Bump OpenMS to 3.6.0

CHANGELOG.md

@@ -5,6 +5,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## 3.4.0dev
 
+### `Changed`
+
+- Bumped OpenMS and pyOpenMS modules to 3.6.0, renamed `COMET:xcorr` to its PSI-MS term name `Comet:xcorr` in the result TSV added a `USI` column (`--usi_dataset_id`) to the result TSV and fixed the ion annotator ignoring `--remove_precursor_peak false` and samples without PSMs after FDR filtering crashing `--quantify` [#494](https://github.com/nf-core/mhcquant/pull/494)
+
+### `Dependencies`
+
+| Dependency          | Old version | New version |
+| ------------------- | ----------- | ----------- |
+| `OpenMS`            | 3.5.0       | 3.6.0       |
+| `OpenMS-thirdparty` | 3.5.0       | 3.6.0       |
+| `Percolator`        | 3.7.1       | 3.9         |
+| `pyOpenMS`          | 3.4.1       | 3.6.0       |
+
 ## 3.3.0 - Favorite - 29/09/26
 
 ### `Added`

bin/get_ion_annotations.py

@@ -83,8 +83,7 @@ def parse_arguments() -> Tuple[argparse.ArgumentParser, argparse.Namespace]:
     parser.add_argument(
         "-rpp",
         "--remove_precursor_peak",
-        type=bool,
-        default=False,
+        action="store_true",
         help="Do not consider precursor masses in the theoretical spectrum generation",
     )
 
@@ -143,12 +142,10 @@ def flatten(ls: list) -> list:
 
 def __main__():
     parser, args = parse_arguments()
-    protein_ids = []
-    peptide_ids = []
     # Each peptide ID should only contain one hit in the FDR_filtered idXML
-    IdXMLFile().load(args.filtered_idXML, protein_ids, peptide_ids)
+    protein_ids, peptide_ids = IdXMLFile().load(args.filtered_idXML)
     # Get the list of mzML files that have been merged together by IDmerger previously
-    filenames = [filename.decode("utf-8") for filename in protein_ids[0].getMetaValue("spectra_data")]
+    filenames = protein_ids[0].getMetaValue("spectra_data")
     # Define empty lists that collect all the necessary information, which is comprised in DataFrames
     ions = []
     spectra_mz = []
@@ -172,7 +169,7 @@ def __main__():
         MzMLFile().load(file, exp)
         # Create lookup object for easy spectrum reference.
         spectrum_lookup = SpectrumLookup()
-        spectrum_lookup.readSpectra(exp, "scan=(?<SCAN>\\d+)")
+        spectrum_lookup.readSpectra(exp)
         # Iterate over the FDR filtered peptideIDs
         for peptide_id in peptide_ids:
             # Check if the PeptideHit originates from the current mzML file
@@ -184,19 +181,18 @@ def __main__():
             spectrum_mz, spectrum_intensities = spectrum.get_peaks()
             spectra_mz.append(spectrum_mz)
             spectra_intensities.append(spectrum_intensities)
-            sequence = peptide_id.getHits()[0].getSequence()
+            sequence = peptide_id.getHits()[0].getSequence().toString()
             spectra_peptides.append(np.repeat(sequence, len(spectrum_mz)))
             is_matching_ion_peptide = np.repeat(False, len(spectrum_mz))
             spectra_nativeIDs.append(np.repeat(peptide_id.getMetaValue("spectrum_reference"), len(spectrum_mz)))
             # Generate theoretical spectrum of peptide hit
             theo_spectrum = generate_theoretical_spectrum(peptide_id, args)
             # Align both spectra
-            alignment = []
-            spa.getSpectrumAlignment(alignment, theo_spectrum, spectrum)
+            alignment = spa.getSpectrumAlignment(theo_spectrum, spectrum)
             # Obtain ion annotations from theoretical spectrum if there are at least two matching ions
             if len(alignment) > 1:
                 for theo_idx, obs_idx in alignment:
-                    ion_name = theo_spectrum.getStringDataArrays()[0][theo_idx].decode()
+                    ion_name = theo_spectrum.getStringDataArrays()[0][theo_idx]
                     ion_charge = theo_spectrum.getIntegerDataArrays()[0][theo_idx]
                     obs_mz = spectrum[obs_idx].getMZ()
                     obs_int = spectrum[obs_idx].getIntensity()

bin/summarize_results.py

@@ -6,7 +6,7 @@
 import json
 import pandas as pd
 import numpy as np
-from pyopenms import AASequence, TransformationXMLFile, TransformationDescription
+from pyopenms import AASequence, ControlledVocabulary, File, TransformationXMLFile
 from argparse import ArgumentParser
 import re
 from collections import Counter
@@ -94,15 +94,6 @@ def parse_multiTSV(file_path):
             elif line.startswith("#UNASSIGNEDPEPTIDE"):
                 unassigned_peptide_cols = line.strip().split('\t')[1:]
 
-    # Workaround for OpenMS 3.5.0 TextExporter bug (https://github.com/OpenMS/OpenMS/issues/9120):
-    # consensusXML export writes a phantom column in data rows between 'end' and 'FFId_category'
-    # that is missing from the header. Remove it to realign columns.
-    for rows, cols in [(peptide_rows, peptide_cols), (unassigned_peptide_rows, unassigned_peptide_cols)]:
-        if rows and len(rows[0]) > len(cols) and 'end' in cols:
-            extra_idx = cols.index('end') + 1
-            for i, row in enumerate(rows):
-                rows[i] = row[:extra_idx] + row[extra_idx + 1:]
-
     peptide_df = pd.DataFrame(peptide_rows, columns=peptide_cols)
     consensus_df = pd.DataFrame(consensus_rows, columns=consensus_cols)
     # Concatenate CONSENSUS and PEPTIDE columns
@@ -159,6 +150,18 @@ def write_box_stats(values, sample, plot_id, path):
         json.dump({"id": plot_id, "data": {sample: stats}}, f, indent=4)
 
 
+def decode_cv_columns(data):
+    """Rename PSI-MS accession columns (e.g. MS:1002252) to their term names (e.g. Comet:xcorr)"""
+    cv = ControlledVocabulary()
+    cv.loadFromOBO("PSI-MS", File.find("CV/psi-ms.obo", []))
+    data.rename(columns={col: cv.getTerm(col).name for col in data.columns if cv.exists(col)}, inplace=True)
+
+
+def format_usi(data):
+    """Use the original run name (without .mzML) in USIs"""
+    data["USI"] = data["USI"].fillna("").astype(str).str.replace(r"\.mzML(?=:(?:scan|index|nativeId):)", "", regex=True, flags=re.IGNORECASE)
+
+
 def process_file(file, prefix, quantify, keep_cols):
     """Extract all the relevant information and write it to a TSV file for the MultiQC report
 
@@ -177,8 +180,12 @@ def process_file(file, prefix, quantify, keep_cols):
         data.rename(columns={data.columns[0]: data.columns[0].replace('#', '')}, inplace=True)
         n_psms = 0
 
+    decode_cv_columns(data)
+    if "USI" in data.columns:
+        format_usi(data)
+
     # Check if all required columns are present in the DataFrame
-    required_columns = ['sequence', 'accessions', 'mz', 'rt', 'score', 'COMET:xcorr']
+    required_columns = ['sequence', 'accessions', 'mz', 'rt', 'score', 'Comet:xcorr']
     missing_columns = set(required_columns) - set(data.columns)
     if data.shape[0] > 0:  # If the DataFrame is not empty
         if missing_columns:
@@ -247,7 +254,7 @@ def process_file(file, prefix, quantify, keep_cols):
     # ---------------------------------
     # Box plots
     # ---------------------------------
-    write_box_stats(data["COMET:xcorr"], prefix, "scores_plot_xcorr", f"{prefix}_xcorr_scores.json")
+    write_box_stats(data["Comet:xcorr"], prefix, "scores_plot_xcorr", f"{prefix}_xcorr_scores.json")
     if 'intensity_cf' in data.columns:
         with np.errstate(divide='ignore'):
             log_intensity = np.log2(data["intensity_cf"].astype(float))
@@ -299,8 +306,7 @@ def write_aligned_residuals(trafoxml_paths):
     (max reference RT) so boxes are comparable across runs and LC setups.
     """
     for path in trafoxml_paths:
-        td = TransformationDescription()
-        TransformationXMLFile().load(path, td, True)
+        td = TransformationXMLFile().load(path, True)
         points = td.getDataPoints()
         gradient = max((pt.second for pt in points), default=0)
         if gradient <= 0:

conf/modules.config

@@ -276,7 +276,7 @@ process {
 process {
     withName: 'NFCORE_MHCQUANT:MHCQUANT:QUANT:OPENMS_IDSCORESWITCHER' {
         ext.args   = [
-            "-new_score COMET:xcorr",
+            "-new_score MS:1002252",
             "-new_score_orientation higher_better",
             "-old_score q-value"
         ].join(' ').trim()
@@ -469,7 +469,7 @@ process {
 
     withName: 'OPENMS_TEXTEXPORTER' {
         ext.prefix  = { "${meta.id}_exported" }
-        ext.args    = '-id:peptides_only -id:add_hit_metavalues 0 -id:add_metavalues 0'
+        ext.args    = { "-id:peptides_only -id:add_hit_metavalues 0 -id:add_metavalues 0 -id:add_usi -id:usi_dataset_id '${params.usi_dataset_id}'" }
         publishDir  = [
             enabled: false
         ]
@@ -481,6 +481,7 @@ process {
             [
                 "peptidoform",
                 "sequence",
+                "USI",
                 "score", "score_type",
                 "psm",
                 "rt",
@@ -489,7 +490,7 @@ process {
                 "accessions",
                 "aa_before", "aa_after",
                 "start", "end",
-                "COMET:deltaCn", "COMET:deltaLCn", "COMET:lnExpect", "COMET:xcorr",
+                "COMET:deltaCn", "COMET:deltaLCn", "COMET:lnExpect", "Comet:xcorr",
                 "rt_diff_best",
                 "observed_retention_time_best", "predicted_retention_time_best",
                 "spec_pearson",
@@ -527,6 +528,8 @@ process {
 
     withName: 'OPENMS_TEXTEXPORTER_GLOBAL' {
         label = 'process_high_memory'
+        // The global TSV is not post-processed by SUMMARIZE_RESULTS, which corrects the OpenMS USIs
+        ext.args    = '-id:peptides_only -id:add_hit_metavalues 0 -id:add_metavalues 0'
         publishDir  = [
             path: {"${params.outdir}/global_fdr"},
             mode: params.publish_dir_mode,
@@ -550,7 +553,7 @@ process {
         ext.args   = { [
             "--precursor_charge ${meta.prec_charge}",
             "--fragment_mass_tolerance ${meta.fragment_mass_tolerance}",
-            "--remove_precursor_peak ${params.remove_precursor_peak}"
+            params.remove_precursor_peak ? "--remove_precursor_peak" : ""
         ].join(' ').trim() }
         publishDir  = [
             path: {"${params.outdir}/intermediate_results/ion_annotations"},