Publications by authors named "R D MacCallum"

Article Synopsis
  • * It provides tools for analyzing omics data, integrates various datasets, and features a user-friendly data mining platform, allowing easy comparisons across organisms and data types.
  • * Recent updates include new data types, enhanced search features, an improved Galaxy workspace, and a redesigned MapVEu interface for analyzing spatially resolved data like vector surveillance.
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The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) represents the 2019 merger of VectorBase with the EuPathDB projects. As a Bioinformatics Resource Center funded by the National Institutes of Health, with additional support from the Welllcome Trust, VEuPathDB supports >500 organisms comprising invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts.

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Background: Whole genome re-sequencing provides powerful data for population genomic studies, allowing robust inferences of population structure, gene flow and evolutionary history. For the major malaria vector in Africa, Anopheles gambiae, other genetic aspects such as selection and adaptation are also important. In the present study, we explore population genetic variation from genome-wide sequencing of 765 An.

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Arthropods play a dominant role in natural and human-modified terrestrial ecosystem dynamics. Spatially-explicit arthropod population time-series data are crucial for statistical or mathematical models of these dynamics and assessment of their veterinary, medical, agricultural, and ecological impacts. Such data have been collected world-wide for over a century, but remain scattered and largely inaccessible.

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Extending work by Waller (2008) on fungible regression coefficients, we propose a method for computation of fungible parameter estimates in structural equation modeling. Such estimates are defined as distinct alternative solutions for parameter estimates, where all fungible solutions yield identical model fit that is only slightly worse than the fit provided by optimal estimates. When such alternative estimates are found to be highly discrepant from optimal estimates, then substantive interpretation based on optimal estimates is called into question.

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