A de novo MS1 feature detector for the Bruker timsTOF Pro.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36449500", "kind": "science-record", "revision": 2, "content_hash": "sha256:c82ac2790275a54ccb46c5d36c44a1c50ec1869b8983332528c86c5d40033b72", "created_at": "2026-11-01T19:00:55Z", "updated_at": "2026-11-01T21:29:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A de novo MS1 feature detector for the Bruker timsTOF Pro.", "doi": "10.1371/journal.pone.0277122", "pmid": "36449500", "authors": ["Daryl Wilding-McBride", "Andrew I Webb"], "journal": "PloS one", "year": 2022, "abstract": "Identification of peptides by analysis of data acquired by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment spectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragment spectra with a peptide database. In DIA, a peptide's fragment spectra are targeted for extraction and matched with observed spectra. Although fragment ion matching is a central aspect in most peptide identification strategies, the precursor ion in the MS1 data reveals important characteristics as well, including charge state, intensity, monoisotopic m/z, and apex in retention time. Most importantly, the precursor's mass is essential in determining the potential chemical modification state of the underlying peptide sequence. In the timsTOF, with its additional dimension of collisional cross-section, the data representing the precursor ion also reveals the peptide's peak in ion mobility. However, the availability of tools to survey precursor ions with a wide range of abundance in timsTOF data across the full mass range is very limited. Here we present a de novo feature detector called three-dimensional intensity descent (3DID). 3DID can detect and extract peptide features down to a configurable intensity level, and finds many more features than several existing tools. 3DID is written in Python and is freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). The dataset used for validation of the algorithm is publicly available (ProteomeXchange identifier PXD030706).", "claims": [{"id": "claim-method", "statement": "To determine the identifiability of a peptide feature solely from MS1 and in the absence of fragment ion information, inspired by MSTracer [2] we constructed a neural network classifier to predict whether an extracted feature is likely to be identified from MS2 spectra.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36449500", "locator": "sections[2] \"2 Results\" paragraph 17", "quote": "To determine the identifiability of a peptide feature solely from MS1 and in the absence of fragment ion information, inspired by MSTracer [2] we constructed a neural network classifier to predict whether an extracted feature is likely to be identified from MS2 spectra."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD030706 and 10.6019/PXD030706.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36449500", "locator": "sections[10] \"Data Availability\" paragraph 2", "quote": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD030706 and 10.6019/PXD030706."}], "state": "complete"}, {"id": "claim-target-result", "statement": "3DID detected 89% of the features detected by Biosaur (13,577), and nearly ten times more (135,218) (Fig 7A).", "kind": "quantitative", "target": {"value": 89.0, "unit": "per cent", "tolerance": null, "source": "3DID detected 89% of the features detected by Biosaur (13,577), and nearly ten times more (135,218) (Fig 7A)."}, "evidence": [{"asset": "source:pmid:36449500", "locator": "sections[3] \"2.1 Comparing feature detection with Biosaur\" paragraph 3", "quote": "3DID detected 89% of the features detected by Biosaur (13,577), and nearly ten times more (135,218) (Fig 7A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "3DID detected 89% of the features detected by Biosaur (13,577), and nearly ten times more (135,218) (Fig 7A).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36449500"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36449500", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0277122", "sha256": "sha256:5a5f107ff7fa192a7b620f5a036f838d16266a279abf71dbcd156d6ef7619eba", "bytes": 28229, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1371/journal.pone.0277122", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9710787", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "neural network classifier", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To determine the identifiability of a peptide feature solely from MS1 and in the absence of fragment ion information, inspired by MSTracer [2] we constructed a neural network classifier to predict whether an extracted feature is likely to be identified from MS2 spectra."}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": null, "expected_value": 89.0, "unit": "per cent", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (neural network classifier), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": ["PXD030706 and 10.6019/PXD030706"], "resolved_accessions": [], "statements": ["The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD030706 and 10.6019/PXD030706."], "basis": "The paper names PXD030706 and 10.6019/PXD030706, but the accession does not resolve in the repository (no reason recorded), so the data cannot be reached from here. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:36449500"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:17:25Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 8526, "completion_tokens": 1480, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36449500", "type": "paper", "label": "pmid:36449500"}, {"id": "source:pmid:36449500", "type": "manuscript", "label": "PMID 36449500 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36449500", "type": "paper", "label": "pmid:36449500"}, {"id": "source:pmid:36449500", "type": "manuscript", "label": "PMID 36449500 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36449500", "type": "extraction", "used": ["source:pmid:36449500"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36449500", "parent_hash": "sha256:c85df216bd14787c9b61fa2320190dcdf4622065cbdc459b04101c3129030378", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "mass_spectrometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["pectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragme", "by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment", "” tool from Bruker (1.4) using the “denoised proteomics complex” settings. Default settings were use"], "order_basis": "paper_text_order:115", "id": "assay:1"}]}