MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

Kohler, Devon; Staniak, Mateusz; Tsai, Tsung-Heng; Huang, Ting; Shulman, Nicholas; Bernhardt, Oliver M.; MacLean, Brendan; Nesvizhskii, Alexey I.; Reiter, Lukas; Sabidó, Eduard; Choi, Meena; Vitek, Olga

doi:10.1021/acs.jproteome.2c00834

Cited by 31 publications

(20 citation statements)

References 44 publications

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“…For simple experimental designs such as these, differential expression analysis does not require complicated statistical procedures. For more complex designs we recommend MSstats, which can model a variety of experimental designs in a statistically rigorous manner …”

Section: Discussionmentioning

confidence: 99%

Evaluating Proteomics Imputation Methods with Improved Criteria

Harris,

Fondrie,

et al. 2023

J. Proteome Res.

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Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, “downstream-centric” criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

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Section: Discussionmentioning

confidence: 99%

Evaluating Proteomics Imputation Methods with Improved Criteria

Harris,

Fondrie,

et al. 2023

J. Proteome Res.

View full text Add to dashboard Cite

show abstract

“…The reporter ion intensities of all the peptide ions mapped to a cysteine site were summarized into a single site level intensity in each channel and plex by MSstats (v4.8.3). 19 For each cysteine site, the local normalization on the summaries was performed to reduce the systematic bias between plexes. For the local normalization, we created an artifact reference channel by averaging over all the 18 channels for each cysteine site and plex.…”

Section: Methodsmentioning

confidence: 99%

Increasing the Throughput and Reproducibility of Activity-Based Proteome Profiling Studies with Hyperplexing and Intelligent Data Acquisition

Budayeva,

Ma,

Wang

et al. 2023

Preprint

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Intelligent data acquisition (IDA) strategies, such as real-time database search (RTS), have improved the depth of proteome coverage for experiments that utilize isobaric labels and gas phase purification techniques (i.e., SPS-MS3). While most applications of IDA have been focused on the analysis of protein abundance, these approaches have recently been applied to activity-based proteome profiling (ABPP) studies aimed at characterization of protein site engagement by small molecules. In this work, we extend IDA capabilities offered by vendor software through a program called InSeqAPI. First, we demonstrate robust performance of InSeqAPI in the analysis of biotinylated cysteine peptides from ABPP experiments. Then, we describe PairQuant, a method within InSeqAPI designed for the hyperplexing approach that utilizes protein-level isotopic labeling and peptide-level TMT labeling. PairQuant allows for TMT analysis of 36 conditions in a single sample and achieves ~98% coverage of both peptide pair partners in a hyperplexed experiment as well as a 40% improvement in the number of quantified cysteine sites compared to non-RTS acquisition. We applied this method in ABPP study of ligandable cysteine sites in the nucleus leading to an identification of additional druggable sites on protein- and DNA-interaction domains of transcription regulators and on nuclear ubiquitin ligases.

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“…The reporter ion intensities of all the peptide ions mapped to a cysteine site were summarized into a single site level intensity in each channel and plex by MSstats (v4.8.3). 22 For each cysteine site, the local normalization on the summaries was performed to reduce the systematic bias between plexes. For the local normalization, we created an artifact reference channel by averaging over all the 18 channels for each cysteine site and plex.…”

Section: Data Analysis For Abpp Workflowmentioning

confidence: 99%

Increasing the Throughput and Reproducibility of Activity-Based Proteome Profiling Studies with Hyperplexing and Intelligent Data Acquisition

Budayeva,

Ma,

Wang

et al. 2024

J. Proteome Res.

Self Cite

View full text Add to dashboard Cite

Intelligent data acquisition (IDA) strategies, such as a real-time database search (RTS), have improved the depth of proteome coverage for experiments that utilize isobaric labels and gas phase purification techniques (i.e., SPS-MS3). In this work, we introduce inSeqAPI, an instrument application programing interface (iAPI) program that enables construction of novel data acquisition algorithms. First, we analyze biotinylated cysteine peptides from ABPP experiments to demonstrate that a real-time search method within inSeqAPI performs similarly to an equivalent vendor method. Then, we describe PairQuant, a method within inSeqAPI designed for the hyperplexing approach that utilizes protein-level isotopic labeling and peptide-level TMT labeling. PairQuant allows for TMT analysis of 36 conditions in a single sample and achieves ∼98% coverage of both peptide pair partners in a hyperplexed experiment as well as a 40% improvement in the number of quantified cysteine sites compared with non-RTS acquisition. We applied this method in the ABPP study of ligandable cysteine sites in the nucleus leading to an identification of additional druggable sites on protein-and DNA-interaction domains of transcription regulators and on nuclear ubiquitin ligases.

show abstract

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

Cited by 31 publications

References 44 publications

Evaluating Proteomics Imputation Methods with Improved Criteria

Evaluating Proteomics Imputation Methods with Improved Criteria

Increasing the Throughput and Reproducibility of Activity-Based Proteome Profiling Studies with Hyperplexing and Intelligent Data Acquisition

Increasing the Throughput and Reproducibility of Activity-Based Proteome Profiling Studies with Hyperplexing and Intelligent Data Acquisition

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