Mercurial > repos > maciek > spamr_vet_tools
changeset 2:d7b099fbb003 draft default tip
Corrected file names and updated tool wrappers for consistency.
| author | maciek |
|---|---|
| date | Tue, 25 Mar 2025 13:35:00 +0000 |
| parents | e57a908b9d3d |
| children | |
| files | spamr_vet_tools_v2/1_qualitty_script_fastp_bracken_v2.py spamr_vet_tools_v2/1_qualitty_script_fastp_bracken_v2.xml spamr_vet_tools_v2/2_quast_get_fasta_v2.py spamr_vet_tools_v2/2_quast_get_fasta_v2.xml spamr_vet_tools_v2/3_MLST_AMRFINDER_STARMAR_v2.py spamr_vet_tools_v2/3_MLST_AMRFINDER_STARMAR_v2.xml spamr_vet_tools_v2/mlst_amrfinder_staramr.py spamr_vet_tools_v2/mlst_amrfinder_staramr.xml spamr_vet_tools_v2/quality_script_fastp_bracken.py spamr_vet_tools_v2/quality_script_fastp_bracken.xml spamr_vet_tools_v2/quast_get_fasta.py spamr_vet_tools_v2/quast_get_fasta.xml |
| diffstat | 12 files changed, 521 insertions(+), 521 deletions(-) [+] |
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--- a/spamr_vet_tools_v2/1_qualitty_script_fastp_bracken_v2.py Tue Feb 25 14:14:39 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,134 +0,0 @@ -import json -import csv -import sys -import os - -def extract_software_data(json_data, software_name): - """ - Extract data for a specific software from the JSON input - - For "bracken", add a "contamination" column where the value is "pass" - if fraction_total_reads > 0.6, otherwise "fail". - - For "fastp", include only specific columns. - """ - # Ensure json_data is a dictionary - if isinstance(json_data, list): - json_data = next((entry for entry in json_data if "analysis_software_name" in entry and entry["analysis_software_name"] == software_name), None) - - if not isinstance(json_data, dict): - print(f"Invalid JSON format for {software_name} extraction.") - return - - results = json_data.get("results", []) - extracted_data = [] - headers = [] # Use list to collect headers to maintain order - output_csv_file = f"{software_name}_output.csv" - - # Define specific columns for "fastp" - fastp_columns = [ - "summary_sequencing", - "summary_before_filtering_total_reads", - "summary_before_filtering_total_bases", - "summary_before_filtering_q20_bases", - "summary_before_filtering_q30_bases", - "summary_before_filtering_q20_rate", - "summary_before_filtering_q30_rate", - "summary_before_filtering_read1_mean_length", - "summary_before_filtering_read2_mean_length", - "summary_before_filtering_gc_content", - "summary_after_filtering_total_reads", - "summary_after_filtering_total_bases", - "summary_after_filtering_q20_bases", - "summary_after_filtering_q30_bases", - "summary_after_filtering_q20_rate", - "summary_after_filtering_q30_rate", - "summary_after_filtering_read1_mean_length", - "summary_after_filtering_read2_mean_length", - "summary_after_filtering_gc_content", - "filtering_result_passed_filter_reads", - "filtering_result_low_quality_reads", - "filtering_result_too_many_N_reads", - "filtering_result_too_short_reads", - "filtering_result_too_long_reads", - "duplication_rate", - "insert_size_peak", - ] - - for entry in results: - if "content" in entry and isinstance(entry["content"], list): - for content_item in entry["content"]: - row_data = {} - if software_name == "fastp": - for key, value in content_item.items(): - if isinstance(value, dict): - for sub_key, sub_value in value.items(): - if isinstance(sub_value, dict): - for sub_sub_key, sub_sub_value in sub_value.items(): - column_name = f"{key}_{sub_key}_{sub_sub_key}" - if column_name in fastp_columns: - row_data[column_name] = sub_sub_value - if column_name not in headers: - headers.append(column_name) - else: - column_name = f"{key}_{sub_key}" - if column_name in fastp_columns: - row_data[column_name] = sub_value - if column_name not in headers: - headers.append(column_name) - else: - if key in fastp_columns: - row_data[key] = value - if key not in headers: - headers.append(key) - elif software_name == "bracken": - for key, value in content_item.items(): - if isinstance(value, dict): - for sub_key, sub_value in value.items(): - column_name = f"{key}_{sub_key}" - row_data[column_name] = sub_value - if column_name not in headers: - headers.append(column_name) - else: - row_data[key] = value - if key not in headers: - headers.append(key) - - # Add contamination column for "bracken" - fraction_total_reads = row_data.get("fraction_total_reads", 0) - row_data["contamination"] = "pass" if float(fraction_total_reads) > 0.6 else "fail" - if "contamination" not in headers: - headers.append("contamination") - - extracted_data.append(row_data) - - if not extracted_data: - print(f"No data extracted for {software_name}") - # Create empty file to prevent Galaxy error - with open(output_csv_file, "w", newline="", encoding="utf-8") as f: - f.write("No data available\n") - return - - with open(output_csv_file, "w", newline="", encoding="utf-8") as f: - writer = csv.DictWriter(f, fieldnames=headers) - writer.writeheader() - writer.writerows(extracted_data) - - print(f"CSV file successfully generated: {output_csv_file}") - -if __name__ == "__main__": - if len(sys.argv) != 2: - print("Usage: python extract_software_data.py input.json") - sys.exit(1) - - input_json_file = sys.argv[1] - - try: - with open(input_json_file, "r", encoding="utf-8") as file: - json_data = json.load(file) - extract_software_data(json_data, "fastp") - extract_software_data(json_data, "bracken") - sys.exit(0) - except Exception as e: - print(f"Error processing file: {e}") - sys.exit(1)
--- a/spamr_vet_tools_v2/1_qualitty_script_fastp_bracken_v2.xml Tue Feb 25 14:14:39 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,43 +0,0 @@ -<tool id="quality_script_fastp_bracken" - name="Quality Control FastP Bracken" - version="0.1.0+galaxy0" - profile="21.05"> - - <description>Quality control using FastP and Bracken</description> - - <requirements> - <requirement type="package" version="3.12">python</requirement> - </requirements> - - <command detect_errors="exit_code"> - <![CDATA[ - python '$__tool_directory__/extract_software_data.py' '$json_input' - ]]> - </command> - - <inputs> - <param name="json_input" type="data" format="json" label="Input JSON Data"/> - </inputs> - - <outputs> - <data name="fastp_output" format="csv" from_work_dir="fastp_output.csv" label="FastP Summary Report on ${on_string}"/> - <data name="bracken_output" format="csv" from_work_dir="bracken_output.csv" label="Bracken Summary Report on ${on_string}"/> - </outputs> - - <help>< - A tool for fast and efficient quality control. -- [Bracken](https://github.com/jenniferlu717/Bracken) - A tool for accurate species abundance estimation. - -For support, please contact the tool maintainers. - ]]></help> - -</tool>
--- a/spamr_vet_tools_v2/2_quast_get_fasta_v2.py Tue Feb 25 14:14:39 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,134 +0,0 @@ -import json -import csv -import sys -import os - -def extract_software_data(json_data, software_name): - """ - Extract QUAST data from JSON and create a CSV with assembly metrics. - For "quast", include specific columns and calculate a Filter_N50 based on "N50". - """ - # Ensure json_data is a dictionary - if isinstance(json_data, list): - json_data = next((entry for entry in json_data if "analysis_software_name" in entry and entry["analysis_software_name"] == software_name), None) - - if not isinstance(json_data, dict): - print(f"Invalid JSON format for {software_name} extraction.") - return - - results = json_data.get("results", []) - extracted_data = [] - headers = [ - "Assembly", - "contigs_(>=_0_bp)", - "contigs_(>=_1000_bp)", - "Total_length_(>=_0_bp)", - "Total_length_(>=_1000_bp)", - "contigs", - "Largest_contig", - "Total_length", - "GC", - "N50", - "Filter_N50", - "N90", - "auN", - "L50", - "L90", - "total_reads", - "left", - "right", - "Mapped", - "Properly_paired", - "Avg._coverage_depth", - "Coverage_>=_1x", - "N's_per_100_kbp" - ] - output_csv_file = f"{software_name}_output.csv" - - for entry in results: - if "content" in entry and isinstance(entry["content"], list): - for content_item in entry["content"]: - n50 = content_item.get("N50", "") - try: - n50_value = float(n50) if n50 else 0 - filter_n50 = "pass" if n50_value > 20000 else "fail" - except ValueError: - filter_n50 = "fail" # If the value is non-numeric, consider it as "fail" - - extracted_data.append({ - "Assembly": content_item.get("Assembly", ""), - "contigs_(>=_0_bp)": content_item.get("contigs_(>=_0_bp)", ""), - "contigs_(>=_1000_bp)": content_item.get("contigs_(>=_1000_bp)", ""), - "Total_length_(>=_0_bp)": content_item.get("Total_length_(>=_0_bp)", ""), - "Total_length_(>=_1000_bp)": content_item.get("Total_length_(>=_1000_bp)", ""), - "contigs": content_item.get("contigs", ""), - "Largest_contig": content_item.get("Largest_contig", ""), - "Total_length": content_item.get("Total_length", ""), - "GC": content_item.get("GC", ""), - "N50": content_item.get("N50", ""), - "Filter_N50": filter_n50, - "N90": content_item.get("N90", ""), - "auN": content_item.get("auN", ""), - "L50": content_item.get("L50", ""), - "L90": content_item.get("L90", ""), - "total_reads": content_item.get("total_reads", ""), - "left": content_item.get("left", ""), - "right": content_item.get("right", ""), - "Mapped": content_item.get("Mapped", ""), - "Properly_paired": content_item.get("Properly_paired", ""), - "Avg._coverage_depth": content_item.get("Avg._coverage_depth", ""), - "Coverage_>=_1x": content_item.get("Coverage_>=_1x", ""), - "N's_per_100_kbp": content_item.get("N's_per_100_kbp", "") - }) - - with open(output_csv_file, "w", newline="", encoding="utf-8") as f: - writer = csv.DictWriter(f, fieldnames=headers) - writer.writeheader() - writer.writerows(extracted_data) - - print(f"CSV file successfully generated: {output_csv_file}") - -def extract_contigs_to_fasta(json_data): - """ - Extract contigs information from "shovill" and save it as a FASTA file. - """ - if isinstance(json_data, list): - json_data = next((entry for entry in json_data if "analysis_software_name" in entry and entry["analysis_software_name"] == "shovill"), None) - - if not isinstance(json_data, dict): - print("Invalid JSON format for shovill extraction.") - return - - results = json_data.get("results", []) - output_fasta_file = "shovill_contigs.fasta" - - with open(output_fasta_file, "w", encoding="utf-8") as f: - for entry in results: - if "content" in entry and isinstance(entry["content"], list): - for content_item in entry["content"]: - name = content_item.get("name", "unknown") - length = content_item.get("length", "unknown") - coverage = content_item.get("coverage", "unknown") - sequence = content_item.get("sequence", "") - - header = f">{name}_{length}_{coverage}" - f.write(f"{header}\n{sequence}\n") - - print(f"FASTA file successfully generated: {output_fasta_file}") - -if __name__ == "__main__": - if len(sys.argv) != 2: - print("Usage: python script.py input.json") - sys.exit(1) - - input_json_file = sys.argv[1] - - try: - with open(input_json_file, "r", encoding="utf-8") as file: - json_data = json.load(file) - extract_software_data(json_data, "quast") - extract_contigs_to_fasta(json_data) - sys.exit(0) - except Exception as e: - print(f"Error processing file: {e}") - sys.exit(1)
--- a/spamr_vet_tools_v2/2_quast_get_fasta_v2.xml Tue Feb 25 14:14:39 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,67 +0,0 @@ -<tool id="quast_get_fasta" - name="QUAST Analysis and FASTA Generator" - version="0.1.0+galaxy0" - profile="21.05"> - - <description>Extracts QUAST metrics and generates FASTA files from JSON input.</description> - - <requirements> - <requirement type="package" version="3.12">python</requirement> - </requirements> - - <command detect_errors="exit_code"> - <![CDATA[ - python '$__tool_directory__/extract_quast_fasta.py' '$json_input' - ]]> - </command> - - <inputs> - <param name="json_input" type="data" format="json" label="Input JSON File" - help="Provide a JSON file containing QUAST and Shovill results."/> - </inputs> - - <outputs> - <data name="csv_output" format="csv" from_work_dir="quast_output.csv" - label="QUAST Summary on ${on_string}"/> - <data name="fasta_output" format="fasta" from_work_dir="shovill_contigs.fasta" - label="Shovill Contigs on ${on_string}"/> - </outputs> - - <tests> - <test> - <param name="json_input" value="example_input.json"/> - <output name="csv_output" file="expected_output.csv" compare="diff"/> - <output name="fasta_output" file="expected_output.fasta" compare="diff"/> - </test> - </tests> - - <help><![CDATA[ -QUAST Analysis and FASTA Generator -================================== - -This tool extracts key statistics from **QUAST** and generates a **FASTA** file containing assembled contigs from **Shovill**. - -Usage Instructions ------------------- -1. Upload or provide a **JSON file** containing **QUAST** and **Shovill** results. -2. The tool will: - - Extract **assembly metrics** from QUAST and save them as a CSV. - - Convert **contigs from Shovill** into a FASTA file. -3. The outputs will be: - - `quast_output.csv` (QUAST summary metrics) - - `shovill_contigs.fasta` (FASTA file with contigs) - -Outputs -------- -- **CSV File:** Contains QUAST summary metrics such as `N50`, `GC content`, `total length`, `L50`, and other key assembly statistics. -- **FASTA File:** Extracts contigs from **Shovill**, formatting them properly for downstream analysis. - -References ----------- -- `QUAST <http://bioinf.spbau.ru/quast>`_ - Quality assessment tool for genome assemblies. -- `Shovill <https://github.com/tseemann/shovill>`_ - A tool for rapid bacterial genome assembly using SPAdes. - -For questions or issues, please contact the tool maintainers. - ]]></help> - -</tool>
--- a/spamr_vet_tools_v2/3_MLST_AMRFINDER_STARMAR_v2.py Tue Feb 25 14:14:39 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,73 +0,0 @@ -import json -import csv -import sys - -def generate_csv_from_json(json_data): - """ - Parse the JSON and generate CSV files based on the analysis_software_name Abricate, AMRfinder plus and STARamr. - Additionally, extract and process the 'mlst_file' content into its own CSV. - """ - for entry in json_data: - analysis_software = entry.get("analysis_software_name", "unknown") - results = entry.get("results", []) - - if results: - csv_file = f"{analysis_software}_output.csv" - extracted_data = [] - headers = [] - - for result in results: - if result.get("name") == "mlst_file": - mlst_file_path = "mlst.csv" - mlst_content = result.get("content", []) - mlst_headers = ["Isolate ID", "Scheme", "Sequence Type", "Locus"] - - # Write the MLST CSV file - if mlst_content: - with open(mlst_file_path, "w", newline="", encoding="utf-8") as f: - writer = csv.DictWriter(f, fieldnames=mlst_headers) - writer.writeheader() - for row in mlst_content: - writer.writerow({ - "Isolate ID": row.get("Isolate ID", ""), - "Scheme": row.get("Scheme", ""), - "Sequence Type": row.get("Sequence Type", ""), - "Locus": "; ".join(row.get("Locus", [])) - }) - - print(f"MLST CSV file successfully generated: {mlst_file_path}") - - if "content" in result and isinstance(result["content"], list): - for content_item in result["content"]: - extracted_data.append(content_item) - for key in content_item.keys(): - if key not in headers: - headers.append(key) # Maintain the original order of the JSON keys - - # Write the CSV file if there is data - if extracted_data: - with open(csv_file, "w", newline="", encoding="utf-8") as f: - writer = csv.DictWriter(f, fieldnames=headers) - writer.writeheader() - for row in extracted_data: - writer.writerow({key: row.get(key, "") for key in headers}) - - print(f"CSV file successfully generated: {csv_file}") - else: - print(f"No content found for {analysis_software}.") - -if __name__ == "__main__": - if len(sys.argv) != 2: - print("Usage: python script.py input.json") - sys.exit(1) - - input_json_file = sys.argv[1] - - try: - with open(input_json_file, "r", encoding="utf-8") as file: - json_data = json.load(file) - generate_csv_from_json(json_data) - sys.exit(0) - except Exception as e: - print(f"Error processing file: {e}") - sys.exit(1)
--- a/spamr_vet_tools_v2/3_MLST_AMRFINDER_STARMAR_v2.xml Tue Feb 25 14:14:39 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,70 +0,0 @@ -<tool id="mlst_amr_staramr" - name="MLST, AMRfinder, and STARamr Analysis" - version="0.1.0+galaxy0" - profile="21.05"> - - <description>Extracts MLST, AMRfinder Plus, and STARamr results from JSON input.</description> - - <requirements> - <requirement type="package" version="3.12">python</requirement> - </requirements> - - <command detect_errors="exit_code"> - <![CDATA[ - python '$__tool_directory__/extract_mlst_amr.py' '$json_input' - ]]> - </command> - - <inputs> - <param name="json_input" type="data" format="json" label="Input JSON File" - help="Provide a JSON file containing MLST, AMRfinder Plus, and STARamr results."/> - </inputs> - - <outputs> - <data name="mlst_csv" format="csv" from_work_dir="mlst.csv" - label="MLST Summary on ${on_string}"/> - <data name="amr_csv" format="csv" from_work_dir="AMRfinderPlus_output.csv" - label="AMRfinder Plus Results on ${on_string}"/> - <data name="staramr_csv" format="csv" from_work_dir="STARamr_output.csv" - label="STARamr Results on ${on_string}"/> - </outputs> - - <tests> - <test> - <param name="json_input" value="example_input.json"/> - <output name="mlst_csv" file="expected_mlst.csv" compare="diff"/> - <output name="amr_csv" file="expected_amr.csv" compare="diff"/> - <output name="staramr_csv" file="expected_staramr.csv" compare="diff"/> - </test> - </tests> - - <help><![CDATA[ -MLST, AMRfinder, and STARamr Analysis -===================================== - -This tool extracts MLST, AMRfinder Plus, and STARamr results from JSON input and converts them into CSV format. - -Usage Instructions ------------------- -1. Provide a **JSON file** containing **MLST, AMRfinder Plus, and STARamr** results. -2. The tool will process the data and generate: - - `mlst.csv`: MLST typing results. - - `AMRfinderPlus_output.csv`: Results from **AMRfinder Plus**. - - `STARamr_output.csv`: Results from **STARamr**. - -Outputs -------- -- **MLST CSV File:** Contains MLST typing information, including sequence type and scheme. -- **AMRfinder Plus CSV File:** Lists detected antimicrobial resistance genes. -- **STARamr CSV File:** Includes resistance profiles and sequence typing. - -References ----------- -- `MLST <https://pubmlst.org/>`_ - Multi-locus sequence typing database. -- `AMRfinder Plus <https://www.ncbi.nlm.nih.gov/pathogens/antimicrobial-resistance/AMRFinder/>`_ - Antimicrobial resistance gene detection. -- `STARamr <https://github.com/phac-nml/staramr>`_ - Salmonella sequence typing and resistance analysis. - -For questions or issues, please contact the tool maintainers. - ]]></help> - -</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/spamr_vet_tools_v2/mlst_amrfinder_staramr.py Tue Mar 25 13:35:00 2025 +0000 @@ -0,0 +1,73 @@ +import json +import csv +import sys + +def generate_csv_from_json(json_data): + """ + Parse the JSON and generate CSV files based on the analysis_software_name Abricate, AMRfinder plus and STARamr. + Additionally, extract and process the 'mlst_file' content into its own CSV. + """ + for entry in json_data: + analysis_software = entry.get("analysis_software_name", "unknown") + results = entry.get("results", []) + + if results: + csv_file = f"{analysis_software}_output.csv" + extracted_data = [] + headers = [] + + for result in results: + if result.get("name") == "mlst_file": + mlst_file_path = "mlst.csv" + mlst_content = result.get("content", []) + mlst_headers = ["Isolate ID", "Scheme", "Sequence Type", "Locus"] + + # Write the MLST CSV file + if mlst_content: + with open(mlst_file_path, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=mlst_headers) + writer.writeheader() + for row in mlst_content: + writer.writerow({ + "Isolate ID": row.get("Isolate ID", ""), + "Scheme": row.get("Scheme", ""), + "Sequence Type": row.get("Sequence Type", ""), + "Locus": "; ".join(row.get("Locus", [])) + }) + + print(f"MLST CSV file successfully generated: {mlst_file_path}") + + if "content" in result and isinstance(result["content"], list): + for content_item in result["content"]: + extracted_data.append(content_item) + for key in content_item.keys(): + if key not in headers: + headers.append(key) # Maintain the original order of the JSON keys + + # Write the CSV file if there is data + if extracted_data: + with open(csv_file, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=headers) + writer.writeheader() + for row in extracted_data: + writer.writerow({key: row.get(key, "") for key in headers}) + + print(f"CSV file successfully generated: {csv_file}") + else: + print(f"No content found for {analysis_software}.") + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python script.py input.json") + sys.exit(1) + + input_json_file = sys.argv[1] + + try: + with open(input_json_file, "r", encoding="utf-8") as file: + json_data = json.load(file) + generate_csv_from_json(json_data) + sys.exit(0) + except Exception as e: + print(f"Error processing file: {e}") + sys.exit(1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/spamr_vet_tools_v2/mlst_amrfinder_staramr.xml Tue Mar 25 13:35:00 2025 +0000 @@ -0,0 +1,70 @@ +<tool id="mlst_amr_staramr" + name="MLST, AMRfinder, and STARamr Analysis" + version="0.1.0+galaxy0" + profile="21.05"> + + <description>Extracts MLST, AMRfinder Plus, and STARamr results from JSON input.</description> + + <requirements> + <requirement type="package" version="3.12">python</requirement> + </requirements> + + <command detect_errors="exit_code"> + <![CDATA[ + python '$__tool_directory__/mlst_amrfinder_staramr.py' '$json_input' + ]]> + </command> + + <inputs> + <param name="json_input" type="data" format="json" label="Input JSON File" + help="Provide a JSON file containing MLST, AMRfinder Plus, and STARamr results."/> + </inputs> + + <outputs> + <data name="mlst_csv" format="csv" from_work_dir="mlst.csv" + label="MLST Summary on ${on_string}"/> + <data name="amr_csv" format="csv" from_work_dir="AMRfinderPlus_output.csv" + label="AMRfinder Plus Results on ${on_string}"/> + <data name="staramr_csv" format="csv" from_work_dir="STARamr_output.csv" + label="STARamr Results on ${on_string}"/> + </outputs> + + <tests> + <test> + <param name="json_input" value="example_input.json"/> + <output name="mlst_csv" file="expected_mlst.csv" compare="diff"/> + <output name="amr_csv" file="expected_amr.csv" compare="diff"/> + <output name="staramr_csv" file="expected_staramr.csv" compare="diff"/> + </test> + </tests> + + <help><![CDATA[ +MLST, AMRfinder, and STARamr Analysis +===================================== + +This tool extracts MLST, AMRfinder Plus, and STARamr results from JSON input and converts them into CSV format. + +Usage Instructions +------------------ +1. Provide a **JSON file** containing **MLST, AMRfinder Plus, and STARamr** results. +2. The tool will process the data and generate: + - `mlst.csv`: MLST typing results. + - `AMRfinderPlus_output.csv`: Results from **AMRfinder Plus**. + - `STARamr_output.csv`: Results from **STARamr**. + +Outputs +------- +- **MLST CSV File:** Contains MLST typing information, including sequence type and scheme. +- **AMRfinder Plus CSV File:** Lists detected antimicrobial resistance genes. +- **STARamr CSV File:** Includes resistance profiles and sequence typing. + +References +---------- +- `MLST <https://pubmlst.org/>`_ - Multi-locus sequence typing database. +- `AMRfinder Plus <https://www.ncbi.nlm.nih.gov/pathogens/antimicrobial-resistance/AMRFinder/>`_ - Antimicrobial resistance gene detection. +- `STARamr <https://github.com/phac-nml/staramr>`_ - Salmonella sequence typing and resistance analysis. + +For questions or issues, please contact the tool maintainers. + ]]></help> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/spamr_vet_tools_v2/quality_script_fastp_bracken.py Tue Mar 25 13:35:00 2025 +0000 @@ -0,0 +1,134 @@ +import json +import csv +import sys +import os + +def extract_software_data(json_data, software_name): + """ + Extract data for a specific software from the JSON input + + For "bracken", add a "contamination" column where the value is "pass" + if fraction_total_reads > 0.6, otherwise "fail". + + For "fastp", include only specific columns. + """ + # Ensure json_data is a dictionary + if isinstance(json_data, list): + json_data = next((entry for entry in json_data if "analysis_software_name" in entry and entry["analysis_software_name"] == software_name), None) + + if not isinstance(json_data, dict): + print(f"Invalid JSON format for {software_name} extraction.") + return + + results = json_data.get("results", []) + extracted_data = [] + headers = [] # Use list to collect headers to maintain order + output_csv_file = f"{software_name}_output.csv" + + # Define specific columns for "fastp" + fastp_columns = [ + "summary_sequencing", + "summary_before_filtering_total_reads", + "summary_before_filtering_total_bases", + "summary_before_filtering_q20_bases", + "summary_before_filtering_q30_bases", + "summary_before_filtering_q20_rate", + "summary_before_filtering_q30_rate", + "summary_before_filtering_read1_mean_length", + "summary_before_filtering_read2_mean_length", + "summary_before_filtering_gc_content", + "summary_after_filtering_total_reads", + "summary_after_filtering_total_bases", + "summary_after_filtering_q20_bases", + "summary_after_filtering_q30_bases", + "summary_after_filtering_q20_rate", + "summary_after_filtering_q30_rate", + "summary_after_filtering_read1_mean_length", + "summary_after_filtering_read2_mean_length", + "summary_after_filtering_gc_content", + "filtering_result_passed_filter_reads", + "filtering_result_low_quality_reads", + "filtering_result_too_many_N_reads", + "filtering_result_too_short_reads", + "filtering_result_too_long_reads", + "duplication_rate", + "insert_size_peak", + ] + + for entry in results: + if "content" in entry and isinstance(entry["content"], list): + for content_item in entry["content"]: + row_data = {} + if software_name == "fastp": + for key, value in content_item.items(): + if isinstance(value, dict): + for sub_key, sub_value in value.items(): + if isinstance(sub_value, dict): + for sub_sub_key, sub_sub_value in sub_value.items(): + column_name = f"{key}_{sub_key}_{sub_sub_key}" + if column_name in fastp_columns: + row_data[column_name] = sub_sub_value + if column_name not in headers: + headers.append(column_name) + else: + column_name = f"{key}_{sub_key}" + if column_name in fastp_columns: + row_data[column_name] = sub_value + if column_name not in headers: + headers.append(column_name) + else: + if key in fastp_columns: + row_data[key] = value + if key not in headers: + headers.append(key) + elif software_name == "bracken": + for key, value in content_item.items(): + if isinstance(value, dict): + for sub_key, sub_value in value.items(): + column_name = f"{key}_{sub_key}" + row_data[column_name] = sub_value + if column_name not in headers: + headers.append(column_name) + else: + row_data[key] = value + if key not in headers: + headers.append(key) + + # Add contamination column for "bracken" + fraction_total_reads = row_data.get("fraction_total_reads", 0) + row_data["contamination"] = "pass" if float(fraction_total_reads) > 0.6 else "fail" + if "contamination" not in headers: + headers.append("contamination") + + extracted_data.append(row_data) + + if not extracted_data: + print(f"No data extracted for {software_name}") + # Create empty file to prevent Galaxy error + with open(output_csv_file, "w", newline="", encoding="utf-8") as f: + f.write("No data available\n") + return + + with open(output_csv_file, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=headers) + writer.writeheader() + writer.writerows(extracted_data) + + print(f"CSV file successfully generated: {output_csv_file}") + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python extract_software_data.py input.json") + sys.exit(1) + + input_json_file = sys.argv[1] + + try: + with open(input_json_file, "r", encoding="utf-8") as file: + json_data = json.load(file) + extract_software_data(json_data, "fastp") + extract_software_data(json_data, "bracken") + sys.exit(0) + except Exception as e: + print(f"Error processing file: {e}") + sys.exit(1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/spamr_vet_tools_v2/quality_script_fastp_bracken.xml Tue Mar 25 13:35:00 2025 +0000 @@ -0,0 +1,43 @@ +<tool id="quality_script_fastp_bracken" + name="Quality Control FastP Bracken" + version="0.1.0+galaxy0" + profile="21.05"> + + <description>Quality control using FastP and Bracken</description> + + <requirements> + <requirement type="package" version="3.12">python</requirement> + </requirements> + + <command detect_errors="exit_code"> + <![CDATA[ + python '$__tool_directory__/extract_software_data.py' '$json_input' + ]]> + </command> + + <inputs> + <param name="json_input" type="data" format="json" label="Input JSON Data"/> + </inputs> + + <outputs> + <data name="fastp_output" format="csv" from_work_dir="fastp_output.csv" label="FastP Summary Report on ${on_string}"/> + <data name="bracken_output" format="csv" from_work_dir="bracken_output.csv" label="Bracken Summary Report on ${on_string}"/> + </outputs> + + <help>< - A tool for fast and efficient quality control. +- [Bracken](https://github.com/jenniferlu717/Bracken) - A tool for accurate species abundance estimation. + +For support, please contact the tool maintainers. + ]]></help> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/spamr_vet_tools_v2/quast_get_fasta.py Tue Mar 25 13:35:00 2025 +0000 @@ -0,0 +1,134 @@ +import json +import csv +import sys +import os + +def extract_software_data(json_data, software_name): + """ + Extract QUAST data from JSON and create a CSV with assembly metrics. + For "quast", include specific columns and calculate a Filter_N50 based on "N50". + """ + # Ensure json_data is a dictionary + if isinstance(json_data, list): + json_data = next((entry for entry in json_data if "analysis_software_name" in entry and entry["analysis_software_name"] == software_name), None) + + if not isinstance(json_data, dict): + print(f"Invalid JSON format for {software_name} extraction.") + return + + results = json_data.get("results", []) + extracted_data = [] + headers = [ + "Assembly", + "contigs_(>=_0_bp)", + "contigs_(>=_1000_bp)", + "Total_length_(>=_0_bp)", + "Total_length_(>=_1000_bp)", + "contigs", + "Largest_contig", + "Total_length", + "GC", + "N50", + "Filter_N50", + "N90", + "auN", + "L50", + "L90", + "total_reads", + "left", + "right", + "Mapped", + "Properly_paired", + "Avg._coverage_depth", + "Coverage_>=_1x", + "N's_per_100_kbp" + ] + output_csv_file = f"{software_name}_output.csv" + + for entry in results: + if "content" in entry and isinstance(entry["content"], list): + for content_item in entry["content"]: + n50 = content_item.get("N50", "") + try: + n50_value = float(n50) if n50 else 0 + filter_n50 = "pass" if n50_value > 20000 else "fail" + except ValueError: + filter_n50 = "fail" # If the value is non-numeric, consider it as "fail" + + extracted_data.append({ + "Assembly": content_item.get("Assembly", ""), + "contigs_(>=_0_bp)": content_item.get("contigs_(>=_0_bp)", ""), + "contigs_(>=_1000_bp)": content_item.get("contigs_(>=_1000_bp)", ""), + "Total_length_(>=_0_bp)": content_item.get("Total_length_(>=_0_bp)", ""), + "Total_length_(>=_1000_bp)": content_item.get("Total_length_(>=_1000_bp)", ""), + "contigs": content_item.get("contigs", ""), + "Largest_contig": content_item.get("Largest_contig", ""), + "Total_length": content_item.get("Total_length", ""), + "GC": content_item.get("GC", ""), + "N50": content_item.get("N50", ""), + "Filter_N50": filter_n50, + "N90": content_item.get("N90", ""), + "auN": content_item.get("auN", ""), + "L50": content_item.get("L50", ""), + "L90": content_item.get("L90", ""), + "total_reads": content_item.get("total_reads", ""), + "left": content_item.get("left", ""), + "right": content_item.get("right", ""), + "Mapped": content_item.get("Mapped", ""), + "Properly_paired": content_item.get("Properly_paired", ""), + "Avg._coverage_depth": content_item.get("Avg._coverage_depth", ""), + "Coverage_>=_1x": content_item.get("Coverage_>=_1x", ""), + "N's_per_100_kbp": content_item.get("N's_per_100_kbp", "") + }) + + with open(output_csv_file, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=headers) + writer.writeheader() + writer.writerows(extracted_data) + + print(f"CSV file successfully generated: {output_csv_file}") + +def extract_contigs_to_fasta(json_data): + """ + Extract contigs information from "shovill" and save it as a FASTA file. + """ + if isinstance(json_data, list): + json_data = next((entry for entry in json_data if "analysis_software_name" in entry and entry["analysis_software_name"] == "shovill"), None) + + if not isinstance(json_data, dict): + print("Invalid JSON format for shovill extraction.") + return + + results = json_data.get("results", []) + output_fasta_file = "shovill_contigs.fasta" + + with open(output_fasta_file, "w", encoding="utf-8") as f: + for entry in results: + if "content" in entry and isinstance(entry["content"], list): + for content_item in entry["content"]: + name = content_item.get("name", "unknown") + length = content_item.get("length", "unknown") + coverage = content_item.get("coverage", "unknown") + sequence = content_item.get("sequence", "") + + header = f">{name}_{length}_{coverage}" + f.write(f"{header}\n{sequence}\n") + + print(f"FASTA file successfully generated: {output_fasta_file}") + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python script.py input.json") + sys.exit(1) + + input_json_file = sys.argv[1] + + try: + with open(input_json_file, "r", encoding="utf-8") as file: + json_data = json.load(file) + extract_software_data(json_data, "quast") + extract_contigs_to_fasta(json_data) + sys.exit(0) + except Exception as e: + print(f"Error processing file: {e}") + sys.exit(1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/spamr_vet_tools_v2/quast_get_fasta.xml Tue Mar 25 13:35:00 2025 +0000 @@ -0,0 +1,67 @@ +<tool id="quast_get_fasta" + name="QUAST Analysis and FASTA Generator" + version="0.1.0+galaxy0" + profile="21.05"> + + <description>Extracts QUAST metrics and generates FASTA files from JSON input.</description> + + <requirements> + <requirement type="package" version="3.12">python</requirement> + </requirements> + + <command detect_errors="exit_code"> + <![CDATA[ + python '$__tool_directory__/quast_get_fasta.py' '$json_input' + ]]> + </command> + + <inputs> + <param name="json_input" type="data" format="json" label="Input JSON File" + help="Provide a JSON file containing QUAST and Shovill results."/> + </inputs> + + <outputs> + <data name="csv_output" format="csv" from_work_dir="quast_output.csv" + label="QUAST Summary on ${on_string}"/> + <data name="fasta_output" format="fasta" from_work_dir="shovill_contigs.fasta" + label="Shovill Contigs on ${on_string}"/> + </outputs> + + <tests> + <test> + <param name="json_input" value="example_input.json"/> + <output name="csv_output" file="expected_output.csv" compare="diff"/> + <output name="fasta_output" file="expected_output.fasta" compare="diff"/> + </test> + </tests> + + <help><![CDATA[ +QUAST Analysis and FASTA Generator +================================== + +This tool extracts key statistics from **QUAST** and generates a **FASTA** file containing assembled contigs from **Shovill**. + +Usage Instructions +------------------ +1. Upload or provide a **JSON file** containing **QUAST** and **Shovill** results. +2. The tool will: + - Extract **assembly metrics** from QUAST and save them as a CSV. + - Convert **contigs from Shovill** into a FASTA file. +3. The outputs will be: + - `quast_output.csv` (QUAST summary metrics) + - `shovill_contigs.fasta` (FASTA file with contigs) + +Outputs +------- +- **CSV File:** Contains QUAST summary metrics such as `N50`, `GC content`, `total length`, `L50`, and other key assembly statistics. +- **FASTA File:** Extracts contigs from **Shovill**, formatting them properly for downstream analysis. + +References +---------- +- `QUAST <http://bioinf.spbau.ru/quast>`_ - Quality assessment tool for genome assemblies. +- `Shovill <https://github.com/tseemann/shovill>`_ - A tool for rapid bacterial genome assembly using SPAdes. + +For questions or issues, please contact the tool maintainers. + ]]></help> + +</tool>