Biology subjects

Vicedomini, R.

Publications and source records attributed to Vicedomini, R..

9 recordsLinked to original sources

bioRxiv2026

Sourmash accurately profiles strains from long-read metagenomes preserving gene content

Background. Metagenomic strain-level profiling remains challenging because closely related bacterial strains often share more than 99% average nucleotide identity (ANI). However, they can differ substantially in gene content and functional potential. Sourmash uses k-mer sketches with FracMinHash to compare sequencing reads against reference databases, which makes it an interesting approach for strain-level profiling. Because it compares sketches rather than full alignments, it enables fast searches against large reference databases. However, its ability to recover biologically relevant strains and preserve community gene content has not been specifically assessed. Here, we evaluated the performance of sourmash for strain-level identification abundance, functional gene content recovery, and abundance estimations using five mock communities of increasing complexity with known composition. Results. We first assessed whether nucleotide similarity reflects functional similarity between strains. Gene content similarity was generally consistent with ANI but revealed important divergences between closely related strains. For example, Escherichia coli strains sharing more than 98% ANI showed gene content similarities ranging from 20% to 57%, highlighting that nucleotide similarity alone is insufficient to assess functional proximity and consequently the accuracy of strain identification. Above sufficient genome coverage, sourmash identified strains either exactly or with closely related strains sharing highly conserved gene content. These closely related strains provided a biologically relevant representation of the expected functional potential of the mock communities. Gene content recovery was highly accurate, with F1 scores ranging from 0.942 to 0.998 across the five mock communities. Genome abundances estimated against sourmash-identified genomes closely matched the expected community composition: Pearson correlation coefficients ranged from 0.823 to 1 at strain level and from 0.978 to 1 at species level. Conclusions. Our results show that sourmash provides a biologically relevant approach for strain-level characterisation in microbial ecosystems. Even when the exact strain could not be identified, closely related strains retained most of the expected gene content and abundance estimations were accurate, supporting the use of sourmash for downstream functional profiling or metabolic network reconstruction.

bioinformatics↗

bioRxiv2025

Accurate MAG reconstruction from complex soil microbiome through combined short- and HiFi long-reads metagenomics

BackgroundAdvances in high-fidelity long-read (HiFi-LR) sequencing technologies have opened new opportunities to explore the microbial genomic diversity of complex environments, such as soils. While short-read (SR) sequencing has enabled broad insights at the gene level, the limited read length constrains the reconstruction of complete genomes. HiFi-LRs, in contrast, improve assembly continuity and completeness, supporting higher-resolution taxonomic and functional annotation. However, the cost and relatively low throughput of HiFi-LR sequencing can limit genome recovery--particularly at the binning stage, where coverage depth is critical. In this study, we assess the benefit of combining HiFi-LR and SR sequencing for genome-resolved characterization of a soil microbiome. ResultsWe generated metagenomic data for a tunnel-cultivated soil sample using high coverage Illumina SRs as well as a combination of two HiFi-LR sequencing platforms (PacBio Sequel II and PacBio Revio). We found that assemblies generated from pooled HiFi-LR data alone exhibited higher completeness compared to those from ultra-deep SR data. Incorporating SR-derived coverage information for the binning of HiFi-LR contigs further increased both the number and quality of recovered metagenome-assembled genomes (MAGs), with a 24% increase in MAG recovery (313 vs. 252) and lower contamination levels (116 vs. 132 contaminated bins; mean 7.09 vs. 8.07), compared to using HiFi-LR data alone. This approach enabled the recovery of 61 additional MAGs, including 67% of low-abundance and taxonomically diverse lineages such as Archaea, representing 36 novel lineages. ConclusionOur results demonstrate that integrating HiFi-LR and SR sequencing markedly enhances genome recovery and binning accuracy in a highly diverse environment such as soil. The hybrid approach employed leverages the strengths of both technologies, leading to more contiguous assemblies and enabling the recovery of a broader range of genomes, including low-abundance and taxonomically diverse taxa. While factors such as sequencing depth, cost, and DNA quality remain important considerations, our study provides practical guidance for designing future soil metagenomics projects and underscores the value of adopting long-read technologies for more comprehensive characterization of complex microbial communities.

genomics↗

bioRxiv2025

MADRe: Strain-Level Metagenomic Classification Through Assembly-Driven Database Reduction

AbstractStrain-level metagenomic classification is essential for understanding microbial diversity and functional potential, but remains challenging, par- ticularly in the absence of prior knowledge about the composition of the sample. In this paper we present MADRe, a modular and scalable pipeline for long-read strain-level metagenomic classification, enhanced with Metagenome Assembly-Driven Database Reduction. MADRe com- bines long-read metagenome assembly, contig-to-reference mapping reas- signment based on an expectation-maximization algorithm for database reduction, and probabilistic read mapping reassignment to achieve sensi- tive and precise classification. We extensively evaluated MADRe on sim- ulated datasets, mock communities, and a real anaerobic digester sludge metagenome, demonstrating that it consistently outperforms existing tools by achieving higher precision with reduced false positives. MADRes de- sign allows users to apply either the database reduction or read classi- fication step individually. Using only the read classification step shows results on par with other tested tools. MADRe is open source and pub- licly available at https://github.com/lbcb-sci/MADRe.

bioinformatics↗

bioRxiv2025

Mapler: Assessing assembly quality in taxonomically rich metagenomes sequenced with HiFi reads

SummaryMetagenome assembly seeks to reconstruct the most high-quality genomes from sequencing data of microbial ecosystems. Despite technological advancements that facilitate assembly, such as Hi-Fi long reads, the process remains challenging in complex environmental samples consisting of hundreds to thousands of populations. Mapler is a metagenome assembly and evaluation pipeline with a focus on evaluating the quality of Hi-Fi long read metagenome assemblies. It incorporates several state-of-the-art metrics, as well as novel metrics assessing the diversity that remains uncaptured by the assembly process. Mapler facilitates the comparison of assembly strategies and helps identify methodological bottlenecks that hinder genome reconstruction. Availability and ImplementationMapler is open source and publicly available under the AGPL-3.0 licence at https://github.com/Nimauric/Mapler. Source code is implemented in Python and Bash as a Snakemake pipeline. Contactsnicolas.maurice@inria.fr, clemence.frioux@inria.fr. Supplementary informationAvailable online.

bioinformatics↗

bioRxiv2023

decOM: Similarity-based microbial source tracking of ancient oral samples using k-mer-based methods

BackgroundThe analysis of ancient oral metagenomes from archaeological human and animal samples is largely confounded by contaminant DNA sequences from modern and environmental sources. Existing methods for Microbial Source Tracking (MST) estimate the proportions of environmental sources, but do not perform well on ancient metagenomes. We developed a novel method called decOM for Microbial Source Tracking and classification of ancient and modern metagenomic samples using k-mer matrices. ResultsWe analysed a collection of 360 ancient oral, modern oral, sediment/soil and skin metagenomes, using stratified five-fold cross-validation. decOM estimates the contributions of these source environments in ancient oral metagenomic samples with high accuracy, outperforming two state-of-the-art methods for source tracking, FEAST and mSourceTracker. ConclusionsdecOM is a high-accuracy microbial source tracking method, suitable for ancient oral metagenomic data sets. The decOM method is generic and could also be adapted for MST of other ancient and modern types of metagenomes. We anticipate that decOM will be a valuable tool for MST of ancient metagenomic studies.

bioinformatics↗

bioRxiv2021

Dietary adaptation in Neandertal, Denisovan and Sapiens revealed by gene copy number variation

Dietary adaptation involves evolving an efficient system to digest food available in an ecosystem. The diet of archaic humans is traditionally reconstructed by isotopic analyses of human remains combined with the faunal assemblages found on the sites, and, recently, from metagenomic analyses of dental calculus. Here, we propose a new computational approach to find the genetic basis for human dietary adaptation. We searched 15 genomes from Neandertal, Denisovan and Early Sapiens for food digestion genes that tend to have more or fewer copies than the modern human reference genome. We identify 50 genes, including 10 gene clusters, with discernible copy number variation (CNV) trends at the population level, from an analysis of the full set of 20,000 human genes. The genomic variation of 19 of these genes shows how metabolic pathways for carbohydrates, lipids, liver lipids and brown fat in archaic humans adapted to metabolize food from animal or plant sources. The remaining 31 genes are all highly expressed in tissues of the digestive apparatus and are involved in immune response, environmental response and obesity. Analysis of the CNV profiles, compared to 64 modern human individuals belonging to distinct ethnic groups in Eurasia, Africa, Oceania, suggests that Homo sapiens may have had an evolutionary advantage compared to Neandertal and Denisovan in adapting to cold and temperate ecosystems. Significance StatementUnderstanding dietary strategies and foraging behaviors of past populations are among the goals of paleoanthropological and prehistoric studies. Based on a new computational approach, we seek for the genetic basis for human dietary adaptation. Gene copy number variation (CNV) is a major type of structural genome variation that we used as indicator of efficient metabolic processes for food digestion. By analysing billions of short sequences from 15 archaic human genomes and 64 modern human ones, across the full set of 20,000 human genes, we identify 50 genes whose population-wide discernable CNV trends point to lipid metabolism as being crucial for Neandertal, the efficiency in carbohydrate metabolism for Sapiens diet, and the importance of lipid metabolism and brown fat metabolism for both Neandertal and early Sapiens compared to modern humans. Preprint availability: an early version, where the CNV analysis of ancient genomes was conducted on the human reference genome hg19, was deposited on BioRxiv on November 2, 2021 (DOI: 10.1101/2021.10.30.466563). The current version, which utilises CNV estimates based on the human reference genome hg38 and includes comparisons with modern human populations, was deposited in BioRxiv in November 2024.

genomics↗

bioRxiv2021

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from [~]1,700 novel and known microbial genomes, as well as [~]600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

bioinformatics↗

bioRxiv2021

Automated strain separation in low-complexity metagenomes using long reads

High-throughput short-read metagenomics has enabled large-scale species-level analysis and functional characterization of microbial communities. Microbiomes often contain multiple strains of the same species, and different strains have been shown to have important differences in their functional roles. Despite this, strain-level resolution from metagenomic sequencing remains challenging. Recent advances on long-read based methods enabled accurate assembly of bacterial genomes from complex microbiomes and an as-yet-unrealized opportunity to resolve strains. Here we present Strainberry, a metagenome assembly method that performs strain separation in single-sample low-complexity metagenomes and that relies uniquely on long-read data. We benchmarked Strainberry on mock communities and showed it consistently produces strain-resolved assemblies with near-complete reference coverage and 99.9% base accuracy. We also applied Strainberry on real datasets for which it improved assemblies generating 20-118% additional genomic material than conventional metagenome assemblies on individual strain genomes. Our results hence demonstrate that strain separation is possible in low-complexity microbiomes using a single regular long read dataset. We show that Strainberry is also able to refine microbial diversity in a complex microbiome, with complete separation of strain genomes. We anticipate this work to be a starting point for further methodological improvements aiming to provide better strain-resolved metagenome assemblies in environments of higher complexities.

bioinformatics↗

bioRxiv2019

ProfileView: multiple probabilistic models resolve protein families functional diversity

Sequence functional classification has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple probabilistic models whose construction explores evolutionary information in available databases, and a new definition of a representation space where to look at sequences from the point of view of probabilistic models combined together. ProfileView classifies families of proteins for which functions should be discovered or characterised within known groups. We validate ProfileView on seven classes of widespread proteins, involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. ProfileView agrees with the large set of functional data collected for these proteins from the literature regarding the organisation into functional subgroups and residues that characterize the functions. Furthermore, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of new biological functions. ProfileView proves to outperform three functional classification approaches, CUPP, PANTHER, and a recently developed neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.

bioinformatics↗