Biology subjects

Belda, E.

Publications and source records attributed to Belda, E..

7 recordsLinked to original sources

bioRxiv2026

CurateMake: an auditable workflow for multi-source ITS reference database harmonisation and phylogenetic validation

O_LIReference databases shape the taxonomic resolution, uncertainty, and reproducibility of metabarcoding analyses. For ITS barcodes, public references are distributed across repositories with different taxonomic conventions, geographic coverage, and annotation practices, creating conflicts, missing ranks, and misannotations when databases are merged or compared. C_LIO_LIWe introduce CurateMake, a reproducible Snakemake workflow for ITS reference database construction, harmonisation, and validation. It integrates four public sources (UNITE, BOLD, PLANiTS, and CALeDNA) and user-supplied databases, combines Catalogue of Life name harmonisation with ITSx-based region standardisation, MSA/HMM-based alignment grouping, and SATIVA phylogenetic validation. Raw, CoL-harmonised, and SATIVA-validated annotation layers are retained throughout to compare curation effects while preserving flagged records for review. C_LIO_LIWe evaluated CurateMake on 3.58 million ingested sequences and controlled error-injection simulations. ITSx expanded the final harmonised database to 5.19 million barcode-resolved entries by recovering ITS1 and ITS2 sub-regions from full-length ITS records. Across the full dataset, normalised intra-cluster entropy decreased from Raw to CoL-harmonised to SATIVA-validated annotations, consistent with improved taxonomic coherence. In simulations, CurateMake achieved the highest correction rate across 1%-50% corruption and, at 15% corruption, corrected 42% {+/-} 1% of introduced errors, compared with 28% {+/-} 1% for CoL alone and 0% for SATIVA without the workflows alignment infrastructure. C_LIO_LIThese results show that nomenclatural harmonisation and phylogeny-informed validation address complementary error classes, with phylogenetic validation contributing measurably only within taxon-coherent alignments in this benchmark. CurateMake therefore provides a reproducible, provenance-tracked framework for auditable ITS reference database curation in metabarcoding workflows. C_LI

bioinformatics↗

bioRxiv2025

Spatial characterization of microbiota profiles along the gut in a widely used mouse model: effects of high-fat diet and fructooligosaccharides

The intestinal microbiota plays a pivotal role in regulating metabolic processes, and its imbalance is linked to metabolic disorders. Modulating the gut microbiota composition and function through prebiotic supplementation has been repeatedly shown to improve host metabolism. While most studies have focused on the fecal microbiota due to the ease of sampling, fecal samples do not reflect the microbial dynamics throughout the gastrointestinal tract, as regional environmental conditions shape distinct microbiota composition. Given the metabolic significance of the proximal intestine and the potential influence of microbiota on these processes, we characterized the microbiota composition along the gastrointestinal tract in a widely used model for studying host-microbiota interactions: C57Bl6J male mice fed a high-fat diet (HFD) with or without prebiotics supplementation (FOS, fructooligosaccharides). The microbiota composition, determined by long-read Nanopore sequencing, was markedly altered by HFD and FOS supplementation not only in feces but also in the jejunum, ileum and caecum. In contrast to previous observations made in humans, obesity in mice was associated with a decrease in microbiome diversity in the small intestine, highlighting species-specific microbial responses to metabolic challenges. Additionally, the pronounced bifidogenic effect of FOS supplementation in the ileum suggests FOS fermentation in the small intestine in mice, contrary to what has been previously described in humans. Finally, we report a relative homogeneity of microbiome composition along the digestive tract, possibly due to the coprophagic behavior of mice. These findings challenge the translational relevance of rodent models for studying the role of the small intestinal microbiota in human metabolic disease.

microbiology↗

bioRxiv2025

MetagenBERT: a Transformer Architecture using Foundational DNA Read Embedding Models to enhance Disease Classification

Microbial ecosystems constitute complex yet information-rich environments whose characterization is crucial for understanding host health and disease. Among them, the human gut microbiome has emerged as a key "super-integrator", owing to its dense interactions with host physiology and its established associations with a wide spectrum of pathologies. Driven by advances in high-throughput sequencing technologies and the continuous decline in associated costs, metagenomic studies have expanded exponentially, generating massive amounts of sequencing data and opening new avenues for data-driven disease modeling. Conventional approaches to microbiome analysis predominantly rely on the alignment of DNA sequencing reads against reference databases to infer microbial composition and profiling at the species level. While effective, these methods are inherently constrained by reference bias and limited taxonomic resolution. Recent advances in artificial intelligence--particularly in Natural Language Processing (NLP) offer new methodological perspectives for metagenomic data representation. In this study, we present MetagenBERT, a Transformer-based framework to embed metagenomes that relies on the foundational models DNABERT-2 and DNABERT-S for the embedding of DNA sequencing reads. Our approach encodes gut microbiome metagenome in a taxonomy-agnostic manner, enabling direct downstream application to disease classification tasks. We demonstrate that MetagenBERT reaches similar performance to state-of-the-art abundance-based models for cirrhosis prediction and surpasses them in the more challenging context of type 2 diabetes. Furthermore, we introduce an alternative representation of metagenomes based on read-level embeddings aggregated into abundance vectors, demonstrating their complementarity with conventional species-level abundance metrics.

bioinformatics↗

bioRxiv2025

CroCoDeEL: accurate control-free detection of cross-sample contamination in metagenomic data

Metagenomic sequencing provides profound insights into microbial communities, but it is often compromised by technical biases, including cross-sample contamination. This phenomenon arises when microbial content is inadvertently exchanged among concurrently processed samples, distorting microbial profiles and compromising the reliability of metagenomic data and downstream analyses. Existing detection methods often rely on negative controls, which are inconvenient and do not detect contamination within real samples. Meanwhile, strain-level bioinformatics approaches fail to distinguish contamination from natural strain sharing and lack sensitivity. To fill this gap, we introduce CroCoDeEL, a decision-support tool for detecting and quantifying cross-sample contamination. Leveraging linear modeling and a pre-trained supervised model, CroCoDeEL identifies specific contamination patterns in species abundance profiles. It requires no negative controls or prior knowledge of sample processing positions, offering improved accuracy and versatility. Benchmarks across three public datasets demonstrate that CroCoDeEL accurately detects contaminated samples and identifies their contamination sources, even at low rates (<0.1%), provided sufficient sequencing depth. Notably, we discovered critical contamination cases in highly cited studies, calling some of their results into question. Our findings suggest that cross-sample contamination is a widespread yet underexplored issue in metagenomics and emphasize the necessity of systematically integrating contamination detection into sequencing quality control.

bioinformatics↗

bioRxiv2023

Deep Learning Methods in Metagenomics: a systematic review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analyzing metagenomic data remains challenging due to several factors, including reference catalogs, sparsity, and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification, and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews deep learning approaches in metagenomics, including convolutional networks (CNNs), autoencoders, and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiomes key role in our health. Author summaryIn our study, we look at the vast world of research in metagenomics, the study of genetic material from environmental samples, spurred by the increasing affordability of sequencing technologies. Our particular focus is the human gut microbiome, an environment teeming with microscopic life forms that plays a central role in our health and well-being. However, navigating through the vast amounts of data generated is not an easy task. Traditional methods hit roadblocks due to the unique nature of metagenomic data. Thats where deep learning (DL), a today well known branch of artificial intelligence, comes in. DL-based techniques complement existing methods and open up new avenues in microbiome research. Theyre capable of tackling a wide range of tasks, from identifying unknown pathogens to predicting disease based on a patients unique microbiome. In our article, we provide a very comprehensive review of different DL strategies for metagenomics, including convolutional networks, autoencoders, and attention-based models. We are convinced that these techniques significantly enhance the field of metagenomic analysis in its entirety, paving the way for more accurate data analysis and, ultimately, better patient care. The PRISMA augmented diagram of our review is illustrated in Fig 1. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/552187v2_fig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@9f0060org.highwire.dtl.DTLVardef@d1cd1aorg.highwire.dtl.DTLVardef@1f0a5fborg.highwire.dtl.DTLVardef@211b5a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1.C_FLOATNO PRISMA-type diagram for article selection of this review. The method developed here enriches the research equation selection with Connected Papers, this diagram represents the selection along with this enrichment in green C_FIG

bioinformatics↗

bioRxiv2019

The great tit HapMap project: a continental-scale analysis of genomic variation in a songbird

A major aim of evolutionary biology is to understand why patterns of genomic diversity vary among populations and species. Large-scale genomic studies of widespread species are useful for studying how the environment and demographic history shape patterns of genomic divergence, and with the continually decreasing cost of sequencing and genotyping, such studies are now becoming feasible. Here, we carry out one of the most geographically comprehensive surveys of genomic variation in a wild vertebrate to date; the great tit (Parus major) HapMap project. We screened ca 500,000 SNP markers across 647 individuals from 29 populations, spanning almost the entire geographic range of the European great tit subspecies. We found that genome-wide variation was consistent with a recent colonisation across Europe from a single refugium in South-East Europe, with bottlenecks and reduced genetic diversity in island populations. Differentiation across the genome was highly heterogeneous, with clear "islands of differentiation" even among populations with very low levels of genome-wide differentiation. Low local recombination rate in the genome was a strong predictor of high local genomic differentiation (FST), especially in island and peripheral mainland populations, suggesting that the interplay between genetic drift and recombination is a key driver of highly heterogeneous differentiation landscapes. We also detected genomic outlier regions that were confined to one or more peripheral great tit populations, most likely as a result of recent directional selection at the range edges of this species. Haplotype-based measures of selection were also related to recombination rate, albeit less strongly, and highlighted population-specific sweeps that likely resulted from positive selection. These regions under positive selection contained candidate genes associated with morphology, thermal adaptation and colouration, providing promising avenues for future investigation. Our study highlights how comprehensive screens of genomic variation in wild organisms can provide unique insights into evolution.

evolutionary biology↗

bioRxiv2018

De novo profiling of RNA viruses in Anopheles malaria vector mosquitoes from forest ecological zones in Senegal and Cambodia

BackgroundMosquitoes are colonized by a large but mostly uncharacterized natural virome of RNA viruses. Anopheles mosquitoes are efficient vectors of human malaria, and the composition and distribution of the natural RNA virome may influence the biology and immunity of Anopheles malaria vector populations.\n\nResultsAnopheles vectors of human malaria were sampled in forest village sites in Senegal and Cambodia, including Anopheles funestus, Anopheles gambiae group sp., and Anopheles coustani in Senegal, and Anopheles hyrcanus group sp., Anopheles maculatus group sp., and Anopheles dirus in Cambodia. Small and long RNA sequences were depleted of mosquito host and de novo assembled to yield non-redundant contigs longer than 500 nucleotides. Analysis of the assemblies by sequence similarity to known virus families yielded 125 novel virus sequences, 39 from Senegal Anopheles and 86 from Cambodia. Important monophyletic virus clades in the Bunyavirales and Mononegavirales orders are found in these Anopheles from Africa and Asia. Small RNA size and abundance profiles were used to cluster non-host RNA assemblies that were unclassified by sequence similarity. 39 unclassified non-redundant contigs >500 nucleotides strongly matched a pattern of classic RNAi processing of viral replication intermediates, and 1566 unclassified contigs strongly matched a pattern consistent with piRNAs. Analysis of piRNA expression in Anopheles coluzzii after infection with Onyong nyong virus (family Togaviridae) suggests that virus infection can specifically alter abundance of some piRNAs.\n\nConclusionsRNA viruses ubiquitously colonize Anopheles vectors of human malaria worldwide. At least some members of the mosquito virome are monophyletic with other arthropod viruses. However, high levels of collinearity and similarity of Anopheles viruses at the peptide level is not necessarily matched by similarity at the nucleotide level, indicating that Anopheles from Africa and Asia are colonized by closely related but clearly diverged virome members. The interplay between small RNA pathways and the virome may represent an important part of the homeostatic mechanism maintaining virome members in a commensal or nonpathogenic state, and host-virome interactions could influence variation in malaria vector competence.

genomics↗