Maura John

Maura John serves as a Research Associate at the Chair of Bioinformatics at Hochschule Weihenstephan-Triesdorf's Straubing Campus for Sustainable Resource Use. Her research focuses on developing advanced computational methods for biological data analysis, with particular expertise in genome-wide association studies and protein structure prediction.

Her primary research interests include:

• Genome-wide association studies with permutation-based significance thresholds that preserve population structure
• Development of bioinformatics tools like permGWAS2 and easyPheno
• Protein thermostability prediction using machine learning approaches
• Genomic selection methodologies for crop breeding applications

Dr. John's recent publications demonstrate a strong focus on methodological improvements in computational biology, particularly addressing limitations of traditional approaches in handling skewed phenotype distributions and population structure. Her work bridges theoretical statistical methods with practical biological applications across plant genomics and protein science.

Notable contributions include:

• permGWAS2: An improved method that maintains population structure during permutations
• ProLaTherm: A protein language model-based thermophilicity predictor outperforming existing methods
• easyPheno: A comprehensive Python framework for phenotype prediction model comparison

Her research program demonstrates strong collaborative efforts with Dominik Grimm's group and other bioinformatics researchers, focusing on developing open-source tools that address critical challenges in genomic data analysis. The work has practical applications in plant breeding, protein engineering, and understanding genotype-phenotype relationships.