生物信息学与计算生物学

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.

The rhizosphere, a 2–10 mm region surrounding the root surface, is colonized by numerous microorganisms, known as the rhizosphere microbiome. These microorganisms interact with each other, leading to emergent properties that affect plant fitness. Mapping these interactions is crucial to understanding microbial ecology in the rhizosphere and predicting and manipulating plant health. However, current methods do not capture the chemistry of the rhizosphere environment, and common plant–microbe interaction study setups do not map bacterial interactions in this niche. Additionally, studying bacterial interactions may require the creation of transgenic bacterial lines with markers for antibiotic resistance/fluorescent probes and even isotope labeling. Here, we describe a protocol for both in silico prediction and in vitro validation of bacterial interactions that closely recapitulate the major chemical constituents of the rhizosphere environment using a widely used Murashige & Skoog (MS)-based gnotobiotic plant growth system. We use the auto-fluorescent Pseudomonas, abundantly found in the rhizosphere, to estimate their interactions with other strains, thereby avoiding the need for the creation of transgenic bacterial strains. By combining artificial root exudate medium, plant cultivation medium, and a synthetic bacterial community (SynCom), we first simulate their interactions using genome-scale metabolic models (GSMMs) and then validate these interactions in vitro, using growth assays. We show that the GSMM-predicted interaction scores correlate moderately, yet significantly, with their in vitro validation. Given the complexity of interactions among rhizosphere microbiome members, this reproducible and efficient protocol will allow confident mapping of interactions of fluorescent Pseudomonas with other bacterial strains within the rhizosphere microbiome.

DNA methylation is a crucial epigenetic modification that influences gene expression and plays a role in various biological processes. High-throughput sequencing techniques, such as bisulfite sequencing (BS-seq) and enzymatic methyl sequencing (EM-seq), enable genome-wide profiling of DNA methylation patterns with single-base resolution. In this protocol, we present a bioinformatics pipeline for analyzing genome-wide DNA methylation. We outline the step-by-step process of the essential analyses, including quality control using FASTQ for BS- and EM-seqs raw reads, read alignment with commonly used aligners such as Bowtie2 and BS-Seeker2, DNA methylation calling to generate CGmap files, identification of differentially methylated regions (DMRs) using tools including MethylC-analyzer and HOME, data visualization, and post-alignment analyses. Compared to existing workflows, this pipeline integrates multiple steps into a single protocol, lowering the technical barrier, improving reproducibility, and offering flexibility for both plant and animal methylome studies. To illustrate the application of BS-seq and EM-seq, we demonstrate a case study on analyzing a mutant in Arabidopsis thaliana with a mutation in the met1 gene, which encodes a DNA methyltransferase, and results in global CG hypomethylation and altered gene regulation. This example highlights the biological insights that can be gained through systematic methylome analysis. Our workflow is adaptable to any organism with a reference genome and provides a robust framework for uncovering methylation-associated regulatory mechanisms. All scripts and detailed instructions are provided in GitHub repository: https://github.com/PaoyangLab/Methylation_Analysis.

Weighted gene co-expression network analysis (WGCNA) is widely used in transcriptomic studies to identify groups of highly correlated genes, aiding in the understanding of disease mechanisms. Although numerous protocols exist for constructing WGCNA networks from gene expression data, many focus on single datasets and do not address how to compare module stability across conditions. Here, we present a protocol for constructing and comparing WGCNA modules in paired tumor and normal datasets, enabling the identification of modules involved in both core biological processes and those specifically related to cancer pathogenesis. By incorporating module preservation analysis, this approach allows researchers to gain deeper insights into the molecular underpinnings of oral cancer, as well as other diseases. Overall, this protocol provides a framework for module preservation analysis in paired datasets, enabling researchers to identify which gene co-expression modules are conserved or disrupted between conditions, thereby advancing our understanding of disease-specific vs. universal biological processes.

Thousands of RNAs are localized to specific subcellular locations, and these localization patterns are often required for optimal cell function. However, the sequences within RNAs that direct their transport are unknown for almost all localized transcripts. Similarly, the RNA content of most subcellular locations remains unknown. To facilitate the study of subcellular transcriptomes, we developed the RNA proximity labeling method OINC-seq. OINC-seq utilizes photoactivatable, spatially restricted RNA oxidation to specifically label RNA in proximity to a subcellularly localized bait protein. After labeling, these oxidative RNA marks are then read out via high-throughput sequencing due to their ability to induce predictable misincorporation events by reverse transcriptase. These induced mutations are then quantitatively assessed for each gene using our software package PIGPEN. The observed mutation rate for a given RNA species is therefore related to its proximity to the localized bait protein. This protocol describes procedures for assaying RNA localization via OINC-seq experiments as well as computational procedures for analyzing the resulting data using PIGPEN.

Transcriptional pausing dynamically regulates spatiotemporal gene expression during cellular differentiation, development, and environmental adaptation. Precise measurement of pausing duration, a critical parameter in transcriptional control, has been challenging due to limitations in resolution and confounding factors. We introduce Fast TV-PRO-seq, an optimized protocol built on time-variant precision run-on sequencing (TV-PRO-seq), which enables genome-wide, single-base resolution mapping of RNA polymerase II pausing times. Unlike standard PRO-seq, Fast TV-PRO-seq employs sarkosyl-free biotin-NTP run-on with time gradients and integrates on-bead enzymatic reactions to streamline workflows. Key improvements include (1) reducing experimental time from 4 to 2 days, (2) reducing cell input requirements, and (3) improved process efficiency and simplified command-line operations through the use of bash scripts.

The root meristem navigates the highly variable soil environment where water availability limits water absorption, slowing or halting growth. Traditional studies use uniform high osmotic potentials, poorly representing natural conditions where roots gradually encounter increasing osmotic potentials. Uniform high osmotic potentials reduce root growth by inhibiting cell division and shortening mature cell length. This protocol describes a simple and effective in vitro system using a gradient mixer that generates a vertical gradient in an agar gel based on the principle of communicating vessels, exploiting gravity to generate a continuous mannitol concentration gradient (from 0 to 400 mM mannitol) reaching osmotic potentials of -1,2 MPa. It enables long-term Arabidopsis root growth analysis under progressive water deficit, improving phenotyping and molecular studies in soil-like conditions.

The complexity of the human transcriptome poses significant challenges for complete annotation. Traditional RNA-seq, often limited by sensitivity and short read lengths, is frequently inadequate for identifying low-abundant transcripts and resolving complex populations of transcript isoforms. Direct long-read sequencing, while offering full-length information, suffers from throughput limitations, hindering the capture of low-abundance transcripts. To address these challenges, we introduce a targeted RNA enrichment strategy, rapid amplification of cDNA ends coupled with Nanopore sequencing (RACE-Nano-Seq). This method unravels the deep complexity of transcripts containing anchor sequences—specific regions of interest that might be exons of annotated genes, in silico predicted exons, or other sequences. RACE-Nano-Seq is based on inverse PCR with primers targeting these anchor regions to enrich the corresponding transcripts in both 5' and 3' directions. This method can be scaled for high-throughput transcriptome profiling by using multiplexing strategies. Through targeted RNA enrichment and full-length sequencing, RACE-Nano-Seq enables accurate and comprehensive profiling of low-abundance transcripts, often revealing complex transcript profiles at the targeted loci, both annotated and unannotated.