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PubMed2026

nf-core/pacsomatic: a scalable somatic analytic pipeline using PacBio HiFi data.

MOTIVATION: Pacific Biosciences (PacBio) HiFi long-read sequencing enables robust characterization of complex genomic regions, repetitive elements, and structural variants (SVs) that are often inaccessible to short-read technologies. To fully leverage HiFi reads to advance cancer genomics and epigenetics, researchers require an end-to-end, scalable and optimized bioinformatics workflow. The nf-core framework meets this need by providing rigorously tested, community-curated pipelines that ensure reproducibility, transparency, and broad compatibility across computational environments. RESULTS: We present nf-core/pacsomatic, an automated Nextflow DSL2 pipeline designed for comprehensive paired tumor-normal somatic analysis using PacBio HiFi data. The workflow includes steps for read alignments against reference genome, somatic SNV/indel, SV, and CNV calling, CpG methylation profiling and differential methylation region (DMR) detection. Additional downstream modules support functional annotation, mutational signature analysis, tumor purity and ploidy estimation, and homologous recombination deficiency (HRD) assessment. Utilizing nf-core's modular design and containerized execution, nf-core/pacsomatic provides a stable framework for the reproducible discovery of biological insights. AVAILABILITY: nf-core/pacsomatic is available under the MIT License at nf-core (https://nf-co.re/pacsomatic) and github (https://github.com/nf-core/pacsomatic).

Software↗

PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗

PubMed2026

The need for standardization and improved open (meta)data practices in metaproteomics.

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices. Video Abstract.

Proteomics↗

PubMed2006

Technical innovations for the automated identification of gel-separated proteins by MALDI-TOF mass spectrometry.

The combination of gel-based two-dimensional protein separations with protein identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is the workhorse for the large-scale analyses of proteomes. Such high-throughput proteomic approaches require automation of all post-separation steps and the in-gel digest of proteins especially is often the bottleneck in the protein identification workflow. With the objective of reaching the same high performance of manual low-throughput in-gel digest procedures, we have developed a novel stack-type digestion device and implemented it into a commercially available robotic liquid handling system. This modified system is capable of performing in-gel digest, extraction of proteolytic peptides, and subsequent sample preparation for MALDI-MS without any manual intervention, but with a performance at least identical to manual procedures as indicated on the basis of the sequence coverage obtained by peptide mass fingerprinting. For further refinement of the automated protein identification workflow, we have also developed a motor-operated matrix application device to reproducibly obtain homogenous matrix preparation of high quality. This matrix preparation was found to be suitable for the automated acquisition of both peptide mass fingerprint and fragment ion spectra from the same sample spot, a prerequisite for high confidence protein identifications on the basis of peptide mass and sequence information. Due to the implementation of the stack-type digestion device and the motor-operated matrix application device, the entire platform works in a reliable, cost-effective, and sensitive manner, yielding high confidence protein identifications even for samples in the concentration range of as low as 100 fmol protein per gel plug.

Automation↗

PubMed2003

Technical considerations for the performance of Nucleic acid Amplification Technology (NAT). The NAT Task Force Group.

The complexity of Nucleic acid Amplification Technology (NAT(1)), comprising sample preparation, amplification and detection methods, requires specific design considerations for both the laboratory and the procedures utilized in such testing. The purpose of this paper is to establish technical considerations for the performance of NAT. These include the collection, handling and assay of specimens and the design of laboratories to routinely and reliably detect low levels of nucleic acid sequences. The sensitivity of NAT due to the exponential amplification of nucleic acids makes contamination a major concern from specimen collection to sample detection. Therefore, laboratories need to be designed to prevent and control contamination through adequate equipment and appropriate workflow. These technical considerations should provide a basis for establishing a robust and reproducible NAT system.

Humans↗

PubMed2003

Validation of a novel, fully integrated and flexible microarray benchtop facility for gene expression profiling.

Here we describe a novel microarray platform that integrates all functions needed to perform any array-based experiment in a compact instrument on the researcher's laboratory benchtop. Oligonucle otide probes are synthesized in situ via a light- activated process within the channels of a three-dimensional microfluidic reaction carrier. Arrays can be designed and produced within hours according to the user's requirements. They are processed in a fully automatic workflow. We have characterized this new platform with regard to dynamic range, discrimination power, reproducibility and accuracy of biological results. The instrument detects sample RNAs present at a frequency of 1:100 000. Detection is quantitative over more than two orders of magnitude. Experiments on four identical arrays with 6398 features each revealed a mean coefficient of variation (CV) value of 0.09 for the 6398 unprocessed raw intensities indicating high reproducibility. In a more elaborate experiment targeting 1125 yeast genes from an unbiased selection, a mean CV of 0.11 on the fold change level was found. Analyzing the transcriptional response of yeast to osmotic shock, we found that biological data acquired on our platform are in good agreement with data from Affymetrix GeneChips, quantitative real-time PCR and--albeit somewhat less clearly--to data from spotted cDNA arrays obtained from the literature.

Automation↗

PubMed2026

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor antigens. Early clinical studies have demonstrated the feasibility, safety, and immunogenicity of these vaccines across multiple solid tumors, with encouraging outcomes particularly when combined with immune checkpoint blockade. However, broader clinical translation remains limited by sequential bottlenecks across the vaccine development pipeline, including false-positive neoantigen selection,  imperfect modeling of antigen processing and HLA presentation, limited prediction of T-cell receptor recognition, and challenges in formulation, delivery, and manufacturing. Artificial intelligence and advanced computational workflows are increasingly integrated into this pipeline to improve candidate prioritization and support more reproducible decision-making. In this review, we summarize clinical progress and key translational barriers in personalized neoantigen vaccination, and discuss how AI-enabled approaches may contribute across four major stages: multi-omics integration for neoantigen discovery, processing-aware HLA presentation prediction, structure-aware and TCR-informed immunogenicity modeling, and data-driven formulation optimization, particularly for lipid nanoparticle-based delivery systems. These approaches are able to help narrow biological and chemical search spaces, improve prioritization, and provide mechanistic insights into antigen presentation and immune recognition rather than replacing experimental validation. This articlefurther addresses future implementation challenges, including dataset diversity, model interpretability, prospective benchmarking, manufacturing traceability, and evolving regulatory frameworks for individualized mRNA cancer immunotherapies. Integrating computational innovation with rigorous immunological validation, scalable manufacturing, and regulatory oversight will be essential for advancing personalized neoantigen vaccines toward broader clinical implementation.

Cancer Vaccines↗

PubMed2026

Evaluation of one-step amplicon-based targeted enrichment for SARS-CoV-2 whole-genome sequencing using the Midnight amplicon scheme.

Genomic surveillance proved invaluable during the COVID-19 pandemic for tracking SARS-CoV-2 variants and guiding outbreak responses, underscoring the ongoing need to reduce whole-genome sequencing (WGS) costs and improve workflow efficiency to ensure accessibility in resource limited settings. Here, we evaluated a one-step reverse transcription polymerase chain reaction (RT-PCR) approach using the Midnight V2 primer scheme for targeted amplification of the SARS-CoV-2 genome, assessed its compatibility with Illumina sequencing, and compared its performance to a well-established two-step method. Initially, we determined optimal RT-PCR reaction conditions using the Midnight V2 primer panel for the one-step RT-PCR kit and scaled reaction volumes for both RT-PCR and library preparation. Clinical specimens (n = 53) that had undergone routine WGS for surveillance purposes using the established two-step RT-PCR method were compared using the one-step RT-PCR assay. For samples with genome completeness greater than 70%, both methods gave comparable results with similar sequence coverage and 100% concordance for lineage assignment. Further investigation revealed a higher percentage of reads aligning to the SARS-CoV-2 genome with a greater depth of coverage using the one-step method compared to the two-step method. Finally, analysis of scaled one-step and library reaction volumes revealed significant cost savings for samples undergoing WGS. Overall, the results presented here verify the accuracy and reproducibility of one-step targeted amplification and offer an efficient and cost-effective workflow for routine SARS-CoV-2 genomic surveillance.

Humans↗

PubMed2006

Challenges and opportunities in proteomics data analysis.

Accurate, consistent, and transparent data processing and analysis are integral and critical parts of proteomics workflows in general and for biomarker discovery in particular. Definition of common standards for data representation and analysis and the creation of data repositories are essential to compare, exchange, and share data within the community. Current issues in data processing, analysis, and validation are discussed together with opportunities for improving the process in the future and for defining alternative workflows.

Databases, Protein↗

PubMed2026

CRISPRessoSea: streamlined analysis and comparison of pooled amplicon CRISPR screens.

BACKGROUND: CRISPR genome editing enables precise modification of genomic targets but may also induce unintended edits at off-target sites with similar sequences. Pooled amplicon sequencing can assess on- and off-target editing across many samples, yet analyzing, aggregating, and visualizing results from multiple pooled experiments remains challenging. Tools to simplify and standardize these analyses are needed to provide reproducible and comparable interpretation of editing data. RESULTS: We developed CRISPRessoSea, a software package that processes, compares, and visualizes genome editing rates from pooled amplicon sequencing experiments. The tool provides standardized workflows for analyzing editing across multiple targets and samples, supports both nuclease- and base-editing modalities, and generates clear, data-rich summaries suitable for downstream interpretation. CONCLUSIONS: CRISPRessoSea facilitates reproducible, scalable analysis of CRISPR editing outcomes across diverse experimental designs, enabling more efficient and transparent assessment of genome editing specificity. The software is freely available at https://github.com/clementlab/CRISPRessoSea .

Software↗

PubMed2025

PopGLen-a Snakemake pipeline for performing population genomic analyses using genotype likelihood-based methods.

SUMMARY: PopGLen is a Snakemake workflow for performing population genomic analyses within a genotype-likelihood framework, integrating steps for raw sequence processing of both historical and modern DNA, quality control, multiple filtering schemes, and population genomic analysis. Currently, the population genomic analyses included allow for estimating linkage disequilibrium, kinship, genetic diversity, genetic differentiation, population structure, inbreeding, and allele frequencies. Through Snakemake, it is highly scalable, and all steps of the workflow are automated, with results compiled into an HTML report. PopGLen provides an efficient, customizable, and reproducible option for analyzing population genomic datasets across a wide variety of organisms. AVAILABILITY AND IMPLEMENTATION: PopGLen is available under GPLv3 with code, documentation, and a tutorial at https://github.com/zjnolen/PopGLen. An example HTML report using the tutorial dataset is included in the Supplementary Material.

Software↗

PubMed2026

TriosCompass: a snakemake workflow for integrated detection of SNVs, indels, STRs, and structural de novo variants in parent-child trios.

MOTIVATION: The accurate and sensitive identification of de novo variants, which are unique to an individual and not found in the parents' germlines, is critical for understanding the genetic basis of rare diseases, developmental disorders, and evolutionary processes. Existing de novo variant detection pipelines often lack the flexibility to handle multiple variant types, struggle with speed and reproducibility across computational environments, demand extensive manual configuration, or require bioinformatics expertise for downstream curation and analysis, limiting their scalability and usability for large genomic studies. Accordingly, there is a pressing need to better address these challenges. RESULTS: We introduce TriosCompass, an open-source Snakemake workflow that addresses these challenges by providing a modular, accelerated, and environmentally-configurable end-to-end solution for comprehensive de novo variant discovery. It integrates state-of-the-art tools into a reproducible framework, empowering researchers to discover novel genetic insights with greater efficiency and reliability. AVAILABILITY: TriosCompass is implemented as a Snakemake workflow and is freely available at https://github.com/NCI-CGR/TriosCompass_v2 or on Zenodo (10.5281/zenodo.17981062). SUPPLEMENTARY INFORMATION: Supplementary data is available on GitHub at https://github.com/NCI-CGR/TriosCompass_v2/tree/manuscript/report_dashboards. Supplementary methods on DeepTrio benchmark runs can be viewed at: https://github.com/NCI-CGR/TriosCompass_v2/blob/manuscript/TriosCompass_Supp_Methods_deeptrio_benchmark.md.

Software↗

PubMed2006

Web-accessible scientific workflow system for performance monitoring.

We describe the design and implementation of a web-accessible scientific workflow system for environmental performance monitoring. This workflow environment integrates distributed automated data acquisition with server side data management and information visualization through flexible browser-based data access tools. Component technologies include a rich browser-based client, a back-end server for methodical data processing, user management, and result delivery, and third party applications which are invoked by the back-end using web services. This environment allows for reproducible, transparent result generation by a diverse user base, and provides a seamless integration between data selection, analysis applications, and result delivery. This workflow system has been implemented for several sites and monitoring systems with different degrees of complexity.

Databases, Factual↗

PubMed2026

A point-of-use SERS assay for rapid detecting difenoconazole and flusilazole residues in fruit juices using Au/COF substrate.

We developed a ready-to-use surface-enhanced Raman scattering (SERS) sensor for rapid, pretreatment-free detection of difenoconazole (DIF) and flusilazole (FLU) in peach and lychee juices. The substrate combines Au nanoparticles (AuNPs) with covalent organic frameworks (COF) and is implemented on a portable 25-well plate, enabling in situ testing. Juices can be directly applied to the SERS-active Au/COF composite, allowing simultaneous adsorption and signal generation. The correlation between SERS intensity and logarithmic concentration yielded R-values between 0.925 and 0.986, meeting the monitoring needs of non-laboratory scenarios. The entire workflow completes within 12 min, offering a faster alternative to conventional methods while maintaining high sensitivity and reproducibility. Detection limits reach 0.96-1.22 ppb for DIF and FLU, both of which are below the regulatory maximum residue limits. Distinct SERS fingerprints enable reliable discrimination of mixed residues across juice matrices, supporting rapid on-site monitoring and cost-effective pesticide surveillance.

Triazoles↗

PubMed2026

The transformative impact of stem cell core facilities in biomedical research.

Over the past three decades, advances in human pluripotent stem cell (hPSC) technologies, including induced pluripotent stem cells, gene editing, and 2D/3D models, have transformed biomedical research. These powerful tools have revolutionized disease modeling, drug discovery, and the development of advanced therapy medicinal products (ATMPs), while driving the establishment of stem cell core facilities. By providing specialized expertise, standardized workflows, and access to advanced technologies, these facilities support both fundamental and translational research, promote rigor and reproducibility, and foster collaboration. This manuscript highlights their role as hubs of excellence and discusses current challenges and future opportunities for the global stem cell community.

Humans↗

PubMed2026

A Rapid Poly(ethylene glycol)-Assisted Magnetic Isolation Approach for High-Throughput Extracellular Vesicle Isolation and Subsequent Biomarker Analysis.

Extracellular vesicles (EVs) are crucial mediators of intercellular communication and have the potential to serve as biomarkers for disease diagnosis and therapeutic monitoring. However, most EV isolation methods often require large sample volumes and specialized instruments or involve trade-offs between purity, yield, cost, and scalability. We developed MagPEG, a workflow that combines poly(ethylene glycol) (PEG)-mediated EV aggregation with magnetic beads to provide a simple, reproducible alternative to ultracentrifugation, size-exclusion chromatography, and commercial precipitation kits. Our optimization experiments clarified the PEG concentration, ionic strength, and bead surface chemistry that collectively influence EV aggregation, capture efficiency, and contaminant coprecipitation, allowing us to define conditions that improve purity while maintaining high recovery. Compared with commonly used methods, MagPEG produced EVs with comparable size distribution, EV markers, and proteomic profiles while relying only on standard laboratory supplies. A key feature of the platform is that EVs and EV-associated DNA, RNA, and proteins can be sequentially extracted from the same bead-bound material, reducing sample loss and hands-on time and enabling multiomic analysis for limited clinical or small animal samples. MagPEG is compatible with downstream applications including proteomics, bead-based assays, and miRNA quantification. When applied to human serum, the method supported high-throughput EV proteomic profiling and enabled the identification of Alzheimer's disease-associated protein signatures, illustrating its utility for biomarker discovery. Overall, our results establish MagPEG as a powerful, rapid, scalable, and high-throughput solution for translational applications in biomarker discovery.

Polyethylene Glycols↗

PubMed2026

Quantitative RNA modification mapping by mass spectrometry with isobaric tags and nucleobase fragment analysis.

RNA modifications regulate RNA stability, translation, stress responses, and disease processes, yet their function remains poorly understood due to technical limitations in sequence analysis. Here, we present an RNA-specific isobaric tandem mass tagging (RMT) platform for omic-scale quantitative mapping of RNA modifications. The platform combines RNA-specific tags adapted from proteomics with an end-to-end workflow spanning sample preparation through data processing. Validation using synthetic oligonucleotides and total tRNA from Pseudomonas aeruginosa yielded reproducible quantification, with coefficients of variation below 5%. Together with nucleobase fragment analysis, we identified and quantified 24 RNA modifications in PA14 tRNAs, including previously undescribed m2A38 and Gm/Cm39, and assigned their corresponding writer enzymes. Further analyses of tRNAs from writer knockout strains and stressed cells revealed dynamic modification patterns, modification interdependencies, and their potential roles in stress adaptation. This method provides a robust, cost-effective platform for quantitative RNA modification mapping, enabling deeper biological insights.

RNA, Transfer↗

PubMed2026

dcHiChIP: a comprehensive Nextflow-based pipeline for multiscale analysis of chromatin architecture from HiChIP data.

MOTIVATION: Despite the growing use of HiChIP to investigate protein-directed chromatin architecture, a comprehensive and reproducible pipeline for analysing these datasets-from raw reads to multiscale 3D genome features-remains lacking. Existing tools often focus on isolated components, such as loop calling or matrix generation, but fall short in integrating structural annotation, functional enrichment, and spatial modeling within a unified framework. To address this gap, we developed dcHiChIP, a modular, scalable Nextflow-based workflow that streamlines the analysis of HiChIP data, enabling both routine processing and in-depth exploration of chromatin organization and regulatory interactions. RESULTS: dcHiChIP enables robust and reproducible analysis of HiChIP datasets across multiple scales of chromatin architecture. It accepts raw sequencing data as input and generates high-quality loop calls, domain annotations, and 3D genome models. It also performs functional annotation and motif enrichment analyses. Applied to benchmark CTCF HiChIP datasets, dcHiChIP identifies major chromatin architectural features such as TADs/CCDs, A/B compartments, and chromatin stripes, and offers efficient, end-to-end execution with support for batch processing and workflow resumability. AVAILABILITY: dcHiChIP is publicly available on GitHub at https://github.com/SFGLab/dcHiChIP, with documentation at https://sfglab.github.io/dcHiChIP/. The software version used in this study is archived at Zenodo: https://doi.org/10.5281/zenodo.22030542.

Chromatin↗