Abstract
Summary Biomolecules shift their structures as a function of temperature and concentrations of protons, ions, small molecules, proteins, and nucleic acids. These transitions impact or underlie biological function and are being monitored at increasingly high throughput. For example, folding transitions for large collections of RNAs can now be monitored at single residue resolution by chemical mapping techniques. LIkelihood-based Fits of Folding Transitions (LIFFT) quantifies these data through well-defined thermodynamic models. LIFFT implements a Bayesian framework that takes into account data at all measured residues and enables visual assessment of modeling uncertainties that can be overlooked in least-squares fits. The framework is appropriate for multimodal techniques ranging from chemical mapping including multi-wavelength spectroscopy.
Availability Freely available MATLAB package at https://ribokit.stanford.edu/LIFFT/.
Contact rhiju{at}stanford.edu
Supplementary information Supplementary data are available at Bioinformatics online.