Steven Maere

Autocorrelation-aware bivariate Moran's I, bivariate Gaussian process tests and generalized additive models (R package sbivar) test cross-modality colocalisation on disjoint coordinates; many existing permutation tests inflate false positives.

This study identifies 132 ancient whole-genome duplication events in angiosperms, showing they cluster around periods of environmental upheaval and extinction.

This study uses reciprocally retained gene families to detect whole-genome multiplications in plants, showing improved detection with extended sets of these families.

The paper introduces smoppix, a nonparametric method for analyzing single-molecule spatial omics data, identifying localization patterns across tissues and cells.

This study shows that SIM and CCS52A1 regulate Arabidopsis root hair cell volume through endoreplication and cell wall modifications.

The paper generates a high-resolution spatiotemporal transcriptome dataset of maize shoot organs under well-watered and drought conditions, revealing conserved and organ-specific gene regulation patterns.

The paper addresses the winners curse in statistical estimation under dependence, proposing a density convolution method to improve empirical Bayes approaches and demonstrating its effectiveness through simulations and a real-world application.

This study uses a year-long metatranscriptomic dataset to analyze seasonal metabolic dynamics of microeukaryotic plankton in the Southern Bight of the North Sea, revealing species-specific metabolic strategies and ecological roles.

The paper links rosette-stage gene expression in Brassica napus to field-grown plant yield traits by analyzing individual plants in the field.

ksrates positions ancient whole-genome duplication events relative to speciation events by comparing KS distributions of paralogs and orthologs.

This study profiles individual maize plants in the field to link genes to functions and phenotypes, showing field data can predict gene function and plant traits as effectively as lab data.