Biology subjects

Courtney, E.

Publications and source records attributed to Courtney, E..

4 recordsLinked to original sources

bioRxiv2026

Efficient and scalable modelling of cotranscriptional RNA folding with deterministic and iterative RNA structure sampling

AO_SCPLOWBSTRACTC_SCPLOWRNA structure sampling is central to modelling RNA ensembles, yet stochastic sampling methods are non-exhaustive, scale poorly, and are biased towards low-free-energy structures, while current suboptimal folding approaches generate an unpredictable exponential number of structures. These limitations are particularly problematic for modelling cotranscriptional folding, where vectorial synthesis continuously reshapes the energy landscape during transcription, stabilising transient out-of-equilibrium structures. Here we introduce iterative sampling, a deterministic framework that enumerates unique RNA secondary structures in strict order of increasing free energy, enabling progressive and exhaustive exploration of the structure space up to an arbitrary stopping criterion. To implement this approach, we developed two scalable algorithms, iterative deepening and a persistent data structure approach, that incrementally traverse the expansion tree by evolving partial structures in place, avoiding redundant recomputation and fixed energy windows. Implemented in memerna, this approach achieves orders-of-magnitude speedups over existing tools (10x over ViennaRNA; 100x over RNAstructure). Integration within the sample-and-select framework (R2D2) improves structural diversity and identifies conformations with greater agreement with experimental data. Comprehensive sampling further enables direct comparison of equilibrium and cotranscriptionally restrained ensembles. Analysis of the resulting structural probability distributions uncovers kinetic traps and putative transcriptional pause sites, supporting an intuitive cotranscriptional folding mechanism in which local 3'-hairpin formation transiently stabilises upstream structure to delay large-scale rearrangement. Together, these results establish iterative sampling as a scalable and general framework for resolving out-of-equilibrium RNA cotranscriptional folding. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/720041v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@dee200org.highwire.dtl.DTLVardef@c7af12org.highwire.dtl.DTLVardef@1b6f54dorg.highwire.dtl.DTLVardef@a2c97a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

The MicroMap is a network visualisation resource for microbiome metabolism

The human microbiome plays a crucial role in metabolism and thereby influences health and disease. Constraint-based reconstruction and analysis (COBRA) has proven an attractive framework to generate mechanism-derived hypotheses along the nutrition-host-microbiome-disease axis within the computational systems biology community. Unlike for human, no large-scale visualisation resource for microbiome metabolism has been available to date. To address this gap, we created the MicroMap, a manually curated microbiome metabolic network visualisation, which captures the metabolic content of over a quarter million microbial genome-scale metabolic reconstructions. The MicroMap contains 5,064 unique reactions and 3,499 unique metabolites, including for 98 drugs. The MicroMap allows users to intuitively explore microbiome metabolism, inspect microbial metabolic capabilities, and visualise computational modelling results. Further, the MicroMap shall serve as an educational tool to make microbiome metabolism accessible to broader audiences beyond computational modellers. For example, we utilised the MicroMap to generate a comprehensive collection of 257,429 visualisations, corresponding to the entire scope of our current microbiome reconstruction resources, to enable users to visually compare and contrast the metabolic capabilities for diaerent microbes. The MicroMap seamlessly integrates with the Virtual Metabolic Human (VMH, www.vmh.life) and the COBRA Toolbox (opencobra.github.io), and is freely accessible at the MicroMap dataverse (https://dataverse.harvard.edu/dataverse/micromap), in addition to all the generated reconstruction visualisations.

systems biology↗

bioRxiv2023

memerna: Sparse RNA Folding Including Coaxial Stacking

AO_SCPLOWBSTRACTC_SCPLOWDetermining RNA secondary structure is a core problem in computational biology. Fast algorithms for predicting secondary structure are fundamental to this task. We describe a modified formulation of the Zuker-Stiegler algorithm with coaxial stacking, a stabilizing interaction in which the ends of multi-loops are stacked. In particular, optimal coaxial stacking is computed as part of the dynamic programming state, rather than inline. We introduce a new notion of sparsity, which we call replaceability. The modified formulation along with replaceability allows sparsification to be applied to coaxial stacking as well, which increases the speed of the algorithm. We implemented this algorithm in software we call memerna, which we show to have the fastest exact RNA folding implementation out of several popular RNA folding packages supporting coaxial stacking. We also introduce a new notation for secondary structure which includes coaxial stacking, terminal mismatches, and dangles (CTDs) information.

bioinformatics↗

bioRxiv2022

Fitness Functions for RNA Structure Design

AO_SCPLOWBSTRACTC_SCPLOWAn RNA design algorithm takes a target RNA structure and finds a sequence that folds into that structure. This is fundamentally important for engineering therapeutics using RNA. Computational RNA design algorithms are guided by fitness functions, but not much research has been done on the merits of these functions. We survey current RNA design approaches with a particular focus on the fitness functions used. We experimentally compare the most widely used fitness functions in RNA design algorithms on both synthetic and natural sequences. It has been almost 20 years since the last comparison was published, and we find similar results with a major new result: maximizing probability outperforms minimizing ensemble defect. The probability is the likelihood of a structure at equilibrium and the ensemble defect is the weighted average number of incorrect positions in the ensemble. Also, we observe that many recently published approaches minimize structure distance to the minimum free energy prediction, which we find to be a poor fitness function.

bioinformatics↗