Frequently Asked Questions
Find answers to common questions about PalmLab tools and data.
FAQ Categories
Data Sources & Quality
Q: How do I switch between Literature and Mass Spectrometry data in analysis tools?
Most analysis tools now feature a data source selector that allows you to choose between two complementary data types:
Literature Data:
- Curated from published research papers
- Includes experimental validation information
- Higher confidence for individual site identification
- May have variable detection conditions across studies
Mass Spectrometry Data:
- Uniformly processed with MaxQuant workflow
- Consistent quantitative comparisons across samples
- Better for expression pattern analysis
- Directly comparable across different studies
Note: The Hotspot Mutation Analysis tool currently only supports Literature data due to insufficient sample size for statistical calculation in the mass spectrometry dataset.
Q: How is the tissue/cell line expression data curated and what does TSI index represent?
Our tissue and cell line expression data undergoes rigorous quality control:
- Data Integration: Compiled from multiple proteomics studies and public databases
- Normalization: All data normalized to enable cross-study comparisons
- Quality Filtering: Low-quality samples and outliers are removed
TSI (Tissue Specificity Index): Measures how specific a protein's palmitoylation is to particular tissues:
- High TSI (>0.8): Protein is palmitoylated in very few tissues (tissue-specific)
- Medium TSI (0.3-0.8): Moderate tissue specificity
- Low TSI (<0.3): Widely palmitoylated across many tissues (ubiquitous)
Q: What do the conservation scores (phyloP and phastCons) indicate about palmitoylation sites?
Conservation scores help identify functionally important palmitoylation sites:
phyloP Conservation:
- Positive scores: Evolutionary conservation (higher = more conserved)
- Negative scores: Accelerated evolution
- Near zero: Neutral evolution
phastCons Conservation:
- 0-1 scale: Probability of being in conserved element
- Close to 1: Highly conserved, likely functional
- Close to 0: Fast evolving
Interpretation: Sites with high conservation scores across species are more likely to be functionally important and may represent critical regulatory modifications.
Search & Browse Functions
Q: What input formats are supported for protein searches?
PalmLab supports multiple input formats for maximum flexibility:
Supported Identifiers:
- UniProt IDs: P01116, P04637, P35579
- Gene Symbols: KRAS, TP53, MYH9, BRAF
- Protein Names: "GTPase KRas", "Cellular tumor antigen p53"
Input Methods:
- Single entry: P01116
- Multiple entries: P01116 KRAS TP53
- Separators: Spaces, commas, tabs, or newlines
- Mixed input: "P01116 KRAS TP53"
Note: The system automatically validates all inputs and provides feedback on unrecognized entries.
Analysis Tools
Q: Which analysis tools support data source selection (Literature vs Mass Spectrometry)?
PalmLab provides data source selection for most analysis tools. Below is the complete support matrix:
| Analysis Tool | Literature Data | Mass Spectrometry Data | Notes |
|---|---|---|---|
| Differential Palmitoylation Analysis | ✅ Supported | ✅ Supported | Both data sources available |
| Palmitoylated Protein Network | ✅ Supported | ✅ Supported | Both data sources available |
| Palmitoylated Protein Correlation | ✅ Supported | ✅ Supported | Both data sources available |
| Multi-Protein Palmitoylation Pattern | ✅ Supported | ✅ Supported | Both data sources available |
| Motif Discovery Analysis | ✅ Supported | ✅ Supported | Both data sources available |
| Hotspot Mutation Analysis | ✅ Supported | ❌ Not Available | Mass spectrometry dataset lacks sufficient sample size for statistical calculation |
How to select data source: When using any supported tool, you will see a dropdown or toggle button labeled "Data Source" that allows you to switch between Literature and Mass Spectrometry data. The analysis will automatically use the selected data source for all calculations.
Hotspot Mutation Analysis Limitation: The Mass Spectrometry data option is intentionally disabled for Hotspot Mutation Analysis because the current dataset does not have enough samples with both mutation and palmitoylation information to produce statistically meaningful results. We are actively collecting more mass spectrometry data to enable this feature in future releases.
Q: What is the difference between co-occurrence and mutual exclusion in network analysis?
These relationships reveal different biological patterns. Both Literature and Mass Spectrometry data sources are supported:
Co-occurrence (Positive Association):
- Definition: Proteins tend to be palmitoylated together in the same samples
- Statistical indicator: OR > 1, Jaccard > 0
- Biological implication: May indicate:
- Functional cooperation
- Same pathway membership
- Protein complex formation
- Coordinated regulation
Mutual Exclusion (Negative Association):
- Definition: Proteins rarely palmitoylated together in same samples
- Statistical indicator: OR < 1, Jaccard ≈ 0
- Biological implication: May indicate:
- Functional redundancy
- Different cellular states
- Compensatory mechanisms
- Mutually exclusive pathways
Data Source Comparison: Literature data typically has higher site-level confidence, while Mass Spectrometry data provides better quantitative comparability across samples. Use the data source selector to compare results from both sources.
Q: How should I interpret the results from Hotspot Mutation Analysis?
The mutation analysis provides statistical evidence for associations between palmitoylation and mutations. Note: This tool currently only supports Literature data due to insufficient sample size for statistical calculation in the mass spectrometry dataset.
Key Statistical Metrics:
- Q (Logit) < 0.05: Statistically significant after multiple testing correction
- Coef_logit > 0: Palmitoylation increases mutation probability
- Coef_logit < 0: Palmitoylation decreases mutation probability
- Color coding: Green = significant positive association, Red = significant negative association
Sample Count Interpretation:
| Variable | Description | Biological Meaning |
|---|---|---|
| n1 | Mutated & palmitoylated | Samples with both features |
| n2 | Mutated & non-palmitoylated | Mutated without palmitoylation |
| m1 | Wildtype & palmitoylated | Normal with palmitoylation |
| m2 | Wildtype & non-palmitoylated | Normal without palmitoylation |
Q: What is the recommended workflow for Multi-Protein Expression Pattern Analysis with different data sources?
For optimal results, follow this workflow:
Step 1: Select Data Source
- Literature Data: Best for site-level confidence and known biological validation
- Mass Spectrometry Data: Best for quantitative comparisons and consistent sample coverage
- Comparative approach: Run analysis with both sources to identify robust patterns
Step 2: Input Selection
- Small-scale exploration: Start with 5-10 proteins to understand patterns
- Pathway-based analysis: Use pre-defined cancer pathways for hypothesis-driven research
- Validation: Check the analysis summary to ensure all proteins were found in selected data source
Step 3: Pattern Identification
- Heatmap patterns: Look for vertical (sample clusters) and horizontal (protein co-expression) patterns
- UMAP clusters: Identify sample groups with similar expression profiles
- Tissue specificity: Note tissue-colored labels for context
Step 4: Biological Interpretation
- Co-expression clusters: May indicate functional modules
- Tissue-specific patterns: Reveal context-dependent regulation
- Outliers: Unique samples may represent special conditions or errors
- Cross-source validation: Patterns observed in both data sources are more reliable
Q: How do I interpret motif discovery results and E-values?
Motif analysis identifies conserved sequence patterns around palmitoylation sites. You can perform this analysis on either Literature or Mass Spectrometry data:
Key Interpretation Points:
- E-value: Expected number of false positives (lower = more significant)
- E-value < 0.05: Statistically significant
- E-value < 0.01: Highly significant
- E-value < 0.001: Very strong evidence
- Sequence Logo: Height indicates information content (conservation)
- Consensus Sequence: Most common amino acids at each position
Biological significance: Statistically significant motifs may represent binding sites for palmitoyltransferases or structural features important for modification.
Consider comparing motifs from both data sources to identify robust patterns.
Technical Issues
Q: Why are some analysis results limited or unavailable for certain proteins? Why doesn't Hotspot Mutation Analysis support Mass Spectrometry data?
Data availability depends on several factors:
- Experimental coverage: Not all proteins have been studied for palmitoylation
- Tissue specificity: Some analyses require data from specific tissues
- Statistical power: Analyses may require minimum sample sizes
- Species limitations: Some tools are human-specific
Hotspot Mutation Analysis - Mass Spectrometry Limitation:
The Hotspot Mutation Analysis tool currently only supports Literature data because the mass spectrometry dataset does not have sufficient sample size to perform reliable statistical calculations for mutation association analysis. We are actively working to expand the mass spectrometry dataset to enable this feature in future releases.
Recommendation: For proteins with limited data in one source, try switching to the other data source. For comprehensive analysis, consider using both Literature and Mass Spectrometry data when available.
Q: How current is the data in PalmLab and how often is it updated?
PalmLab maintains a regular update schedule:
- Major updates: Quarterly releases with new data and features
- Literature curation: Continuous addition of new experimental data
- Mass Spectrometry data: Regular reprocessing of raw data with updated MaxQuant workflows
- Database synchronization: Monthly updates from external databases
- Bug fixes: Ongoing maintenance and improvements
Check the About page for the most recent update information and version details.