TL;DR
PALACE, a conjugate-graph-based framework for assembling high-quality phage genomes from metagenomes, incorporates homology-based and deep-learning-based methods to detect phage signals and constructs a conjugate graph from the metagenomic sample.
PHORAGER (Prophage Hunting, vOtu Retrieval, Annotation and Genomic ExploRation), a scalable Nextflow pipeline for the standardised identification and quality assessment of prophages from bacterial genomes, validated using 30,824 publicly available ESKAPE pathogen genomes.
Applications across healthcare, environmental science, agriculture, biotechnology, and industry are reviewed with particular emphasis on clinical metagenomic next-generation sequencing (mNGS) for infectious disease diagnostics, antimicrobial resistance (AMR) surveillance, gut microbiome research, and precision medicine.
The GenomeCompendium is released, a public database and interactive analysis tool for complete prokaryotic genomes and it is shown that complex, repeat-rich genomes are more common than previously estimated.
Abstract Human RNA sequencing (RNA-seq) data originally generated for human transcriptome profiling are overwhelmingly dominated by host sequences, yet they often contain a small fraction of non-human reads that can be exploited for microbial detection. When such datasets are repurposed for secondary microbiome-oriented analyses, extracting and accurately classifying this weak microbial signal becomes technically challenging, and no ready-to-use pipeline currently exists. In this study, we evaluate computational strategies for filtering host reads and classifying microbial transcripts in host-dominated RNA sequencing data. We compare assembly-based approaches similar to those used in a previous study focusing on microbial translocation with state-of-the-art assembly-free methods, and assess their respective strengths and limitations using simulated datasets reflecting low microbial abundance. Our results show that assembly-based methods yield accurate taxonomic predictions but struggle at low read depth, whereas assembly-free methods are more robust in sparse settings at the cost of reduced precision. To leverage the complementarity of both approaches, we propose a hybrid pipeline that integrates assembly-based and assembly-free classification. On simulated data, this hybrid strategy improves microbial classification performance compared with either approach alone. Application to a real human metatranscriptomic dataset analyzed in a microbial translocation context illustrates the broader microbial signal captured by the hybrid approach, despite intrinsic challenges related to the absence of reliable ground truth and the risk of host read misclassification. Our work provides a framework for extracting microbial signals from host-dominated human metatranscriptomes, enabling the reuse of existing transcriptomic datasets for microbiome-related analyses, including but not limited to microbial translocation studies.
By introducing support for ancient DNA data in nf-core/mag, this paper aims to improve the ability of researchers to more regularly integrate de novo assembled ancient microbial data into broader metagenomics studies of microbial ecology and evolution.
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