Reverse Engineering Byos .blgc Files: Automating the 80/20 of Peptide Mapping

In the world of biopharmaceutical mass spectrometry, Byos (Protein Metrics) is a staple for peptide mapping. However, even with powerful software, the “80/20 rule” remains a painful reality: while 90% of your data might be processed perfectly, the remaining 10%—the complex samples or low-quality signals—consumes 80% of your manual analysis time.

I recently discovered a way to break this bottleneck: Byos output files (.blgc) are actually SQLite database files.

The Discovery: Under the Hood of .blgc

By realizing that .blgc files are standard SQL databases, I opened up the possibility of using Python to query, retrieve, and even edit the underlying data directly. This is a game-changer for automating the tedious manual adjustments typically done through the GUI.

Workflow Phase 1: AI-Driven Schema Research

The first challenge was understanding the complex database schema. I employed an “auto-research” strategy using AI agents like Codex and Claude.

Instead of manually guessing table relationships, I provided the agents with real .blgc data. I instructed them to:

1. Open the SQLite database.
2. Inspect all tables and their column definitions.
3. Identify the relationships between spectral data, peptide assignments, and integration parameters.
4. Document the entire schema into structured reference files.

By combining real-world data inspection with web-based research, the AI was able to build a comprehensive map of how Byos stores its analysis results.

Workflow Phase 2: Python-Based Automation

With the schema understood, I began implementing Python scripts to edit these databases. The most critical step was ensuring software compatibility: I verified that any file edited via Python could still be opened and read by the Byos software without corruption.

Key Automation Use Cases

I am focusing on automating two of the most time-consuming manual tasks:

1. Peak Integration Alignment: Often, integration time windows aren’t perfectly aligned across different samples or charge states, especially for weak signals. I now use Python to optimize and align these Integration Time Windows automatically, ensuring consistency across the entire dataset.
2. Correcting Peptide Assignments: For complex spectra or long retention times, Byos often provides multiple assignment alternatives. If the default choice isn’t the best one, I can now use scripts to evaluate the alternatives and programmatically switch to the correct assignment based on pre-defined logic.

Conclusion: Ending the Manual Grind

By moving beyond the GUI and interacting directly with the .blgc SQLite database, I am transforming the way I handle “difficult” data. Instead of manually clicking through hundreds of peaks, I am using Vibe Coding and AI-powered research to build tools that handle the heavy lifting, letting me focus on the science rather than the spreadsheet.