Publications

Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome

Published in bioRxiv, 2022

This preprint details how pairwise correlation of antimicrobial resistance abundances highlights potential co-selection occuring in different environments at a global scale.

Citation: 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. https://www.biorxiv.org/content/10.1101/2022.12.19.519133v1

A curated data resource of 214K metagenomes for characterization of the global antimicrobial resistome

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.

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. https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001792

Global distribution of mcr gene variants in 214K metagenomic samples

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.

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. https://journals.asm.org/doi/10.1128/msystems.00105-22

NetSolP: predicting protein solubility in E. coli using language models

Published in Bioinformatics, 2022

This study aims to predict solubility and usability of proteins by applying deep learning protein language models.

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 https://doi.org/10.1093/bioinformatics/btab801

Deep protein representations enable recombinant protein expression prediction

Published in Computational Biology and Chemistry, 2021

Protein representations improve the success of predicting whether a protein can be recombinantly expressed in Bacillus subtilis.

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 https://www.sciencedirect.com/science/article/pii/S1476927121001663