Biology subjects

Seppey, M.

Publications and source records attributed to Seppey, M..

5 recordsLinked to original sources

bioRxiv2025

LEMMIv2: Benchmarking Framework for Metagenomic and 16S Amplicon Profilers with a Catalogue of Evaluated Tools.

Sequencing has transformed microbial studies, enabling metagenomic analysis of microbial communities without the need for culturing or prior knowledge of sample composition. The essential analysis of the primary sequencing reads, however, is complex and led to the development of diverse computational strategies. The plethora of available methods and their numerous parameters poses a practical challenge for practitioners and creates a visibility barrier for developers of novel approaches. In addition to technical limitations related to user computing environment, algorithmic solutions, and their scalability, there are critical considerations regarding the reference database, i.e. the knowledge against which the data is interpreted, as well as the target of the analysis, expected sample composition, and peculiarities of read data from different sequencing platforms. To facilitate informed decision-making, we introduced the LEMMI platform for continuous benchmarking of software tools for metagenomic analyses, where developers can receive impartial benchmarks for method publication and users benefit from a standardized and benchmarked catalogue of tools. Here we present developments of LEMMI version 2, including assessments of different target scenarios, long- and short-read sequencing data, alternative taxonomies, and a standalone pipeline (https://lemmi.ezlab.org). In addition to LEMMI, which focuses on shotgun metagenomic profiling, we extended this approach to bacteria profiling with 16S amplicon sequencing (https://lemmi16S.ezlab.org).

genomics↗

bioRxiv2020

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as "healthy" or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

bioinformatics↗

bioRxiv2018

LEMMI: A Live Evaluation of Computational Methods for Metagenome Investigation

Studies of microbiomes are booming, as well as the diversity of computational tools to make sense out of the sequencing data and the volumes of accumulated microbial genotypes. LEMMI (https://lemmi.ezlab.org) is a novel concept of a benchmarking platform of computational tools for metagenome composition assessments that introduces: a continuous integration of tools, their multi-objective ranking, and an effective distribution through software containers. Here, we detail the workflow and discuss the evaluation of some recently released methods. We see this platform eventually as a community-driven effort: where method developers can showcase novel approaches and get unbiased benchmarks for publications, while users can make informed choices and obtain standardized and easy-to-use tools.

microbiology↗

bioRxiv2018

Genomic signatures accompanying the dietary shift to phytophagy in polyphagous beetles

BackgroundThe diversity and evolutionary success of beetles (Coleoptera) are proposed to be related to the diversity of plants on which they feed. Indeed the largest beetle suborder, Polyphaga, mostly includes plant-eaters among its ~315,000 species. In particular, plants defend themselves with a diversity of specialized toxic chemicals. These may impose selective pressures that drive genomic diversification and speciation in phytophagous beetles. However, evidence of changes in beetle gene repertoires driven by such interactions remains largely anecdotal and without explicit hypothesis testing.\n\nResultsTo address this, we explored the genomic consequences of beetle-plant trophic interactions by performing comparative gene family analyses across 18 species representing the two most species-rich beetle suborders. We contrasted the gene contents of species from the mostly plant-eating suborder Polyphaga with those of the mainly predatory Adephaga. We found gene repertoire evolution to be more dynamic, with significantly more adaptive lineage-specific expansions, in the more speciose Polyphaga. Testing the specific hypothesis of adaptation to plant-feeding, we identified families of enzymes putatively involved in beetle-plant interactions that underwent adaptive expansions in Polyphaga. There was especially strong support for the selection hypothesis on large gene families for glutathione S-transferase and carboxylesterase detoxification enzymes.\n\nConclusionsOur explicit modeling of the evolution of gene repertoires across 18 species identifies adaptive lineage-specific gene family expansions that accompany the dietary shift towards plants in beetles. These genomic signatures support the popular hypothesis of a key role for interactions with plant chemical defenses, and for plant-feeding in general, in driving beetle diversification.

evolutionary biology↗

bioRxiv2017

BUSCO applications from quality assessments to gene prediction and phylogenomics

Genomics promises comprehensive surveying of genomes and metagenomes, but rapidly changing technologies and expanding data volumes make evaluation of completeness a challenging task. Technical sequencing quality metrics can be complemented by quantifying completeness in terms of the expected gene content of Benchmarking Universal Single-Copy Orthologs (BUSCO, http://busco.ezlab.org). Now in its third release, BUSCO utilities extend beyond quality control to applications in comparative genomics, gene predictor training, metagenomics, and phylogenomics.

bioinformatics↗