Astrid Rosa Mach-Aigner

Genome sequencing and physiological characterization of three Neoarthrinium moseri strains

This study sequenced the genomes of three Neoarthrinium moseri strains and characterized their metabolic potential, revealing high biosynthetic gene clusters and ecological versatility.

TU_MyCo-Vision: A Deep Learning Tool for Detection of Cell Morphologies in Fungal Microscopic Images

The paper introduces TU_MyCo-Vision, a deep learning tool for detecting 13 fungal morphotypes in bright-field images, showing strong performance on Aureobasidium pullulans and other fungal species.

Dark matter of an orchid: metagenome of the microbiome associated with the rhizosphere of Dactylorhiza traunsteineri

The paper analyzes the microbiome of the endangered orchid Dactylorhiza traunsteineri, identifying key bacterial phyla and revealing metabolic potential in its rhizosphere.

In vivo activation of the dia BGC allows consolidation of the biosynthetic pathways of diaporthin, dichlorodiaporthin, diaporthinic acid, and diaporthinol

This study activates the dia biosynthetic gene cluster in Trichoderma reesei, identifying diaporthinic acid as the primary metabolite and elucidating the roles of key genes in its biosynthesis.

Discovery of a novel antifungal compound, ilicicolin K, through genetic activation of the ilicicolin biosynthetic pathway in Trichoderma reesei

This paper activates the ilicicolin biosynthetic pathway in Trichoderma reesei to discover a novel antifungal compound, ilicicolin K, with similar antifungal activity to ilicicolin H.

FunOrder 2.0 - a fully automated method for the identification of co-evolved genes

FunOrder 2.0 automates the identification of co-evolved genes, improving speed and specificity over its semi-automated predecessor.

The Functional Order (FunOrder) tool - Identification of essential biosynthetic genes through computational molecular co-evolution

The paper introduces FunOrder, a tool that identifies essential biosynthetic genes in BGCs using computational molecular co-evolution, improving upon existing methods that struggle with distinguishing essential genes from gap genes.