DIA proteomics data from a UPS1-spiked E.coli protein mixture processed with six software tools

Gotti, Clarisse; Roux‐Dalvai, Florence; Joly-Beauparlant, Charles; Mangnier, Loïc; Leclercq, Mickaël; Droit, Arnaud

doi:10.1016/j.dib.2022.107829

Cited by 6 publications

(10 citation statements)

References 9 publications

(20 reference statements)

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“…This combination resulted in inter- and intrasample differential abundancies for the 48 proteins, ranging from 1.2 amol up to 1.2 pmol (Table ). Our benchmarking approach thus covers a larger dynamic range than found in similar studies, , thereby allowing a more precise determination of the limits of detection and quantification in the different workflows. Moreover, our benchmark samples more closely resemble actual complex proteomics samples compared to previously used samples for DIA benchmarking, , such as the triple proteome LFQ benchmark sample set, in which a human protein digest is mixed with an E.…”

Section: Resultsmentioning

confidence: 99%

“…A narrow overlapping window scheme was used in the DIA method to obtain low cluttered spectra and to be as sensitive as possible, as was shown by Gotti et al and Amodei et al LC conditions were chosen to achieve optimal resolution and sufficient data points across the peak (3–4 at fwhm).…”

Section: Resultsmentioning

confidence: 99%

“…Hence, starting from a ground truth of only 48 proteins, spiked in at known amounts, the limits of detectability, accuracy, and precision can be more correctly assessed. A narrow overlapping window scheme was used in the DIA method to obtain low cluttered spectra and to be as sensitive as possible, as was shown by Gotti et al 41 and Amodei et al 51 LC conditions were chosen to achieve optimal resolution and sufficient data points across the peak (3−4 at fwhm).…”

Section: Methodsmentioning

confidence: 99%

“…By using independent in silico spectral library predictions, a solution can be more easily provided to alter the predictions. This kind of evaluation was not performed before in any previous benchmark, providing a valuable addition of this study to other benchmark studies. ,− And by analyzing the dilution series of the spiked UPS2 standard we mimicked true biological proteome samples and therefore obtained a more realistic view on the different performance parameters. Because of our large dynamic range, we are able to truly define the limits of the DIA workflows.…”

Section: Introductionmentioning

confidence: 99%

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Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows

Staes,

Mendes Maia,

Dufour

et al. 2024

J. Proteome Res.

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Data-independent acquisition (DIA) has become a well-established method for MS-based proteomics. However, the list of options to analyze this type of data is quite extensive, and the use of spectral libraries has become an important factor in DIA data analysis. More specifically the use of in silico predicted libraries is gaining more interest. By working with a differential spike-in of human standard proteins (UPS2) in a constant yeast tryptic digest background, we evaluated the sensitivity, precision, and accuracy of the use of in silico predicted libraries in data DIA data analysis workflows compared to more established workflows. Three commonly used DIA software tools, DIA-NN, EncyclopeDIA, and Spectronaut, were each tested in spectral library mode and spectral library-free mode. In spectral library mode, we used independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to classical data-dependent acquisition (DDA)-based spectral libraries. In total, we benchmarked 12 computational workflows for DIA. Our comparison showed that DIA-NN reached the highest sensitivity while maintaining a good compromise on the reproducibility and accuracy levels in either library-free mode or using in silico predicted libraries pointing to a general benefit in using in silico predicted libraries.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows

Staes,

Mendes Maia,

Dufour

et al. 2024

J. Proteome Res.

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“…Till 2021, proteome standard material has not been considered in the quality standards in research facilities (25). Currently, a mixture of 18~48 recombinant proteins are used as a "test standard" or spike-in for proteomics (5,(26)(27)(28)(29). However, such small number of proteins could hardly form a reference standard for complex proteome samples.…”

Section: Introductionmentioning

confidence: 99%

A stable reference human transcriptome and proteome as a standard for reproducible omics experiments

Lü²,

Zheng³

et al. 2022

Preprint

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In recent years, the development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits its application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. However, the transcriptome and proteome of most cell lines shift during culturing, which limits their applicability to serve as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (R2=0.966-0.995) and proteome (R2=0.934-0.976 for DDA, R2=0.942-0.986 for DIA) after 9 subculturing generations, which allows this stable standard sample to be stably produced on an industrial scale for several decades. Moreover, this stability was maintained across labs and platforms. In sum, our results justified a omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

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A multi-omics dataset of human transcriptome and proteome stable reference

Lü

Zheng

et al. 2023

Sci Data

View full text Add to dashboard Cite

The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

show abstract

DIA proteomics data from a UPS1-spiked E.coli protein mixture processed with six software tools

Cited by 6 publications

References 9 publications

Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows

Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows

A stable reference human transcriptome and proteome as a standard for reproducible omics experiments

A multi-omics dataset of human transcriptome and proteome stable reference

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