Dan MacLean

The paper introduces SNI, a framework to identify atypical protein assemblies, and applies it to NLRs, finding an undecameric assembly in NRC7 orthologs.

The paper develops a machine learning model to improve effector prediction by integrating multiple existing programs' outputs, achieving better performance across plant pathogens.

This study identifies Pwl2 as a virulence factor in the rice blast fungus that suppresses host immunity by altering the localization of HIPP43.

This study uses metagenomic and metatranscriptomic analysis to reveal the microbial complexity and gene expression dynamics in the lichen Xanthoria parietina across developmental stages.

The paper develops deepredeff, a deep learning model ensemble for classifying plant pathogen effector proteins from sequence data, achieving higher accuracy and lower false positives than existing tools.

This paper uses a convolutional neural network to classify transcriptional regulators in Arabidopsis gene expression data, finding that classifications are based on positive expression correlations.

DeepStomata automates stomatal aperture measurement using image segmentation, achieving accuracy comparable to manual methods with higher sensitivity and much faster processing.

The paper analyzes the genetic diversity of Hymenoscyphus fraxineus, showing the European population was founded by two divergent haplotypes with adaptive potential.

This paper introduces SDM, a reference-independent method to identify causative mutations in unordered genomes by analyzing SNP distributions, and demonstrates its effectiveness in Arabidopsis thaliana datasets.

This paper introduces an algorithm to find SNPs in k-mer graphs by enumerating 2k + 2 cycles, achieving high accuracy across the genome in Arabidopsis thaliana.

The paper reports on a crowdsourced project analyzing ash dieback fungus data, revealing insights into its genome and biology through community contributions.