Search

One Health surveillance of antimicrobial resistance (AMR) depends on a harmonised method for detection of AMR. Metagenomics-based surveillance offers the possibility to compare resistomes within and between different target populations. Its potential to be embedded into policy in the future calls for a timely and integrated knowledge dissemination strategy. We developed a blended training (e-learning and a workshop) on the use of metagenomics in surveillance of pathogens and AMR. The objectives were to highlight the potential of metagenomics in the context of integrated surveillance, to demonstrate its applicability through hands-on training and to raise awareness to bias factors. The target participants included staff of competent authorities responsible for AMR monitoring and academic staff. The training was organized in modules covering the workflow, requirements, benefits and challenges of surveillance by metagenomics. The training had 41 participants. The face-to-face workshop was essential to understand the expectations of the participants about the transition to metagenomics-based surveillance. After the training, the e-learning component was revised and released as a Massive Open Online Course (MOOC), now available at https://www.coursera.org/learn/metagenomics. This course has run in more than 20 sessions, with more than 3000 learners enrolled, from more than 120 countries. Blended learning and MOOCs are useful tools to deliver knowledge globally and across disciplines. The released MOOC can be a reference knowledge source for the international public health community engaged in the application of metagenomics in surveillance.

The COVID-19 pandemic negatively impacted the 2019/20 WHO European Region influenza surveillance. Compared with previous 4-year averages, antigenic and genetic characterisations decreased by 17% (3,140 vs 2,601) and 24% (4,474 vs 3,403). Of subtyped influenza A viruses, 56% (26,477/47,357) were A(H1)pdm09, 44% (20,880/47,357) A(H3). Of characterised B viruses, 98% (4,585/4,679) were B/Victoria. Considerable numbers of viruses antigenically differed from northern hemisphere vaccine components. In 2020/21, maintaining influenza virological surveillance, while supporting SARS-CoV-2 surveillance is crucial.

Metagenomics is the research field pertaining to the analysis of genetic material taken directly from an environment as opposed to the tissue of a single organism. Metagenomics have a plethora of use cases: Analysis of the human gut microbiome is a metagenomic task, and is getting an increasing amount of attention these years. Medical- and biotech-companies do metagenomic analysis of environments to discover microorganisms with industrially relevant genes. At the time of writing, the global 2020 coronavirus pandemic is raging, caused by a virus whose origin seem to have been uncovered by a metagenomic study. As in many other sprawling fields, a great number of analytical tools are available for metagenomic researchers. One of these tools is metagenomic binning, a process by which genetic sequences from an environment is grouped, or binned, such that each resulting bin is presumed to correspond to a genome from a single organism in the environment. Binning has been used in a number of high-profile articles the last years. Despite the quick pace of progress within the field, binning remains an error-prone process whose results leave much room for improvement.The work presented in this thesis is about method development within metagenomics, and in particular metagenomic binning. This thesis is composed primarily of two articles written during my Ph.D scholarship, each describing a specific contribution to the metagenomic toolkit. Besides the articles, the thesis consists of introduction and discussion sections which puts the articles in context, and which contains relevant results that could not be included in the articles due to space concerns. The contributions of this thesis can be summarized in brief:1. In the first article we present Vamb, a new method for binning, as well as the software implementing the method. Vamb uses variational autoencoders to represent metagenomic sequences before the representation is clustered using a novel homemade algorithm. We use Vamb to group a collection of synthetic metagenomes and thus demonstrate that Vamb creates more accurate bins than comparable software. By binning a large natural dataset with 1,000 human feces samples and almost 6 million contigs, we show that Vamb can handle larger datasets than other binners. We also show that Vamb can recreate bacterial strains with high phylogenetic resolution.2. The second article is a comparison between the domain-specific language Seq and BioJulia, a package for bioinformatic data analysis. The comparison refutes central claims in an article published in 2019 by reproducing the results from the original article and shows how these results do not support claims in the article. This article illustrates the possibilities of BioJulia, a package I have contributed to the development of. The tools behind these articles are meant for different parts of a complete metagenomic workflow. Vamb is a specific tool for one single part of the workflow, and may directly subsitute other binning tools. In contrast, BioJulia is of a more general nature, and creates a solid foundation for metagenomic tool development. Together, these articles represent a small contribution to metagenomic methods.

Identification and reconstruction of microbial species from metagenomics wide genome sequencing data is an important and challenging task. Current existing approaches rely on gene or contig co-abundance information across multiple samples and k-mer composition information in the sequences. Here we use recent advances in deep learning to develop an algorithm that uses variational autoencoders to encode co-abundance and compositional information prior to clustering. We show that the deep network is able to integrate these two heterogeneous datasets without any prior knowledge and that our method outperforms existing state-of-the-art by reconstructing 1.8 - 8 times more highly precise and complete genome bins from three different benchmark datasets. Additionally, we apply our method to a gene catalogue of almost 10 million genes and 1,270 samples from the human gut microbiome. Here we are able to cluster 1.3 - 1.8 million extra genes and reconstruct 117 - 246 more highly precise and complete bins of which 70 bins were completely new compared to previous methods. Our method Variational Autoencoders for Metagenomic Binning (VAMB) is freely available at: https://github.com/jakobnissen/vamb