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A multi-center study benchmarks software tools for label-free proteome quantification

View Article: PubMed Central - PubMed

ABSTRACT

The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.

No MeSH data available.


Study workflow.Two proteome-hybrid samples A and B were prepared containing known quantities of peptide digestions of human, yeast, and E.Coli organisms. The samples were analyzed in three technical replicates in SWATH-MS acquisition mode on two different MS instrument platforms (TripleTOF 5600 and TripleTOF 6600) with/using two different swath windows setups (32 fixed size windows and 64 variable size windows). This resulted in four benchmarking datasets. The datasets were analyzed in five software tools: OpenSWATH, SWATH 2.0, Skyline, Spectronaut, and DIA-Umpire. Benchmark analyses of each dataset and software tool were performed based on the output reports generated by the newly developed benchmarking software LFQbench.
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Figure 1: Study workflow.Two proteome-hybrid samples A and B were prepared containing known quantities of peptide digestions of human, yeast, and E.Coli organisms. The samples were analyzed in three technical replicates in SWATH-MS acquisition mode on two different MS instrument platforms (TripleTOF 5600 and TripleTOF 6600) with/using two different swath windows setups (32 fixed size windows and 64 variable size windows). This resulted in four benchmarking datasets. The datasets were analyzed in five software tools: OpenSWATH, SWATH 2.0, Skyline, Spectronaut, and DIA-Umpire. Benchmark analyses of each dataset and software tool were performed based on the output reports generated by the newly developed benchmarking software LFQbench.

Mentions: As a benchmarking sample, two hybrid proteome samples consisting of tryptic digests of human, yeast and E.coli proteins were mixed in defined proportions16 (Figure 1) to yield expected peptide and protein ratios of 1:1 for human, 2:1 for yeast, and 1:4 for E.coli proteins if samples A and B are compared. This sample set is referred to as HYE124 (Supplementary Figure 1). While the absolute amounts of individual proteins are not known, these samples provide a defined ground truth for bioinformatics analysis, i.e., defined relative changes between samples, and a sufficiently large number of peptides to enable the in-depth evaluation of both precision and accuracy of relative label-free quantification16. We analyzed HYE124 samples A & B in technical triplicates on two different instrument platforms (TripleTOF 5600 and TripleTOF 6600) using two different SWATH-MS acquisition modes (Supplementary Figure 1), generating a total of four benchmark datasets. To individually address the effects of SWATH window number (32 vs. 64 windows) and window size (fixed vs. variable), we generated a second sample set with higher ratio differences (termed HYE110, see online methods), which was analyzed in four different acquisition modes on the TripleTOF 6600 platform (Supplementary Figure 1). This allowed us to test the performance of the software tools on data generated using a variety of instruments and settings of different sensitivity and co-fragmentation frequency.


A multi-center study benchmarks software tools for label-free proteome quantification
Study workflow.Two proteome-hybrid samples A and B were prepared containing known quantities of peptide digestions of human, yeast, and E.Coli organisms. The samples were analyzed in three technical replicates in SWATH-MS acquisition mode on two different MS instrument platforms (TripleTOF 5600 and TripleTOF 6600) with/using two different swath windows setups (32 fixed size windows and 64 variable size windows). This resulted in four benchmarking datasets. The datasets were analyzed in five software tools: OpenSWATH, SWATH 2.0, Skyline, Spectronaut, and DIA-Umpire. Benchmark analyses of each dataset and software tool were performed based on the output reports generated by the newly developed benchmarking software LFQbench.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120688&req=5

Figure 1: Study workflow.Two proteome-hybrid samples A and B were prepared containing known quantities of peptide digestions of human, yeast, and E.Coli organisms. The samples were analyzed in three technical replicates in SWATH-MS acquisition mode on two different MS instrument platforms (TripleTOF 5600 and TripleTOF 6600) with/using two different swath windows setups (32 fixed size windows and 64 variable size windows). This resulted in four benchmarking datasets. The datasets were analyzed in five software tools: OpenSWATH, SWATH 2.0, Skyline, Spectronaut, and DIA-Umpire. Benchmark analyses of each dataset and software tool were performed based on the output reports generated by the newly developed benchmarking software LFQbench.
Mentions: As a benchmarking sample, two hybrid proteome samples consisting of tryptic digests of human, yeast and E.coli proteins were mixed in defined proportions16 (Figure 1) to yield expected peptide and protein ratios of 1:1 for human, 2:1 for yeast, and 1:4 for E.coli proteins if samples A and B are compared. This sample set is referred to as HYE124 (Supplementary Figure 1). While the absolute amounts of individual proteins are not known, these samples provide a defined ground truth for bioinformatics analysis, i.e., defined relative changes between samples, and a sufficiently large number of peptides to enable the in-depth evaluation of both precision and accuracy of relative label-free quantification16. We analyzed HYE124 samples A & B in technical triplicates on two different instrument platforms (TripleTOF 5600 and TripleTOF 6600) using two different SWATH-MS acquisition modes (Supplementary Figure 1), generating a total of four benchmark datasets. To individually address the effects of SWATH window number (32 vs. 64 windows) and window size (fixed vs. variable), we generated a second sample set with higher ratio differences (termed HYE110, see online methods), which was analyzed in four different acquisition modes on the TripleTOF 6600 platform (Supplementary Figure 1). This allowed us to test the performance of the software tools on data generated using a variety of instruments and settings of different sensitivity and co-fragmentation frequency.

View Article: PubMed Central - PubMed

ABSTRACT

The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.

No MeSH data available.