Biology subjects

Cadieu, E.

Publications and source records attributed to Cadieu, E..

6 recordsLinked to original sources

bioRxiv2025

Benchmarking circRNA Detection Tools from Long-Read Sequencing Using Data-Driven and Flexible Simulation Framework

AO_SCPLOWBSTRACTC_SCPLOWCircular RNAs (circRNAs) are unique non-coding RNAs with covalently closed loop structures formed through backsplicing events. Their stability, tissue-specific expression patterns, and potential as disease biomarkers have garnered increasing attention. However, their circular structure and diverse size range pose challenges for conventional sequencing technologies. Long-read Oxford Nanopore (ONT) sequencing offers promising capabilities for capturing entire circRNA molecules without fragmentation, yet the effectiveness of bioinformatic tools for analyzing this data remains understudied. This study presents the first comprehensive benchmark comparison of three specialized tools for circRNA detection from ONT long-read data: CIRI-long (Zhang et al., 2021), IsoCIRC (Xin et al., 2021), and circNICK-Irs (Rahimi et al., 2021). To address the lack of standardized evaluation frameworks, we developed a novel computational pipeline, open-source and freely available, to generate realistic simulated circRNA ONT long-read datasets. Our pipeline integrates several molecular features of circRNAs extracted from established databases and real datasets into NanoSim tool (Hafezqorani et al., 2020) and outputs FASTQ reads reflecting therefore biological diversity and technical properties. We systematically assessed tool performance across key metrics, including precision, recall, specificity, accuracy, and F1 score. Our analysis revealed distinct performance profiles: while all tools exhibited high specificity, they varied in precision and their ability to detect different circRNA subtypes, often showing limited sensitivity and precision. Notably, the overlap in detected circRNAs among tools was relatively low. Additionally, computational efficiency varied significantly across the tools. This suggests that relying on a single tool might not be ideal, and combining tools or improving algorithms could be necessary for more accurate circRNA detection from ONT data. This benchmark provides valuable insights for researchers selecting appropriate tools for circRNA studies using ONT sequencing. Furthermore, our customizable simulation framework, offering a resource to optimize detection approaches and advance bioinformatic tool development for circRNA research is freely available at: https://gitlab.com/bioinfog/circall/nano-circ.

bioinformatics↗

bioRxiv2025

Facilitating genome annotation using ANNEXA and long-read RNA sequencing

With the advent of complete genome assemblies, genome annotation has become essential for the functional interpretation of genomic data. Long-read RNA sequencing (LR-RNAseq) technologies have significantly improved transcriptome annotation by enabling full-length transcript reconstruction for both coding and non-coding RNAs. However, challenges such as transcript fragmentation and incomplete isoform representation persist, highlighting the need for robust quality control (QC) strategies. This study presents ANNEXA, a pipeline designed to enhance genome annotation using LR-RNAseq data while also providing QC for reconstructed genes and transcripts. ANNEXA integrates two transcriptome reconstruction tools, StringTie2 and Bambu, applying stringent filtering criteria to improve annotation accuracy. It also incorporates deep learning models to evaluate transcription start sites (TSSs) and employs the tool FEELnc for the systematic annotation of long non-coding RNAs (lncRNAs). Additionally, the pipeline offers intuitive visualisations for comparative analyses of coding and non-coding repertoires. Benchmarking against multiple reference annotations revealed distinct patterns of sensitivity and precision for both known and novel genes and transcripts and mRNAs and lncRNAs. To demonstrate its utility, ANNEXA was applied in a comparative oncology study involving LR-RNAseq of two human and eight canine cancer cell lines. The pipeline successfully identified novel genes and transcripts across species, expanding the catalog of protein-coding and lncRNA annotations in both species. Implemented in Nextflow for scalability and reproducibility, ANNEXA is available as an open-source tool: https://github.com/IGDRion/ANNEXA.

bioinformatics↗

bioRxiv2021

Canine oral melanoma genomic and transcriptomic study defines two molecular subgroups with different therapeutical targets

Mucosal melanoma (MM) is a rare and aggressive clinical cancer that occurs mostly in the head, neck, and anogenital regions. Despite recent advances in genetics and the development of revolutionary treatments, such as immunotherapy, the prognosis for MM remains poor. Canine MM shares several clinical, histological, and genetic features with its human counterpart, offering a relevant spontaneous and immunocompetent model to decipher the genetic bases and explore treatment options for human MM. We performed an integrative genomic and transcriptomic analysis of 32 canine MM samples, which allowed us to identify two molecular subgroups differing in microenvironment and structural variant (SV) content. The overexpression of genes related to the microenvironment and T-cell response was associated with tumors harboring a lower content of structural variants, whereas the overexpression of pigmentation-related pathways and oncogenes such as TERT were associated with a high SV burden. To detail the SVs, especially those with focal amplifications, whole-genome sequencing was performed on four canine MM cell lines. We showed that focal amplifications characterized complex chromosomal rearrangements targeting oncogenes such as MDM2 or CDK4 and a recurrently amplified region on canine chromosome 30, comprising the genes TRPM7, GABPB1, USP8, and SPPL2A, were candidate oncogenes for MM. We showed that the copy numbers of these genes were significantly correlated with their expression levels. Finally, we demonstrated that the genes TRPM7, GABPB1, and SPPL2A play a role in cell proliferation; thus, these may be considered new candidate oncogenes for human MM. Our findings suggest the existence of two MM molecular subgroups that may benefit from dedicated therapies, such as immune checkpoint inhibitors or targeted therapies. These results illustrate the relevance of dog models for deciphering genetic mechanisms in spontaneous MM, along with the potential to screen for efficient targeted therapies for rare and aggressive cancers in humans.

cancer biology↗

bioRxiv2020

The tree that hides the forest: identification of common predisposing loci in several hematopoietic cancers and several dog breeds.

Histiocytic sarcoma (HS) is a rare but aggressive cancer in humans and dogs. The spontaneous canine model, with the clinical, epidemiological and histological similarities with human HS and specific breed predispositions, is a unique model/opportunity to unravel the genetic bases of this cancer. In this study, we aimed to identify germline risk factors associated with the development of HS in canine predisposed breeds. We used a methodology that combined several genome-wide association studies in a multi-breed and multi-cancer approach, as well as targeted next generation sequencing, and imputation combining several breeds (Bernese mountain dog, Rottweiler, flat coated retriever and golden retriever) and three haematopoietic cancers (HS, lymphoma and mast cell tumor). Results showed that we not only refined the previously identified HS risk CDKN2A locus but we identified new loci on canine chromosomes 2, 5, 12, 14, 20, 26 and X. Capture and targeted sequencing of specific loci pointed towards the existence of regulatory variants in non coding regions and/or methylation mechanisms linked to risk haplotypes, leading to strong cancer predispositions in specific dog breeds. Our results showed that these canine cancer predisposing loci appear to be due to the additive effect of several risk haplotype involved also in other haematopoietic cancers such lymphoma or mast cell tumor, illustrating the pleiotropic nature of these canine cancer loci as observed in human oncology, thus reinforcing the interest of predisposed dog breeds to study cancer initiation and progression.

genetics↗

bioRxiv2019

Cross-species analysis of melanoma enhancer logic using deep learning

Genomic enhancers form the central nodes of gene regulatory networks by harbouring combinations of transcription factor binding sites. Deciphering the combinatorial code by which these binding sites are assembled within enhancers is indispensable to understand their regulatory involvement in establishing a cells phenotype, especially within biological systems with dysregulated gene regulatory networks, such as melanoma. In order to unravel the enhancer logic of the two most common melanoma cell states, namely the melanocytic and mesenchymal-like state, we combined comparative epigenomics with machine learning. By profiling chromatin accessibility using ATAC-seq on a cohort of 27 melanoma cell lines across six different species, we demonstrate the conservation of the two main melanoma states and their underlying master regulators. To perform an in-depth analysis of the enhancer architecture, we trained a deep neural network, called DeepMEL, to classify melanoma enhancers not only in the human genome, but also in other species. DeepMEL revealed the presence, organisation and positional specificity of important transcription factor binding sites. Together, this extensive analysis of the melanoma enhancer code allowed us to propose the concept of a core regulatory complex binding to melanocytic enhancers, consisting of SOX10, TFAP2A, MITF and RUNX, and to disentangle their individual roles in regulating enhancer accessibility and activity.

genomics↗

bioRxiv2018

Characterisation and functional predictions of canine long non-coding RNAs

Long non-coding RNAs (lncRNAs) are a family of heterogeneous RNAs that play major roles in multiple biological processes. We recently identified an extended repertoire of more than 10,000 lncRNAs of the domestic dog however, predicting their biological functionality remains challenging. In this study, we have characterised the expression profiles of 10,444 canine lncRNAs in 26 distinct tissue types, representing various anatomical systems. We showed that lncRNA expressions are mainly clustered by tissue type and we highlighted that 44% of canine lncRNAs are expressed in a tissue-specific manner. We further demonstrated that tissue-specificity correlates with specific families of canine transposable elements. In addition, we identified more than 900 conserved dog-human lncRNAs for which we show their overall reproducible expression patterns between dog and humans through comparative transcriptomics. Finally, co-expression analyses of lncRNA and neighbouring protein-coding genes identified more than 3,400 canine lncRNAs, suggesting that functional roles of these lncRNAs act as regulatory elements. Altogether, this genomic and transcriptomic integrative study of lncRNAs constitutes a major resource to investigate genotype to phenotype relationships and biomedical research in the dog species.

genomics↗