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Use bioconductor-msstatslip to prepare a Spectronaut conversion, LiP/TrP summarization, and group-comparison workflow for [matched LiP and TrP exports], [protein FASTA], and [condition and replicate design], ensuring matched condition names and unique biological replicates.
Required inputs
[matched LiP and TrP exports]
[protein FASTA]
[condition and replicate design]
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SKILL.md
name: bioconductor-msstatslipdescription: Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages.when_to_use: Use when: MSstatsLiP Workflow: An example workflow and analysis of the MSstatsLiP package; MSstatsLiP Proteolytic Workflow; Differential expression analysis (MSstatsLiP); Proteomics quantification (MSstatsLiP); Data normalization and preprocessing (MSstatsLiP). Not for: Requires R ≥ 4.1 and Bioconductor ≥ 3.16user-invocable: false
MSstatsLiP
Workflows
Proteolytic Resistance Analysis
library(MSstatsLiP)
# Calculate proteolytic resistance ratios and perform differential analysis
Accessibility <- calculateProteolyticResistance(
MSstatsLiP_Summarized,
fasta_file,
differential_analysis = TRUE
)
# Plot proteolytic resistance results as a barcode along the protein sequence
ResistanceBarcodePlotLiP(
Accessibility,
fasta_file,
which.prot = "P16622",
which.condition = "F1",
differential_analysis = TRUE,
which.comp = "F1 vs F2",
address = FALSE
)
Input: Summarized LiP-MS and TrP-MS datasets (MSstatsLiP_Summarized) and a FASTA file (fasta_file).
Output: Proteolytic resistance ratios and sequence-aligned barcode plots.
Standard Workflow
library(MSstatsLiP)
# Convert raw Spectronaut data to MSstatsLiP format
msstats_data <- SpectronauttoMSstatsLiPFormat(
raw_lip,
fasta_file,
raw_prot
)
# Summarize the preprocessed data
MSstatsLiP_Summarized <- dataSummarizationLiP(
msstats_data,
normalization.LiP = "equalizeMedians"
)
# Perform group comparison modeling
MSstatsLiP_model <- groupComparisonLiP(MSstatsLiP_Summarized)
Input: Raw Spectronaut LiP and TrP datasets, and a FASTA file.
Output: Preprocessed, summarized, and modeled LiP-MS and TrP-MS datasets.
When to Use
Analyzing Limited Proteolysis-coupled Mass Spectrometry (LiP-MS) datasets alongside Trypsin-only control (TrP-MS) datasets.
Preprocessing Spectronaut exports using SpectronauttoMSstatsLiPFormat.
Performing differential analysis of proteolytic resistance patterns across conditions using calculateProteolyticResistance and groupComparisonLiP.
Visualizing peptide-level proteolytic resistance changes along a protein sequence using ResistanceBarcodePlotLiP.
When NOT to Use
For standard bottom-up shotgun proteomics data without limited proteolysis, use MSstats directly.
For differential expression analysis of RNA-seq data, use packages like limma, edgeR, or DESeq2.
Data Requirements
Raw LiP-MS and TrP-MS datasets (e.g., Spectronaut exports) containing columns like PG.ProteinAccessions, PEP.GroupingKey, EG.Qvalue, and F.PeakArea.
A FASTA file containing the protein sequences of interest.
Key Parameters
normalization.LiP ("equalizeMedians"): Normalization method for LiP data in dataSummarizationLiP.
differential_analysis (TRUE): Whether to perform differential analysis in calculateProteolyticResistance.
which.prot (NULL): Protein accession to plot in ResistanceBarcodePlotLiP.
which.condition (NULL): Condition to plot in ResistanceBarcodePlotLiP.
address (FALSE): Whether to save the plot to a file or display it in ResistanceBarcodePlotLiP.
Best Practices
Filter out half-tryptic (HT) peptides and retain only fully tryptic (FT) peptides for proteolytic resistance analysis using calculateTrypticity.
Ensure that the Condition nomenclature is identical in both LiP and TrP datasets before summarization.
Ensure unique BioReplicate nomenclature for case-control experiments.
Common Pitfalls
Mismatched condition names between LiP and TrP datasets, causing errors in summarization or modeling; verify using unique(msstats_data[["LiP"]]$Condition) %in% unique(msstats_data[["TrP"]]$Condition).
High memory usage during summarization; clear memory cache using rm() and gc() if needed.
Alternatives
MSstats for standard proteomics without limited proteolysis.
limma for general linear modeling of expression data.
Citations
Cappelletti et al., 2021 (referenced in vignette text)