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Results for “High throughput workflow”

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PubMed2026

VisPan: real-time visualisation of multiplex amplicon-based sequencing panels for rapid syndromic surveillance and pathogen detection.

MOTIVATION: Infectious diseases persist as a major global public health challenge. Diverse factors, including climate change, globalization, deforestation, human-animal interactions, lifestyle choices, and various biological factors, can contribute to their emergence and reemergence. Rapid detection and characterization of (re)emerging pathogens are therefore critical for effective outbreak management and for enhancing our understanding of epidemics by monitoring the transmission, spread, evolution, and genomics of pathogens. In this context, next-generation sequencing technologies (NGS), particularly long-read platforms such as Oxford Nanopore Technologies (ONT), have opened new avenues for real-time pathogen monitoring. However, the bioinformatics bottleneck remains a challenge, emphasizing the need for efficient, accessible, and user-friendly analysis tools. RESULTS: Here, we present a tool adapted from the RAMPART software that enables real-time data visualisation of multiplex PCR syndromic panels combined with Oxford Nanopore sequencing. This real-time analysis enables rapid pathogen detection, from raw data acquisition to taxonomic assignment, within minutes. The interface offers dynamic visual tracking of the sequencing run and amplicon coverage, facilitating immediate insights during diagnostic workflows. Validation experiments confirmed the system's reliability, accurately identifying all pathogens present in complex clinical or environmental samples. This tool provides an integrated, user-friendly solution for genomic pathogen surveillance in field or clinical settings.

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PubMed2025

Targeted next-generation sequencing for drug-resistant tuberculosis diagnosis: implementation considerations for bacterial load, regimen selection and diagnostic algorithm placement.

INTRODUCTION: Early and accurate diagnosis of drug-resistant tuberculosis (DR-TB) is essential for improving treatment outcomes. Phenotypic drug susceptibility testing (pDST) is comprehensive but slow, while rapid molecular assays provide resistance information for a limited number of drugs. Targeted next-generation sequencing (tNGS) offers the potential for broad and rapid resistance detection, but its integration into diagnostic algorithms has been hindered by uncertainty about its placement within existing workflows. METHODS: This study evaluated the extent to which two tNGS solutions-Deeplex Myc-TB (GenoScreen) and TB Drug Resistance Test (Oxford Nanopore Technologies, ONT)-provided interpretable drug resistance results that could inform regimen design, in comparison to other WHO-recommended molecular assays and pDST. Data were collected from three high-burden DR-TB settings under the Seq&Treat study. Sequencing success rates and drug resistance detection were analysed based on: (1) the initial Xpert MTB/RIF result (very low, low, medium, high), (2) resistance results for drugs in WHO-recommended regimens and (3) performance relative to other WHO-endorsed assays. The potential impact of different algorithms on the estimates was also considered. Key factors influencing successful tNGS adoption within diagnostic pathways were identified, leveraging insights from the Seq&Treat diagnostic accuracy study. RESULTS: Sequencing success rates were 88.5% (GenoScreen) and 93.1% (ONT) across 763 samples. While tNGS provided complete resistance data for 73%-86% of drugs in recommended regimens, pDST achieved 92%-93%. Both tNGS solutions matched or exceeded the sensitivity of WHO-recommended molecular assays. CONCLUSIONS: This study highlights the critical role of tNGS as a centralised tool for comprehensive drug resistance testing to inform DR-TB treatment decisions following initial screening assays. By complementing existing molecular tests with tNGS, diagnostic workflows can be optimised to ensure timely and comprehensive resistance detection. These findings support policy updates to integrate tNGS into global TB diagnostic algorithms. TRIAL REGISTRATION NUMBER: NCT04239326.

Humans↗

PubMed2026

Utilization of long-read sequencing for the detection of structural rearrangements with AgileStructure.

MOTIVATION: Changes in genome organisation contribute to genetic disease when they disrupt gene function or regulation. Structural rearrangements may interrupt coding sequence or alter expression through promoter loss or gain, chromatin changes, copy-number variation, or disruption of short-range regulatory elements. Although short-read sequencing excels at detecting small variants, it performs poorly at resolving breakpoints of large rearrangements, especially in repetitive or low-complexity regions. Long-read sequencing overcomes these limitations, but analytical tools have not kept pace, making accurate identification and annotation of large structural variants challenging. RESULTS: We developed AgileStructure, a desktop application for locating and annotating large‑scale genomic rearrangements using aligned long‑read data. The software enables user‑guided exploration of breakpoint‑spanning reads, supporting accurate interpretation of complex events and filling a key gap in current structural variant analysis workflows. AVAILABILITY AND IMPLEMENTATION: Source code, binaries, user guide, and example aligned read data, are available on GitHub: https://github.com/msjimc/AgileStructure. An archived version is also available on Zenodo at https://doi.org/10.5281/zenodo.18610110.

Software↗

PubMed2026

An open-source clinical bioinformatics pipeline for real-world NGS implementation: translating genomic variants into actionable treatment strategies in oncology.

BACKGROUND: Next-Generation Sequencing (NGS) has become a cornerstone technology in clinical practice, yet its adoption presents significant challenges. Physicians and oncologists must manage vast amounts of genome-scale data and transform it into actionable insights for complex decision-making. While commercial systems exist to synthesize data from NGS experiments into clinical reports, many are hindered by limitations such as closed-source designs that restrict transparency and customization. Additionally, some fail to leverage publicly available genomic databases, missing opportunities to integrate valuable external data. Furthermore, the rigidity of many tools in accommodating diverse NGS panels limits their applicability across varied clinical scenarios. METHODS: To address these limitations, we developed OncoReport, an open-source tool that generates comprehensive reports from NGS analyses. By integrating publicly accessible databases, OncoReport provides a robust, user-friendly environment equipped with essential tools for NGS analysis. This design aims to enhance data interpretation and support informed clinical decision-making. RESULTS: Rigorous testing has demonstrated OncoReport’s effectiveness in producing detailed, actionable reports that are clear and easy to use. By automating key aspects of the workflow, the tool significantly reduces manual effort and expedites the synthesis and interpretation of NGS results, making genomic insights more accessible to clinicians. CONCLUSION: OncoReport offers a transparent, flexible, and efficient framework for clinicians to analyze and apply genomic data in patient care. By streamlining workflows and leveraging open-source principles, it empowers healthcare professionals to make informed, data-driven decisions. OncoReport is freely available at https://oncoreport.atlas.dmi.unict.it, with source code and issue tracking on GitHub: https://github.com/knowmics-lab/oncoreport .

Humans↗

PubMed2020

Genomic Analysis of Circulating Tumor Cells at the Single-Cell Level.

Circulating tumor cells (CTCs) have a great potential for noninvasive diagnosis and real-time monitoring of cancer. A comprehensive evaluation of four whole genome amplification (WGA)/next-generation sequencing workflows for genomic analysis of single CTCs, including PCR-based (GenomePlex and Ampli1), multiple displacement amplification (Repli-g), and hybrid PCR- and multiple displacement amplification-based [multiple annealing and loop-based amplification cycling (MALBAC)] is reported herein. To demonstrate clinical utilities, copy number variations (CNVs) in single CTCs isolated from four patients with squamous non-small-cell lung cancer were profiled. Results indicate that MALBAC and Repli-g WGA have significantly broader genomic coverage compared with GenomePlex and Ampli1. Furthermore, MALBAC coupled with low-pass whole genome sequencing has better coverage breadth, uniformity, and reproducibility and is superior to Repli-g for genome-wide CNV profiling and detecting focal oncogenic amplifications. For mutation analysis, none of the WGA methods were found to achieve sufficient sensitivity and specificity by whole exome sequencing. Finally, profiling of single CTCs from patients with non-small-cell lung cancer revealed potentially clinically relevant CNVs. In conclusion, MALBAC WGA coupled with low-pass whole genome sequencing is a robust workflow for genome-wide CNV profiling at single-cell level and has great potential to be applied in clinical investigations. Nevertheless, data suggest that none of the evaluated single-cell sequencing workflows can reach sufficient sensitivity or specificity for mutation detection required for clinical applications.

Carcinoma, Non-Small-Cell Lung↗

PubMed2026

Protocol for Duplex Sequencing of Mitochondrial DNA in Single Human Oocytes.

Oocytes are densely packed with mitochondria, the energy-producing organelles that contain their own genome, mitochondrial DNA (mtDNA). Each cell contains multiple copies of mtDNA, with copy number varying among tissue types. Oocytes possess the highest mtDNA copy number, containing hundreds of thousands of mtDNA molecules per cell. Because mitochondria are inherited exclusively through the maternal lineage, accurate detection of mtDNA variants is essential for studies of inheritance, aging, and disease. The presence of multiple mtDNA copies allows wild-type and mutant molecules to coexist within the same cell, a condition known as heteroplasmy, in which low-frequency and de novo variants may occur at frequencies below 1%. Conventional next-generation sequencing (NGS) lacks sufficient accuracy to reliably distinguish these rare variants from errors introduced during library preparation and sequencing. Here, we present a protocol for enriching mtDNA from single human oocytes using Exonuclease V to remove linear DNA, followed by duplex sequencing library preparation for highly accurate mtDNA analysis. This workflow enables error-corrected sequencing of individual oocytes, facilitating reliable detection of low-frequency mtDNA variants and analysis of heteroplasmy and de novo mutagenesis. The protocol provides a reproducible approach for investigating mitochondrial genome variation in single oocytes using Illumina-compatible sequencing platforms.

Humans↗

PubMed2025

Automated chromatin profiling with spa-ChIP-seq uncovers the impacts of condition variations.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to study the genomic localization of DNA-associated proteins. However, conventional protocols include multiple manual steps that can introduce inconsistency and limit scalability, thereby restricting the inclusion of appropriate replicates and controls. Although the introduction of liquid handling platforms has improved reproducibility, most existing efforts have automated only a subset of the workflow, and extending automation to efficiently map non-histone proteins, such as chromatin regulators, remains challenging. Here, we present a fully automated implementation of our previously developed single-pot ChIP-seq protocol (Texari et al. 2021), named spa-ChIP-seq, which enables scalable processing of 8 to 96 ChIP-seq samples from crosslinked cells to sequencing-ready library in approximately three days with an estimated cost of $70 per sample. Benchmarking spa-ChIP-seq against manual ChIP-seq performed in parallel demonstrates comparable signal-to-noise ratio between the two workflows. Using spa-ChIP-seq, we systematically evaluate multiple parameters including shearing and crosslinking conditions, buffer compositions, and the ratio of antibody to cell-number. We find, for the first time to our knowledge, that weaker genomic localization signals are sensitive to changing the antibody to cell-number ratio, whereas the stronger signals remain unaffected. This finding underscores the importance of maintaining consistent antibody-to-cell-number ratio for comparative studies, such as treatment responses or chromatin-QTL mapping. The spa-ChIP-seq protocol is publicly available, including deck setups, operational parameters, and scripts. We envision that this robust, cost-efficient protocol will facilitate high-throughput, reproducible ChIP-seq analyses, supporting large-scale studies of antibody validation, compound screening, population genomics, and diagnostic frameworks.

Journal Article↗

PubMed2025

MRDagent: iterative and adaptive parameter optimization for stable ctDNA-based MRD detection in heterogeneous samples.

MOTIVATION: Minimal residual disease (MRD) as critical biomarker for cancer prognosis and management plays a crucial role in improving patient outcomes. However, detecting MRD via next-generation sequencing-based circulating tumor DNA variant calling remains unstable due to the extremely low variant allele frequency and significant inter- and intra-sample heterogeneity. Although parameter optimization can theoretically enhance the detection performance of variants, achieving stable MRD detection remains challenging due to three key factors: (i) the necessity for individualized parameter tuning across numerous heterogeneous genomic intervals within each sample, (ii) the tightly interdependent parameter requirements across different stages of variant detection workflows, and (iii) the limitations of current automated parameter optimization methods. RESULTS: In this study, we propose MRDagent, a novel variant detection tool designed specifically for MRD detection. MRDagent incorporates an iterative and self-adaptive optimization framework capable of handling unknown objectives, varying constraints, and highly coupled parameters across stages. A key innovation of MRDagent is the integration of a convolutional neural network-based meta-model, trained on historical data to enable rapid parameter prediction. This significantly enhances computational efficiency and generalization performance. Extensive evaluations on simulated and real-world datasets demonstrate MRDagent's superior and stable performance, providing an efficient, reliable solution for MRD detection in clinical and high-throughput research applications. AVAILABILITY AND IMPLEMENTATION: MRDagent is freely available at https://github.com/aAT0047/MRDagent.git. The corresponding dataset and software archive are available at Zenodo: https://doi.org/10.5281/zenodo.15458496.

Circulating Tumor DNA↗

PubMed2026

MHASS: Microbiome HiFi Amplicon Sequencing Simulator.

SUMMARY: Microbiome HiFi Amplicon Sequence Simulator (MHASS) creates realistic synthetic PacBio HiFi amplicon sequencing datasets for microbiome studies, by integrating genome-aware abundance modeling, realistic dual-barcoding strategies, and empirically derived pass-number distributions from actual sequencing runs. MHASS generates datasets tailored for rigorous benchmarking and validation of long-read microbiome analysis workflows, including ASV clustering and taxonomic assignment. AVAILABILITY AND IMPLEMENTATION: Implemented in Python with automated dependency management, the source code for MHASS is freely available at https://github.com/rhowardstone/MHASS along with installation instructions. Our code is also published on Zenodo at https://doi.org/10.5281/zenodo.17486364. The data underlying this article are available on GitHub at https://github.com/rhowardstone/MHASS_evaluation/.

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PubMed2026

QCatch: a framework for quality control assessment and analysis of single-cell sequencing data.

MOTIVATION: Single-cell sequencing data analysis requires robust quality control (QC) to mitigate technical artifacts and ensure reliable downstream results. While tools like alevin-fry and simpleaf (and augmented execution context for the alevin-fry), offer flexibility and computational efficiency to process single-cell data, this ecosystem will further benefit from a standardized QC reporting tailored for its outputs. RESULTS: We introduce QCatch, a Python-based command-line tool that generates comprehensive and interactive HTML QC reports designed specifically for single-cell quantification results. Taking the output directory of alevin-fry or simpleaf as the input, QCatch is able to perform essential processing steps, like cell calling, and generate detailed QC reports that contain informative visualizations and statistics, including unique molecular identifier (UMI) count distributions, sequencing saturation estimates, and splicing status information, for QC assurance. Built for seamless integration into downstream analysis workflows, QCatch exports the processed results in a richly-annotated H5AD format file, a widely used data format common among many downstream single-cell data analysis tools. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of QCatch are available on GitHub at https://github.com/COMBINE-lab/QCatch. QCatch can be installed via both Bioconda and PyPI.

Single-Cell Analysis↗

PubMed2026

Prospective clinical validation of targeted long-read sequencing for preimplantation genetic testing of α-thalassaemia.

BACKGROUND: Preimplantation genetic testing for monogenic disorders (PGT-M) can prevent transmission of severe α-thalassaemia, but conventional workflows remain limited by family-specific assay design for direct variant detection, dependence on additional family samples for haplotype construction, and labour-intensive multi-step procedures across several platforms. Targeted long-read sequencing-based PGT-M for α-thalassaemia (tlrPGT-α-thal) integrates direct variant detection and haplotype linkage analysis within a single assay, but prospective clinical validation is lacking. METHODS: This prospective clinical study enrolled 103 families at high risk of transmitting α-thalassaemia at a reproductive medicine centre between August 2024 and March 2025. All families underwent blinded parallel analysis using both conventional NGS-based PGT-M (comparator) and tlrPGT-α-thal. RESULTS: In the primary concordance analysis, tlrPGT-α-thal was fully concordant with conventional NGS-based PGT-M (507/507, 100.0%; exact 95% CI, 99.3-100.0). Direct variant detection was successful in 501/507 embryos (98.82%; 95% CI, 97.4-99.6), haplotype linkage was established in 505/507 embryos (99.61%; 95% CI, 98.6-100.0), and one meiotic recombination event was identified. Among 93 families proceeding to embryo transfer, 57 pregnancies underwent invasive prenatal diagnosis, and all were concordant with the corresponding tlrPGT-α-thal results. Of the 26 comparator-inconclusive embryos, tlrPGT-α-thal resolved 6 complex cases, including cases with incomplete pedigrees or insufficient informative SNPs. Among the remaining 20 embryos with HBA-region aneuploidies, genotype and parental origin could be determined in 12. CONCLUSIONS: The findings show that tlrPGT-α-thal enables direct detection of diverse α-thalassaemia-causing variants together with efficient haplotype linkage analysis within a single workflow, without requiring family-specific assay design or additional family samples. The method demonstrated high diagnostic accuracy while providing added value in complex scenarios. Taken together, tlrPGT-α-thal represents a simplified and broadly applicable strategy for α-thalassaemia PGT-M.

Humans↗

PubMed2027

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗

PubMed2026

Optimizing GRIDSS for clinical use: A targeted NGS filtering strategy for germline structural variant detection.

Detecting intermediate-sized structural variants (SVs) remains challenging in diagnostics, as tools for single-nucleotide and copy-number variants, particularly read-depth-based methods, are often insufficient. GRIDSS addresses this gap by integrating paired-end mapping, split-read analysis, and assembly-based approaches. However, its use in targeted sequencing and diagnostic workflows remains complex. NGS panel data from 9726 patients with suspected hereditary cancer were analyzed using GRIDSS. A filtering strategy was developed to prioritize clinically relevant germline SVs. Multiple parameter settings were tested to optimize performance. The initial dataset of 1,307,592 variants was reduced to 89 candidates after applying the selected filtering strategy. Of these, 24 had been previously detected by routine callers and were not further analyzed. Among the remaining 65, 13 were considered likely true positives after visual inspection using IGV. Experimental validation was performed by Sanger/Nanopore long-read sequencing for these variants, all of which were confirmed. Eight were classified as (likely) pathogenic, including two frameshift duplications in MSH6, one splicing variant in BARD1, and five mobile element insertions in APC, BRCA2, and PALB2. Altogether, GRIDSS implementation increased diagnostic yield while maintaining feasibility for diagnostic workflows. Comprehensive workflow scheme for germline structural variant detection and results in our diagnostic setting.

Humans↗

PubMed2025

RBC-GEM: A genome-scale metabolic model for systems biology of the human red blood cell.

Advancements with cost-effective, high-throughput omics technologies have had a transformative effect on both fundamental and translational research in the medical sciences. These advancements have facilitated a departure from the traditional view of human red blood cells (RBCs) as mere carriers of hemoglobin, devoid of significant biological complexity. Over the past decade, proteomic analyses have identified a growing number of different proteins present within RBCs, enabling systems biology analysis of their physiological functions. Here, we introduce RBC-GEM, one of the most comprehensive, curated genome-scale metabolic reconstructions of a specific human cell type to-date. It was developed through meta-analysis of proteomic data from 29 studies published over the past two decades resulting in an RBC proteome composed of more than 4,600 distinct proteins. Through workflow-guided manual curation, we have compiled the metabolic reactions carried out by this proteome to form a genome-scale metabolic model (GEM) of the RBC. RBC-GEM is hosted on a version-controlled GitHub repository, ensuring adherence to the standardized protocols for metabolic reconstruction quality control and data stewardship principles. RBC-GEM represents a metabolic network is a consisting of 820 genes encoding proteins acting on 1,685 unique metabolites through 2,723 biochemical reactions: a 740% size expansion over its predecessor. We demonstrated the utility of RBC-GEM by creating context-specific proteome-constrained models derived from proteomic data of stored RBCs for 616 blood donors, and classified reactions based on their simulated abundance dependence. This reconstruction as an up-to-date curated GEM can be used for contextualization of data and for the construction of a computational whole-cell models of the human RBC.

Humans↗

PubMed2025

ChIP-Rx: Arabidopsis Chromatin Profiling Using Quantitative ChIP-Seq.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is widely used to probe the chromatin landscape of transcription factors, chromatin components, and associated proteins. Conventional ChIP normalization procedures robustly allow estimating differences in local enrichment across genomic regions. Yet, inter-sample comparisons can be biased by technical variability and biological differences. This is notably the case when samples display large differences in the abundance of the target protein or its enrichment at chromatin. For example, epigenome defects are improperly detected or quantified upon large-effect genetic or chemical inhibition of chromatin modifiers. To circumvent these caveats and robustly determine biological variations while minimizing technical variability, ChIP adaptations using an external reference have flourished. Here, we describe a step-by-step protocol employing a reference exogenous chromatin (ChIP-Rx) that allows absolute comparisons of epigenome variations in Arabidopsis samples displaying drastic differences in chromatin mark abundance. In contrast to the originally published ChIP-Rx approach, which assumes that exogenous spike-in references are constant across samples, the method detailed here involves the sequencing of each input sample to account for technical variability in initial reference chromatin contents. We also report a detailed computational workflow with an accompanying Github resource to help in calculating spike-in normalization factors, applying them to normalize epigenome tracks, and performing spike-in normalized inter-sample differential analyses. We propose two ways of computing the spike-in factor: a classically used method based on raw counts and a noise-corrected method using peak detection on the exogenous genome.

Arabidopsis↗

PubMed2026

Performance comparison of rapid and native barcoding methods for Oxford Nanopore sequencing of Poliovirus Viral Protein 1 (VP1) amplicons.

Accurate and timely sequencing of poliovirus is critical for global eradication efforts, particularly for molecular epidemiology based on the typing region of the genome, viral protein 1 (VP1). While Oxford Nanopore Technologies (ONT) sequencing has expanded capabilities for poliovirus surveillance, the relative performance of different ONT library preparation methods, including ligation-based (Native Barcoding) and transposase-based (Rapid Barcoding) approaches, has not been systematically evaluated. In this study, we compared rapid barcoding and native barcoding workflows for sequencing VP1 amplicons from 17 type 2 poliovirus-positive samples, each processed in triplicate. Native barcoding generated significantly more sequencing output, producing approximately 2.3-fold greater total read yield than rapid barcoding, and demonstrated higher run-to-run reproducibility (R2 = 0.979-0.998 vs. 0.847-0.929, respectively; p < 0.001). In addition, native barcoding generated 80% of the total yield achieved by rapid barcoding within approximately 7 h, whereas rapid barcoding required approximately 40 h to reach the same output. Despite these differences, both methods produced identical VP1 consensus sequences across all samples, with comparable read quality (median per-base Q-scores of approximately Q17-Q18). Rapid barcoding provided substantial practical advantages, reducing hands-on library preparation time (55 vs. 200 min) and per-sample cost ($12.82 vs. $16.54), while simplifying workflow and reducing technical complexity. These findings indicate that sequencing yield may not be a determinant of downstream analytical outcomes for poliovirus VP1 ONT sequencing. Rapid barcoding therefore represents a cost-effective and efficient approach for routine poliovirus surveillance, whereas native barcoding remains advantageous in applications requiring rapid data generation or maximal sequencing depth.

Poliovirus↗

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↗

PubMed2026

OmicsQ: a user-friendly platform for interactive quantitative omics data analysis.

MOTIVATION: High-throughput omics technologies generate complex datasets with thousands of features that are quantified across multiple experimental conditions, but often suffer from incomplete measurements, missing values, and individually fluctuating variances. This requires analytical tools for accurate, deep and insightful biological interpretation, capable of dealing with a large variety of data properties and different amounts of completeness. Software capable of handling such data complexity and integrating with external applications for downstream analysis remains rare and mostly relies on programming-based environments, limiting accessibility for researchers without computational expertise. RESULTS: We present OmicsQ, an interactive, web-based platform designed to streamline quantitative omics data analysis. OmicsQ provides an intuitive, browser-based visualization interface that integrates established statistical processing tools. Those include robust batch correction, automated experimental design annotation, and handling of missing data without imputation, which maintains data integrity and avoids artifacts from a priori assumptions. OmicsQ seamlessly interacts with external applications (e.g. PolySTest, VSClust, ComplexBrowser) for statistical testing, clustering, analysis of protein complex behavior, and pathway enrichment, offering a comprehensive and flexible workflow from data import to biological interpretation that is broadly applicable across domains. AVAILABILITY AND IMPLEMENTATION: OmicsQ is implemented in R and Shiny and is available at https://computproteomics.bmb.sdu.dk/app_direct/OmicsQ. Source code and installation instructions: https://github.com/computproteomics/OmicsQ, DOI: 10.5281/zenodo.17778420.

Software↗