Jes Frellsen
now a Associate Professor at Technical University of Denmark
Publications
Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts
Wouter Boomsma, Pengfei Tian, Jes Frellsen, Jesper Ferkinghoff-Borg, Thomas Hamelryck, Kresten Lindorff-Larsen, Michele Vendruscolo, 2014. (Proceedings of the National Academy of Sciences). DOI: 10.1073/pnas.1404948111.
Abstract▼
Methods of protein structure determination based on NMR chemical shifts are becoming increasingly common. The most widely used approaches adopt the molecular fragment replacement strategy, in which structural fragments are repeatedly reassembled into different complete conformations in molecular simulations. Although these approaches are effective in generating individual structures consistent with the chemical shift data, they do not enable the sampling of the conformational space of proteins with correct statistical weights. Here, we present a method of molecular fragment replacement that makes it possible to perform equilibrium simulations of proteins, and hence to determine their free energy landscapes. This strategy is based on the encoding of the chemical shift information in a probabilistic model in Markov chain Monte Carlo simulations. First, we demonstrate that with this approach it is possible to fold proteins to their native states starting from extended structures. Second, we show that the method satisfies the detailed balance condition and hence it can be used to carry out an equilibrium sampling from the Boltzmann distribution corresponding to the force field used in the simulations. Third, by comparing the results of simulations carried out with and without chemical shift restraints we describe quantitatively the effects that these restraints have on the free energy landscapes of proteins. Taken together, these results demonstrate that the molecular fragment replacement strategy can be used in combination with chemical shift information to characterize not only the native structures of proteins but also their conformational fluctuations.
Combining the multicanonical ensemble with generative probabilistic models of local biomolecular structure
Jes Frellsen, Thomas Hamelryck, Jesper Ferkinghoff-Borg, 2014. (In Proceedings of the 59th World Statistics Congress of the International Statistical Institute). Hong Kong.
Abstract▼ URL
Markov chain Monte Carlo is a powerful tool for sampling complex systems such as large biomolecular structures. However, the standard Metropolis-Hastings algorithm suffers from a number of deficiencies when applied to systems with rugged free-energy landscapes. Some of these deficiencies can be addressed with the multicanonical ensemble. In this paper we will present two strategies for applying the multicanonical ensemble to distributions constructed from generative probabilistic models of local biomolecular structure. In particular, we will describe how to use the multicanonical ensemble efficiently in conjunction with the reference ratio method.
Bayesian generalised ensemble Markov chain Monte Carlo
Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg, May 2016. (In 19th International Conference on Artificial Intelligence and Statistics). Cadiz, Spain.
Abstract▼
Bayesian generalised ensemble (BayesGE) is a new method that addresses two major drawbacks of standard Markov chain Monte Carlo algorithms for inference in high-dimensional probability models: inapplicability to estimate the partition function, and poor mixing properties. BayesGE uses a Bayesian approach to iteratively update the belief about the density of states (distribution of the log likelihood under the prior) for the model, with the dual purpose of enhancing the sampling efficiency and make the estimation of the partition function tractable. We benchmark BayesGE on Ising and Potts systems and show that it compares favourably to existing state-of-the-art methods.
Adaptable probabilistic mapping of short reads using position specific scoring matrices
Peter Kerpedjiev, Jes Frellsen, Stinus Lindgreen, Anders Krogh, 2014. (BMC bioinformatics). DOI: 10.1186/1471-2105-15-100.
Abstract▼
BACKGROUND: Modern DNA sequencing methods produce vast amounts of data that often requires mapping to a reference genome. Most existing programs use the number of mismatches between the read and the genome as a measure of quality. This approach is without a statistical foundation and can for some data types result in many wrongly mapped reads. Here we present a probabilistic mapping method based on position-specific scoring matrices, which can take into account not only the quality scores of the reads but also user-specified models of evolution and data-specific biases.RESULTS:We show how evolution, data-specific biases, and sequencing errors are naturally dealt with probabilistically. Our method achieves better results than Bowtie and BWA on simulated and real ancient and PAR-CLIP reads, as well as on simulated reads from the AT rich organism P. falciparum, when modeling the biases of these data. For simulated Illumina reads, the method has consistently higher sensitivity for both single-end and paired-end data. We also show that our probabilistic approach can limit the problem of random matches from short reads of contamination and that it improves the mapping of real reads from one organism (D. melanogaster) to a related genome (D. simulans). CONCLUSION: The presented work is an implementation of a novel approach to short read mapping where quality scores, prior mismatch probabilities and mapping qualities are handled in a statistically sound manner. The resulting implementation provides not only a tool for biologists working with low quality and/or biased sequencing data but also a demonstration of the feasibility of using a probability based alignment method on real and simulated data sets.
Comment: Peter Kerpedjiev and Jes Frellsen contributed equally. Additional resources are available at bwa-pssm.binf.ku.dk
On the Accuracy of Short Read Mapping
Peter Menzel, Jes Frellsen, Mireya Plass, Simon H. Rasmussen, Anders Krogh, 2013. (In Deep Sequencing Data Analysis). Springer. DOI: 10.1007/978-1-62703-514-9_3.
Abstract▼
The development of high-throughput sequencing technologies has revolutionized the way we study genomes and gene regulation. In a single experiment, millions of reads are produced. To gain knowledge from these experiments the first thing to be done is finding the genomic origin of the reads, i.e., mapping the reads to a reference genome. In this new situation, conventional alignment tools are obsolete, as they cannot handle this huge amount of data in a reasonable amount of time. Thus, new mapping algorithms have been developed, which are fast at the expense of a small decrease in accuracy. In this chapter we discuss the current problems in short read mapping and show that mapping reads correctly is a nontrivial task. Through simple experiments with both real and synthetic data, we demonstrate that different mappers can give different results depending on the type of data, and that a considerable fraction of uniquely mapped reads is potentially mapped to an incorrect location. Furthermore, we provide simple statistical results on the expected number of random matches in a genome (E-value) and the probability of a random match as a function of read length. Finally, we show that quality scores contain valuable information for mapping and why mapping quality should be evaluated in a probabilistic manner. In the end, we discuss the potential of improving the performance of current methods by considering these quality scores in a probabilistic mapping program.
Comment: Peter Menzel and Jes Frellsen contributed equally.