Rebecca R. Murphy
Bitbucket repository
Departmental Webpage
Email: rrm33 at

I'm a third-year PhD student at the University of Cambridge, working in the Department of Chemistry and supervised by David Klenerman and Sophie Jackson. In my research, I develop techniques that use Bayesian inference to analyse data from single molecule fluorescence experiments. We apply these techniques to problems in protein folding and protein aggregation, using single-molecule FRET.

Outside the lab, I enjoy learning about Machine Learning and Artificial Intelligence and have participated in several MOOCs on Coursera. I completed my undergraudate studies (BA and MSci in Natural Sciences) at the University of Cambridge in 2011.



  • Rebecca R. Murphy, George Danezis, Mathew H. Horrocks, Sophie E. Jackson and David Klenerman. Bayesian Inference of Accurate Intramolecular Distances from Single Diffusing Biomolecules. Analytical Chemistry.
  • Elizabeth F. Werrell, William J.K. Crone, Rebecca R. Murphy, Shang-Te Danny Hsu and Sophie E. Jackson. Oxidative damage to the Parkinsons Disease associated protein UCH-L1 exposes a hidden partially unfolded state. In preparation.
  • Rebecca R. Murphy, Jared O'Connell, Anthony J. Cox and Ole Schulz-Trieglaff. NxRepair: Error correction in de novo sequence assembly using Nextera mate pairs. In preparation.

Conference Presentations

Poster Presentations

  • Rebecca R. Murphy, George Danezis, Sophie E. Jackson and David Klenerman. Accurate Intramolecular Distances by Single Molecule Confocal Spectroscopy: A Monte Carlo Markov Chain Analysis of Fluorescence Data from Freely Diffusing Biomolecules. Presented at the Biophysical Society 57th Annual Meeting, February 2013 and the International Workshop on Single-Molecule Spectroscopy, September 2013

Current Research

  • pyFRET: Open-source python library for analysis of single-molecule FRET data
  • FRETinfer: Bayesian analysis of single-molecule FRET data
  • SizeInfer: Accurate determination of oligomer sizes using Bayesian inference
  • QUENCH: Identifying good labelling sites from protein pdb records