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PubMed2026

IMPACT OF FLUORESCENT DYES ON MUTATIONS IN NEXT GENERATION SEQUENCING LIBRARY GENERATION.

DNA labelling fluorescent dyes such as ethidium bromide have long been considered to be highly mutagenic during DNA replication. While recent studies have pushed back on this narrative, the intercalative nature of these dyes continues to raise the possibility that these dyes can induce mutations. The iconPCR instrument by n6tec uses fluorescent dyes to measure amplification in real time and to adjust cycling conditions. However, since this use of qPCR is preparative and not analytical, mutations introduced by fluorescent dyes would be propagated into the sequencing reaction. To address the impact of these dyes on downstream analyses, we have performed routine mutation calling as well as mutational signature analysis on samples amplified using the iconPCR in the presence of either SYBR or EvaGreen. Sequence analysis revealed very minimal impacts of dyes on the reactions, largely within the noise regimen with only subtle changes in mutation rates seen. Mutational signature analysis was unable to identify any key signatures assignable to the dyes in either substitutions or indel domains. The mutational impact of intercalating dyes during fluorescence-guided amplification is therefore minimal and can be disregarded in all but the most sensitive NGS applications.

Fluorescent Dyes↗

PubMed2026

Sex-specific ethylene responses drive floral sexual plasticity in Cannabis sativa.

Cannabis sativa L. exhibits pronounced sexual plasticity in which both XX and XY plants can undergo floral phenotypic sex reversal in response to ethylene modulation, yet the underlying molecular mechanisms remain poorly defined. Here, we present the most extensive multi-omic analysis of ethylene-induced sex change in C. sativa to date, integrating over 130 RNA-seq libraries, ethylene pathway metabolite quantification, and whole-genome sequencing across three XX and XY genotypes. Treatments with silver thiosulfate and ethephon induced more than 80% phenotypic conversion, but transcriptomic responses diverged sharply between XX and XY plants. Profiling 47 ERGs revealed 14 high-confidence candidates, including CsACS1, CsACO5, CsERF1, and CsMTN, with sex-specific and temporal expression patterns that show dynamic ethylene mediation of plasticity. Early transcriptional activation occurred prior to the emergence of flowers, within 18 h of sex-change treatments and the photoperiod-induced transition to flowering. As opposite-sex floral tissues emerged, ethylene-related gene expression shifted accordingly within developing floral organs, with distinct sets of genes stabilizing the opposite-sex phenotype in XX and XY plants. Several candidates were located in non-recombining regions of the X chromosome or were absent from the Y chromosome, and most exhibited low nucleotide diversity, consistent with functional constraint. These results provide a high-resolution view of ethylene-responsive sexual plasticity in cannabis and show that the shared capacity for sex reversal in XX and XY plants is implemented through distinct regulatory trajectories that produce opposite-sex floral phenotypes. This work expands the mechanistic understanding of sex expression in dioecious species and identifies candidate genes relevant to the development of sex-stable cultivars.

Ethylenes↗

PubMed2024

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Proteomics↗

PubMed2026

A scalable, low-cost, sample hashing workflow for multiomic single-cell analysis using the Seq-Well S3 platform.

In-depth analyses of clinical samples have the potential to provide unparalleled insights into the cellular mechanisms that underlie both health and disease, as well as therapeutic and prophylactic responses. However, these specimens are often paucicellular, necessitating the use of workflows that maximize the amount of information that can be learned. Here we provide a detailed protocol for generating and analyzing single-cell multiomic data from low-input samples with the Seq-Well S3 platform. We further describe a matched pipeline for sample hashing that reduces costs and sources of technical variation in the resulting data while also enhancing throughput. In brief, our streamlined and efficient methodology involves: (1) optionally staining single-cell suspensions with antibody-oligonucleotide conjugates for cell surface protein quantification and/or sample multiplexing; (2) generating Seq-Well S3 sequencing libraries; (3) optionally producing bulk-RNA sequencing libraries via SMART-seq2 to support genetic demultiplexing; and (4) computationally analyzing the resulting data. Each step herein has been designed to leverage readily available reagents and standard laboratory equipment, substantially lowering barriers to entry for researchers. The overall Protocol can yield high-quality multiomic insights from samples in under a week.

Single-Cell Analysis↗

PubMed2026

DeepGeSeq: deep learning library for genomic sequence modeling and analysis.

MOTIVATION: Deep learning methods have demonstrated significant potential in genomics, enabling broad applications such as sequence activity prediction, regulatory rule identification, and variant effect quantification. However, their widespread adoption is often hindered by the steep computational learning curve required for model construction, training, and downstream biological interpretation. Here, we introduce DeepGeSeq, a user-friendly Deep-learning library tailored for Genomic Sequence modeling and analysis. RESULTS: By integrating state-of-the-art architectural modules, DeepGeSeq streamlines the entire deep learning workflow, requiring minimal user input via a simple configuration file and an intuitive agentic skill. We comprehensively validate the efficacy of DeepGeSeq through diverse case studies, encompassing pipeline verification using synthetic datasets, the reproduction and application of established models, and model fine-tuning coupled with biological interpretation on user-defined data. Furthermore, we demonstrate DeepGeSeq's versatility in domain-specific applications, including single-cell ATAC-seq modeling for cell-type clustering, and MPRA data modeling coupled with in silico saturation mutagenesis to dissect cis-regulatory elements. Ultimately, DeepGeSeq bridges the gap between computational complexity and biological discovery, providing an accessible resource that facilitates the development and broad application of deep learning methods in genomics research. AVAILABILITY AND IMPLEMENTATION: https://github.com/JiaqiLi1024/DeepGeSeq.

Deep Learning↗

PubMed2026

A porcine spectral assay library to quantify brain proteome by DIA-MS.

Neurological disorders are the leading cause of health loss worldwide. The growing number of patients suffering from such conditions calls for improved strategies for their prevention, diagnosis, and therapy. To better understand human pathologies, relevant models and methodologies must be made available. In this study, we focused on a biomedical model capable of recapitulating the complexity of human pathology, the pig (Sus scrofa). Brain tissue and cerebrospinal fluid samples from a transgenic minipig model of Huntington's disease were subjected to multiple extraction and fractionation steps. A proteomic mass spectrometry (MS) methodology then allowed the generation of a porcine spectral library for 8,321 proteins. Using data-independent acquisition (DIA), we demonstrated that our porcine spectral library substantially enhanced the quantitative potential of this untargeted MS approach, generating reproducible proteome-wide data. The porcine library also provides a comprehensive resource for the development of targeted MS assays, enabling the quantification of selected proteins with a key role not only in neuroscience.

Animals↗

PubMed2022

A custom library construction method for super-resolution ribosome profiling in Arabidopsis.

BACKGROUND: Ribosome profiling, also known as Ribo-seq, is a powerful technique to study genome-wide mRNA translation. It reveals the precise positions and quantification of ribosomes on mRNAs through deep sequencing of ribosome footprints. We previously optimized the resolution of this technique in plants. However, several key reagents in our original method have been discontinued, and thus, there is an urgent need to establish an alternative protocol. RESULTS: Here we describe a step-by-step protocol that combines our optimized ribosome footprinting in plants with available custom library construction methods established in yeast and bacteria. We tested this protocol in 7-day-old Arabidopsis seedlings and evaluated the quality of the sequencing data regarding ribosome footprint length, mapped genomic features, and the periodic properties corresponding to actively translating ribosomes through open resource bioinformatic tools. We successfully generated high-quality Ribo-seq data comparable with our original method. CONCLUSIONS: We established a custom library construction method for super-resolution Ribo-seq in Arabidopsis. The experimental protocol and bioinformatic pipeline should be readily applicable to other plant tissues and species.

3-nt periodicity↗

PubMed2026

URMD-Seq: A high-throughput method for scalable detection of ultra-rare mutations in the human mitochondrial genome.

The study of mitochondrial genetics has long been limited to polymorphisms and high frequency mutations owing in part to technical and technological limitations in reliably detecting and quantifying rare somatic mutations. Over the past decade or so, the study of rare somatic mitochondrial DNA (mtDNA) variants has expanded and continues to garner increasing interest in a wide range of research fields. Here, we describe Ultra-Rare Mutation Detection-Sequencing (URMD-Seq), a high-throughput method that combines unique molecular identifier (UMI)-based library preparation and Next Generation Sequencing (NGS) for the accurate and scalable detection of ultra-rare mutations in the mtDNA control region. Our method exploits degenerate primers to label individual mtDNA molecules. This is followed by several purification, quantification and amplification steps, to obtain high quality amplicons for sequencing on the Illumina MiSeq platform. Our approach enables the use of total genomic DNA extract as starting point for the assay, overcoming the need for organelle isolation and/or mtDNA enrichment, hence broadening the type of specimen that can be studied, while offering cost and time benefits. The assay described herein has been demonstrated to reliably measure variants present at on average 0.09%, but as low as 0.03%, variant allele frequency in a variety of tissues, including fresh and frozen biobanked specimens. Using this protocol, library preparation of 300 specimens can be completed by a single individual with general nucleic acid handling experience in approximately 20 days. Given its flexibility and scalability, URMD-Seq is particularly well suited for epidemiological studies using a large number of specimens.

Humans↗

PubMed2026

Evaluation of bone preparation approaches using length-based analysis and targeted sequencing for forensic human identification of historic skeletal remains.

Advances in DNA technology have significantly enhanced the forensic community's ability to develop genetic profiles from unidentified human skeletal remains. However, sampling requires mechanical grinding of hard tissues before DNA isolation. This processing can compromise genetic profiles, particularly in aged bones. We compared the industry-standard pulverization method with an alternative powder-free preparation involving prolonged demineralization and subsequent slicing of 19th-century cortical bone. Data from DNA quantification, STR genotyping, and targeted SNP sequencing were used to evaluate powdered samples versus demineralized slices from paired human bones. Average human DNA yields for pulverized samples and demineralized slices were 0.032 ng and 0.692 ng, respectively. Demineralized slices recovered more amplifiable DNA than traditional homogenization methods (p < 0.05). No pulverized samples produced STR profiles, whereas demineralized slices from the same bone samples yielded partial profiles. Samples underwent DNA repair, library preparation, and hybridization capture using the FORensic Capture Enrichment (FORCE) panel. Applying low-coverage (1X) analysis of high-throughput sequencing (HTS) data, demineralized slices outperformed those prepared by traditional pulverization methods (p < 0.05) and substantially increased the information recovered compared with conventional STR analysis methods. Based on HTS data from pulverized samples, DNA fragment length ranged from 27 to 95 bp, and FORCE SNP recovery was 33.23%. In contrast, for demineralized slices, DNA fragment length ranged from 85 to 114 bp, and FORCE SNP recovery was 83.24%. The required reagents and equipment are typically available in forensic labs, and the workflow outlined herein significantly increases the success of DNA recovery from challenging skeletal samples.

Humans↗

PubMed2026

Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow.

As researchers and clinicians seek to identify human genomic alterations relevant to traits and disorders, identifying and aggregating evidence providing mechanistic support for associations between alterations and phenotypes remains challenging. In particular, the study of noncoding genomic variation remains a major challenge because of the lack of accurate functional annotation for activity in a given context and across alleles. Experimental evidence is critical for prioritizing and interpreting functional effects of genetic alterations. Massively parallel reporter assays (MPRAs) have emerged as a powerful high-throughput approach, enabling quantification of regulatory element activity and allelic effects, as well as systematic dissection of gene regulatory logic and variant effects across different contexts. However, the diversity of MPRA designs, lack of standardized formats, and many potential processing parameters hamper data integration, reproducibility, and meta-analyses across studies. To address these challenges, the Impact of Genomic Variation on Function (IGVF) Consortium established an MPRA focus group to develop community standards, including harmonized file formats, and robust analysis pipelines for a wide range of library types and experimental designs. Here, we present these formats and comprehensive computational tools, MPRAlib and MPRAsnakeflow, for uniform processing from raw sequencing reads to counts, processing, and visualization. Using diverse MPRA data sets, we investigated technical variability sources including barcode sequence bias, outlier barcodes, and delivery method (episomal vs. lentiviral). Our results establish best practices for MPRA data generation and analysis, facilitating robust, reproducible research and large-scale integration. The presented tools and standards are publicly available, providing a foundation for future collaborative efforts in regulatory genomics.

Humans↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2026

Measuring economic efficiency in adult intensive care units: A systematic review of methods, metrics, and evidence.

OBJECTIVES: Intensive care units (ICUs) consume substantial hospital resources, yet "efficiency" is inconsistently defined and measured. This study systematically reviewed how economic efficiency has been conceptualised and quantified in adult ICUs and appraised the quality of evidence. METHODS: Following PRISMA 2020 and a PROSPERO-registered protocol (CRD420251107866), we searched MEDLINE, Embase, CINAHL, Cochrane Library and Web of Science (2000-August 2025), plus global grey sources. Eligible studies explicitly defined efficiency and reported an efficiency metric/model linking ICU inputs (e.g., staff, beds/capacity, time, consumables, or costs) to outputs/outcomes (e.g., throughput/discharges, length of stay/resource use, risk-adjusted mortality). Dual independent screening and extraction were performed. Study quality was appraised using MMAT, and findings were synthesised narratively (SWiM), given heterogeneity. RESULTS: 39 studies (2001-2025) from 17 countries were included, all from high-income or upper-middle-income settings. Four methodological families were identified: (1) frontier modelling (predominantly DEA; occasional SFA/RFDH), (2) benchmarking indicators (risk-adjusted mortality and LOS/resource-use ratios; "efficiency matrix" quadrant classification), (3) cost-outcome evaluations, and (4) operational/process metrics. Across families, variation in decision-making units, input/output selection, and risk adjustment limited comparability; long-term and patient-reported outcomes were absent, and equity considerations were uncommon. CONCLUSIONS: ICU efficiency research is feasible but fragmented and often methodologically limited. Standardised definitions, validated risk adjustment, uncertainty quantification, and inclusion of patient-centred and equity-relevant outcomes are needed before efficiency metrics can reliably inform value-based decision making.

Intensive Care Units↗