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

Abstract

Motivation The output of electrospray ionisation - liquid chromatography mass spectrometry (ESI-LC-MS) is influenced by multiple sources of noise and major contributors can be broadly categorised 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 two-dimensional information is lost because the mass-to-charge ratio and retention time dimensions are not considered jointly. Results This paper presents a novel technique for denoising raw ESI-LC-MS data via two-dimensional 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 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 information is available at Bioinformatics online.

N-Terminal Derivatization of Peptides with 4'-Formylbenzo-18-crown-6-ether for Protein and Species Identification

“…[37] Additionally, the application of the algorithm for chemical and random additive noise elimination (CRANE), led to a simultaneous increase in the number of identifications and the quantitative accuracy. [38] In our approach (illustrated in Figure 2), however, the two aforementioned data sets were superimposed within a tolerance limit of ± 0.005 Da for the mass difference.…”

Opportunities for pharmacoproteomics in biomarker discovery

“…There are a range of DIA-MS approaches including SWATH-MS [25], MS E [27], and diaPASEF [28], among others (reviewed in [29]). The utility of DIA-MS is increasing due to associated developments in software [29] such as tools for processing raw data [30][31][32], building a high-quality spectral reference library [33] and downstream analysis steps such as batch correction, normalisation, and missing value imputation [7,[34][35][36]. These advances have enabled the integration of proteomic and drug response datasets at scale [9,37,38].…”

Proteomic-based stratification of intermediate-risk prostate cancer patients

“…To address this limitation, we have compiled a cohort of 290 patients procured from the Prostate Cancer Outcomes Cohort Study (ProCOC) 30 to generate large-scale proteomic measurement of PCa tissue samples using data-independent acquisition mass spectrometry (DIA-MS). The data have been analysed through purpose-built computational workflows at the Australian Cancer Research Foundation International Centre for the Proteome of Human Cancer (ProCan ® ) in Westmead, Australia 31,32,33,34,35,36,37 . We have identified differentially expressed proteins and pathways involved in PCa development and biochemical recurrence (BCR), including the identification of possible new therapeutic targets.…”