ABSTRACT
Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. Main advantages include greater reproducibility, sensitivity and dynamic range compared to data-dependent acquisition (DDA). However, data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics a multi-functional, automated high-throughput pipeline implemented in Nextflow that allows to easily process proteomics and peptidomics DIA datasets on diverse compute infrastructures. Central components are well-established tools such as the OpenSwathWorkflow for DIA spectral library search and PyProphet for false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and carry out retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from statistical post-processing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is open-source software and available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.
Competing Interest Statement
The authors have declared no competing interest.
- ABBREVIATIONS
- LC-MS/MS
- liquid chromatography coupled mass spectrometry;
- MS
- mass spectrometry;
- DIA
- data-independent acquisition;
- DDA
- data-dependent acquisition;
- FDR
- false discovery rate;
- XIC
- extracted ion chromatogram;
- HPC
- high-performance computing
