A Case Study and Methodology for OpenSWATH Parameter Optimization Using the ProCan90 Data Set and 45 810 Computational Analysis Runs

Abstract

In the current study, we show how ProCan90, a curated data set of HEK293 technical replicates, can be used to optimize the configuration options for algorithms in the OpenSWATH pipeline. Furthermore, we use this case study as a proof of concept for horizontal scaling of such a pipeline to allow 45 810 computational analysis runs of OpenSWATH to be completed within four and a half days on a budget of US $10 000. Through the use of Amazon Web Services (AWS), we have successfully processed each of the ProCan 90 files with 506 combinations of input parameters. In total, the project consumed more than 340 000 core hours of compute and generated in excess of 26 TB of data. Using the resulting data and a set of quantitative metrics, we show an analysis pathway that allows the calculation of two optimal parameter sets, one for a compute rich environment (where run time is not a constraint), and another for a compute poor environment (where run time is optimized). For the same input files and the compute rich parameter set, we show a 29.8% improvement in the number of quality protein (>2 peptide) identifications found compared to the current OpenSWATH defaults, with negligible adverse effects on quantification reproducibility or drop in identification confidence, and a median run time of 75 min (103% increase). For the compute poor parameter set, we find a 55% improvement in the run time from the default parameter set, at the expense of a 3.4% decrease in the number of quality protein identifications, and an intensity CV decrease from 14.0% to 13.7%.

Toffee – a highly efficient, lossless file format for DIA-MS

“…Data sets. Three publicly available data sets are used in the current work: Swath Gold Standard and TRIC data available from PeptideAtlas raw data repository with accession number PASS00289 10 and PASS00788 11 , respectively, and the ProCan90 dataset can be obtained from the PRIDE archive under the identifier PXD011093 12 .…”

Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines

“…These applications range from useful utilities (file format conversions, peak picking) to wrappers for known applications like peptide identification search engines. These two frameworks have been used recently to analyse big datasets [36, 37]. Though these frameworks have been fully implemented as component-based frameworks, they have been really slow to implement and promote standard file formats between each component.…”

Toffee – a highly efficient, lossless file format for DIA-MS

“…A PREPRINT - MAY 11, 2019 Using openms-toffee I have conducted a thorough investigation into OpenSWATH with a variety of mzML conversions, and toffee itself. Using three public data sets covering both TripleTOF5600 (Swath Gold Standard [2] and TRIC manual validation set, only the y-and b-ions are included in the analysis, [15]) and TripleTOF6600 (ProCan90, including only the first injection from each mass spectrometer, [16]) the raw vendor files were converted to mzML using 'msconvert' in both profile and vendor peak-picking centroid mode, each with and without 'msnumpress' [17], as well as the 'sciex/wiffconverter' Docker image [18] in both profile and centroid modes. Toffee files were then produced from the msconvert and Sciex Docker profile mzML files, and the toffee file back to mzML.…”

“…1.1.1 Data setsThree publicly available data sets are used in the current work: Swath Gold Standard and TRIC data available from PeptideAtlas raw data repository with accession number PASS00289[2] and PASS00788[15], respectively, and the ProCan90 dataset can be obtained from the PRIDE archive under the identifier PXD011093[16].Original Sciex wiff files were converted to various forms of mzML, mz5, and toffee files. While conversion methods are provided here for reference, a self-contained script is included in the Supplementary Material convert_mzml.py[12].11 A PREPRINT -MAY 11, 2019 mzML | msconvert | Profile: Conversion of wiff to mzML using msconvert version 3.0.18304 in a Conversion of wiff to mzML using the publicly available Sciex Docker Conversion of the Sciex Docker profile mzML to toffee using the toffee Docker image cmriprocan/toffee:0.12.16 [27] mzml_to_toffee $ { mzml_fname } $ { tof_fname } mzML | Toffee Round Trip | Profile: Using the toffee file created using the Sciex Docker profile mzML, convert back to mzML using the toffee Docker image cmriprocan/toffee:0.12.16 [27] -toffee:0.13.12.dev [28] and a complete python script for marshalling analysis is included in the Supplementary Material pipeline.py…”

“…1.1.1 Data setsThree publicly available data sets are used in the current work: Swath Gold Standard and TRIC data available from PeptideAtlas raw data repository with accession number PASS00289[2] and PASS00788[15], respectively, and the ProCan90 dataset can be obtained from the PRIDE archive under the identifier PXD011093[16].Original Sciex wiff files were converted to various forms of mzML, mz5, and toffee files. While conversion methods are provided here for reference, a self-contained script is included in the Supplementary Material convert_mzml.py[12].…”