Bioinformatics and Computational Biology

Expansion microscopy (ExM) is an innovative and cost-effective super-resolution imaging technique that enables nanoscale visualization of biological structures using conventional fluorescence microscopes. By physically enlarging biological specimens, ExM circumvents the diffraction limit and has become an indispensable tool in cell biology. Ongoing methodological advances have further enhanced its spatial resolution, labeling versatility, and compatibility with diverse sample types. However, ExM imaging is often hindered by sample drift during image acquisition, caused by subtle movements of the expanded hydrogel. This drift can distort three-dimensional reconstruction, compromising both visualization accuracy and quantitative analysis. To overcome this limitation, we developed 3D-Aligner, an advanced and user-friendly image analysis software that computationally corrects sample drift in fluorescence microscopy datasets, including but not limited to those acquired using ExM. The algorithm accurately determines drift trajectories across image stacks by detecting and matching stable background features, enabling nanometer-scale alignment to restore structural fidelity. We demonstrate that 3D-Aligner robustly corrects drift across ExM datasets with varying expansion factors and fluorescent labels. This protocol provides a comprehensive, step-by-step workflow for implementing drift correction in ExM datasets, ensuring reliable three-dimensional imaging and quantitative assessment.

Accurate profiling of soil and root-associated bacterial communities is essential for understanding ecosystem functions and improving sustainable agricultural practices. Here, a comprehensive, modular workflow is presented for the analysis of full-length 16S rRNA gene amplicons generated with Oxford Nanopore long-read sequencing. The protocol integrates four standardized steps: (i) quality assessment and filtering of raw reads with NanoPlot and NanoFilt, (ii) removal of plant organelle contamination using a curated Viridiplantae Kraken2 database, (iii) species-level taxonomic assignment with Emu, and (iv) downstream ecological analyses, including rarefaction, diversity metrics, and functional inference. Leveraging high-performance computing resources, the workflow enables parallel processing of large datasets, rigorous contamination control, and reproducible execution across environments. The pipeline’s efficiency is demonstrated on full-length 16S rRNA gene datasets from yellow pea rhizosphere and root samples, with high post-filter read retention and high-resolution community profiles. Automated SLURM scripts and detailed documentation are provided in a public GitHub repository (https://github.com/henrimdias/emu-microbiome-HPC; release v1.0.2, emu-pipeline-revised) and archived on Zenodo (DOI: 10.5281/zenodo.17764933).

Hair cells are the sensory receptors of the auditory and vestibular systems in the inner ears of all vertebrates. Hair cells also serve to detect water flow in the lateral line system in amphibians and fish. The zebrafish lateral line serves as a well-established model for investigating hair cell development and function, including research on genetic mutations associated with deafness and environmental factors that cause hair cell damage. Rheotaxis, the ability to orient and swim in response to water flow, is a behavior mediated by multiple sensory modalities, including the lateral line organ. In this protocol, we describe a rheotaxis assay in which station holding behavior, which employs positive rheotaxis to maintain position in oncoming water flow, serves as a sensitive measure of lateral line function in larval zebrafish. This assay provides a valuable tool for researchers assessing the functional consequences of genetic or environmental disruptions of the lateral line system.

Plants move chloroplasts in response to light, changing the optical properties of leaves. Low irradiance induces chloroplast accumulation, while high irradiance triggers chloroplast avoidance. Chloroplast movements may be monitored through changes in leaf transmittance and reflectance, typically in red light. We present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light. We show how to employ machine learning methods to classify leaves according to the chloroplast positioning. The convolutional network is a method of choice for the analysis of the reflectance spectra, as it allows low levels of misclassification. As a complementary approach, we propose a vegetation index, called the Chloroplast Movement Index (CMI), which is sensitive to chloroplast positioning. Our method offers a high-throughput, contactless way of chloroplast movement detection.

The exploration of microbial genomes through next-generation sequencing (NGS) and genome mining has transformed the discovery of natural products, revealing an immense reservoir of previously untapped chemical diversity. Bacteria remain a prolific source of specialized metabolites with potential applications in medicine and biotechnology. Here, we present a protocol to access novel biosynthetic gene clusters (BGCs) that encode natural products from soil bacteria. The protocol uses a combination of Oxford Nanopore Technology (ONT) sequencing, de novo genome assembly, antiSMASH for BGC identification, and transformation-associated recombination (TAR) for cloning the BGCs. We used this protocol to allow the detection of large BGCs at a relatively fast and low-cost DNA sequencing. The protocol can be applied to diverse bacteria, provided that sufficient high-molecular-weight DNA can be obtained for long-read sequencing. Moreover, this protocol enables subsequent cloning of uncharacterized BGCs into a genome engineering-ready vector, illustrating the capabilities of this powerful and cost-effective strategy.

Understanding how lipids interact with lipid transfer proteins (LTPs) is essential for uncovering their molecular mechanisms. Yet, many available LTP structures, particularly those thought to function as membrane bridges, lack detailed information on where their native lipid ligands are located. Computational strategies, such as docking or AI-methods, offer a valuable alternative to overcome this gap, but their effectiveness is often restricted by the inherent flexibility of lipid molecules and the lack of large training sets with structures of proteins bound to lipids. To tackle this issue, we introduce a reproducible computational pipeline that uses unbiased coarse-grained molecular dynamics (CG-MD) simulations on a free and open-source software (GROMACS) with the Martini 3 force-field. Starting from a configuration of a lipid in bulk solvent, we run CG-MD simulations and observe spontaneous binding of the lipid to the protein. We show that this protocol reliably identifies lipid-binding pockets in LTPs and, unlike docking methods, suggests potential entry routes for lipid molecules with no a priori knowledge other than the protein’s structure. We demonstrate the utility of this approach in investigating bridge LTPs whose internal lipid-binding positions remain unresolved. Altogether, our study provides a cost-effective, efficient, and accurate framework for mapping binding sites and entry pathways in diverse LTPs.

Quantitative analysis of biological membrane morphology is essential for understanding fundamental cellular processes such as organelle biogenesis and remodeling. While manual annotation has been the standard for complex structures, it is laborious and subjective, and conventional automated methods often fail to accurately delineate overlapping objects in 2D projected microscopy images. This protocol provides a complete, step-by-step workflow for the quantitative analysis of overlapping prospore membranes (PSMs) in sporulating yeast. The procedure details the synchronous induction of sporulation, acquisition of 3D fluorescence images and their conversion to 2D maximum intensity projections (MIPs), and the generation of a custom-annotated dataset using a semi-automated pipeline. Finally, it outlines the training and application of our mask R-CNN-based model, DeMemSeg, for high-fidelity instance segmentation and the subsequent extraction of morphological parameters. The primary advantage of this protocol is its ability to enable accurate and reproducible segmentation of individual, overlapping membrane structures from widely used 2D MIP images. This framework offers an objective, efficient, and scalable solution for the detailed quantitative analysis of complex membrane morphologies.

Real-time quantitative PCR (qPCR) is a pivotal technique for analyzing gene expression and DNA copy number variations. However, the limited availability of user-friendly software tools for qPCR data analysis presents a significant challenge for experimental biologists with limited computational skills. To address this issue, we developed Click-qPCR, a user-friendly and web-based Shiny application for qPCR data analysis. Click-qPCR streamlines ΔCq and ΔΔCq calculations using user-uploaded CSV data files. The interactive interface of the application allows users to select genes and experimental groups and perform Welch’s t tests and one-way analysis of variance with Dunnett’s post-hoc test for pairwise and multi-group comparisons, respectively. Results are visualized via interactive bar plots (mean ± standard deviation with individual data points) and can be downloaded as publication-quality images, along with summary statistics. Click-qPCR empowers researchers to efficiently process, interpret, and visualize qPCR data regardless of their programming experience, thereby facilitating routine analysis tasks. Click-qPCR Shiny application is available at https://kubo-azu.shinyapps.io/Click-qPCR/, while its source code and user guide are available at https://github.com/kubo-azu/Click-qPCR.

In neuropharmacology and drug development, in silico methods have become increasingly vital, particularly for studying receptor–ligand interactions at the molecular level. Membrane proteins such as GABA (A) receptors play a central role in neuronal signaling and are key targets for therapeutic intervention. While experimental techniques like electrophysiology and radioligand binding provide valuable functional data, they often fall short in resolving the structural complexity of membrane proteins and can be time-consuming, costly, and inaccessible in many research settings. This study presents a comprehensive computational workflow for investigating membrane protein–ligand interactions, demonstrated using the GABA (A) receptor α5β2γ2 subtype and mitragynine, an alkaloid from Mitragyna speciosa (Kratom), as a case study. The protocol includes homology modeling of the receptor based on a high-resolution template, followed by structure optimization and validation. Ligand docking is then used to predict binding sites and affinities at known modulatory interfaces. Finally, molecular dynamics (MD) simulations assess the stability and conformational dynamics of receptor–ligand complexes over time. Overall, this workflow offers a robust, reproducible approach for structural analysis of membrane protein–ligand interactions, supporting early-stage drug discovery and mechanistic studies across diverse membrane protein targets.

Insects rely on chemosensory proteins, including gustatory receptors, to detect chemical cues that regulate feeding, mating, and oviposition behaviours. Conventional approaches for studying these proteins are limited by the scarcity of experimentally resolved structures, especially in non-model pest species. Here, we present a reproducible computational protocol for the identification, functional annotation, and structural modelling of insect chemosensory proteins, demonstrated using gustatory receptors from the red palm weevil (Rhynchophorus ferrugineus) as an example. The protocol integrates publicly available sequence data with OmicsBox for functional annotation and ColabFold for high-confidence structure prediction, providing a step-by-step framework that can be applied to genome-derived or transcriptomic datasets. The workflow is designed for broad applicability across insect species and generates structurally reliable protein models suitable for downstream applications such as ligand docking or molecular dynamics simulations. By bridging functional annotation with structural characterisation, this protocol enables reproducible studies of chemosensory proteins in agricultural and ecological contexts and supports the development of novel pest management strategies.