Current protocols最新文献

Genetic ancestry inference has become essential in population and medical genetics, especially for studies of admixed populations. Accurate determination of both global ancestry (GA) proportions and local ancestry (LA) segmental origins requires careful selection of computational methods and reference panels. Here, we present a practical, protocol-oriented guide that (i) clarifies key concepts (GA vs. LA, reference panel selection, phasing requirements), (ii) organizes methods into model-based clustering and dimensionality-reduction approaches for GA and hidden Markov model–based, window-based machine learning, and deep learning frameworks for LA and (iii) provides concise guidance on tool selection for GA and LA. Step-by-step protocols are provided for a typical ADMIXTURE-based GA analysis and for a SHAPEIT5 + RFMix LA inference pipeline, with practical considerations for genotype array and whole-genome sequencing data. We also discuss quality control, method validation, and downstream applications of ancestry inference. Finally, we address current challenges and highlight recent advances, including fast algorithms, deep learning models, improved phasing, and integrative tools. This guide aims to help researchers select and implement appropriate ancestry inference methods for diverse study designs and datasets. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Basic Protocol 1: Global ancestry analysis (ADMIXTURE pipeline)

Basic Protocol 2: Local ancestry analysis (phasing + RFMix pipeline)

Differential abundance or expression analyses are routinely performed on metagenomic, metatranscriptomic, and amplicon sequencing data. In such datasets, analysts usually have no information regarding the true scale (i.e., size) of the microbial community or sample under study, with inter-sample differences in sequencing depth instead being driven by technical variation rather than biological factors. Recent work has demonstrated that normalizations used in all analysis tools make incorrect assumptions about the biological scale of the system in question, leading to unacceptably high false-discovery rates in the output. To mitigate this, analysts can acknowledge and account for the uncertainty of the overall system scale during normalization by building scale models of the data—a feature that has been integrated into the ALDEx2 R package. Here, we provide reproducible examples that demonstrate how to incorporate scale models into differential expression analyses of RNA-seq data using bulk transcriptome and metatranscriptomic datasets, as well as the consequences of not doing so. We also show how to use the output of ALDEx2 to create high-level exploratory visualizations of their data through principal component analysis. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Basic Protocol 1: Using a simple scale model for differential expression analysis to avoid dual-cutoff P value/significance thresholds

Basic Protocol 2: Implementing a full informed scale model to correct scale-related data asymmetry in differential expression analyses

Basic Protocol 3: Visualizing ALDEx2 outputs using a compositional approach: Principal component analysis

Next-generation RNA sequencing (RNA-seq) allows researchers to study gene expression across the whole genome. However, its analysis often needs powerful computers and advanced command-line skills, which can be challenging when resources are limited. This protocol provides a simple, start-to-finish RNA-seq data analysis method that is easy to follow, reproducible, and requires minimal local hardware. It uses free tools such as SRA Toolkit, FastQC, Trimmomatic, BWA/HISAT2, Samtools, and Subread, along with Python and R for further analysis using Google Colab. The process includes downloading raw data from NCBI GEO/SRA, checking data quality, trimming adapters and low-quality reads, aligning sequences to reference genomes, converting file formats, counting reads, normalizing to TPM, and creating visualizations such as heatmaps, bar plots, and volcano plots. Differential gene expression is analyzed with pyDESeq2, and functional enrichment is done using g:Profiler. Troubleshooting in RNA-seq generally involves configuring essential tools, resolving path and dependency issues, and ensuring proper handling of paired-end reads during analysis. By running the heavy computational steps on cloud platforms, this workflow makes RNA-seq analysis affordable and accessible to more researchers. © 2026 Wiley Periodicals LLC.

Basic Protocol 1: Extracting and processing a high-throughput RNA-seq dataset with the command prompt and Windows Subsystem for Linux

Basic Protocol 2: Normalization and visualization of processed RNA-seq dataset with Google Colab and Python 3

Pseudomonas syringae complex (Psc) causes bacterial blight and canker on blueberry and other fruit crops. Reliable and cost-effective pathogen detection is essential for managing crop diseases. The Oxford Nanopore MinION has emerged as a powerful tool in plant disease diagnostics and genetic characterizations of plant pathogens due to streamlined library preparation protocols, rapid sequencing turnaround, the ability to generate long reads, and enhanced taxonomic resolution. However, standardized and reproducible methods for whole-genome sequencing of the plant pathogenic Psc remains limited, especially for pathogens infecting blueberry tissues. Therefore, we describe several protocols including DNA extraction and quality control from both pure bacterial cultures and infected blueberry tissues (e.g., infected leaves and stem tissues), library preparation, and whole-genome sequencing using the MinION platform. The challenges of working with low bacterial pathogen biomass in plant tissue, contamination from host DNA, and sample variability are addressed through optimized workflows and troubleshooting strategies. Furthermore, we provide a modular bioinformatics software pipeline ranging from user-friendly EPI2ME tools to command-line analyses with Kraken2 and Krona for genome assembly and annotation and taxonomic identification, and comparative and evolutionary studies of Psc. These methods provide a reproducible framework for the genomic characterization and diagnostics of the Psc in the context of agricultural disease diagnostics, with the potential flexibility for adaptation to other bacterial taxa and sample types. © 2026 His Majesty the King in Right of Canada. Current Protocols published by Wiley Periodicals LLC. Reproduced with the permission of the Minister of Agriculture and Agri-Food. Basic Protocol 1: Isolation of pseudomonad cultures from infected plant tissues Basic Protocol 2: DNA extraction from pure cultures of pseudomonads Basic Protocol 3: DNA isolation from infected host tissues Basic Protocol 4: Quality control, library preparation, and whole-genome sequencing Basic Protocol 5: Taxonomic identification using EPI2ME Desktop application Support Protocol: Taxonomic identification using Kraken2 and Krona in Linux Basic Protocol 6: Bioinformatics analyses for genome assembly and annotation.

Oligonucleotide-based therapeutics are now widely used in clinical settings. From the late 1980s to the mid-1990s, efforts to improve therapeutic efficacy focused on imparting drug-like properties to oligonucleotides, emphasizing nuclease stability and target sequence affinity. These efforts resulted in the standard gapmer design for RNase H–mediated antisense and the prevalent use of chemical modification such as phosphorothioate and 2′-substituted oligoribonucleotides in oligonucleotide therapeutics. Progress made in the antisense field also enabled the development of splice-modulating oligonucleotide therapeutics and later siRNA therapies. All three modes of action are now widely employed in >25 approved drugs. Since then, we have learned that oligonucleotides and their chemical modifications can interact with pattern recognition receptors as well as various other proteins. This can have both positive and negative effects, such as aiding in oligonucleotide delivery or activating the intracellular innate immune system. My current work aims to optimize the drug-like properties of oligonucleotides by combining the early chemical advances with the more recent insights into off-target protein binding. The present article describes how this resulted in several different cyclic structured oligonucleotide designs, in which 3′ and 5′ ends are transiently held together via Watson-Crick base pairing. The transient nature of these cyclic structures protects the functional parts of the structure against nucleases during delivery and cell entry while allowing effective release of the oligonucleotide drug into the intracellular environment. These cyclic designs demonstrate significant improvements in potency and specificity over gapmer antisense and are broadly applicable to potentially all types of RNA therapeutics, irrespective of their mechanism of action. © 2026 Wiley Periodicals LLC.

The IntelliCage is a home-cage-based radiofrequency identification (RFID) test system for studying learning behavior of group-housed mice. Specifically, the mice must learn where and how to get water within the IntelliCage. The protocol describes how to set up the IntelliCage, how to set up and start a learning experiment, what to consider before starting an experiment, and how to clean the system. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Basic Protocol 1: General instructions for using the IntelliCage with mice

Basic Protocol 2: IntelliCage cleaning

A major, serendipitous psychiatric discovery is monoamine-transporter reuptake inhibition as an antidepressant mechanism of action. Chronic treatment with such antidepressants is efficacious, with onset requiring 1-2 weeks, in many but by no means all patients with major depressive or another stress-related neuropsychiatric disorder. The forced swim test (FST) in rats and mice involves the acute, moderate stressor of placement in a container of water: at test onset, the predominant reaction is swimming, interpreted cautiously as active “struggling”; over minutes, this is replaced by floating, described objectively as immobility. Acute administration of monoamine transporter inhibitors immediately prolongs “struggling.” Although this readout is of behavioral pharmacological interest, the FST has no (back-)translational relevance to the neurobiological and neuropsychological symptoms/states of stress-related psychiatric disorders. The persistent adoption of the FST to measure “depression-like state,” based on interpretation of immobility as “despair,” “helplessness,” or “passive coping,” is a major weakness in applied behavioral neuroscience. Rodents do have a concept of learned uncontrollability, such that tests showing this depict an adaptive, not a “depression-like,” state. Recent psychiatry-neuroscience initiatives, such as the Research Domain Criteria framework, increase the accessibility of specific, transdiagnostic symptoms/states to behavioral neuroscience methods, thereby facilitating the establishment of animal models. Such animal models must incorporate clinically valid (1) etiological factors, such as prolonged psychosocial stress, and (2) neurobehavioral readouts with face and construct validity for specific symptoms/states. Increased reactivity to an acute threat, measured as increased Pavlovian aversion learning-memory (PALM), is a neurobehavioral state common in major depression and other disorders. Mice that have undergone chronic social stress exhibit generalized excessive PALM. Therefore, although stating that the FST measures “depression-like state” is erroneous, mouse models of specific symptoms/states can be achieved by back-translating etiology and neurobehavioral readouts. Though complex and moderately severe, such models have the potential to provide much-needed benefits in terms of preclinical neuropharmacological target discovery and validation. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Multiple myeloma is a bone marrow–derived neoplastic proliferation of plasma cells with low levels of circulating tumor cells. Cytogenetic abnormalities are present in >90% of patients, when assessed by fluorescence in situ hybridization (FISH) on bone marrow, and the specific abnormalities provide vital prognostic information. Provided is a protocol for assessing the high-risk abnormalities del(17p) and amp(1q21) in the circulating plasma cells of myeloma patients by flow cytometry. This utilizes positive plasma cell identification by standard immunophenotyping and then chromosomal analysis by FISH using an imaging flow cytometer. Integrating cell phenotype and FISH in one test, a method called “immuno-flowFISH” allows for the detection of cytogenetic abnormalities in plasma cells identified by their antigenic profile. This method can be applied to both bone marrow and blood samples to detect primary abnormalities [i.e., hyperdiploidy; immunoglobulin heavy locus (IGH) translocation] and secondary abnormalities [i.e., del(17p) found in 10% of patients, and gain(1q) present in ∼40% of patients with myeloma]. Here we describe the protocol for the simultaneous detection of these secondary abnormalities in blood and bone marrow samples from myeloma patients that enables detection of single or “double hit” abnormalities, with the latter classified as ultra high–risk disease. The protocol includes the data analysis strategy, as well as the statistics used to confirm the presence of these abnormalities. Applying this method will facilitate blood-based monitoring for the presence and evolution of these critical cytogenetic abnormalities. © 2026 Wiley Periodicals LLC.

Basic Protocol: Immuno-flowFISH assessment of del(17p) and amp(1q21) in multiple myeloma

Support Protocol: Validation of single fluorophore for positive markers in multiple myeloma

Identifying viral sequences from metagenomic datasets is critical for investigating their origins, evolutionary patterns, and ecological functions. Previously, we developed a novel deep learning software, DeepVirFinder, to predict viral sequences from shotgun metagenomic assemblies. This method employs a twin convolutional neural network model to extract features from known viral and prokaryotic host genomic sequences for binary classification of input query sequences. With the rapid accumulation of environmental metagenomic data, this approach has accelerated the discovery of novel viruses from diverse environments through an alignment-free and reference-free deep learning strategy. To facilitate the rapid adoption of this software for beginning users, here we have further improved DeepVirFinder by optimizing its runtime performance, while maintaining the essential user interface of the original version. This comprehensive guide provides basic workflows for the most common use cases of DeepVirFinder. Additionally, to assist users in downstream analyses, supplementary scripts were provided in the software for extracting viral sequences and inspecting the results, thereby helping researchers more effectively mine viral information from metagenomic datasets. © 2026 Wiley Periodicals LLC.

Basic Protocol 1: Predicting viral sequences in metagenomic assemblies

Basic Protocol 2: An integrated pipeline for viral sequence analysis: Prediction, extraction, and visualization

Basic Protocol 3: Retraining the DeepVirFinder model using a customized dataset

Virus-like particles (VLPs) are widely recognized as safe and versatile nanostructures with broad applications in vaccine development, gene delivery, and nanotechnology, but their production typically requires time-consuming procedures, incurs high costs, and yields products of limited purity. This article outlines a rapid, scalable, and cost-effective method for producing high-quality VLPs using the VP1s capsid protein from murine polyomavirus. The procedure comprises four principal stages: expression of the capsid protein in Escherichia coli, extraction and stabilization of the protein, purification to yield high-quality capsomeres, and controlled in vitro assembly of VLPs. Compared with conventional protocols that rely on in vivo assembly and labor-intensive purification procedures, such as ultracentrifugation- or affinity tag-based methods, the described approach provides a streamlined and rapid workflow that avoids affinity tags, achieves consistently high yields, and enables precise regulation of assembly conditions, resulting in a scalable and highly cost-efficient strategy for VLP production within 5 days. © 2026 Wiley Periodicals LLC.

Basic Protocol 1: Expression and preparation of polyomavirus VP1 protein in E. coli

Basic Protocol 2: Polymomavirus VP1 purification

Basic Protocol 3: VP1 assembly

Basic Protocol 4: Quality control