Published in In review, 2025
Study of sewage resistomes to identify regional differences
Recommended citation: Yu, Z., Gray D.A., Fick, J., Waters, N., Lindberg, R., Grabic, R., Tysklind, M., Ekwanzala, M. D., Martiny, H.-M., Flach, C.-F., Aarestrup, F. M., Larsson, D G J. (2025). Geographics and bacterial networks shape the global urban sewage resistome. In review.
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Published in In review, 2025
Study of sewage resistomes to identify regional differences
Recommended citation: Martiny, H.-M., Munk, P., Fuschi, A., Becsei, A., Pyrounakis, N., Brinch, C., Global Sewage Consortium, Larsson, D G J., Koopmans, M., Remondini, D., Csabai, I., Aarestrup, F. M. (2025). Geographics and bacterial networks shape the global urban sewage resistome. In review.
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Published in In review, 2025
Exploratory data analysis of mining metagenomes for potential new sources of LAB bacteriocins.
Recommended citation: Wätjen, A. P., Martiny, H.-M,, Huangc, W., Turner, M. S., Bang-Berthelsen C. H. (2025). Uncovering natural sources of LAB bacteriocins through the use of metagenomics. In review.
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Published in Microbiology Spectrum, 2024
This preprint details how pairwise correlation of antimicrobial resistance abundances highlights potential co-selection occuring in different environments at a global scale.
Recommended citation: Martiny, H. M., Munk, P., Brinch, C., Aarestrup, F. M., Calle, M. L., & Petersen, T. N. (2024). Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome. Microbiology Spectrum, 12(7), e04108-23.
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Published in Bioinformatics, 2024
Our workflow ARGprofiler is designed to handle large quantities of metagenomic datasets to profile antimicrobial resistance genes and their flanking regions
Recommended citation: Martiny, H. M., Pyrounakis, N., Petersen, T. N., Lukjančenko, O., Aarestrup, F. M., Clausen, P. T., & Munk, P. (2024). ARGprofiler—a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets. Bioinformatics, 40(3), btae086.
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Published in PLOS BIOLOGY, 2022
This paper details the large collection of 214K metagenomes that we curated to analyze the distribution of antimicrobial resistance genes at a global scale.
Recommended citation: Martiny, H. M., Munk, P., Brinch, C., Aarestrup, F. M., & Petersen, T. N. (2022). A curated data resource of 214K metagenomes for characterization of the global antimicrobial resistome. PLoS biology, 20(9), e3001792.
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Published in mSystems, 2022
This paper analyzes the abundance levels of nine mcr gene variants in metagenomic samples from different locations, years and sampling hosts sources within 214K metagenomes.
Recommended citation: Martiny, H. M., Munk, P., Brinch, C., Szarvas, J., Aarestrup, F. M., & Petersen, T. N. (2022). Global Distribution of mcr Gene Variants in 214K Metagenomic Samples. Msystems, e00105-22.
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Published in Bioinformatics, 2022
This study aims to predict solubility and usability of proteins by applying deep learning protein language models.
Recommended citation: Vineet Thumuluri, Hannah-Marie Martiny, Jose J Almagro Armenteros, Jesper Salomon, Henrik Nielsen, Alexander Rosenberg Johansen, NetSolP: predicting protein solubility in Escherichia coli using language models, Bioinformatics, Volume 38, Issue 4, 15 February 2022, Pages 941–946, https://doi.org/10.1093/bioinformatics/btab801
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Published in Computational Biology and Chemistry, 2021
Protein representations improve the success of predicting whether a protein can be recombinantly expressed in Bacillus subtilis.
Recommended citation: Martiny, H. M., Armenteros, J. J. A., Johansen, A. R., Salomon, J., & Nielsen, H. (2021). Deep protein representations enable recombinant protein expression prediction. Computational Biology and Chemistry
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