Functional Assessment of Orphan Proteins in the Streptomyces Pan-Proteome through Genome-Wide Synteny Analysis

Functional Assessment of Orphan Proteins in the Streptomyces Pan-Proteome through Genome-Wide Synteny Analysis The scripts (Python 3.12.2) and datasets described were used to calculate the co-occurrence between unknown genes, clustered using ALFATClust ( https://doi.org/10.1186/s12859-022-04643-9), and KAAS-annotated genes located within syntenic regions. Dependencies, questions, and other technical details can be asked directly to the authors ([e-mail]) Datasets Dataset1_redundant_dataset_R-D (file txt) Dataset2_non-redundant_dataset_NR-D (file txt) Dataset3_annotation_list.txt (file txt) Dataset4_non-redundant_unannotated_dataset_NR-U-D (file txt) Dataset5_Clusters (file zip) Dataset6_Coordinates (file zip) Scripts Script_1_protein_length.py#Before running, set the following paths input_folder = "/Users/path/fasta_folder" #folder cointaining .faa files output_folder = "/Users/path/output1” Script_2_protein_length_plot.py#Before running, set the following path folder_path = '/Users/path/output1' #from script_1 Script_3_AA_%.py#Before running, set the following paths input_folder = "/Users/path/fasta_folder" #folder cointaining .faa files output_folder = "/Users/path/output2” Script_4_var.py#Before running, set the following paths input_folder = "/Users/path/output2" #from script_3 output_file = "/Users/path/combined_variances.txt" Script_5_plot.py#Before running, set the following paths variance_file = '/Users/path/combined_variances.txt’ #from script_4 output_pdf = '/Users/path/plot_variance_all_aminoacids.pdf' Script_6_Identical_prot_50.py#Before running, set the following paths input_file = '/Users/path//Desktop/Script_6_INPUT_all_identifiers.txt' output_file = '/Users/path/filtered_protein_ids_with_numbers.txt' Script_7_count_cluster_protein_in_genomes.py#Before running, set the following paths cluster_file = "/Users/path/Desktop/Clusters.txt" fasta_folder = "/Users/path/fasta_folder" #folder cointaining .faa files output_file = ""/Users/path/Clusters_count.txt" #This script can be run using as input any file (Clusters.txt) that has this format (like the one produced by ALFATClust): #Cluster 1WP_xxxxxxxxx.xWP_xxxxxxxxx.xWP_xxxxxxxxx.x#Cluster 2WP_xxxxxxxxx.xWP_xxxxxxxxx.xWP_xxxxxxxxx.x where WP_xxxxxxxxx.x are the protein accession numbers, variable in abundance, and code for each cluster Script_8_cluster_name_list.py#Before running, set the following paths cluster_file = "/Users/path/Desktop/Clusters.txt" # as in script 7 output_file = "/Users/path/Desktop/cluster_name.txt" Script_9_count_repetition_cluster.pyScript_10_count_repetition_num_cluster.py#Before running, set the following paths selected_clusters_file = "/Users/path/Desktop/cluster_name.txt" #from script_8 counts_file = "/Users/path/Desktop/output_counts.txt" output_file = "/Users/path/Desktop/output_counts_summary.txt" #The difference between these two scripts is that the first one generates a file containing the accession number of the genome (GCF_xxxxxxxxx.x) while the second one only counts the repeats. Script_11_filtering_75.py#Before running, set the following paths input_file = = "/Users/path/Desktop/output_counts_summary.txt" #from script_10 output_file = "/Users/path/Desktop/output_num_filtered_summary.txt" Script_12_cluster_neighbor_selection.py#Before running, set the following paths cluster_file_path = "/Users/path//Desktop/Cluster_nn.txt" #file containing the accession number of a single cluster folder_path = "/Users/path//Desktop/Coordinates" #folder dataset 6 Script_13_cluster_neighbor_Knum.py#Before running, set the following paths output_file_path = "/Users/path/Desktop/output.csv" #output from script_12 annotation_file_path = "/Users/path/Desktop/annotation.txt" #file dataset 3 Script_14_csv_filtration.py#Before running, set the following path output_file_path = "/Users/path/Desktop/output_with_knumbers.csv" #output from script_13 Script_15_unique_k_numbers_list.py#Before running, set the following path file_path = "/Users/path/Desktop/output_filtered.csv" #output from script_14 Script_16_k_numbers_count.py#Before running, set the following paths file_path = "/Users/path/Desktop/output_filtered.csv" #output from script_14 k_numbers_file = "/Users/path/Desktop/unique_k_numbers.txt" #output from script_15 Script_17_analysis_cluster.py#Before running, set the following paths csv_file = 'k_numbers_file = "/Users/path/Desktop/output_filtered.csv" #output from script_14 k_numbers_file = "/Users/path/Desktop/unique_k_numbers.txt" #output from script_15 #Scripts 16 and 17 are similar. However, Script 16 returns a list of all K-numbers, whereas Script 17 also provides their counts, reflecting the number of times each K-number is repeated within the syntenic region. Script_18_cutoff.py#Before running, set the following paths csv_file = '/Users/path/Desktop/output_filtered.csv' #output from script_14 Script_19_analysis_cluster_filtering.py#Before running, set the following paths csv_file = 'k_number_analysis.csv' #output from script_17 cutoff_file = 'cutoff.txt' #output from script_18 #output: k_number_analysis_filtered.csv. Containing co-occurence value for KASS-annotated genes These scripts can be automated and executed in bulk (for example: Scripts 1 and 2; Scripts 3, 4, and 5; Scripts 8, 9/10, and 11; and Scripts 12 through 18). For this purpose, a Bash (.sh) file can be created and implemented as follows: Filtered_cluster_list.txt#txt file containing the 330 conserved clusters localized in syntetinc region (cluster name and protein ID) Filtered_cluster_name_list.txt#txt file containing the 330 conserved clusters localized in syntetinc region (only cluster name) Batch These scripts can be automated and executed in bulk (for example: Scripts 1 and 2; Scripts 3, 4, and 5; Scripts 8, 9/10, and 11; and Scripts 12 through 18). For this purpose, a Bash (.sh) file can be created and implemented as follows: ______________________________________________________________________ #!/bin/bash set -e # Path to the folder containing the scriptsSCRIPT_DIR="/Users/matteocalcagnile/Desktop/YourScriptsFolder" # Run Script 3echo "▶ Running Script_3_AA_%.py"python3 "$SCRIPT_DIR/Script_3_AA_%.py" # Run Script 4echo "▶ Running Script_4_var.py"python3 "$SCRIPT_DIR/Script_4_var.py" # Run Script 5echo "▶ Running Script_5_plot.py"python3 "$SCRIPT_DIR/Script_5_plot.py" echo "✅ All scripts executed successfully." ______________________________________________________________________ After saving the file (for example, as run_pipeline.sh), make it executable using the chmod +x command, and then run it with ./. Commands: chmod +x run_pipeline.sh ./run_pipeline.sh Other files Script_6_INPUT_all_identifiers.txt#txt file, input of script_6 Filtered_cluster_list.txt#txt file containing the 330 conserved clusters localized in syntetinc region (cluster name and protein ID) Filtered_cluster_name_list.txt#txt file containing the 330 conserved clusters localized in syntetinc region (only cluster name)