Embedded Files
Protein structures are now being resolved at the atomic level, but deciphering their molecular organization in the cell remains a challenge.
SuperResNET is an integrated machine learning-based analysis software for visualizing and quantifying 3D point cloud data acquired by single molecule localization microscopy (SMLM).
The computational modules of SuperResNET include correction for multiple blinking of a single fluorophore, denoising, segmentation (clustering), and feature extraction, which are then used for cluster group identification, modularity analysis, blob retrieval and visualization in 2D and 3D.
More recent updates to SuperResNET allow two-channel interaction distance analysis to determine how two proteins interact within macromolecular assemblies.
SuperResNET can be effectively and easily applied to any SMLM event list from which it rapidly learns macromolecular architecture in the intact cell.
SuperResNET makes super-resolution microscopy accessible to biologists, with a GUI version for analysis of individual data sets and a batch analysis version for statistical analysis of multiple replicates and conditions.
SuperResNET is an analysis tool to discover protein architecture and structural diversity in the cell
SuperResNET is an analysis tool to discover protein architecture and structural diversity in the cell
SuperResNET Features
GUI and non-GUI batch analysis versions
Imports file formats: .bin;.ascii; .xyz; .txt; .mat; .csv
Exports high-resolution figures (tiff, png, pdf, eps…) and quantitative data (for Excel, R, Matlab, Python…)
Load and easily switch between multiple datasets
SuperResNET-specific analysis methods
Alpha filtering of random network-like blinks
Merge analysis to correct for multiple blinking
30 features (Size, Shape, Topology, Network)
Feature selection and normalization
Convex hull analysis
Modularity analysis
Dual-channel interaction distance analysis
Established Analysis Methods
Ripley’s H function
K-means and DBSCAN clustering
Mean shift segmentation
Network analysis
Visualization
2D and 3D point clouds
Networks
Pairwise feature visualization
Convex hull
Retrieval of most representative blobs
Identification of blob communities (modularity)
Paired datasets loaded simultaneously
Page updated
Google Sites
Report abuse