Biology subjects

Nissen, J. N.

Publications and source records attributed to Nissen, J. N..

7 recordsLinked to original sources

bioRxiv2025

Accurate plasmid reconstruction from metagenomics data using assembly-alignment graphs and contrastive learning

Plasmids are extrachromosomal DNA molecules that enable horizontal gene transfer in bacteria, often conferring advantages such as antibiotic resistance. Despite their significance, plasmids are underrepresented in genomic databases due to challenges in assembling them, caused by mosaicism and micro-diversity. Current plasmid assemblers rely on detecting circular paths in single-sample assembly graphs, but face limitations due to graph fragmentation and entanglement, and low coverage. We introduce PlasMAAG (Plasmid and organism Metagenomic binning using Assembly Alignment Graphs), a framework to recover plasmids and organisms from metagenomic samples that leverages an approach that we call "assembly-alignment graphs" alongside common binning features. On synthetic benchmark datasets, PlasMAAG reconstructed 50-121% more near-complete plasmids than competing methods and improved the Matthews Correlation Coefficient of geNomad contig classification by 28-106%. On hospital sewage samples, PlasMAAG outperformed all other methods, reconstructing 33% more plasmid sequences. PlasMAAG enables the study of organism-plasmid associations and intra-plasmid diversity across samples, offering state-of-the-art plasmid reconstruction with reduced computational costs.

bioinformatics↗

bioRxiv2024

Exploring genetic signatures of zoonotic influenza A virus at the swine-human interface with phylogenetic and ancestral sequence reconstruction

Influenza A viruses (IAVs) in swine have zoonotic potential and pose a continuous threat of causing human pandemics, as demonstrated by the H1N1 pandemic in 2009. Despite increased genomic surveillance, we have limited knowledge of the IAV evolutionary dynamics leading to such zoonotic events and no clear understanding of genetic markers associated with interspecies transmission of IAV between humans and swine. To explore this, we analyzed a comprehensive publicly available whole genome dataset of human and swine IAV sequences. We conducted phylogenetic analyses and inference of ancestral host and sequence states for each IAV segment and mapped inferred mutations that were associated with transmission within and between swine and human hosts. We developed a custom python library to combine information from host and ancestral sequence annotated trees and applied statistical models to identify genetic markers associated with intra- or interspecies transmissions between swine and humans. This included analyzing mutation rates and the selective pressures acting on the viral proteins following intra- and interspecies transmissions and using a scalable, gradient-boosted decision tree machine learning approach to predict key amino acid positions critical for different transmission types. Our analyses indicated complex mutational patterns within and across viral proteins, but also suggested that specific protein regions and amino acid positions of especially several of the internal gene segments were more important for interspecies transmission. Our findings identify potential genetic signatures across the IAV proteins associated with host adaptation and zoonotic potential, offering valuable markers for early-warning genomic surveillance systems to enhance animal health and minimize the potential for zoonotic transmission of IAV.

bioinformatics↗

bioRxiv2024

BinBencher: Fast, flexible and meaningful benchmarking suite for metagenomic binning

New methods for metagenomic binning are typically evaluated using benchmarking software, and become tuned to maximize whatever criterion is measured by the benchmark. Subtleties in benchmarking procedures can cause misleading evaluations, derailing method development. Differences between procedures used to evaluate binning tools make them hard to compare, which slows progress in the field. We introduce BinBencher, a free software suite for benchmarking, and show how BinBencher produces evaluations that are more biologically meaningful than alternative benchmarking approaches.

bioinformatics↗

bioRxiv2023

N-terminal toxin signal peptides efficiently load therapeutics into a natural nano-injection system

Targeted delivery of therapeutics to specific cells is a major bottleneck towards personalized medicine. The extracellular injection system (eCIS) of Serratia entomophila, the antifeeding prophage (Afp), promises potential for drug delivery purposes. However, the precise mechanism of action, toxin location, and Afp loading remain unclear. Here, we reveal a minimal N-terminal signal peptide (NtSP) of the toxin Afp18, that plays a key role in toxin packing. By engineering fusion proteins, we demonstrate that Afp18s NtSP can shuttle effectors for Afp loading. We packed non-eCIS effectors, including CRISPR-Cas protein Cas{Phi}-2 from Biggiephage, and a human antimicrobial peptide, LL37, into Afp. Additionally, NtSPs from eCIS effectors of other species facilitate loading of Cas{Phi}-2 into Afp. We observed cargo being packed inside the Afp tail tube through cryo-EM single particle analysis. The presented results enhance our understanding of eCIS toxin packing and contribute to their development as targeted delivery systems. TeaserA novel use of the Afp nano injection systems N-terminal signal peptide in targeted therapeutics delivery

molecular biology↗

bioRxiv2023

Adversarial and variational autoencoders improve metagenomic binning

Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by regrouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biological diversity of metagenomic samples. Here we present Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning approach that integrates sequence co-abundances and tetranucleotide frequencies into a common denoised space that enables precise clustering of sequences into microbial genomes. When benchmarked, AAMB presented similar or better results compared with the state-of-the-art reference-free binner VAMB, reconstructing [~]7% more near-complete (NC) genomes across simulated and real data. In addition, genomes reconstructed using AAMB had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we implemented a pipeline integrating VAMB and AAMB that enabled improved binning, recovering 20% and 29% more simulated and real NC genomes, respectively, compared to VAMB with moderate additional runtime. AAMB is freely available at https://github.com/RasmussenLab/VAMB.

bioinformatics↗

bioRxiv2021

Genome binning of viral entities from bulk metagenomics data

Despite the accelerating number of uncultivated virus sequences discovered in metagenomics and their apparent importance for health and disease, the human gut virome and its interactions with bacteria in the gastrointestinal are not well understood. In addition, a paucity of whole-virome datasets from subjects with gastrointestinal diseases is preventing a deeper understanding of the viromes role in disease and in gastrointestinal ecology as a whole. By combining a deep-learning based metagenomics binning algorithm with paired metagenome and metavirome datasets we developed the Phages from Metagenomics Binning (PHAMB) approach for binning thousands of viral genomes directly from bulk metagenomics data. Simultaneously our methodology enables clustering of viral genomes into accurate taxonomic viral populations. We applied this methodology on the Human Microbiome Project 2 (HMP2) cohort and recovered 6,077 HQ genomes from 1,024 viral populations and explored viral-host interactions. We show that binning can be advantageously applied to existing and future metagenomes to illuminate viral ecological dynamics with other microbiome constituents.

genomics↗

bioRxiv2018

Binning microbial genomes using deep learning

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

bioinformatics↗