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

Abstract

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. Results 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. Availability 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). Supplementary information Supplementary data are available at Bioinformatics online.