FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data

Schlaffner, Christoph N.; Kahnert, Konstantin; Muntel, Jan; Chauhan, Ruchi; Renard, Bernhard Y.; Steen, Judith A.; Steen, Hanno

doi:10.7554/elife.58783

Cited by 3 publications

(13 citation statements)

References 40 publications

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“…The FLEXIQuant-LF method employs the RANSAC algorithm 14 to compute a robust linear regression between the precursor intensities of a protein from a sample of interest and from a reference sample. 13 RANSAC linear regression iteratively identifies outliers and fits the regression line solely based on the inliers. For every peptide precursor of the protein in the sample of interest, the distance to the regression line is calculated and normalized by the slope of the regression line and the intensities of the corresponding reference precursor.…”

Section: Multiflex-lfmentioning

confidence: 99%

“…The previously published FLEXIQuant-LF method was tested with a small subset of proteins from this data set (ProteomeXchange identifier: PXD018411). 13 In brief, HeLa S3 cells were treated with thymidine for 20 h to synchronize them in the S (synthesis) phase (0 h). For the M (mitotic) phase, the cells were treated with nocodazole after having been cultured for 3 h in fresh media.…”

Section: Multiflex-lf Applicationmentioning

confidence: 99%

“…The FLEXIQuant 12 method requires a purified and labeled standard for every protein of interest and is therefore a laborious, time-intensive, and expensive process. Hence, building on the same concept, Schlaffner et al (2020) 13 developed the label-free computational method FLEXIQuant-LF (FLEXIQuant-Label Free). FLEXIQuant-LF infers the degree of differential modification solely based on the intensities of the unmodified peptides of a single protein in time-series or case-control studies, where a certain time point or control group is used as a reference.…”

Section: ■ Introductionmentioning

confidence: 99%

“…On the basis of the model, the distance to the regression line is calculated for every peptide precursor. 13 Afterward, the distances are normalized by the Figure 1. Overview of multiFLEX-LF.…”

Section: ■ Introductionmentioning

confidence: 99%

“…multiFLEX-LF requires label-free quantification data of unmodified peptide precursors of a set of proteins and a within study reference to identify differentially modified precursors and compute the modification extent. To this end, an adapted FLEXIQuant-LF 13 using robust linear regression is applied to every protein consecutively. The intensities of the unmodified precursors of a protein are compared with the reference intensities of that protein by fitting a linear regression line.…”

Section: ■ Introductionmentioning

confidence: 99%

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multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets

et al. 2022

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In liquid-chromatography−tandem-mass-spectrometrybased proteomics, information about the presence and stoichiometry of protein modifications is not readily available. To overcome this problem, we developed multiFLEX-LF, a computational tool that builds upon FLEXIQuant, which detects modified peptide precursors and quantifies their modification extent by monitoring the differences between observed and expected intensities of the unmodified precursors. multiFLEX-LF relies on robust linear regression to calculate the modification extent of a given precursor relative to a within-study reference. multiFLEX-LF can analyze entire label-free discovery proteomics data sets in a precursorcentric manner without preselecting a protein of interest. To analyze modification dynamics and coregulated modifications, we hierarchically clustered the precursors of all proteins based on their computed relative modification scores. We applied multiFLEX-LF to a data-independentacquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C) isolated at various time points during mitosis. The clustering of the precursors allows for identifying varying modification dynamics and ordering the modification events. Overall, multiFLEX-LF enables the fast identification of potentially differentially modified peptide precursors and the quantification of their differential modification extent in large data sets using a personal computer. Additionally, multiFLEX-LF can drive the large-scale investigation of the modification dynamics of peptide precursors in time-series and case-control studies. multiFLEX-LF is available at https://gitlab.com/SteenOmicsLab/multiflex-lf.

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Section: Multiflex-lfmentioning

confidence: 99%

Section: Multiflex-lf Applicationmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

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multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets

et al. 2022

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The potential for using shell proteins in gastropod systematics, assessed in patellogastropod limpets

Colgan

2021

Zoological Journal of the Linnean Society

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This investigation of the application of shell protein information to gastropod systematics initially utilized available Lottia gigantea sequences and a transcriptome of Patelloida mimula developed here. Levels of differentiation between predicted sequences of reciprocal best-hit potential homologues in P. mimula and L. gigantea suggested that they could be useful within families, and possibly in higher taxa using some shell-associated proteins, particularly the peroxidases. Subsequently, proteomic analyses of the acid-soluble fraction of extractions from 17 shells and five tissue samples were conducted by combined liquid chromatography/mass spectrometry with nano-electrospray ionization. All proteins with abundance more than 1.2% in the L. gigantea shell proteome were identified with 100% confidence in most extractions by SearchGui/PeptideShaker analyses. In total, 259 of 379 peptides predicted from in silico digestion of L. gigantea shell proteins were represented by validated peptide spectrum matches in one or more specimens. Systematics applications were investigated by analysing metrics such as protein coverage by peptides and phylogenetic analyses of peptide presence/absence. The investigation suggested that diagnostic profiles based on fixed presence/absence differences can be used to separate species pairs. However, further development of analytical techniques and accumulation of reference databases is required for realising fully the systematics potential of the shell proteome.

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Detecting Differential Alternative Splicing in Mass Spectrometry-based Proteomics Data

Ammar,

Csaba,

Hadziahmetovic

et al. 2023

Preprint

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Alternative splicing can substantially diversify biological cell states and influence cellular function. The functional impact of splicing has to be estimated at protein level, typically by mass spectrometry (MS) -based proteomics. Although this technology measures increasingly large peptides sets, distinguishing isoform-specific peptides are rare, limiting detection and quantification of splicing. We introduce MS-EmpiReS, a quantification-based computational approach for differential alternative splicing detection in proteomics data. Its core principle is to differentially quantify peptides mapping to different regions of genes. This approach increased the number of testable peptides hundred-fold in a clinical cancer cohort, resulting in a large number of cancer-relevant splicing candidates. Splicing events detected by both MS-EmpiReS and deep RNA sequencing correlated well but also provided complementary information. The proteomics data allowed us to define a per-sample splicing score to separate cancer conditions. Finally, deep brain proteomes from different mice separated strongly by the lower abundance protein splicing isoform.

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FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data

Cited by 3 publications

References 40 publications

multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets

multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets

The potential for using shell proteins in gastropod systematics, assessed in patellogastropod limpets

Detecting Differential Alternative Splicing in Mass Spectrometry-based Proteomics Data

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