Improved identification and quantification of peptides in mass spectrometry data via chemical and random additive noise elimination (CRANE).

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34323970", "kind": "science-record", "revision": 3, "content_hash": "sha256:bfc2b95c373cf758101b9597feda41cb4f77f91ead12aaa12756edbe063f51e8", "created_at": "2026-11-01T15:41:45Z", "updated_at": "2026-11-01T21:25:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Improved identification and quantification of peptides in mass spectrometry data via chemical and random additive noise elimination (CRANE).", "doi": "10.1093/bioinformatics/btab563", "pmid": "34323970", "authors": ["Akila J Seneviratne", "Sean Peters", "David Clarke", "Michael Dausmann", "Michael Hecker", "Brett Tully", "Peter G Hains", "Qing Zhong"], "journal": "Bioinformatics (Oxford, England)", "year": 2021, "abstract": "MOTIVATION: The output of electrospray ionization-liquid chromatography mass spectrometry (ESI-LC-MS) is influenced by multiple sources of noise and major contributors can be broadly categorized as baseline, random and chemical noise. Noise has a negative impact on the identification and quantification of peptides, which influences the reliability and reproducibility of MS-based proteomics data. Most attempts at denoising have been made on either spectra or chromatograms independently, thus, important 2D information is lost because the mass-to-charge ratio and retention time dimensions are not considered jointly.\nRESULTS: This article presents a novel technique for denoising raw ESI-LC-MS data via 2D undecimated wavelet transform, which is applied to proteomics data acquired by data-independent acquisition MS (DIA-MS). We demonstrate that denoising DIA-MS data results in the improvement of peptide identification and quantification in complex biological samples.\nAVAILABILITY AND IMPLEMENTATION: The software is available on Github (https://github.com/CMRI-ProCan/CRANE). The datasets were obtained from ProteomeXchange (Identifiers-PXD002952 and PXD008651). Preliminary data and intermediate files are available via ProteomeXchange (Identifiers-PXD020529 and PXD025103).\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "The Levene’s statistic is calculated to test the null hypothesis that the variance of the abundance ratio distributions of RAW_OSW1, CARNE_OSW0 and CRANE_OSW1 is that same as that of RAW_OSW0 for each species (Supplementary Tables S10–S13).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34323970", "locator": "sections[16] \"Quantification performance\" paragraph 4", "quote": "The Levene’s statistic is calculated to test the null hypothesis that the variance of the abundance ratio distributions of RAW_OSW1, CARNE_OSW0 and CRANE_OSW1 is that same as that of RAW_OSW0 for each species (Supplementary Tables S10–S13)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets were obtained from ProteomeXchange (Identifiers—PXD002952 and PXD008651). 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