Massively parallel analysis of single-molecule dynamics on next generation sequencing chips - Aguirre Rivera, Javier; Mao, Guanzhong; Sabantcev, Anton; Panfilov, Mikhail; Lindell, Magnus; Chanez, Christelle; Ritort, Felix; Jinek, Martin; Deindl, Sebastian; Hou, Qinhan (Uppsala University, 2024)
Data and codes underlying the findings of the manuscript "Massively parallel analysis of single-molecule dynamics on next generation sequencing chips". Please see the Materials and Methods section of the manuscript for the description of experimental procedures. For every figure in the paper a README file is provided, listing the datasets used for the figure, as well as the MATLAB analysis codes used to generate the respective plots. The latest version of the Python codes for matching single-molecule FRET traces with sequenced clusters is available at https://github.com/deindllab/MUSCLE/.

The abstract of the manuscript: "Single-molecule techniques are ideally poised to characterize complex dynamics but are typically limited to investigating a small number of different samples. However, a large sequence or chemical space often needs to be explored to derive a comprehensive understanding of complex biological processes. Here we describe MUltiplexed Single-molecule Characterization at the Library scalE (MUSCLE), a method that combines single-molecule fluorescence microscopy with next-generation sequencing to enable highly multiplexed observations of complex dynamics. We comprehensively profile the sequence dependence of DNA hairpin properties and Cas9-induced target DNA unwinding/rewinding dynamics. For Cas9, the ability to explore a large sequence space allowed us to identify a number of target sequences with unexpected behaviors. We envision that MUSCLE will enable the mechanistic exploration of many fundamental biological processes."



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Keywords: Biochemistry and cell biology not elsewhere classified; Medical and Health Sciences; Medicin och hälsovetenskap; Biochemistry; Biokemi