生物信息学与计算生物学

DNA epigenetic modifications play crucial roles in regulating gene expression and cellular function across diverse organisms. Among them, 5-glyceryl-methylcytosine (5gmC), a unique DNA modification first discovered in Chlamydomonas reinhardtii, represents a novel link between redox metabolism and epigenetic regulation. Accurate genome-wide detection of 5gmC is essential for investigating its biological functions, yet no streamlined method has been available. Here, we present deaminase-assisted sequencing (DEA-seq), a simple and robust approach for base-resolution mapping of 5gmC. DEA-seq employs a single DNA deaminase that efficiently converts unmodified cytosines (C) and 5-methylcytosine (5mC) into uracils or thymines, while leaving 5gmC intact. This selective resistance generates a clear sequence signature that enables precise identification of 5gmC sites across the genome. The method operates under mild reaction conditions and is compatible with low-input DNA, minimizing sample loss and improving detection sensitivity. Overall, DEA-seq provides an accessible, efficient, and highly accurate protocol for profiling 5gmC, offering clear advantages in workflow simplicity, DNA integrity, and analytical performance.

Serial spatial omics technologies capture genome-wide gene expression patterns in thin tissue sections but lose spatial continuity along the third dimension. Reconstructing these two-dimensional measurements into coherent three-dimensional volumes is necessary to relate molecular domains, gradients, and tissue architecture within whole organs or embryos. sc3D is an open-source Python framework that registers consecutive spatial transcriptomic sections, interpolates bead coordinates in three dimensions, and stores the result in an AnnData object compatible with Scanpy. The workflow performs slice alignment, 3D reconstruction, optional downsampling, and interactive visualization in a napari-sc3D-viewer, enabling virtual in situ hybridization and spatial differential gene expression analysis. We tested sc3D on Slide-seq and Stereo-seq datasets, including E8.5 and E16.5 mouse embryos, recovering continuous tissue morphologies, cardiac anatomical markers, and the expected anterior–posterior gradients of Hox gene expression. These results show that sc3D allows reproducible reconstruction and analysis of volumetric spatial omics data across different samples and experimental platforms.

The deep learning revolution has accelerated discovery in cell biology by allowing researchers to outsource their microscopy analyses to a new class of tools called cell segmentation models. The performance of these models, however, is often constrained by the limited availability of annotated data for them to train on. This limitation is a consequence of the time cost associated with annotating training data by hand. To address this bottleneck, we developed Cell-APP (cellular annotation and perception pipeline), a tool that automates the annotation of high-quality training data for transmitted-light (TL) cell segmentation. Cell-APP uses two inputs—paired TL and fluorescence images—and operates in two main steps. First, it extracts each cell’s location from the fluorescence images. Then, it provides these locations to the promptable deep learning model μSAM, which generates cell masks in the TL images. Users may also employ Cell-APP to classify each annotated cell; in this case, Cell-APP extracts user-specified, single-cell features from the fluorescence images, which can then be used for unsupervised classification. These annotations and optional classifications comprise training data for cell segmentation model development. Here, we provide a step-by-step protocol for using Cell-APP to annotate training data and train custom cell segmentation models. This protocol has been used to train deep learning models that simultaneously segment and assign cell-cycle labels to HeLa, U2OS, HT1080, and RPE-1 cells.

Pinpointing causal genes for complex traits from genome-wide association studies (GWAS) remains a central challenge in crop genetics, particularly in species with extensive linkage disequilibrium (LD) such as rice. Here, we present CisTrans-ECAS, a computational protocol that overcomes this limitation by integrating population genomics and transcriptomics. The method’s core principle is the decomposition of gene expression into two distinct components: a cis-expression component (cis-EC), regulated by local genetic variants, and a trans-expression component (trans-EC), influenced by distal genetic factors. By testing the association of both components with a phenotype, CisTrans-ECAS establishes a dual-evidence framework that substantially improves the reliability of causal inference. This protocol details the complete workflow, demonstrating its power not only to identify causal genes at loci with weak GWAS signals but also to systematically reconstruct gene regulatory networks. It provides a robust and powerful tool for advancing crop functional genomics and molecular breeding.

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.