CV

Work experience

2022 - : PostDoc, Research Group for Genomic Epidemiology, Technical University of Denmark (current)
2019-2023 : PhD student, Research Group for Genomic Epidemiology, Technical University of Denmark
2022 : Visiting PhD Student, University of Vic - Central University of Catalonia, Spain.
2019 : Research Assistant, Research Group for Genomic Epidemiology, Technical University of Denmark
2017-2019: Student Assistant, in the data science team Advanced Analytics, Novozymes, Copenhagen

Education

Ph.D in Bioinformatics in the Research Group for Genomic Epidemiology, Technical University of Denmark, 2019-2022
M.Sc. in Bioinformatics and Systems Biology, Technical University of Denmark, 2017-2019
B.Sc. in Biotechnology, Technical University of Denmark, 2014-2017

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

Presentations

Large-scale analysis of 214K metagenomes for antimicrobial resistance surveillance

September 06, 2022

Talk at DTU Microbes Initative Conference 2022, Kongens Lyngby, Denmark

Unraveling the co-occurence patterns of antimicrobial resistance genes within 214K metagenomic samples

June 12, 2022

Poster at ASM Microbe 2022 & ISME18, Washington DC, USA

Identifying genetic signatures in regions around antimicrobial resistance genes

November 18, 2021

Poster at 6th Annual Danish Bioinformatic Conference, Aalborg, Denmark

Global distribution of AMR gene variants in 214,095 metagenomic samples

November 10, 2021

Talk at Novo Nordisk Foundation Symposium 2021 on Local Landscape and Outlook Regarding Solutions to Antimicrobial Resistance Challenges, Hellerup, Denmark

Using publicly available metagenomes to describe the distribution of mcr gene variants

March 23, 2021

Talk at VEO Symposium 2021, Online

Digging deep or AMR: Monitoring Antimicrobial Resistance in 100k Host and Environmental Samples

December 09, 2020

Talk at ASM NGS 2020, Online

Monitoring antimicrobial resitance in 100K host and environmental samples

November 04, 2020

Poster at Antimicrobial Resistance - Genomes, Big Data and Emerging Technologies, Online

Teaching & Supervision

2022 : Lecture for students at University of Vic on “Fighting antimicrobial resistance with genomic surveillance”.
2022 : Lecture on Inferring Correlations with Compositional Data in the master course Compositional Data Analysis with Applications in Genomics
2021 : Lecture on Compositional Data Analysis in Metagenomics in the master couse 23260 Applied methods in metagenomics
2021 : Teaching Assistant in the master course Compositional Data Analysis with Applications in Genomics
2021 : Co-supervision of Master thesis project focusing on automating the discovery of novel AMR gene variants in sewage samples.
2020 : Co-supervision of Bachelor thesis project on the topic of distribution and propagation of AMR genes in historical metagenomic data.

Skills

R
Python
SQL
Bash
High-Performance Computing

Projects

Deep protein representations enable recombinant protein expression (2019-2021): Originally my Master thesis project, where the goal was to predict recombinant gene expression using deep learning methods. After I finished in 2019, we wrote the findings into a paper that has been published in Computational Biology and Chemistry.
The Language of Life (2018-2019): applying deep learning methods to predict amino acids in protein sequences. Project originally started as part of the course 02456 Deep Learning at DTU. Applied Word2Vec and CNNs to protein sequence data.
iGEM 2018: Hyphae Hackers, DTU Biobuilders. Participated in 2018 as part of the team at TechnicalUniversity of Denmark. The project investigated how we can use mycelium from fungi as building materials, where I personally worked on modelling of hyphal growth, and was part of the design team for the webpage.
Microbesoft (2018): a command-line tool to visualize alignment files. The project was built as part of participation in Copenhacks 2018, a hackathon held in Copenhagen for students, where our team won 2nd place.

Hobbies

I do like to draw and paint in my spare time, but more recently, I have taken up crochet - a good way to keep my supplies of dishcloths up to date.. I sometimes post my art at Instagram under the user hmmartiny.