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Lescai, F.

Publications and source records attributed to Lescai, F..

11 recordsLinked to original sources
bioRxiv2026

nanorepertoire: an end-to-end Nextflow pipeline for nanobody repertoire analysis

Camelid heavy-chain antibodies, and particularly their variable domains known as nanobodies or VHHs, combine full antigen-binding capacity with a compact and highly stable scaffold, which makes them attractive for both fundamental immunology and therapeutic development. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) allows nanobody repertoires to be profiled at great depth, but the analyses applied to VHH data are typically assembled ad hoc from standalone scripts, which limits standardisation and reproducibility across laboratories. Here we present nanorepertoire, an end-to-end Nextflow DSL2 pipeline dedicated to camelid VHH repertoires. It takes paired-end AIRR-seq FASTQ files through quality control, adapter trimming, read merging, in-silico translation, CD-HIT clonotyping and deep-learning CDR3 annotation with nanoCDR-X (Bagordo et al., 2026), and returns an interactive HTML report describing clonal architecture, CDR3 length and amino-acid composition, intra-clonal homogeneity and repertoire diversity, together with the computational carbon footprint of the run. Applied to two publicly available SARS-CoV-2 RBD-selected llama libraries sampled before and after phage-display enrichment (4.8 million paired-end reads in total), the pipeline completed in 59 min on a 16-vCPU cloud instance and recovered 41,363 distinct CDR3 paratopes, reproducing the expected contraction of clonal diversity upon selection. nanorepertoire is open source under the MIT licence at https://github.com/lescailab/nanorepertoire, is archived on Zenodo, and runs unchanged on local, HPC and cloud infrastructures.

bioinformatics↗
bioRxiv2026

SIEVE: Sparse Interpretable Exome Variant Explainer

Whole-exome case-control studies contain rare and common variation, yet analytical methods usually partition the frequency spectrum, discard positional context, or depend on fixed annotations. We present SIEVE, a deep-learning framework for interpretable variant and gene prioritisation. It reads every observed exonic variant without a frequency filter, represents genomic position through self-attention, and calibrates attributions against a permuted-label null. Across coronary artery disease, early-onset myocardial infarction and Crohns disease, discrimination matches the liability-threshold expectation for each trait, while recovery of catalogued associations rises with annotation depth. Against burden testing, single-variant association and polygenic scoring, SIEVE recovers overlapping but largely distinct candidates.

bioinformatics↗
bioRxiv2026

Bioinf-Farma: supervised integration of epitope prediction and recombinant protein developability for automated vaccine candidate prioritization

Vaccine antigen discovery requires prioritizing protein candidates according to both immunogenic potential and recombinant expression feasibility. These properties are typically evaluated using separate computational tools, requiring researchers to integrate heterogeneous outputs through ad hoc workflows. Here, we present BIOINF-farma, a modular platform integrating epitope prediction and developability assessment for rational antigen selection within a unified environment. Candidates can be submitted as amino acid sequences or three-dimensional structures. When experimental structures are unavailable, BIOINF-farma automatically searches for models in AlphaFold DB or performs structure prediction using Boltz-2, ensuring a standardized structural representation for downstream analyses. Antigenicity is quantified by combining structure-based conformational epitope signals (MLCE/REBELOT-BEPPE) and sequence-based linear epitope propensity scores (BepiPred 3.0) into a protein-level Antigenicity Score, with a classification threshold optimized on a manually curated validation dataset. Developability is evaluated through two supervised Random Forest meta-learners that integrate three solubility predictors (DeepSoluE, SoluProt, Protein-Sol) and three thermal stability predictors (TemStaPro, ProLaTherm, BertThermo), whose outputs are combined into an Expression Efficiency Score (EES). By integrating complementary predictive signals, the meta-learning framework achieves greater accuracy and robustness than individual predictors while maintaining performance across a broad range of sequence identities. The Antigenicity Score effectively discriminates antigenic from non-antigenic proteins with a large effect size, whereas EES successfully distinguishes soluble from insoluble outcomes on an independent panel of recombinant proteins expressed in Escherichia coli. BIOINF-farma jointly assesses antigenicity and expression feasibility within a single framework. Its modular architecture facilitates the incorporation of future predictive methods, while its web-based interface makes the full pipeline accessible to users without programming expertise, supporting rapid candidate triage in vaccine research and emerging pathogen responses. Author SummaryVaccine development begins with a critical step: identifying, among the many proteins encoded in a pathogen genome, those most suitable as candidate antigens. A promising candidate must satisfy two requirements that are rarely evaluated together. It must be recognized by the immune system, so that vaccination elicits a protective response; and it must be amenable to recombinant production, since antigens that cannot be obtained in sufficient quantity and quality are of limited practical use. Current computational tools typically address only one of these aspects, and researchers must integrate their outputs manually, through procedures that are time-consuming and prone to inconsistency. We developed BIOINF-farma, an automated platform that brings these two assessments into a single analytical framework. Starting from a protein sequence or an experimental structure, the platform retrieves or predicts a three-dimensional model, evaluates the proteins antigenic potential by combining complementary epitope predictors, and estimates its expression feasibility by integrating multiple solubility and stability predictors through supervised machine learning. A web-based interface makes the full workflow available to experimental immunologists and vaccine developers without requiring computational expertise, supporting rational candidate prioritization in routine vaccine research and during emerging pathogen responses.

bioinformatics↗
bioRxiv2025

Eduomics: a Nextflow pipeline to simulate -omics data for education

Moving past learning just algorithms and code is a key challenge of bioinformatics education: the ideal goal is for students to acquire higher-order knowledge such as the ability to solve biological problems with the appropriate tools, and more importantly learn to interpret the results in the broader context where bioinformatics is needed. To design such a teaching and learning experience, data simulations play a key role: however, there is a massive barrier to adoption. Different data types are produced by different tools, requiring educators to learn each of them and adapt their workflow to the necessary dependencies, requirements and input files. Additionally, most existing data simulation solutions are meant for benchmarking and methods development rather than education and cannot provide the context needed to teach students the critical interpretation skills they need to move beyond problem-based learning to what we call storyline-based learning. A significant effort must be placed also when many datasets with the same characteristics are needed, such as in tutoring or assessment in higher education. Here, we present eduomics: a Nextflow pipeline meant to automate the simulation workflow for both genomic and transcriptomic next-generation sequencing data, and to produce realistic clinical scenarios to provide students with clues and a biomedical story necessary for the interpretation of their results. Eduomics removes barriers to adoption, by requiring the user to just decide which chromosome datasets should be simulated on, and which type of data they would like to simulate. There is no need to learn specific tools and resolve their dependencies. The use of Gemini API provides an innovative approach to generate plausible clinical scenarios, consistent with the genes where either a pathological mutation or differential expression has been simulated. With eduomics, we offer an accessible and scalable solution to design comprehensive learning experiences and innovate bioinformatics education. Author SummaryModern bioinformatics education faces a dual challenge: students must learn how to analyse data and interpret results within the biological problems they aim to solve, while instructors who wish to design such a comprehensive learning experience should introduce appropriately simulated data in their teaching. However, the complexity of existing simulation tools is an often daunting barrier to overcome: current solutions require different software for different data types and provide no support for teaching interpretation skills. Most available simulators were designed for benchmarking and method development, not for education: therefore they lack the biological and clinical context needed to move beyond the traditional problem-based learning. To address these challenges, we developed eduomics, a Nextflow-based end-to-end pipeline that automates the simulation of genomic and transcriptomic data in a fully automated manner for educational use. Educators only need to choose the type of data to simulate, while the pipeline handles all underlying tools and dependencies. As a groundbreaking element, we integrated the Google Gemini API to generate patient-inspired clinical scenarios that gives students realistic clues and a biomedical narrative in which to interpret the results. This approach complements the traditional problem-based learning by offering a more complete, storyline-based learning experience. By combining automated data simulation and clinical storytelling, eduomics provides an accessible and scalable solution that support an engaging and immersive learning experience for both educators and students.

bioinformatics↗
bioRxiv2025

Adaptive disorder as the hallmark of nanobodies antigen-binding loops

Nanobodies are antigen-binding proteins of great interest as diagnostics and therapeutics. Accurate and fast characterization of their complementarity-determining regions (CDRs) is crucial to uncover the principles guiding their design. Yet, this task remains challenging, as random recombination and somatic mutations generate highly diverse CDR sequences that escape motif-based or structure-prediction approaches currently used to identify them. To overcome this hurdle, we employed two independent strategies that converged on the same conclusion. At the sequence level, we developed a deep learning model to identify nanobody CDRs directly from the primary sequence. At the structural level, we applied an energy decomposition method, revealing CDRs as residues highly uncoupled to the rest of the fold. Explainability analyses showed the network captured intrinsic CDR properties, which notably aligned with these energy values. CDRs emerge as fuzzy regions capable of adopting diverse conformational ensembles, from which a preferred state is selected upon antigen binding. This finding supports a model where chaos in both sequence and structure appears adaptive and disorder emerges as the hallmark of nanobody CDRs. This work aims to advance the definition of rules for the design of antigen binding regions, paving the way for the next-generation immune diagnostics and therapeutics.

immunology↗
bioRxiv2025

Spiroplasma impairs testes gene expression in Glossina fuscipes fuscipes

Glossina fuscipes fuscipes is a riverine tsetse fly species, the primary vector of human and animal trypanosomiasis in Sub-Saharan Africa. Controlling tsetse fly populations is crucial for mitigating the socio-economic impact of this disease, as effective treatments remain challenging. Indeed, the development of control strategies is hindered by the species unique reproductive biology: adenotrophic viviparity, in which the female retains and nourishes the developing larva in her uterus throughout the pregnancy. The discovery of Spiroplasma in some G. f. fuscipes populations has drawn attention as a potential tool to enhance tsetse fly control strategies. Although Spiroplasma does not exhibit in G. f. fuscipes the male-killing phenotype observed in Drosophila melanogaster, evidence suggests that it may confer refractoriness to Trypanosoma infection. This has led to further investigations into its broader effects on G. f. fuscipes biology, particularly its potential impact on Glossina reproductive fitness. In this study, we considered Spiroplasma effect on the male reproduction. For this, we performed a differential gene expression analysis on testes and male accessory glands (MAGs) between Spiroplasma-infected and uninfected males. A significant downregulation of genes was observed in testes while a minor effect has been detected on MAGs. Downregulation of testes genes associated with functions related to sperm motility, energy metabolism, and mitochondrial function has been observed. Additionally, differentially expressed genes involved in antimicrobial activity and circadian rhythm regulation were observed. These findings provide valuable insights into the potential fitness costs of Spiroplasma infection for the fly and its implications for the bacterium use as biological control strategies targeting G. f. fuscipes.

genomics↗
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↗
bioRxiv2022

hgtseq: a standard pipeline to study horizontal gene transfer

Horizontal gene transfer (HGT) is well described in prokaryotes, it plays a crucial role in evolution, and has functional consequences in insects and plants: less is known about HGT in Humans. Studies have reported bacterial integrations in cancer patients, and microbial sequences have been detected in data from well-known Human sequencing projects. Few of the existing tools to investigate HGT are highly automated. Thanks to the adoption of Nextflow for life sciences work-flows, and the standards and best practices curated by communities such as nf-core, fully automated, portable, and scalable pipelines can now be developed. Here we present nf-core/hgtseq, to facilitate the analysis of HGT from sequencing data in different organisms. We showcase its performance by analysing six exome datasets from five mammals. Hgtseq can be run seamlessly in any computing environment and accepts data generated by existing exome and whole-genome sequencing projects: this will enable researchers to expand their analyses into this area. Fundamental questions are still open, about the mechanisms and the extent or the role of horizontal gene transfer: by releasing hgtseq we provide a standardised tool which will enable a systematic investigation of this phenomenon, thus paving the way for a better understanding of HGT.

bioinformatics↗
bioRxiv2019

Meta-analysis of Scandinavian Schizophrenia Exomes

Rare genetic variants may play a prominent role in schizophrenia. We report on the to date largest whole exome sequencing study of schizophrenia case-control samples from related populations and combine with other available sequence data, analysing in total 34,084 individuals (14,302 cases). Three genes showed significant association at FDR < 0.10 (SETD1A, TAF13 and MKI67) and gene-set analyses highlighted the involvement of the synaptome and excitatory neurons, and demonstrated shared architecture with high-functioning autism.

genetics↗
bioRxiv2017

Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum

There is a limited understanding about the impact of rare protein truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals. Protein truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization and reduced age. Gene sets implicated from GWAS did not show a significant protein truncating variants-burden beyond what captured by established Mendelian genes. In conclusion, we provide the most thorough investigation to date of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.\n\nMain abbreviations

genetics↗