Create README.md

Author: tkimhofer

README.md

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+# MSFileParserApp
+
+**MSFileParserApp** is a Python Dash web application that parses targeted mass spectrometry experiment from **Skyline** and converts them into a **2D matrix format** for downstream statistical or bioinformatics analysis.
+
+This tool is ideal for labs and analysts looking for a fast, browser-based interface to convert Skyline output into a pivoted matrix (samples × analytes).
+
+---
+
+## 🔍 What It Does
+
+- Parses and decodes .TXT` Skyline data files
+- Extracts quantification metrics like **Concentration**, **Response**, or **Area**
+- Supports toggling of **internal standard inclusion**
+- Transforms long-form MS data into a **pivoted matrix**: samples × analytes
+- Allows direct **CSV download** of the processed matrix
+- Provides a clean, responsive UI with `dash-mantine-components`
+
+---
+
+## 📁 Project Structure
+
+```
+MSFileParserApp/
+├── app.py                   # Dash app (recommend renaming to massspec_dash_app.py)
+├── scr/
+│   └── conv.py              # Core logic for file decoding and data transformation
+├── requirements.txt         # Python dependencies
+├── README.md                # Project overview and usage
+```
+
+---
+
+## 🛠️ Installation
+
+```bash
+# Clone the repo
+git clone https://github.com/tkimhofer/MSFileParserApp.git
+cd MSFileParserApp
+
+# Set up a virtual environment (optional but recommended)
+python -m venv venv
+source venv/bin/activate  # or venv\Scripts\activate on Windows
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+---
+
+## 🚀 Run the App
+
+```bash
+python app.py
+```
+
+Navigate to [http://127.0.0.1:8050](http://127.0.0.1:8050) in your browser.
+
+---
+
+## 📤 Output Format
+
+- A **pivoted data matrix**: rows = samples, columns = analytes
+- Index includes path and sample name
+- Exported as `.csv` with dynamic naming:
+  ```
+  df_conc_[datetime].csv
+  ```
+
+---
+
+## 🎯 Use Cases
+
+- Preprocessing of Skyline output for statistical analysis
+- Generating clean feature matrices for machine learning
+- Quick QC of targeted LC-MS data via web interface
+
+---
+
+## 📦 Dependencies
+
+- Dash
+- Pandas
+- Plotly
+- dash-mantine-components
+
+---
+
+## 📖 License
+
+MIT License
+
+---
+
+## 👩‍🔬 Acknowledgements
+
+- Developed by [@tkimhofer](https://github.com/tkimhofer)
+- Built with [Dash](https://dash.plotly.com/)
+- UI powered by [dash-mantine-components](https://www.dash-mantine-components.com/)