE S. aureus dataset.NAE_RE_input.txt. The input used for running CURED_FindREs.py with the USA300 NAE S. aureus dataset. NAE_RE_output.txt. The output from running CURED_FindREs.py with the USA300 NAE S. aureus dataset.NAE_diagnostic_alleles.faa. Diagnostic alleles for the NAE clade from Bianco et al.SAE_diagnostic_alleles.faa. Diagnostic alleles for the SAE clade from Bianco et al. calculate_sensitivity_specificity.py. Python script used to calculate the final sensitivity and specificity of the k-mer identified by CURED using the Pyseer output files. batching_case_control_all.txt. All genomes with their corresponding case or control designation used in Figure 2a.controls_with_kmer.txt. Genomes that were not classified as RdJ genomes, but had the RdJ k-mer. These were excluded from Figure 2a. randomly_subset_data.py. Python script used to randomly select subsets of the dataset for Figure 2a. ToolComparisonFigure.R. R script used to generate Figure 2a.kec_output.raw. Output from running KEC with the entire dataset.kec_output.parsed. The output from running count_seqs.py with kec_output.rawAcinetobacter_Main_output.txt. The output from running CURED_Main.py with the A. baumannii dataset.Acinetobacter_RE_input.txt. The input used when running CURED_FindREs.py with the A. baumannii dataset. Acinetobacter_RE_output.txt. The output from running CURED_FindREs.py with the A. baumannii dataset.Cdiff_Main_output.txt. The output from running CURED_Main.py with the C. difficile dataset.Cdiff_RE_input.txt. The input used when running CURED_FindREs.py with the C. difficile dataset. Cdiff_RE_output.txt. The output from running CURED_FindREs.py with the C. difficile dataset.Cluster1_Main_output.txt. The output from running CURED_Main.py with the CHOP dataset for Cluster 1.Cluster1_SimpleMode_input.txt. The input for running CURED_Main.py in simple mode with the global dataset for Cluster 1. Cluster1_SimpleMode_output.txt. The output from running CURED_Main.py in simple mode with the global dataset for Cluster 1.Cluster1_SimpleMode_UniqueKmers.txt. The unique k-mers, that restriction enzymes unique to Cluster 1 in the local dataset, identified by CURED_Main.py in simple mode using the global dataset. Cluster1_RE_local_input.txt. The input from running CURED_FindREs.py with the CHOP dataset for Cluster 1. Cluster1_RE_local_output.txt. The output from running CURED_FindREs.py with the CHOP dataset for Cluster 1.Cluster1_Global_input.txt. The input used when running CURED_FindREs.py with the global dataset for Cluster 1. Cluster1_Global_output.txt. The output from running CURED_FindREs.py with the global dataset for Cluster 1.Cluster1_UniqueEnzymes_PCR_Regions.txt. The in-silico PCR target region for each of the k-mers with a unique restriction enzyme site for Cluster 1.