SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis.

ABSTRACT: The morphology of protein assemblies impacts their behaviour and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system- and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data

SUBMITTER: Bender SWB 

PROVIDER: S-EPMC10897458 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature