I tried to use the chatbot to create a new workflow and it created a malformed json:
{
"format_version": "1.0",
"steps": {
"0": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/thermorawfileparser/thermorawfileparser/1.4.0",
"tool_state": {
"input_file": {"src": "hda", "id": "__raw_input__"},
"output_format": "mzML",
"centroid": "true"
},
"outputs": {
"output_file": {"src": "hda", "id": "__mzml_output__"}
},
"annotation": "Convert Thermo *.raw* to centroided mzML"
},
"1": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/msqc/msqc/1.0.0",
"tool_state": {
"input_file": {"src": "hda", "id": "__mzml_output__"},
"report_type": "HTML"
},
"inputs": {
"input_file": {"src": "output", "id": "0", "output_name": "output_file"}
},
"outputs": {
"report": {"src": "hda", "id": "__qc_report__"}
},
"annotation": "Basic QC of converted mzML"
},
"2": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/msfragger/msfragger/3.5.0",
"tool_state": {
"input_file": {"src": "hda", "id": "__mzml_output__"},
"fasta_file": {"src": "hda", "id": "__fasta_input__"},
"enzyme": "Trypsin",
"missed_cleavages": "2",
"fixed_mods": ["Carbamidomethyl (C)", "TMT6plex (K)", "TMT6plex (N-term)"],
"variable_mods": ["Oxidation (M)", "Acetyl (Protein N-term)"],
"precursor_mass_tolerance": "10ppm",
"fragment_mass_tolerance": "0.02Da",
"tmt_label": "__tmt_plex__",
"output_format": "pepXML"
},
"inputs": {
"input_file": {"src": "output", "id": "0", "output_name": "output_file"},
"fasta_file": {"src": "hda", "id": "__fasta_input__"}
},
"outputs": {
"pepXML": {"src": "hda", "id": "__pepXML_output__"}
},
"annotation": "MSFragger search (TMT‑aware)"
},
"3": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/peptide_shaker/peptide_shaker/2.2.0",
"tool_state": {
"input_file": {"src": "hda", "id": "__pepXML_output__"},
"fasta_file": {"src": "hda", "id": "__fasta_input__"},
"decoy_tag": "rev_",
"fdr_cutoff": "0.01",
"minimum_peptides_per_protein": "2"
},
"inputs": {
"input_file": {"src": "output", "id": "2", "output_name": "pepXML"},
"fasta_file": {"src": "hda", "id": "__fasta_input__"}
},
"outputs": {
"filtered_peptide_report": {"src": "hda", "id": "__filtered_peptides__"},
"protein_report": {"src": "hda", "id": "__protein_report__"}
},
"annotation": "PeptideShaker – FDR filtering and protein inference"
},
"4": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/msstats_tmt/msstats_tmt/2.0.0",
"tool_state": {
"protein_report": {"src": "hda", "id": "__protein_report__"},
"tmt_correction_matrix": {"src": "hda", "id": "__tmt_correction_matrix__"},
"reference_channel": "__reference_channel__",
"normalization_method": "median",
"summarization_method": "sum",
"output_format": "TSV"
},
"inputs": {
"protein_report": {"src": "output", "id": "3", "output_name": "protein_report"},
"tmt_correction_matrix": {"src": "hda", "id": "__tmt_correction_matrix__"}
},
"outputs": {
"quant_table": {"src": "hda", "id": "__quant_table__"},
"summary_report": {"src": "hda", "id": "__msstats_summary__"}
},
"annotation": "MSstatsTMT – reporter ion extraction, impurity correction, normalization, protein‑level summarization"
},
"5": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/limma/limma_voom/1.0.0",
"tool_state": {
"input_table": {"src": "hda", "id": "__quant_table__"},
"design_matrix": {"src": "hda", "id": "__design_matrix__"},
"contrast_matrix": {"src": "hda", "id": "__contrast_matrix__"},
"adjust_method": "BH",
"fdr_cutoff": "0.05"
},
"inputs": {
"input_table": {"src": "output", "id": "4", "output_name": "quant_table"},
"design_matrix": {"src": "hda", "id": "__design_matrix__"},
"contrast_matrix": {"src": "hda", "id": "__contrast_matrix__"}
},
"outputs": {
"differential_results": {"src": "hda", "id": "__diff_results__"},
"volcano_plot": {"src": "hda", "id": "__volcano_plot__"},
"heatmap": {"src": "hda", "id": "__heatmap__"}
},
"annotation": "limma‑voom – statistical testing, volcano plot & heatmap"
}
},
"inputs": {
"__raw_input__": {
"label": "Raw file(s) (Thermo *.raw*)",
"type": "data"
},
"__fasta_input__": {
"label": "Protein FASTA (with decoys if desired)",
"type": "data"
},
"__tmt_correction_matrix__": {
"label": "TMT correction matrix (CSV from manufacturer)",
"type": "data"
},
"__tmt_plex__": {
"label": "TMT plex (e.g., 10plex, 11plex, 16plex)",
"type": "select",
"options": ["10plex", "11plex", "16plex"]
},
"__reference_channel__": {
"label": "Reference channel (e.g., 126, 127N, 128C)",
"type": "text"
},
"__design_matrix__": {
"label": "Design matrix (CSV, samples × conditions)",
"type": "data"
},
"__contrast_matrix__": {
"label": "Contrast matrix (CSV, comparisons)",
"type": "data"
}
},
"annotations": {
"name": "TMT‑DDA Proteomics Pipeline",
"tags": ["proteomics", "TMT", "DDA", "quantification", "MSstatsTMT", "limma"],
"description": "End‑to‑end workflow for TMT‑labelled DDA proteomics: raw → mzML conversion → QC → MSFragger search → PeptideShaker filtering → MSstatsTMT quantification → limma‑
Auto (Router) gpt-oss-120b-llmlb 6852 tokens
Query
Can you create the workflow for me?
Auto (Router)
Below is a ready‑to‑import Galaxy workflow definition (in the standard JSON “.ga” format) that implements the end‑to‑end TMT‑DDA pipeline described earlier.
You can copy the JSON into a text file, name it something like tmt_dda_workflow.ga, and then Import it into any Galaxy instance (via Workflows → Import → Choose File).
After importing, you’ll only need to set a few “user‑input” parameters (e.g., the FASTA file, TMT plex, and correction matrix) before running the workflow on your own data.
1️⃣ Full Workflow JSON
{
"format_version": "1.0",
"steps": {
"0": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/thermorawfileparser/thermorawfileparser/1.4.0",
"tool_state": {
"input_file": {"src": "hda", "id": "__raw_input__"},
"output_format": "mzML",
"centroid": "true"
},
"outputs": {
"output_file": {"src": "hda", "id": "__mzml_output__"}
},
"annotation": "Convert Thermo *.raw* to centroided mzML"
},
"1": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/msqc/msqc/1.0.0",
"tool_state": {
"input_file": {"src": "hda", "id": "__mzml_output__"},
"report_type": "HTML"
},
"inputs": {
"input_file": {"src": "output", "id": "0", "output_name": "output_file"}
},
"outputs": {
"report": {"src": "hda", "id": "__qc_report__"}
},
"annotation": "Basic QC of converted mzML"
},
"2": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/msfragger/msfragger/3.5.0",
"tool_state": {
"input_file": {"src": "hda", "id": "__mzml_output__"},
"fasta_file": {"src": "hda", "id": "__fasta_input__"},
"enzyme": "Trypsin",
"missed_cleavages": "2",
"fixed_mods": ["Carbamidomethyl (C)", "TMT6plex (K)", "TMT6plex (N-term)"],
"variable_mods": ["Oxidation (M)", "Acetyl (Protein N-term)"],
"precursor_mass_tolerance": "10ppm",
"fragment_mass_tolerance": "0.02Da",
"tmt_label": "__tmt_plex__", // user input, e.g., 10plex, 11plex, 16plex
"output_format": "pepXML"
},
"inputs": {
"input_file": {"src": "output", "id": "0", "output_name": "output_file"},
"fasta_file": {"src": "hda", "id": "__fasta_input__"}
},
"outputs": {
"pepXML": {"src": "hda", "id": "__pepXML_output__"}
},
"annotation": "MSFragger search (TMT‑aware)"
},
"3": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/peptide_shaker/peptide_shaker/2.2.0",
"tool_state": {
"input_file": {"src": "hda", "id": "__pepXML_output__"},
"fasta_file": {"src": "hda", "id": "__fasta_input__"},
"decoy_tag": "rev_",
"fdr_cutoff": "0.01",
"minimum_peptides_per_protein": "2"
},
"inputs": {
"input_file": {"src": "output", "id": "2", "output_name": "pepXML"},
"fasta_file": {"src": "hda", "id": "__fasta_input__"}
},
"outputs": {
"filtered_peptide_report": {"src": "hda", "id": "__filtered_peptides__"},
"protein_report": {"src": "hda", "id": "__protein_report__"}
},
"annotation": "PeptideShaker – FDR filtering and protein inference"
},
"4": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/msstats_tmt/msstats_tmt/2.0.0",
"tool_state": {
"protein_report": {"src": "hda", "id": "__protein_report__"},
"tmt_correction_matrix": {"src": "hda", "id": "__tmt_correction_matrix__"},
"reference_channel": "__reference_channel__", // user input (e.g., 126)
"normalization_method": "median",
"summarization_method": "sum",
"output_format": "TSV"
},
"inputs": {
"protein_report": {"src": "output", "id": "3", "output_name": "protein_report"},
"tmt_correction_matrix": {"src": "hda", "id": "__tmt_correction_matrix__"}
},
"outputs": {
"quant_table": {"src": "hda", "id": "__quant_table__"},
"summary_report": {"src": "hda", "id": "__msstats_summary__"}
},
"annotation": "MSstatsTMT – reporter ion extraction, impurity correction, normalization, protein‑level summarization"
},
"5": {
"type": "tool",
"tool_id": "toolshed.g2.bx.psu.edu/repos/iuc/limma/limma_voom/1.0.0",
"tool_state": {
"input_table": {"src": "hda", "id": "__quant_table__"},
"design_matrix": "__design_matrix__", // user‑provided CSV (samples vs. conditions)
"contrast_matrix": "__contrast_matrix__", // user‑provided CSV (comparisons)
"adjust_method": "BH",
"fdr_cutoff": "0.05"
},
"inputs": {
"input_table": {"src": "output", "id": "4", "output_name": "quant_table"}
},
"outputs": {
"differential_results": {"src": "hda", "id": "__diff_results__"},
"volcano_plot": {"src": "hda", "id": "__volcano_plot__"},
"heatmap": {"src": "hda", "id": "__heatmap__"}
},
"annotation": "limma‑voom – statistical testing, volcano plot & heatmap"
}
},
"inputs": {
"__raw_input__": {
"label": "Raw file(s) (Thermo *.raw*)",
"type": "data"
},
"__fasta_input__": {
"label": "Protein FASTA (with decoys if desired)",
"type": "data"
},
"__tmt_correction_matrix__": {
"label": "TMT correction matrix (CSV from manufacturer)",
"type": "data"
},
"__tmt_plex__": {
"label": "TMT plex (e.g., 10plex, 11plex, 16plex)",
"type": "select",
"options": ["10plex", "11plex", "16plex"]
},
"__reference_channel__": {
"
Even when removing the cutoff section I was not able to import the generate the workflow.
Maybe it would be good to detect this automatically and reprompt in this case? Also a download button would be much appreciated.
That being said I am unsure if that is the intended purpose of the chatbot.
