Biology subjects

Pipoli da Fonseca, J.

Publications and source records attributed to Pipoli da Fonseca, J..

4 recordsLinked to original sources

bioRxiv2026

KozakExplorer: an interactive framework for genome-wide Kozak sequence analysis

Translation initiation signals shape gene expression across all domains of life. In eukaryotes, nu-cleotide constraints surrounding the start codon are commonly described by the Kozak Consensus Sequence (KCS), whereas in bacteria and archaea, initiation frequently involves Shine-Dalgarno ribosome-binding motifs. Although these signals have been extensively characterized in model or-ganisms, their large-scale diversity and evolutionary distribution remain incompletely explored. We present KozakExplorer, a reproducible framework for quantitative and comparative analysis of translation initiation contexts from genome assemblies and annotations. The software per-forms strand-aware extraction of start codon environments from FASTA and GFF3 files and ap-plies information-theoretic metrics--including Kullback-Leibler (KL) divergence and information content (IC)--to measure positional nucleotide constraints relative to a background model. Derived summary statistics (Kozak Strength Index [KSI], maximum information content, peak position) con-vert motif patterns into interpretable per-genome signatures suitable for cross-species comparison. Our primary analysis covers 2,282 eukaryotic reference genomes, producing a standardized dataset of translation initiation metrics. Dimensionality reduction via t-SNE on per-position KL divergence, information content, and motif nucleotide frequencies reveals a structured eukaryotic KCS land-scape with kingdom-level clustering and continuous variation in signal strength. A dedicated case study of 216 Apicomplexa genomes shows genus-level structure consistent with host range and phylogeny. An extended analysis across 25,344 reference genomes (22,253 bacteria, 809 archaea) places eukaryotic patterns in a global comparative framework, revealing transitions between sharply localized Kozak motifs and distributed Shine-Dalgarno-type signatures. Implemented within the open-source Sequana ecosystem, KozakExplorer is distributed as a Python module and an interactive web application that accepts local annotated assemblies, GenBank records, or NCBI RefSeq accessions, and exports all computed metrics, embeddings, and coordi-nates for downstream comparative and evolutionary genomics. AvailabilityImplemented in Python within the Sequana framework. Source code available at https://github.com/sequana/sequana and https://github.com/sequana/webapp_kozak. Contactthomas.cokelaer@pasteur.fr

bioinformatics↗

bioRxiv2026

LORA: a polymorphic multi-sample LOng Read Assembly pipeline.

Genome assembly from long-read sequencing data has become a standard approach for resolving complex genomic regions and producing high-contiguity assemblies. However, the diversity of available assemblers, their varying performance across species, and the need for reproducible workflows present ongoing challenges. We developed LORA, an easy-to-use and reproducible application for assembling genomes from long-read data. LORA integrates several well-established assemblers, including Canu, HiFiasm, Flye, and Unicycler, as well as more recent tools such as Necat and Pecat. It is implemented as a Snakemake pipeline to parallelize tasks and support seamless execution on both local machines and computing clusters. LORA includes multiple quality assessment steps, interactive HTML reports for interpretation, BLAST-based taxonomic identification, and completeness evaluation. Together, these features provide users with a comprehensive view of assembly quality and potential problematic. We illustrate the capabilities of LORA using datasets from bacterial genomes and unicellular eukaryotes, sequenced with both PacBio and Oxford Nanopore technologies, highlighting typical outcomes and common pitfalls encountered during long-read assemblies. LORA is distributed as part of the Sequana project, an open-source framework designed for reproducibility, maintainability, and straightforward deployment across computing environments.

bioinformatics↗

bioRxiv2022

Core genome sequencing and genotyping of Leptospira interrogans in clinical samples by target capture sequencing

The life-threatening pathogen Leptospira interrogans is the most common agent of leptospirosis, an emerging zoonotic disease. However, little is known about the strains that are circulating worldwide due to the fastidious nature of the bacteria and its difficulty to be culture isolated. In addition, the paucity of bacteria in blood and other clinical samples has proven to be a considerable challenge for directly genotyping the agent of leptospirosis directly from patient material. Here, to elucidate the genomic diversity of Leptospira circulating strains, hybridization capture followed by Illumina sequencing of the core genome was performed directly from 20 biological samples that were PCR positive for pathogenic Leptospira. A set of samples subjected to capture with RNA probes covering the L. interrogans core genome resulted in 72 to 13,000-fold increase in pathogen reads when compared to standard sequencing without capture. A SNP analysis of the genomes sequenced from the biological samples using 273 Leptospira reference genome was then performed in order to determine the genotype of the infecting strain. For samples with sufficent coverage (19/20 samples with coverage >8X), we could unambigously identify L. interrogans sv Icterohaemorrhagiae (14 samples), L. kirschneri sv Grippotyphosa (4 samples) and L. interrogans sv Pyrogenes (1 sample) as the infecting strain. In conclusion, we obtained for most of our biological samples high quality genomic data at suitable coverage for confident core genome genotyping of the agent of leptospirosis. The ability to generate culture-free genomic data opens new opportunities to better understand the epidemiology and evolution of this fastidious pathogen.

microbiology↗

bioRxiv2022

Transcriptomic landscapes of SARS-CoV-2-infected and bystander lung cells reveal a selective upregulation of NF-κB-dependent coding and non-coding proviral transcripts

Investigations of cellular responses to viral infection are commonly performed on mixed populations of infected and uninfected cells or using single-cell RNA sequencing, leading to inaccurate and low-resolution gene expression interpretations. Here, we performed deep polyA+ transcriptome analyses and novel RNA profiling of SARS-CoV-2 infected lung epithelial cells, sorted based on the expression of the viral spike (S) protein. Infection caused a massive reduction in mRNAs and lncRNAs, including transcripts coding for antiviral factors, such as interferons (IFN). This absence of IFN signaling probably explained the poor transcriptomic response of bystander cells co-cultured with S+ ones. NF-{kappa}B pathway and the inflammatory response escaped the global shutoff in S+ cells. Functional investigations revealed the proviral function of the NF-{kappa}B pathway and the antiviral activity of CYLD, a negative regulator of the pathway. Thus, our transcriptomic analysis on sorted cells revealed additional genes that modulate SARS-CoV-2 replication in lung cells.

microbiology↗