生物信息学与计算生物学

Adaptive behaviors shaped by prior experience are essential for increasing animal survival. Aversive experiences play a pivotal role in memory formation and in updating subsequent learning rules. While the negative value of aversive signals, which are both necessary and sufficient to drive a conditioned response, is considered to be innately specified, it can also be subject to experience-dependent scaling. Previous reports demonstrated synaptic potentiation in nociceptive pathways following robust aversive learning. However, the neuronal basis of experience-dependent value updating remains largely unknown. Recently, we demonstrated that long-term potentiation (LTP) in the parabrachial-central amygdala (PB-CeA) pathway, an important circuit involved in pain processing and aversive learning, enhances the negative value and thereby updates future learning rules. Here, we present a protocol that combines behavioral analysis using pathway-specific optogenetic induction of in vivo LTP with mathematical modeling to examine value modification using Bayesian inference of the unconditioned stimulus value using the Rescorla–Wagner model. This protocol enables investigation of the mechanisms underlying experience-dependent value modulation and learning-rule changes in mice. Potentially, this protocol may provide a framework for understanding learning rules across a wide range of species and for the development of treatments for stress-related disorders.

Postnatal mouse retinal vascular development is a widely used model for studying retinal vascular diseases and evaluating candidate therapies. This is particularly relevant for inherited disorders such as familial exudative vitreoretinopathy (FEVR), in which impaired vascular growth and organization are central to disease pathogenesis. Numerous approaches have been used to assess retinal vasculature in mouse flat mounts, ranging from qualitative descriptions to limited quantitative measurements of vascular growth. However, phenotypic variability across genetic models, including different models of FEVR, complicates comparisons and underscores the need for standardized, comprehensive multi-parameter analyses that are suitable for rapid and cost-effective screening studies. We describe a standardized morphometric protocol using ImageJ software to quantitatively analyze mouse retinal vasculature in a reproducible manner. The protocol begins with measurement of areas of vascular disorganization (meshes) as well as total vascular and retinal area. Two defined regions in the peripheral and midperipheral retina are then selected to quantify cell clusters, followed by image processing, binarization, and skeletonization. From these processed images, vascular density, branch number, branch length and thickness, junction number, triple points, and box-counting fractal dimension and lacunarity are quantified. Overall, this protocol provides a rapid, cost-effective, and standardized framework for quantifying retinal vascular phenotypes across diverse mouse models. By capturing multiple structural features and accommodating phenotypic variability, it is well-suited for comparative studies and therapeutic screening in retinal vascular disease.

Peptidoglycan (PG), a network of glycan strands crosslinked by short peptides, is an essential and bacterial-specific structure that determines cell shape and protects cells from lysis. Understanding how bacteria assemble, maintain, and modify their PG not only addresses fundamental questions in cell biology but also provides a basis for developing strategies to treat bacterial infections. Although several in vitro methods, such as zymography, Remazol Brilliant Blue (RBB) assay, and LC-MS analyses, are available to quantify the activities of PG-modification enzymes, these approaches are not readily applicable in vivo. Here, we describe a single-particle tracking photo-activated localization microscopy (sptPALM)-based method to quantify the binding of enzymes to PG in vivo, which serves as a proxy for their enzymatic activities. Because the PG meshwork is relatively immobile, fluorescently tagged enzymes that transiently or stably bind it exhibit reduced mobility, reflected by lower diffusion coefficients. This approach provides sensitive, quantitative, and real-time insights into enzyme behavior in vivo under diverse physiological conditions or genetic backgrounds. The protocol is particularly valuable for investigating PG-modification enzymes that are essential or functionally redundant, which are often difficult to analyze using traditional genetic methods.

In current genomic research, molecular dating is challenged by both imperfect substitution modeling and analysis efficiency, as genome-scale datasets often exhibit substantial rate heterogeneity and complex patterns of sequence evolution, which can make divergence-time estimation sensitive to modeling assumptions and computational settings. Meanwhile, commonly used molecular dating workflows remain operationally demanding; preparing correctly formatted inputs, implementing model settings, configuring fossil calibrations, and performing basic diagnostics and visualization frequently require multiple tools and extensive manual steps, resulting in high hands-on time and avoidable operational errors. To facilitate the practical implementation of molecular dating analyses and lower the operational barrier for users, this protocol describes a GUI-based workflow in PhyloSuite v2 for molecular dating analysis. Using a dataset of fish nuclear genomes as an example, the tutorial covers multi-format data import, visual configuration of fossil calibrations, automatic selection and implementation of substitution models, automation of complex analytical procedures, and assessment of Markov chain Monte Carlo (MCMC) convergence, along with data visualization. Through this protocol, users can quickly master the full workflow—from input preparation and molecular dating to MCMC sample statistical assessment and timetree visualization—thus significantly enhancing the efficiency of molecular dating analysis and result verification.

Structural proteomics methods allow for the proteome-wide interrogation of protein structural differences between two different conditions. Limited proteolysis mass spectrometry (LiP-MS), as originally implemented by the Picotti lab, utilizes a promiscuous protease to cleave at solvent-exposed regions of a protein to encode structural information, which is then read out with mass spectrometry proteomics. Here, we present a protocol that details experimental steps and data analysis for a LiP-MS workflow. First, tissue is homogenized under native conditions and then subjected to limited proteolysis using proteinase K (PK). The samples are prepared for mass spectrometry, and data are acquired using either data-dependent acquisition (DDA) or data-independent acquisition (DIA). Raw data is processed using FragPipe, and raw ion abundances are processed in FragPipe Limited-Proteolysis Processor (FLiPPR). Proteins with structural changes between the two conditions are identified in a proteome-wide manner.

DNA language models, such as DNABERT-2, have recently enabled the accurate prediction of functional sequence elements across species. However, the practical, protocol-style steps needed to transform these resources into training datasets, fine-tune the official DNABERT-2 model, and evaluate classifier performance have not been explicitly described. Herein, we present a step-by-step computational protocol for preparing training data, fine-tuning DNABERT-2, and evaluating sequence-level binary classifiers using readily available command-line tools. The protocol has been demonstrated using RNA off-target sites induced by cytosine base editors, detected by our PiCTURE pipeline from RNA sequencing (RNA-seq) data, and extended to core promoter prediction using the EPDnew database. We describe how to derive positive and negative sequence sets into DNABERT-2 compatible datasets, and fine-tune the official pretrained model of DNABERT-2 using the datasets. We also demonstrate how to compute the standard performance metrics and compare the model outputs with the baselines. This protocol will help researchers adapt DNA foundation models to new genomic tasks, including the safety assessment of genome editing tools and the functional annotation of regulatory sequences.

Protein–protein interactions (PPIs) govern nearly all aspects of cellular physiology, yet identifying these interactions under native conditions remains challenging. Here, we present TIE-UP-SIN (targeted interactome experiment for unknown proteins by stable isotope normalization), a robust method for in vivo identification and quantification of PPIs in bacterial systems. The protocol combines metabolic labeling with ¹⁵N isotopes, reversible formaldehyde crosslinking, affinity purification, and quantitative mass spectrometry. TIE-UP-SIN preserves transient or weak interactions during purification and quantifies interaction partners using internal light/heavy peptide ratios, reducing experimental variability. The method employs a triple-sample design to distinguish specific from nonspecific interactors and can be adapted to various bacterial species and affinity tags. Data analysis is streamlined through a user-friendly web application (https://shiny-fungene.biologie.uni-greifswald.de/TIE_UP_SIN_app) that automates statistical analysis, normalization, and visualization, requiring no programming expertise. The entire workflow from cell culture to mass spectrometry data acquisition takes approximately 4–5 days, with data analysis completed in 1–2 days using the web application.

Transcription factors (TFs) regulate gene expression by binding to cis-regulatory elements in the genome. Understanding transcriptional regulation requires genome-wide characterization of TF occupancy across different chromatin contexts, yet simultaneous assessment of TF binding for multiple factors remains technically challenging. Here, we describe a detailed and reproducible protocol for cFOOT-seq, a cytosine deaminase–based genomic footprinting assay by sequencing, which enables antibody-independent, base-resolution profiling of chromatin accessibility, nucleosome organization, and TF occupancy. In cFOOT-seq, the double-stranded DNA (dsDNA) cytosine deaminase SsdA_tox converts cytosine to uracil in accessible chromatin, whereas TF binding and nucleosome occupancy locally protect DNA from deamination. Using the FootTrack analysis framework, deamination patterns generated by cFOOT-seq are quantitatively analyzed to derive standardized footprint and chromatin organization profiles at base resolution across the genome. Because cFOOT-seq preserves genomic DNA integrity during deamination-based footprinting, it is compatible with ATAC-seq-based chromatin enrichment. ATAC-combined implementations reduce sequencing depth requirements and improve scalability for footprint-focused analyses, supporting applications in low-input and single-cell settings. This protocol provides a practical framework for genome-wide TF footprint profiling and can be readily applied to dissect gene regulatory mechanisms in development, immunity, and disease, including cancer.

Extrachromosomal circular DNA (eccDNA) is a type of circular DNA that exists independently of chromosomes and has garnered significant attention in various fields, particularly in the context of smaller eccDNAs, which have considerable roles in gene regulation through various mechanisms. Current methods such as Circle-Seq and 3SEP can enrich small eccDNAs during sample preparation, but most bioinformatics pipelines remain challenging, exhibiting low accuracy and efficiency. This protocol describes the detailed workflow of a newly developed bioinformatics analysis pipeline, named EccDNA Caller based on Consecutive Full Pass (ECCFP), to accurately identify eccDNA from long-read Nanopore sequencing data. Compared to other pipelines, ECCFP significantly improves detection sensitivity, accuracy, and runtime efficiency. The process includes raw data quality control, trimming of adapters and barcodes, alignment to a reference genome, and identification of eccDNA, with detailed results encompassing accurate positioning of eccDNA, consensus sequences, and variants of individual eccDNA.

Cellulose synthase complexes (CSCs) play a central role in plant cell wall formation. Their dynamic behavior at the plasma membrane leads to the deposition of cellulose microfibrils into the apoplastic space, thereby shaping the architecture and mechanical properties of the cell wall. Although previous imaging studies have provided important insights into CSC dynamics and localization, standardized and reproducible workflows for quantitative measurements of CSC speed and density remain limited. Here, we present a reproducible live-cell imaging and analysis workflow for quantifying the speed and density of fluorescently labeled CSCs at the plasma membrane in Arabidopsis thaliana. The protocol integrates optimized spinning-disk confocal imaging, surface-based projection of z-stack recordings, automated detection of diffraction-limited CSCs foci, and kymograph-based speed measurements using freely available tools in Fiji. While selected steps, such as region of interest definition and parameter selection for spot detection or trajectory analysis, remain user-guided, these decisions are constrained to well-defined stages within an otherwise standardized pipeline, thereby reducing variability and improving reproducibility across experiments. The workflow has been validated across multiple tissues, reporter lines, genetic backgrounds, and perturbation conditions in Arabidopsis and enables robust comparative analysis of CSC dynamics. Beyond CSCs, this workflow is expected to be adaptable to other fluorescently labeled proteins that appear as diffraction-limited foci at or near the plasma membrane.