Welcome!
I’m Hannah-Marie Martiny, and I am currently doing a PostDoc in Bioinformatics at the Technical University of Denmark in the Research Group for Genomic Epidemiology at DTU Food. My project is about dong large-scale metagenomic analysis to analyze the global distribution of antimicrobial resistance.
Feel free to reach out to me if you have any inquiries!
You can also check out my CV here.
Publications:
Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome
Martiny, H. M., Munk, P., Brinch, C., Aarestrup, F. M., Calle, M. L., & Petersen, T. N. (2022). Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome. bioRxiv, 2022-12.
A curated data resource of 214K metagenomes for characterization of the global antimicrobial resistome
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.
Global distribution of mcr gene variants in 214K metagenomic samples
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.
NetSolP: predicting protein solubility in E. coli using language models
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
Deep protein representations enable recombinant protein expression prediction
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
In case you came looking for the documentation on the 214K metagenomic data collection, which we shared on Zenodo, go here: https://hmmartiny.github.io/mARG/