Biology subjects

Beaudoin, C.

Publications and source records attributed to Beaudoin, C..

2 recordsLinked to original sources

bioRxiv2025

Resting-state functional MRI derivatives: A dataset derived from the The Comprehensive Assessment of Neurodegeneration and Dementia Study

Resting-state functional connectivity (RSFC) holds promise for the detection and characterisation of dementia. The Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) Study, by the Canadian Consortium on Neurodegeneration in Aging (CCNA), provides a unique resource to study deeply phenotyped neurodegenerative conditions. We present RSFC derivatives for 784 participants (data release 7 of the cohort) who were either cognitively unimpaired or diagnosed primarily with Alzheimers disease (AD), mixed dementia (AD with a vascular component), mild cognitive impairment (MCI), vascular MCI, frontotemporal dementia, Parkinsons disease with or without MCI or dementia, Lewy body disease or subjective cognitive impairment. Functional MRI scans were preprocessed using fMRIPrep, and time-series and whole-brain connectomes generated using three atlases at multiple resolutions, denoised using seven different techniques. High-motion artifacts were managed using a liberal quality control threshold appropriate for an older clinical population, resulting in data from 680 participants. These derivatives are made available to the research community to accelerate research on RSFC biomarkers of neurodegenerative disease, reducing duplication of effort, saving computational resources, and improving standardisation across studies.

neuroscience↗

bioRxiv2020

Producing Polished Prokaryotic Pangenomes with the Panaroo Pipeline

Population-level comparisons of prokaryotic genomes must take into account the substantial differences in gene content, resulting from frequent horizontal gene transfer, gene duplication and gene loss. However, the automated annotation of prokaryotic genomes is imperfect, and errors due to fragmented assemblies, contamination, diverse gene families and mis-assemblies accumulate over the population, leading to profound consequences when analysing the set of all genes found in a species. Here we introduce Panaroo, a graph based pangenome clustering tool that is able to account for many of the sources of error introduced during the annotation of prokaryotic genome assemblies. We verified our approach through extensive simulations of de novo assemblies using the infinitely many genes model and by analysing a number of publicly available large bacterial genome datasets. Using a highly clonal Mycobacterium tuberculosis dataset as a negative control case, we show that failing to account for annotation errors can lead to pangenome estimates that are dominated by error. We additionally demonstrate the utility of the improved graphical output provided by Panaroo by performing a pan-genome wide association study in Neisseria gonorrhoeae and by analysing gene gain and loss rates across 51 of the major global pneumococcal sequence clusters. Panaroo is freely available under an open source MIT licence at https://github.com/gtonkinhill/panaroo.

genomics↗