Biology subjects

Barrett, R. L.

Publications and source records attributed to Barrett, R. L..

4 recordsLinked to original sources

bioRxiv2025

MegaTrack: a framework for the anatomically accurate and time-efficient virtual dissection and analysis of large-scale tractography datasets

The increasing prevalence of large-scale neuroimaging initiatives has created a need for efficient and anatomically accurate methods for analysing tractography data. Here, we present MegaTrack, a framework designed to combine the anatomical precision of manual dissections with the efficiency required for big data analysis. At its core, MegaTrack operates by normalising individual tractography datasets to a standard space, concatenating them into a single "mega" tractography dataset the user manually dissects before automatically extracting subject-specific tracts and metrics in native space. This approach permits identifying and extracting tracts not yet available in existing templates or automatic dissection tools. We validated this approach using multiple datasets and applications. Comparison with individual manual dissections showed high spatial agreement (weighted dice coefficient > 0.95) and strong correlations in tract-specific metrics (R2 > 0.9). In a longitudinal dataset, MegaTrack demonstrated improved reproducibility across time points and higher inter-rater reliability compared to manual dissections. In a motor neuron disease cohort, MegaTrack replicated known group differences in corticospinal tract microstructure with comparable sensitivity to manual dissections (p = 0.001) but with significantly reduced processing time. Finally, we showcase MegaTracks scalability by creating a comprehensive white matter atlas from 140 healthy adults and demonstrate its application for lesion analysis in stroke patients. This framework is accompanied by an interactive online tool that allows users to explore these atlases and perform customised lesion analyses. The MegaTrack framework is a time-efficient solution for the anatomically precise analysis of large tractography datasets, enabling the rapid simultaneous dissection of multiple subjects while preserving individual anatomical features. The frameworks ability to efficiently process large datasets makes it an excellent tool for generating training data for machine learning and deep learning algorithms in tractography analysis. This approach has broad applications in both research and clinical settings, from group comparisons to personalised lesion mapping, and can significantly contribute to the development of advanced automated tractography methods.

neuroscience↗

bioRxiv2024

Nuclear phylogenomics of grasses (Poaceae) supports current classification and reveals repeated reticulation

O_LIGrasses (Poaceae) comprise around 11,800 species and are central for human livelihoods and terrestrial ecosystems. Knowing their relationships and evolutionary history is key to comparative research and crop breeding. Advances in genome-scale sequencing allow for increased breadth and depth of phylogenomic analyses, making it possible to infer a new reference species tree of the family. C_LIO_LIWe inferred a comprehensive species tree of grasses by combining new and published sequences for 331 nuclear genes from genome, transcriptome, target enrichment and shotgun data. Our 1,153-tip tree covers 79% of grass genera (including 21 genera sequenced for the first time) and all but two small tribes. We compared it to a 910-tip plastome tree. C_LIO_LIThe nuclear phylogeny matches that of the plastome at most deep branches, with only a few instances of incongruence. Gene tree-species tree reconciliation suggests that reticulation events occurred repeatedly in the history of grasses. C_LIO_LIWe provide a robust framework for the grass tree of life to support research on grass evolution, including modes of reticulation, and genetic diversity for sustainable agriculture. C_LI

plant biology↗

bioRxiv2023

Global analysis of Poales diversification - parallel evolution in space and time into open and closed habitats

O_LIPoales are one of the most species-rich, ecologically and economically important orders of plants and often characterise open habitats, enabled by unique suites of traits. We test the hypotheses that Poales species are assembled into distinct phyloregions, with centres of high phylogenetic diversity and endemism clustered in tropical regions, and that cosmopolitan families show parallel transitions into open and closed habitats at different times. C_LIO_LIWe sampled 42% of Poales species and obtained taxonomic and biogeographic data from the World Checklist of Vascular Plants database, which was combined with open/closed habitat data scored by taxonomic experts. A dated supertree of Poales was constructed. We integrated spatial phylogenetics with regionalization analyses, historical biogeography, ancestral state estimations, and models of contingent evolution. C_LIO_LIDiversification in Poales and assembly of open and closed habitats result from dynamic evolutionary processes that vary across lineages, time, space, and traits, most prominently in tropical and southern latitudes. Our results reveal parallel and recurrent patterns of habitat and trait transitions in the species-rich families Poaceae and Cyperaceae, yet other smaller families display unique evolutionary trajectories. C_LIO_LIThe Poales have achieved global dominance via parallel evolution in open habitats, with notable, spatially and phylogenetically restricted divergences into strictly closed habitats. C_LI

ecology↗

bioRxiv2023

The AusTraits Plant Dictionary

Traits with intuitive names, a clear scope and explicit description are essential for all trait databases. Reanalysis of data from a single database, or analyses that integrate data across multiple databases, can only occur if researchers are confident the trait concepts are consistent within and across sources. The lack of a unified, comprehensive resource for plant trait definitions has previously limited the utility of trait databases. Here we describe the AusTraits Plant Dictionary (APD), which extends the trait definitions included in the new trait database AusTraits. The development process of the APD included three steps: review and formalisation of the scope of each trait and the accompanying trait description; addition of trait meta-data; and publication in both human and machine-readable forms. Trait definitions include keywords, references and links to related trait concepts in other databases, and the traits are grouped into a hierarchy for easy searching. As well as improving the usability of AusTraits, the Dictionary will foster the integration of trait data across global and regional plant trait databases.

ecology↗