Biology subjects

Gabernet, G.

Publications and source records attributed to Gabernet, G..

12 recordsLinked to original sources

bioRxiv2026

Cell-level integration of gene expression and B cell receptor data captures complementary B cell information

Single-cell B cell analyses commonly define relationships among cells using gene expression (GEX), although paired B cell receptor (BCR) sequences provide complementary information about receptor features, differentiation, and clonal relationships. We evaluated multiple approaches for integrating paired GEX and BCR data into cell-level representations and compared their ability to capture complementary aspects of B cell biology. We compared BCR representations based on language-model embeddings and engineered sequence features across multiple integration strategies. Spaces dominated by BCR language-model embeddings captured broad sequence and clonal information but could distort cell type relationships, as assessed using independent cell surface protein measurements. In contrast, concatenating GEX with selected BCR features (isotype, somatic hypermutation frequency, and CDRH3 length) incorporated receptor information while retaining transcriptional and surface-protein-associated structure. The resulting space reduced discordance between GEX-based cell type annotations and BCR characteristics and revealed B-cell substructure supported by both transcriptional and receptor-derived features. Thus, integrating GEX and BCR data captures complementary biological information and provides a richer foundation for single-cell B cell analysis.

bioinformatics↗

bioRxiv2025

AMULETY: A Python package to embed adaptive immune receptor sequences

Large language models have been developed to capture relevant features of adaptive immune receptors, each with unique potential applications. However, the diversity in available models presents challenges in accessibility and usability for downstream applications. Here we present AMULETY (Adaptive imMUne receptor Language model Embedding Tool), a Python-based software package to generate language model embeddings for adaptive immune receptor sequences, enabling users to leverage the strengths of different models without the need for complex configuration. AMULETY offers functions for embedding adaptive immune receptor amino acid sequences using pre-trained protein or antibody language models for paired heavy and light chain or single chain sequences. We showcase the variability on the embedding space for several embeddings on a dataset of antibody binders to several SARS-CoV-2 epitopes and showed that different models may be effective at capturing different aspects of the distinctions between epitope groups. AMULETY is available under GPL v3 license from https://github.com/immcantation/amulety or via pip from the Python Package Index (PyPI) from https://pypi.org/project/amulety/. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/644583v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1c0bafaorg.highwire.dtl.DTLVardef@337816org.highwire.dtl.DTLVardef@111136forg.highwire.dtl.DTLVardef@1300dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Identification of a multi-omics factor predictive of long COVID in the IMPACC study

Following SARS-CoV-2 infection, [~]10-35% of COVID-19 patients experience long COVID (LC), in which often debilitating symptoms persist for at least three months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities. We utilized machine learning methods on biologic analytes and patient reported outcome surveys provided over 12 months after hospital discharge from >500 hospitalized COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor". IMPACC participants who experienced LC had lower recovery factor scores compared to participants without LC. Biologic characterization revealed increased levels of plasma proteins associated with inflammation, elevated transcriptional signatures of heme metabolism, and decreased androgenic steroids in LC patients. The recovery factor was also associated with altered circulating immune cell frequencies. Notably, recovery factor scores were predictive of LC occurrence in patients as early as hospital admission, irrespective of acute disease severity. Thus, the recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.

systems biology↗

bioRxiv2024

Empowering bioinformatics communities with Nextflow and nf-core

Standardised analysis pipelines are an important part of FAIR bioinformatics research. Over the last decade, there has been a notable shift from point-and-click pipeline solutions such as Galaxy towards command-line solutions such as Nextflow and Snakemake. We report on recent developments in the nf-core and Nextflow frameworks that have led to widespread adoption across many scientific communities. We describe how adopting nf-core standards enables faster development, improved interoperability, and collaboration with the >8,000 members of the nf-core community. The recent development of Nextflow Domain-Specific Language 2 (DSL2) allows pipeline components to be shared and combined across projects. The nf-core community has harnessed this with a library of modules and subworkflows that can be integrated into any Nextflow pipeline, enabling research communities to progressively transition to nf-core best practices. We present a case study of nf-core adoption by six European research consortia, grouped under the EuroFAANG umbrella and dedicated to farmed animal genomics. We believe that the process outlined in this report can inspire many large consortia to seek harmonisation of their data analysis procedures.

bioinformatics↗

bioRxiv2024

nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets. Availability and implementationnf-core/airrflow is available free of charge, under the MIT license on GitHub (https://github.com/nf-core/airrflow). Detailed documentation and example results are available on the nf-core website at (https://nf-co.re/airrflow). Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/576147v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@e39504org.highwire.dtl.DTLVardef@14c52aforg.highwire.dtl.DTLVardef@1c87664org.highwire.dtl.DTLVardef@1d4d78b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

How tool combinations in different pipeline versions affect the outcome in RNA-seq analysis

Data analysis tools are continuously changed and improved over time. In order to test how these changes influence the comparability between analyses, the output of different workflow options of the nf-core/rnaseq pipeline were compared. Five different pipeline settings (STAR+Salmon, STAR+RSEM, STAR+featureCounts, HiSAT+featureCounts, pseudoaligner Salmon) were run on three datasets (human, Arabidopsis, zebrafish) containing spike-ins of the External RNA Control Consortium (ERCC). Fold change ratios and differential expression of genes and spike-ins were used for comparative analyses of the different tools and versions settings of the pipeline. An overlap of 85% for differential gene classification between pipelines could be shown. Genes interpreted with a bias were mostly those present at lower concentration. Also, the number of isoforms and exons per gene were determinants. Previous pipeline versions using featureCounts showed a higher sensitivity to detect one-isoform genes like ERCC. To ensure data comparability in long-term analysis series it would be recommendable to either stay with the pipeline version the series was initialized with or to run both versions during a transition time in order to ensure that the target genes are addressed the same way.

bioinformatics↗

bioRxiv2023

Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO2 emissions low. The code is available at https://nf-co.re/sarek.

bioinformatics↗

bioRxiv2023

nf-root: a best-practice pipeline for deep learning-based analysis of apoplastic pH in microscopy images of developmental zones in plantroot tissue

Here we report nextflow-root (nf-root), a novel best-practice pipeline for deep learning-based analysis of fluorescence microscopy images of plant root tissue, aimed at studying hormonal mechanisms associated with cell elongation, given the vital role that plant hormones play in the development and growth of plants. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images, and analysis of apoplastic pH measurements of tissue zones, which is useful for modeling plant hormone signaling and cell physiological responses. Mathematical models of physiological responses of plant hormones, such as brassinolide, have been successfully established for certain root tissue types, by evaluating apoplastic pH via fluorescence imaging. However, the generation of data for this modeling is time-consuming, as it requires the manual segmentation of tissue zones and evaluation of large amounts of microscopy data. We introduce a high-throughput, highly reproducible Nextflow pipeline based on nf-core standards that automates tissue zone segmentation by implementing a deep-learning module, which deploys deterministically trained (i.e. bit-exact reproducible) convolutional neural network models, and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, aiming to facilitate result interpretation and verification by experienced plant biologists. To train our segmentation prediction models, we created a publicly available dataset composed of confocal microscopy images of A. thaliana root tissue using the pH-sensitive fluorescence indicator, and manually annotated segmentation masks that identify relevant tissue zones. We applied this pipeline to analyze exemplary data, and observed a high statistical similarity between the manually generated results and the output of nf-root. Our results indicate that this approach achieves near human-level performance, and significantly reduces the time required to analyze large volumes of data, from several days to hours.

bioinformatics↗

bioRxiv2022

Comparative multi-OMICS single-cell atlas of five COVID-19 (rAdVV and mRNA) vaccines describe unique and distinct mechanisms of action

COVID-19 vaccines based on a range of expression platforms have shown considerable protective efficacy, generating antibody and T cell immune responses. However, molecular pathways underpinning COVID-19 vaccine priming of immunity against the SARS-CoV-2 virus have not yet been explored extensively. This analysis is critical to optimization of future vaccination strategies, schedules, and combinations. Thus, we investigated a cohort of individuals pre- and post-vaccination to understand the humoral and cellular immune response against different COVID-19 vaccines, including recombinant adenoviral vector (rAdVV) and mRNA-based vaccines. Single-cell RNA sequencing allowed characterization of monocytes, T, NK and B cell activation at the transcriptomics/proteomic level, in response to different COVID-19 vaccines. Our data revealed that different COVID-19 vaccines elicit a unique and distinct mechanism of action. Specifically, we revealed that rAdVV vaccines negatively regulate CD4+ T cell activation, leukocytes chemotaxis, IL-18 signalling and antigen presentation by monocytes whilst mRNA vaccines positively regulate NKT cell activation, platelets activation and chemokine signalling pathways. An antigen-specific T cell response was already observed following the 1st vaccine dose and was not further augmented after the subsequent 2nd dose of the same vaccine and it was dependent on the type of vaccination used. Our integrated three layered-analyses highlights that COVID-19 vaccines evoke a strong but divergent immune response at the RNA, protein, and cellular levels. Our approach is able to pinpoint efficacy and mechanisms controlling immunity to vaccination and open the door for better vaccination which could induce innate and adaptive immunity equally in the long term. Key findingsO_LIDecrease in major three cell types classical and non-classical monocytes and NK type III cells after COVID-19 vaccination C_LIO_LIIndividual vaccination (AZ, JJ, MD, PB) has differential effect on various immune cell subsets and regulates unique cell populations, whilst no change was observed for CV vaccination C_LIO_LIrAdVV and mRNA vaccines have different mechanism of action for activation of lymphocytes and monocytes, respectively C_LIO_LIrAdVV vaccines negatively regulates CD4+ T cell activation, leukocytes chemotaxis, IL-18 signalling and antigen presentation whilst mRNA vaccines positively regulate NKT cell activation, platelets activation and chemokine signalling pathways. C_LIO_LIAn antigen-specific T cell response was prompted after the 1st vaccine dose and not augmented after the subsequent 2nd dose of the same vaccine. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/507666v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@11fd7eborg.highwire.dtl.DTLVardef@198a9c7org.highwire.dtl.DTLVardef@1b28735org.highwire.dtl.DTLVardef@1cadbb5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install - all dependencies are provided within containers -, portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All code is hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

bioinformatics↗

bioRxiv2020

Specific induction of double negative B cells during protective and pathogenic immune responses

1Double negative (DN) (CD19+CD20lowCD27-IgD-) B cells are expanded in patients with autoimmune and infectious diseases; however their role in the humoral immune response remains unclear. Using systematic flow cytometric analyses of peripheral blood B cell subsets, we observed an inflated DN B cell population in patients with variety of active inflammatory conditions: myasthenia gravis, Guillain-Barre syndrome, neuromyelitis optica spectrum disorder, meningitis/encephalitis, and rheumatic disorders. Furthermore, we were able to induce DN B cells in healthy subjects following vaccination against influenza and tick borne encephalitis virus. Transcriptome analysis revealed a gene expression profile in DN B cells that clustered with naive B cells, memory B cells, and plasmablasts. Immunoglobulin VH transcriptome sequencing and analysis of recombinant antibodies revealed clonal expansion of DN B cells, that were targeted against the vaccine antigen. Our study suggests that DN B cells are expanded in multiple inflammatory neurologic diseases and represent an inducible B cell population that responds to antigenic stimulation, possibly through an extra-follicular maturation pathway.

immunology↗

bioRxiv2019

Efficient data management infrastructure for the integration of imaging and omics data in life science research

As technical developments in omics and biomedical imaging drive the increase in quality, modality, and throughput of data generation in life sciences, the need for information systems capable of integrative, long-term storage and management of these heterogeneous digital assets is also increasing. Here, we propose an approach based on principles of Service Oriented Architecture design, to allow the integrated management and analysis of multi-omics and biomedical imaging data. The proposed architecture introduces an interoperable image management system, the OMERO server, into the backend of qPortal, a FAIR-compliant web-based platform for omics data management. The implementation of an integrated metadata model, the development of software components to enable the communication with the OMERO server, and an extension to the data management operations of established software, allows for FAIR management of heterogeneous omics and biomedical imaging data within an integrated system, which facilitates metadata queries from web-based scientific applications. The applicability of the proposed architecture is demonstrated in two prototypical use cases, a plant biology study using confocal scanning microscopy, and a clinical study on hepatocellular carcinoma, with data from a variety of medical imaging and omics modalities. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, as the joint analysis of omics and imaging data becomes not only desirable but necessary to derive novel insights into biological processes. In particular for powerful machine learning applications where the availability of large datasets with high quality phenotypic annotations is a requirement.

bioinformatics↗