BioXTAS RAW 2: new developments for a free open-source program for small angle scattering data reduction and analysis.

bioRxiv

The Biophysics Collaborative Access Team (BioCAT), Department of Physics, Illinois Institute of Technology, Chicago, IL 60616, USA.

Published: December 2023

BioXTAS RAW is a free, open-source program for reduction, analysis and modelling of biological small angle scattering data. Here, the new developments in RAW version 2 are described. These include: improved data reduction using pyFAI; updated automated Guinier fitting and finding algorithms; automated series (e.g. SEC-SAXS) buffer and sample region finding algorithms; linear and integral baseline correction for series; deconvolution of series data using REGALS; creation of electron density reconstructions via DENSS; a comparison window showing residuals, ratios, and statistical comparisons between profiles; and generation of PDF reports with summary plots and tables for all analysis. In addition, there is now a RAW API, which can be used without the GUI, providing full access to all of the functionality found in the GUI. In addition to these new capabilities, RAW has undergone significant technical updates, such as adding Python 3 compatibility, and has entirely new documentation available both online and in the program.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557611PMC
http://dx.doi.org/10.1101/2023.09.25.559353DOI Listing

Publication Analysis

Top Keywords

bioxtas raw
8
free open-source
8
open-source program
8
small angle
8
angle scattering
8
scattering data
8
data reduction
8
reduction analysis
8
finding algorithms
8
raw developments
4

Similar Publications

: new developments for a free open-source program for small-angle scattering data reduction and analysis.

J Appl Crystallogr

February 2024

The Biophysics Collaborative Access Team (BioCAT), Department of Physics, Illinois Institute of Technology, Chicago, IL 60616, USA.

is a free open-source program for reduction, analysis and modelling of biological small-angle scattering data. Here, the new developments in version 2 are described. These include improved data reduction using ; updated automated Guinier fitting and finding algorithms; automated series ( size-exclusion chromatography coupled small-angle X-ray scattering or SEC-SAXS) buffer- and sample-region finding algorithms; linear and integral baseline correction for series; deconvolution of series data using regularized alternating least squares (); creation of electron-density reconstructions using electron density via solution scattering (); a comparison window showing residuals, ratios and statistical comparisons between profiles; and generation of PDF reports with summary plots and tables for all analysis.

View Article and Find Full Text PDF

BioXTAS RAW 2: new developments for a free open-source program for small angle scattering data reduction and analysis.

bioRxiv

December 2023

The Biophysics Collaborative Access Team (BioCAT), Department of Physics, Illinois Institute of Technology, Chicago, IL 60616, USA.

BioXTAS RAW is a free, open-source program for reduction, analysis and modelling of biological small angle scattering data. Here, the new developments in RAW version 2 are described. These include: improved data reduction using pyFAI; updated automated Guinier fitting and finding algorithms; automated series (e.

View Article and Find Full Text PDF

is a graphical-user-interface-based free open-source Python program for reduction and analysis of small-angle X-ray solution scattering (SAXS) data. The software is designed for biological SAXS data and enables creation and plotting of one-dimensional scattering profiles from two-dimensional detector images, standard data operations such as averaging and subtraction and analysis of radius of gyration and molecular weight, and advanced analysis such as calculation of inverse Fourier transforms and envelopes. It also allows easy processing of inline size-exclusion chromatography coupled SAXS data and data deconvolution using the evolving factor analysis method.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!