Hannah-Marie Martiny

Hannah-Marie Martiny

Ph.D. in Bioinformatics with a focus on large-scale metagenomic analysis

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

What in the shit?

3 minute read

Published:

Using wastewater as an early warning system for viral outbreaks

portfolio

publications

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

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

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

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

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

talks

Monitoring antimicrobial resitance in 100K host and environmental samples

Published:

The growing amount of NGS data available in public data repositories presents a unique opportunity to explore new ways of monitoring antimicrobial resistance (AMR) across different sample types. The diversity of sample origins, locations and collection dates can provide a detailed picture of the emergence and spread of different AMR genes worldwide. We have downloaded 122 Tbp sequence reads from 116,758 host and environmental metagenomes from the European Nucleotide Archive (ENA), have quality checked and trimmed the raw sequencing reads and mapped them against two reference sequence databases: the Silva database to describe the bacterial content and ResFinder to detect AMR genes. Using compositional data analysis on the output, we have compared the results across different experimental procedures.
We will showcase the feasibility of large-scale metagenomic AMR analysis by focusing on the mcr gene family, which confers resistance to colistin. The spread of mcr genes in recent years poses a significant threat to public health, as colistin is a last-resort against multidrug-resistant infections. By using our data, we have characterized the advancement of mcr genes across animal and human populations. In human samples, mcr-9 were more present as compared to other hosts such as livestock displayed a more diverse distribution of all the genes (p < 0.05). We also see that the mcr genes did not have a global distribution but instead were specific to each demographic region with mcr-9 being more prevalent in North American samples and mcr-1 in Asian samples (p < 0.05). Storing and analyzing large quantities of metagenomic data might not be possible for all research groups, which is why we are planning to setup a database and share it together with the results in a webservice. This will allow the scientific community to explore and use the data in new ways. Furthermore, as new metagenomic data becomes available online, we will add these samples to the database and analyze them.

Identifying genetic signatures in regions around antimicrobial resistance genes

Published:

The advancement of antimicrobial resistance genes (ARGs) is one of the biggest threats to public health. Since ARGs can be found in a diverse range of genetic contexts in different species and environments, determining structural elements in the regions up- and downstream, or flanks, of ARGs can provide useful information on their evolutionary history. We will present our analysis of flanks surrounding members of the mcr gene family in metagenomic samples. The mcr genes confer resistance to colistin, a last-resort antibiotic used against multidrug-resistant bacteria. We have identified and assembled 869 mcr positive metagenomes from various origins and locations and have extracted flanks of 1939 different mcr contigs from the assemblies. Plasmid replicons and mobile genetic elements (MGEs) were identified in the flanks. By setting a minimum flank size of 1,000 bp and clustering on the pairwise k-mer distance, we can observe several unique genetic signatures for each mcr gene variant. For example, we can see that only the ISApl1 MGE appears around mcr-1 contigs and IS903 in mcr-9 flanks. As there were only 138 contigs that passed the size criteria, we are in the process of including mcr flanks from 3327 genomes of single isolates retrieved from NCBI to better understand the elements involved in the dissemination of mcr genes. Our presentation will include these results as well.

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

Published:

The increasing amount of next-generation sequencing (NGS) data available in public repositories has the potential to provide new ways of monitoring the emergence, evolution, and spread of antimicrobial resistance genes (ARGs) with a large-scale metagenomic analysis. We have downloaded more than 442 Tbp of sequencing reads from 214,095 host-derived and environmental metagenomes from the European Nucleotide Archive (ENA). After quality-checking and trimming the reads, we aligned them against ARG templates from the ResFinder database. Here, we present our network analysis of the co-occurrence of ARGs across a highly diverse collection of samples to find the evidence of ARGs conferring resistance to different antibiotic classes being co-selected at a global scale. Due to the compositional nature and sparsity of working with counts of aligned read fragments, we applied SparCC on counts to infer pairwise linear correlations. The poster presents the preliminary results to demonstrate the feasibility of a large-scale analysis to investigate the occurrence of cross-resistances in different microbial settings.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.