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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.

SUBMITTER: Seneviratne AJ

PROVIDER: S-EPMC8711017 | biostudies-literature |

REPOSITORIES: biostudies-literature

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