SAXSier Suite

To view everything at a glance

An integrated, user-friendly suite for reduction, analysis, and visualization of SAXS and SEC-SAXS data.

The idea is to get a maximum of information through various plots (Guinier, Nomalized Kratky, Volume of Corelation) at a glance.

All plots are automatically save as image and txt files in dedicated folder. This (at least for me) helps to keep an insightful trace of the analysis.

Standalone version are available below

A small Wiki is here

📖 Overview

SAXSier is a software toolkit designed to streamline the Small-Angle X-ray Scattering (SAXS) analysis pipeline. It facilitates buffer subtraction for SEC-SAXS, automated Guinier assessment, molecular weight estimation, and Pair-Distance Distribution $P(r)$ calculations, bridging the gap between raw beamline data and biophysical interpretation.

The suite is built with Python 3.10, PySide6, and Matplotlib, ensuring a modern, responsive interface for both Windows MacOS and Linux

🛠️ Included Tools

SAXSier v4.x includes four specialized modules accessible via a central launcher:

1. Ragtime (v5.x) - SEC-SAXS Analysis

Designed for Size-Exclusion Chromatography coupled with SAXS.

Unified Visualization: with I(0)vsRg and I(0)vs MW but also for individual frame form Factor, Guinier fit, Kratky plot, and Volume of Correlation analysis in a single window, enabling immediate data quality assessment at a glance.

It allows you to visualize frame by frame the Saxs curve, Guinier (RG, I(0)) , Normalized Kratky plot, Volume of corelation (MW), in order to peak the best region to average and keep for further analysis.

2. Sexier (v7.x) - Detailed Structural Analysis

A comprehensive tool for analyzing single scattering profiles.

Guinier & Kratky: Instant visualization of linearity and folding state, (RG, I(0)).

P(r) Distribution: Real-space analysis using BIFT (Bayesian Indirect Fourier Transform) for $Dmax$.

Molecular Weight: Estimates MW using the Volume of Correlation ($V_c$) and Porod Invariant.

3. SAXSting (v3.x) - Comparison & Averaging

Curve Superimposition: Compare multiple datasets visually.

Normalization: View raw or normalized data ($I/I_0$).

it also facilitates the visualization of all key parameters ($R_g$, MW, Kratky) to judge the differences between several scattering curves in a single glance.

Averaging: Statistical averaging of selected curves with error propagation.

4. SubMe (v3.x) - Buffer Subtraction

Baseline Correction: Advanced subtraction using linear baselines (drift correction).

Averaging: Standard average buffer subtraction.

Visualization: Real-time preview of the subtracted signal.

⚙️ Installation

Clone or download this repository.

Create the environment using the provided environment.yml file:

conda env create -f environment.yml

Activate the environment:

conda activate saxsier_env

Put all scripts in the same folder then run

Run the launcher:

python SAXSier-v4.x.py

📥 Download

Mac standalone version can be downloaded :

here

If the app doesn't start go to 1. Open System Settings (or System Preferences on older macOS versions). 2. Go to Privacy & Security. 3. Scroll down to the Security section. 4. If macOS has blocked the app, you’ll see a message saying it was prevented from opening (I'm not a officla develloper). 5. Click “Open Anyway” to allow the app to run.

Windows standalone can be found:

here

Linux standalone can be found (coming soon)

🧪 Methodology & References

If you use SAXSier in your research, please cite the original works used within the software:

Guinier Analysis ($R_g$ & $I_0$)

Guinier, A. (1939). La diffraction des rayons X aux très petits angles: application à l'étude de phénomènes ultramicroscopiques. Annales de Physique, 11(12), 161-237.

Molecular Weight (Volume of Correlation)

Rambo, R. P., & Tainer, J. A. (2013). Accurate assessment of mass, models and resolution by small-angle scattering. Nature, 496(7446), 477-481.

P(r) Distribution (BIFT)

The $P(r)$ calculation in Sexier utilizes the Bayesian Indirect Fourier Transform.

Hansen, S. (2000). Bayesian estimation of the pair distance distribution function from small-angle scattering data. Journal of Applied Crystallography, 33(6), 1415-1421.

Glatter, O. (1977). A new method for the evaluation of small-angle scattering data. Journal of Applied Crystallography, 10(5), 415-421.

Hopkins, J. B. (2024). BioXTAS RAW 2: new developments for a free open-source program for small-angle scattering data reduction and analysis. . Journal of Applied Crystallography (2024), 57, 194-208.

Special Thanks:

Acknowledgment to Jesse B. Hopkins with Raw, by making everything available it helps me a lot . (for instence, clearly, I had no idea how to code bift).

📄 License* (because github ask for one)

Basically be aware it has been coded by a stupid biochemist (for this matter me), so might be some problems... And do whatever you want to improve it.