Biology subjects

Charleston, M. A.

Publications and source records attributed to Charleston, M. A..

4 recordsLinked to original sources

bioRxiv2025

Robust Phylogenetics

We present a robust phylogenetic inference method, called the trimmed log-likelihood method, which effectively identifies fast-evolving, saturated, or erroneous sites in both simulated and empirical multiple sequence alignments. This method avoids circularity by dynamically identifying and removing sites without relying on an initial tree, allowing the specific sites removed to change as tree topology and branch lengths are estimated. Our analyses demonstrate that this method outperforms existing approaches, such as the Slow-Fast method, Tree Independent Generation of Evolutionary Rate approach, and Le Quesne Probability statistics, by removing fewer sites while still inferring phylogenies with comparable or greater accuracy. Implemented in IQ-TREE2, the trimmed log-likelihood method is user-friendly with a simple command-line interface. However, challenges remain in addressing heterogeneous evolutionary processes including compositional biases, such as GC bias. Despite these challenges, our approach offers a practical solution for improving phylogenetic inference by automatically and dynamically identifying sites to down-weight during phylogenetic analyses. We recommended that researchers compare trees inferred by varying the proportion of down-weighted sites to monitor changes in tree topology and to identify a set of candidate tree topologies for further consideration.

evolutionary biology↗

bioRxiv2024

Shape-constrained, changepoint additive models for time series omics data with cpam

Time series omics experiments are critical for the study of a wide range of biological processes such as cell differentiation and developmental programs or responses to pathogens and environmental cues. While statistical tools for differential analysis across static conditions have matured, comparable comprehensive methodology is lacking for time series data. Here, we introduce cpam, a novel time series method and user-friendly software that performs temporal differential analysis of omics data for case-only or case-control time series, incorporating quantification uncertainty. Powerful features of cpam include changepoint detection and shape-constrained temporal trend estimation to allocate omics data into similar clusters. Performance evaluation shows that cpam outperforms existing time series methods in terms of control of the false discovery rate versus power to detect temporal changes and accurate changepoint estimation. In particular, we find that trend-based models provide superior performance compared to pairwise comparisons, even with as few as six time points. The software provides an interactive interface with modern customizable visualisations, offering both graphical and statistical insight into complex molecular processes. Application to published transcriptome data illustrates RNA isoform-level modelling together with high resolution clustering during human embryogenesis and the identification of 910 novel genes differentially expressed in response to excess light in Arabidopsis. This method has the potential for application to a range of omics data, including single-cell analyses via pseudobulked data, providing the analytical power to reveal complex temporal patterns of gene expression in any biological system.

bioinformatics↗

bioRxiv2024

Spectral Cluster Supertree: fast and statistically robust merging of rooted phylogenetic trees

AO_SCPLOWBSTRACTC_SCPLOWThe algorithms for phylogenetic reconstruction are central to computational molecular evolution. The relentless pace of data acquisition has exposed their poor scalability and the conclusion that the conventional application of these methods is impractical and not justifiable from an energy usage perspective. Furthermore, the drive to improve the statistical performance of phylogenetic methods produces increasingly parameter-rich models of sequence evolution, which worsens the computational performance. Established theoretical and algorithmic results identify supertree methods as critical to divide-and-conquer strategies for improving scalability of phylogenetic reconstruction. Of particular importance is the ability to explicitly accommodating rooted topologies. These can arise from the more biologically plausible non-stationary models of sequence evolution. We make a contribution to addressing this challenge with Spectral Cluster Supertree, a novel supertree method for merging a set of overlapping rooted phylogenetic trees. It offers significant improvements over Min-Cut supertree and previous state-of-the-art methods in terms of both time complexity and overall topological accuracy, particularly for problems of large size. We perform comparisons against Min-Cut supertree and Bad Clade Deletion. Leveraging two tree topology distance metrics, we demonstrate that while Bad Clade Deletion generates more correct clades in its resulting supertree, Spectral Cluster Supertrees generated tree is generally more topologically close to the true model tree. Over large datasets containing 10000 taxa and -500 source trees, where Bad Clade Deletion usually takes -2 hours to run, our method generates a supertree in on average 20 seconds. Spectral Cluster Supertree is released under an open source license and is available on the python package index as sc-supertree. This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.

bioinformatics↗

bioRxiv2020

Ancient and modern genomes reveal microsatellites maintain a dynamic equilibrium through deep time

Microsatellites are widely used in population genetics, but their evolutionary dynamics remain poorly understood. It is unclear whether microsatellite loci drift in length over time. We identify more than 27 million microsatellites using a novel and unique dataset of modern and ancient Adelie penguin genomes along with data from 63 published chordate genomes. We investigate microsatellite evolutionary dynamics over two time scales: one based on the Adelie penguin samples dating to approximately 46.5 kya, the other dating to the diversification of chordates more than 500 Mya. We show that the process of microsatellite allele length evolution is at dynamic equilibrium; while there is length polymorphism among individuals, the length distribution for a given locus remains stable. Many microsatellites persist over very long time scales, particularly in exons and regulatory sequence. These often retain length variability, suggesting that they may play a role in the maintenance of evolutionary plasticity.

evolutionary biology↗