Deep-LASI, single-molecule data analysis software

Asadiatouei, Pooyeh; Salem, Clemens-Bässem; Wanninger, Simon; Ploetz, Evelyn und Lamb, Don C. (2024): Deep-LASI, single-molecule data analysis software. In: Biophysical Journal [Forthcoming]

Abstract

By avoiding ensemble averaging, single-molecule methods provide novel means of extracting mechanistic insights into function of material and molecules at the nanoscale. However, one of the big limitations is the vast amount of data required for analyzing and extracting the desired information, which is time-consuming and user dependent. Here, we introduce Deep-LASI, a software suite for the manual and automatic analysis of single-molecule traces, interactions, and the underlying kinetics. The software can handle data from one-, two- and three-color fluorescence data, and was particularly designed for the analysis of two- and three-color single-molecule fluorescence resonance energy transfer experiments. The functionalities of the software include: the registration of multiple-channels, trace sorting and categorization, determination of the photobleaching steps, calculation of fluorescence resonance energy transfer correction factors, and kinetic analyses based on hidden Markov modeling or deep neural networks. After a kinetic analysis, the ensuing transition density plots are generated, which can be used for further quantification of the kinetic parameters of the system. Each step in the workflow can be performed manually or with the support of machine learning algorithms. Upon reading in the initial data set, it is also possible to perform the remaining analysis steps automatically without additional supervision. Hence, the time dedicated to the analysis of single-molecule experiments can be reduced from days/weeks to minutes. After a thorough description of the functionalities of the software, we also demonstrate the capabilities of the software via the analysis of a previously published dynamic three-color DNA origami structure fluctuating between three states. With the drastic time reduction in data analysis, new types of experiments become realistically possible that complement our currently available palette of methodologies for investigating the nanoworld.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Chemie und Pharmazie
• Fakultätsübergreifende Einrichtungen: Center for NanoScience (CENS)
• Themengebiete: 500 Naturwissenschaften und Mathematik > 540 Chemie
• ISSN: 00063495
• Sprache: Englisch