Biology subjects

Vermeiren, R. R. J. M.

Publications and source records attributed to Vermeiren, R. R. J. M..

4 recordsLinked to original sources

bioRxiv2023

A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object

BackgroundApplying good data management and FAIR data principles (Findable, Accessible, Interoperable, and Reusable) in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FindingsWe conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. ConclusionsAlong with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice. O_TEXTBOXKey PointsO_LIThe FAIR4RS principles provide guidelines to enhance the discovery and reuse of research software. C_LIO_LIFAIR Digital Objects support Findability, Accessibility, Interoperability, and Reusability by both humans and machines. C_LIO_LIWe here demonstrate the implementation multi-omics data analysis workflow and share it as a FAIR Digital Object. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2022

Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children

Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7 - 12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The mean monozygotic (MZ) and dizygotic (DZ) correlations for urinary metabolites were 0.51 (range: 0.25-0.75) and 0.16 (range: 0.01-0.46) for the amines, 0.52 (range: 0.33-0.64) and 0.23 (range: 0.07-0.35) for the organic acids, and 0.61 (range: 0.43-0.81) and 0.25 (range: 0.11-0.44) for the steroids. Broad-sense heritability was 0.49 (range: 0.25-0.64), 0.50 (range: 0.33-0.62), and 0.64 (range: 0.43-0.81) for 50 amines, 13 organic acids, and 6 steroids, and narrow-sense heritability was 0.50 (range: 0.37-0.68), 0.50 (0.23-0.61), and 0.47 (range: 0.32-0.70) for 6 amines, 7 organic acids, and 4 steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.

genomics↗

bioRxiv2021

A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site- specific profiles in participants demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,343 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,067 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM were more sensitive to the detection of significant case-control differences in regional cortical thickness (X2(3) = 34.339, p < 0.001), and case-control differences in age-related cortical thinning (X2(3) = 15.128, p = 0.002). Specifically, ComBat-GAM led to larger effect size estimates of cortical thickness reductions (corrected p-values < 0.001), smaller age-appropriate declines (corrected p-values < 0.001), and lower female to male contrast (corrected p-values < 0.001) in cases compared to controls relative to other harmonization methods. Harmonization with ComBat-GAM also led to greater estimates of age-related declines in cortical thickness (corrected p-values < 0.001) in both cases and controls compared to other harmonization methods. Our results support the use of ComBat-GAM for harmonizing cortical thickness data aggregated from multiple sites and scanners to minimize confounds and increase statistical power.

neuroscience↗

bioRxiv2021

Structural Covariance Networks in Post-Traumatic Stress Disorder: A Multisite ENIGMA-PGC Study

IntroductionCortical thickness (CT) and surface area (SA) are established biomarkers of brain pathology in posttraumatic stress disorder (PTSD). Structural covariance networks (SCN) constructed from CT and SA may represent developmental associations, or unique interactions between brain regions, possibly influenced by a common causal antecedent. The ENIGMA-PGC PTSD Working Group aggregated PTSD and control subjects data from 29 cohorts in five countries (n=3439). MethodsUsing Destrieux Atlas, we built SCNs and compared centrality measures between PTSD subjects and controls. Centrality is a graph theory measure derived using SCN. ResultsNotable nodes with higher CT-based centrality in PTSD compared to controls were left fusiform gyrus, left superior temporal gyrus, and right inferior temporal gyrus. We found sex-based centrality differences in bilateral frontal lobe regions, left anterior cingulate, left superior occipital cortex and right ventromedial prefrontal cortex (vmPFC). Comorbid PTSD and MDD showed higher CT-based centrality in the right anterior cingulate gyrus, right parahippocampal gyrus and lower SA-based centrality in left insular gyrus. ConclusionUnlike previous studies with smaller sample sizes ([≤]318), our study found differences in centrality measures using a sample size of 3439 subjects. This is the first cross-sectional study to examine SCN interactions with age, sex, and comorbid MDD. Although limited to group level inferences, centrality measures offer insights into a nodes relationship to the entire functional connectome unlike approaches like seed-based connectivity or independent component analysis. Nodes having higher centrality have greater structural or functional connections, lending them invaluable for translational treatments like neuromodulation.

neuroscience↗