Search arXivSearch

arXiv · 2510.10950

ChloroScan: Recovering plastid genome bins from metagenomic data

Abstract

Genome-resolved metagenomics has contributed largely to discovering prokaryotic genomes. When applied to microscopic eukaryotes, challenges such as the high number of introns and repeat regions found in nuclear genomes have hampered the mining and discovery of novel protistan lineages. Organellar genomes are simpler, smaller, have higher abundance than their nuclear counterparts and contain valuable phylogenetic information, but are yet to be widely used to identify new protist lineages from metagenomes. Here we present "ChloroScan", a new bioinformatics pipeline to extract eukaryotic plastid genomes from metagenomes. It incorporates a deep learning contig classifier to identify putative plastid contigs and an automated binning module to recover bins with guidance from a curated marker gene database. Additionally, ChloroScan summarizes the results in different user-friendly formats, including annotated coding sequences and proteins for each bin. We show that ChloroScan recovers more high-quality plastid bins than MetaBAT2 for simulated metagenomes. The practical utility of ChloroScan is illustrated by recovering 16 medium to high-quality metagenome assembled genomes from four protist-size fractioned metagenomes, with several bins showing high taxonomic novelty.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuhao Tong, Vanessa Rossetto Marcelino, Robert Turnbull, Heroen Verbruggen. 2025-10-13. ChloroScan: Recovering plastid genome bins from metagenomic data. https://doi.org/10.1111/1755-0998.70143

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment response prediction a critical unmet need. Transcriptomics-based biomarkers derived from bulk and single-cell RNA sequencing (scRNA-seq) offer a promising avenue for capturing tumour-immune interactions, yet the cross-cohort generalisability of existing prediction models remains unclear.We systematically benchmark nine state-of-the-art transcriptomic ICI response predictors, five bulk RNA-seq-based models (COMPASS, IRNet, NetBio, IKCScore, and TNBC-ICI) and four scRNA-seq-based models (PRECISE, DeepGeneX, Tres and scCURE), using publicly available independent datasets unseen during model development. Overall, predictive performance was modest: bulk RNA-seq models performed at or near chance level across most cohorts, while scRNA-seq models showed only marginal improvements. Pathway-level analyses revealed sparse and inconsistent biomarker signals across models. Although scRNA-seq-based predictors converged on immune-related programs such as allograft rejection, bulk RNA-seq-based models exhibited little reproducible overlap. PRECISE and NetBio identified the most coherent immune-related themes, whereas IRNet predominantly captured metabolic pathways weakly aligned with ICI biology. Together, these findings demonstrate the limited cross-cohort robustness and biological consistency of current transcriptomic ICI prediction models, underscoring the need for improved domain adaptation, standardised preprocessing, and biologically grounded model design.

q-bio.GN

Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.

q-bio.GN

Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation

Single-cell and spatial foundation models promise transferable biological representations, yet their generality remains largely untested across modalities, biological domains and analytical tasks. We benchmarked six representative models, Nicheformer, CellPLM, scGPT-spatial, GenePT, scELMo and Novae, using a harmonised framework spanning scRNA-seq, spatial transcriptomics and Perturb-seq. We evaluated zero-shot and continually pretrained clustering, supervised annotation, marker-gene concordance and perturbation prediction. Model performance was strongly conditional: expression-trained cell-level transformers best resolved many cell-identity tasks, spatial and graph-aware models better preserved tissue architecture, and language-derived gene embeddings were competitive for selected perturbation-response metrics. No model dominated across tasks, and rankings shifted with modality, preprocessing, tokenisation, biological prior, domain shift and metric choice. This benchmark provides practical guidance for model selection and argues that future models should be judged by biological generalisation, interpretability and perturbation-grounded validity, not by scale or leaderboard performance alone.

q-bio.GN