SUMMARY: Metagenomic workflows involve complex multi-step analyses, from quality control and assembly to binning, annotation, and strain-level profiling. Few existing metagenomic pipelines achieve the combination of flexibility, reproducibility, and hybrid assembly support within a unified workflow. We present StrainMake, a Snakemake-based workflow for de novo metagenomic analysis from short, long, or hybrid sequencing data. StrainMake integrates widely used tools across all major steps-quality control, assembly, binning, dereplication, taxonomic and functional annotation-while also providing non-redundant gene catalogues, community-scale metabolic models, and strain-level microdiversity metrics. The modular design enables the use of alternative tools, scalable execution on HPC systems, and full reproducibility through Snakemake and Conda. RESULTS: Applied to the CAMI II strain-madness dataset, StrainMake produced high-quality assemblies and metagenome-assembled genomes (MAGs), while enabling strain-resolved comparisons across samples. Hybrid assemblies improved contiguity, whereas short-read assemblies offered faster runtimes, illustrating the workflow's benchmarking capacity. AVAILABILITY AND IMPLEMENTATION: StrainMake is open source and available at https://github.com/UMMISCO/strainmake, together with comprehensive documentation. Generated data are deposited in Zenodo (doi: 10.5281/zenodo.16950162).

International audience

The large-scale digitization of herbarium specimens—over 10 million in France via the Recolnat portal led by MNHN, and hundreds of millions worldwide—offers unprecedented opportunities for advancing plant biodiversity research. However, extracting reliable morphological traits from these images remains challenging due to the presence of non-vegetal elements (e.g., labels, rulers, envelopes) and visual background noise that can mislead model predictions. In this work, we introduce a comparative study of three deep learning models—YOLOv8, ViT, and ResNet101—applied to botanical trait classification from digitized herbarium images. We evaluate these models on both raw images and segmented versions where non-plant elements are removed. Although models trained on raw images can sometimes yield high accuracy, interpretability analysis (e.g., Grad-CAM) reveal that their predictions often rely on irrelevant background features. In contrast, segmentation consistently drives the models to focus on the plant itself, leading not only to improved performance for several morphological traits, but also laying the groundwork for explainable and scientifically meaningful AI. We argue that segmentation is not merely a preprocessing step but a prerequisite for trustworthy trait recognition and a necessary condition for enabling AI-driven discovery in botanical sciences. This work establishes a methodological foundation for interpretable plant-focused deep learning applied to biodiversity research.

ABSTRACT We introduce the shape‐image multimodal network (SIM‐Net), a novel 2D image classification architecture that integrates 3D point cloud representations inferred directly from RGB images. Our key contribution lies in a pixel‐to‐point transformation that converts 2D object masks into 3D point clouds, enabling the fusion of texture‐based and geometric features for enhanced classification performance. SIM‐Net is particularly well‐suited for the classification of digitised herbarium specimens—a task made challenging by heterogeneous backgrounds, nonplant elements, and occlusions that compromise conventional image‐based models. To address these issues, SIM‐Net employs a segmentation‐based preprocessing step to extract object masks prior to 3D point cloud generation. The architecture comprises a CNN encoder for 2D image features and a PointNet‐based encoder for geometric features, which are fused into a unified latent space. Experimental evaluations on herbarium datasets demonstrate that SIM‐Net consistently outperforms ResNet101, achieving gains of up to 9.9% in accuracy and 12.3% in F‐score. It also surpasses several transformer‐based state‐of‐the‐art architectures, highlighting the benefits of incorporating 3D structural reasoning into 2D image classification tasks.

Discovering complex interactions is an important issue in numerous fields ranging from social sciences to systems biology. Over the past few decades, many network learning methods have exhibited competitive results on various types of data. A commonly reached conclusion is that some learning approaches are more advisable than others depending on the dataset type or the complexity of the underlying network. Another frequently encountered issue relates to the ever increasing number of variables that need to be simultaneously dealt with, especially when only a small number of observations is available. The ScaleNet, a novel reconstruction method which can embed different types of network discovery approaches within a spectral framework for large graphical model was introduced recently. The approach identifies sets of connected variables based on the magnitude and sign of the eigenvector elements of a normalized graph Laplacian matrix, and it learns in parallel multiple relevant sub-graphs of a large network. However, the number of eigenvectors to be used and the size of the sub-graphs are to be fixed by an expensive procedure of cross-validation. In this contribution, we propose heuristics to find both optimal number of eigenvectors and the number of nodes in the sub-networks. We illustrate by the results on standard large-scale data sets and on a real human gut graph reconstruction that the proposed approaches save computational time, i.e. are efficient, and reach the state-of-the-art performance.

BACKGROUND: The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches. RESULTS: We propose a consensus method based on spectral decomposition, named Spectral Consensus Strategy, to reconstruct large networks from high-dimensional datasets. This novel unsupervised approach can be applied to a broad range of biological networks and the associated spectral framework provides scalability to diverse reconstruction methods. The results obtained on benchmark datasets demonstrate the interest of our approach for high-dimensional cases. As a suitable example, we considered the human gut microbiome co-presence network. For this application, our method successfully retrieves biologically relevant relationships and gives new insights into the topology of this complex ecosystem. CONCLUSIONS: The Spectral Consensus Strategy improves prediction precision and allows scalability of various reconstruction methods to large networks. The integration of multiple reconstruction algorithms turns our approach into a robust learning method. All together, this strategy increases the confidence of predicted interactions from high-dimensional datasets without demanding computations.

MOTIVATION: The noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. Therefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets. RESULTS: We propose an original network centrality measure, called annotation transcriptional centrality (ATC) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. ATC computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network. We demonstrate ATC ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures. AVAILABILITY: ATC computational routine is implemented in a publicly available tool named FunNet (www.funnet.info).

UNLABELLED: We describe here an exploratory tool, called FunNet, which implements an original systems biology approach, aiming to improve the biological relevance of the modular interaction patterns identified in transcriptional co-expression networks. A suitable analytical model, involving two abstraction layers, has been devised to relate expression profiles to the knowledge on transcripts' biological roles, extracted from genomic databases, into a comprehensive exploratory framework. This approach has been implemented into a user-friendly web tool to promote its open use by the community. AVAILABILITY: http://www.funnet.info