Biology subjects

Prifti, E.

Publications and source records attributed to Prifti, 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

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↗

bioRxiv2020

Towards end-to-end disease prediction from raw metagenomic data

Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and are stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

genomics↗

bioRxiv2018

A chemogenetic approach for optical monitoring of voltage in neurons

Optical monitoring of neuronal voltage using fluorescent indicators is a powerful approach for interrogation of the cellular and molecular logic of the nervous system. Here we describe a Semisynthetic Tethered Voltage Indicator (STeVI1) based upon Nile Red that displays voltage sensitivity when genetically targeted to neuronal membranes. This environmentally sensitive probe allows for wash-free imaging and faithfully detects supra- and subthreshold activity in neurons.

neuroscience↗

bioRxiv2018

Interpretable and accurate prediction models for metagenomics data

Biomarker discovery using metagenomic data is becoming more prevalent for patient diagnosis, prognosis and risk evaluation. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes. Moreover, they seldom generalize well when learned on small datasets. Here, we introduce an original approach that focuses on three models inspired by microbial ecosystem interactions: the addition, subtraction, and ratio of microbial taxon abundances. While being extremely simple, their performance is surprisingly good and compares to or is better than Random Forest, SVM or Elastic Net. Such models besides being interpretable, allow distilling biological information of the predictive core-variables. Collectively, this approach builds up both reliable and trustworthy diagnostic decisions while agreeing with societal and legal pressure that require explainable AI models in the medical domain.

bioinformatics↗