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Data Access Guide

This document explains how to obtain and prepare data for the Glycoproteomic Data Analysis tutorial.

Sample Data (Included)

Small sample datasets are included in each chapter's training_data/ folder for quick testing. These allow you to run the code immediately without downloading large files.

Chapter Sample File Description
Chapter 1 training_data.csv Newly synthesized protein data with fold changes and GO terms (~100 proteins)
Chapter 2 Generated from Chapter 2 code TMT quantification data
Chapter 3 Generated from Chapter 2 Normalized protein quantification
Chapter 4 Generated from Chapter 3 Differential expression results
Chapter 5 HEK_Nterm_Kd_half_life_sequence.csv, common_Nterm_protein.csv N-terminal peptide sequences

Full Datasets

For complete analysis with your own data or larger datasets, you'll need to obtain additional files.

Chapter 1: R Basics

Required file: training_data.csv

Format:

Column Description
Gene Gene symbol
Btz/DMSO Fold change for Btz vs DMSO
3MA/DMSO Fold change for 3MA vs DMSO
Comb/DMSO Fold change for combination vs DMSO
GO Term columns Binary (0/1) indicators for subcellular localization

Chapter 2: Data Normalization

Required file: Peptide search results from mass spectrometry search software (e.g., Sequest, MSFragger)

Expected columns:

Column Description
Reference Protein reference (should contain "HUMAN")
Peptide Full peptide sequence with modifications
Trimmed.Peptide Clean peptide sequence
XCorr Cross-correlation score
PPM Mass error in parts per million
Sum.Sn Sum of signal-to-noise ratios
TMT channels Columns for each TMT reporter ion (126Sn, 127nSn, etc.)

Note: The raw data file is typically generated by your MS search software. Adjust column names and filtering thresholds based on your search engine.

Chapter 4: Enrichment Analysis

Required external databases:

CORUM Database (Protein Complexes)

  1. Visit: https://mips.helmholtz-muenchen.de/corum/download
  2. Download: UniProt-CORUM Mapping (corum_uniprotCorumMapping.txt)
  3. Place in: Chapter_4_Enrichment_Analysis/training_data/

Format:

Column Description
corum_id CORUM complex identifier
UniProtKB_accession_number UniProt accession

Pfam Database (Protein Domains)

  1. Visit: https://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam37.0/proteomes/
  2. Download: 9606.tsv.gz (human proteome, ~2.8MB)
  3. Decompress and place in: Chapter_4_Enrichment_Analysis/training_data/

Format: Tab-separated file with protein domain annotations. Column X6 contains Pfam IDs, Column X1 contains protein identifiers.

Chapter 5: Structure Analysis

Required files:

Sequence Data

  • HEK_Nterm_Kd_half_life_sequence.csv: Contains N-terminal peptide sequences for physicochemical analysis
  • common_Nterm_protein.csv: Protein list with modification positions

AlphaFold Structure Data

For structural analysis, you need to download AlphaFold predicted structures:

  1. CIF files (Crystallographic Information Files):

    • Download from: https://alphafold.ebi.ac.uk/
    • Or use StructureMap's download_alphafold_cif() function
    • Place in: training_data/alphafold_cif/

  2. PAE files (Predicted Aligned Error):

    • Download from: https://alphafold.ebi.ac.uk/
    • Or use StructureMap's download_alphafold_pae() function
    • Place in: training_data/alphafold_pae/

File naming convention:

  • CIF: AF-{UniProt_ID}-F1-model_v4.cif
  • PAE: AF-{UniProt_ID}-F1-predicted_aligned_error_v4.json

Generating Your Own Data

From Mass Spectrometry Experiments

If you have your own TMT-labeled proteomics data:

  1. Run your raw files through a search engine (Sequest, MSFragger, MaxQuant)

  2. Export peptide-spectrum matches (PSMs) as CSV/TSV

  3. Ensure your export includes:

    • Protein accession (UniProt format preferred)
    • Peptide sequences
    • Quality scores (XCorr, hyperscore, or equivalent)
    • TMT reporter ion intensities
    • Mass error (PPM)

  4. Adjust the filtering thresholds in Chapter 2 based on your search engine and data quality

Using Public Datasets

You can practice with public proteomics data from:

Search for TMT-labeled human proteomics datasets and download the search results.

Data Format Checklist

Before starting each chapter, verify your data meets these requirements:

  • Files are in CSV or TSV format (Excel files work for Chapter 1 only)
  • Column names don't contain special characters that R can't handle
  • Protein identifiers are in UniProt format (e.g., P12345)
  • Numeric values don't contain text or special characters
  • Missing values are empty cells or NA (not "N/A" or "-")

Troubleshooting

Common Issues

"File not found" errors:

  • Check that files are in the correct training_data/ subfolder
  • Verify file names match exactly (case-sensitive on Linux/macOS)

"Column not found" errors:

  • Your data may have different column names than the tutorial
  • Use colnames(your_data) to see available columns
  • Modify the select() and rename() calls accordingly

Encoding issues:

  • Ensure files are saved with UTF-8 encoding
  • On Windows, use read_csv(..., locale = locale(encoding = "UTF-8"))

Getting Help

If you encounter data format issues:

  1. Check the expected format tables above
  2. Open an issue on GitHub with:
    • Chapter number
    • Error message
    • First few lines of your data (redacted if sensitive)