生物信息学与计算生物学

The canonical role of transfer RNAs (tRNAs) in protein synthesis has been extensively characterized; however, recent studies have uncovered novel functions for tRNA as a mediator of long-distance signaling in plants. Several studies have identified dicistronic tRNA-mRNA transcripts that contain a tRNA gene and an adjacent protein-coding gene (PCG) that are transcribed as a single unit. These transcripts are associated with RNA systemic mobility through the plant’s vascular tissues, potentially acting as non-cell-autonomous signaling messengers in coordinating development and stress responses. Here, we report a computational pipeline to detect dicistronic tRNA-mRNA transcripts from short-read next-generation RNA-sequencing datasets; to our knowledge, this is the only established pipeline for the systematic identification of such candidates in plants. The dicistronic RNA transcript version 2 (v2) described here improves on the earlier version DiRT v1 by expanding the repertoire of dicistronic transcripts detected to include tRNA-like structures (TLS) as well as functional tRNAs, which were already supported in the pipeline. The updated protocol also includes detection of dicistronic tRNA or TLS sequences within genomic features such as untranslated regions (UTRs). The accurate detection of both tRNAs and UTR-embedded tRNA-like sequences (TLS) is critical, as these RNA structures have been reported to function as mediators of long-distance RNA mobility. Furthermore, as NGS datasets are prone to sequencing artifacts and potential DNA contamination, we improved the pipeline’s statistical robustness by including read coverage of flanking intronic regions as a baseline control. To account for potential DNA contamination during RNA-seq library preparation, detected tRNA-mRNA transcripts are deemed as putatively dicistronic only if the coverage of their intergenic region is significantly higher (Student’s t-test, FDR < 0.05) than flanking intronic regions. Furthermore, the updated pipeline allows this statistical test to be applied to intronless and single-intron genes. Using this updated protocol, we identified novel tRNA and TLS dicistronic transcripts in both grapevine (Vitis spp. Ruggeri 140) and Arabidopsis thaliana datasets and validated in vitro using RT-PCR. We provide a fast and reliable method to detect dicistronic transcripts that can be applied to any short-read RNA-sequencing dataset, fast-tracking the functional characterization of these newly emerging transcripts.

RNA alternative splicing (AS) is an essential process that expands transcriptomic and proteomic diversity in eukaryotic cells and contributes to cellular heterogeneity across physiological and pathological conditions in humans. With the advent of single-cell RNA sequencing (scRNA-seq), it has become possible to study AS at cellular resolution, although robust and standardized analytical workflows remain to be developed. Here, we present a stepwise protocol for analyzing AS in single cells from pediatric high-grade gliomas (pHGGs) harboring the histone H3.3 lysine 27-to-methionine (H3.3K27M) mutation using SMART-Seq2 scRNA-seq data. Starting from raw sequencing reads, the workflow includes read alignment, gene-level quantification, splice junction and intron quantification, and single-nucleotide variant-based mutation detection. Gene expression–based clustering and cell-type annotation are performed by using the Seurat R package. AS analysis in tumor cells is then conducted using the MARVEL R package in combination with customized scripts to calculate percent spliced-in (PSI) values, identify variable AS events, perform dimensionality reduction, cluster cells, conduct differential AS analysis, and visualize splicing patterns. This protocol provides a reproducible and comprehensive framework for dissecting AS dynamics at single-cell resolution. It is readily adaptable to other SMART-Seq2 datasets and facilitates systematic investigation of splicing heterogeneity in diverse biological contexts.

Functional enrichment analysis is essential for understanding the biological significance of differentially expressed genes. Commonly used tools such as g:Profiler, DAVID, and GOrilla are effective when applied to well-annotated model organisms. However, for non-model organisms, particularly for bacteria and other microorganisms, curated functional annotations are often scarce. In such cases, researchers often rely on homology-based approaches, using tools like BLAST to transfer annotations from closely related species. Although this strategy can yield some insights, it often introduces annotation errors and overlooks unique species-specific functions. To address this limitation, we present a user-friendly and adaptable method for creating custom annotation R packages using genomic data retrieved from NCBI. These packages can be directly imported as libraries into the R environment and are compatible with the clusterProfiler package, enabling effective gene ontology and pathway enrichment analysis. We demonstrate this approach by constructing an R annotation package for Mycobacterium tuberculosis H37Rv, as an example. The annotation package is then utilized to analyze differentially expressed genes from a subset of RNA-seq dataset (GSE292409), which investigates the transcriptional response of M. tuberculosis H37Rv to rifampicin treatment. The chosen dataset includes six samples, with three serving as untreated controls and three exposed to rifampicin for 1 h. Further, enrichment analysis was performed on genes to demonstrate changes in response to the treatment. This workflow provides a reliable and scalable solution for functional enrichment analysis in organisms with limited annotation resources. It also enhances the accuracy and biological relevance of gene expression interpretation in microbial genomics research.

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.