Biology subjects

Jochheim, A.

Publications and source records attributed to Jochheim, A..

4 recordsLinked to original sources

bioRxiv2025

Evaluation of Metagenome Binning: Advances and Challenges

BackgroundSeveral recent deep learning methods for metagenome binning claim improvements in the recovery of high quality metagenome-assembled genomes. These methods differ in their approaches to learn the contig embeddings and to cluster them. Rapid advances in binning require rigorous benchmarking to evaluate the effectiveness of new methods. We have benchmarked newly developed state-of-the-art deep learning binners on CAMI2 datasets, including our own, McDevol. ResultsThe results show that COMEBin and GenomeFace give the best binning accuracy, although not always the best embedding accuracy. Interestingly, post-binning reassembly consistently improves the quality of low coverage bins. We find that binning coassembled contigs with multi-sample coverage is effective for low coverage dataset while binning multi-sample contigs with multi-sample coverage ( multi-sample) is effective for high-coverage samples. In multi-sample binning, splitting the embedding space by sample before clustering showed enhanced performance compared to the standard approach of splitting final clusters by sample. ConclusionsCOMEBin and GenomeFace emerged as the top-performing tools overall, with MetaBAT2 and GenomeFace demonstrating superior speed. To facilitate future development, we provide workflows for standardized benchmarking of metagenome binners.

bioinformatics↗

bioRxiv2024

CarpeDeam: A De Novo Metagenome Assemblerfor Heavily Damaged Ancient Datasets

De novo assembly of ancient metagenomic datasets is a challenging task. Ultra-short fragment size and characteristic postmortem damage patterns of sequenced ancient DNA molecules leave current tools ill-equipped for ideal assembly. We present CarpeDeam, a novel damage-aware de novo assembler designed specifically for ancient metagenomic samples. Utilizing maximum-likelihood frameworks that integrate sample-specific damage patterns, CarpeDeam demonstrates improved recovery of longer continuous sequences and protein sequences in many simulated and empirical datasets compared to existing assemblers. As a pioneering ancient metagenome assembler, CarpeDeam opens the door for new opportunities in functional and taxonomic analyses of ancient microbial communities.

genomics↗

bioRxiv2024

Strain-resolved de-novo metagenomic assembly of viral genomes and microbial 16S rRNAs

BackgroundMetagenomics is a powerful approach to study environmental and human-associated microbial communities and, in particular, the role of viruses in shaping them. Viral genomes are challenging to assemble from metagenomic samples due to their genomic diversity caused by high mutation rates. In the standard de Bruijn graph assemblers, this genomic diversity leads to complex k-mer assembly graphs with a plethora of loops and bulges that are challenging to resolve into strains or haplotypes because variants more than the k-mer size apart cannot be phased. In contrast, overlap assemblers can phase variants as long as they are covered by a single read. ResultsHere, we present PenguiN, a software for strain resolved assembly of viral DNA and RNA genomes and bacterial 16S rRNA from shotgun metagenomics. Its exhaustive detection of all read overlaps in linear time combined with a Bayesian model to select strain-resolved extensions allow it to assemble longer viral contigs, less fragmented genomes, and more strains than existing assembly tools, on both real and simulated datasets. We show a 3-40-fold increase in complete viral genomes and a 6-fold increase in bacterial 16S rRNA genes. ConclusionPenguiN is the first overlap-based assembler for viral genome and 16S rRNA assembly from large and complex metagenomic datasets, which we hope will facilitate studying the key roles of viruses in microbial communities.

bioinformatics↗

bioRxiv2023

A 2-million-year-old microbial and viral communities from the Kap Kobenhavn Formation in North Greenland

Environmental DNA (eDNA) from the 2-million-year-old Kap Kobenhavn Formation of northern Greenland has revealed an ecosystem of plants and animals with no contemporary analogue1. Here, we reconstruct the microbial (bacterial, archaeal, and viral) communities that thrived at the site during this time. By leveraging a novel analytical framework that integrates taxonomic profiling, DNA damage estimates, and functional reconstructions, we identify and distinguish pioneer microbial communities from later permafrost microbial assemblages. We show that at the time of sediment deposition, the terrestrial input at the Kap Kobenhavn site originated from a palustrine wetland, suggesting warmer, non-permafrost conditions. During this period, the detection of methanogenic archaea and signals of their carbon metabolism is consistent with Kap Kobenhavn and similar northern ecosystems contributing moderate methane emissions. Intriguingly, we discover a remarkable nucleotide sequence similarity--exceeding 98%--between pioneer methanogens and present-day analogues in thawing permafrost. This aligns with the concept of "time-traveling" microbes2 surviving across geological time and waiting for conditions to turn favourable rather than evolving to adapt to changing conditions. Importantly, in contrast to the plant and animal communities of the Kap Kobenhavn, a striking similarity in microbial composition to that of a contemporary thawing Arctic suggests that microbial communities may serve as the first indication of broader climate-driven ecosystem disruptions.

microbiology↗