Perin, O. | bioRxiv Science

bioRxiv2026

An in-depth, updated benchmark for 16S amplicon sequencing

Amplicon-based techniques provide a rapid and cost-effective approach for profiling microbial communities. However, the observed microbial diversity is influenced by a wide range of factors, encompassing pre-analytical steps such as the choice of primers and target regions, as well as the bioinformatic pipeline, including the selection of tools, reference databases, and parameter settings. Several benchmarks are already available in the literature, but the updates to important tools and databases, namely LotuS3, the Ribosomal Database Project and GreenGenes2, prompted our investigation. In this study, we conducted a comprehensive benchmark of the main bioinformatic tools and databases. Using seven regions for three publicly available mock communities of increasing complexity, we tested 38 possible combinations of sequence resolution algorithms (DADA2 stand-alone, LotuS3 (DADA2/UPARSE)), taxonomic classifiers and search tools (Kraken2, DECIPHER, RDP, MMseqs2, Lambda, and Metaxa2), and databases (SILVA, GreenGenes2, RDP, RefSeq, and Metaxa2). The region V1-V3, coupled with DADA2+MMseqs2+SILVA, DADA2+Metaxa2, or LotuS3 (DADA2)+RDP yielded the highest-quality estimates of the true diversity according to the metrics. We also demonstrated that even certain dominant genera remain difficult to detect, and that the quantification of all genera can be substantially over- or under-estimated, even when using optimal combinations of tools and reference databases.

bioinformatics↗

bioRxiv2023

New genome scale network modeling and mining workflow for detecting metabolic changes induced by exposure to chemicals

BackgroundThe growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. ResultsTo address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated conditions-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes (PHH) transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone) or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. ConclusionThe proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. All code is freely available as well as data to reproduce results.

bioinformatics↗

bioRxiv2020

Interpretation of network-based integration from multi-omics longitudinal data

Cost reduction of high-throughput technologies has enabled the monitoring of the same biological sample across multiple omics studies and multiple timepoints. The goal is to combine longitudinal multi-omics data to detect temporal relationships between molecules and interactions between omics layers. This can finally lead to uncover new regulation mechanisms and interactions that could be responsible for causing complex phenotype or disease. However multi-omics integration of diverse omics data is still challenging due to heterogeneous data and designs. Moreover, interpretation of multi-omics models is the key to understand biological systems. We propose a generic analytic and integration framework for multi-omics longitudinal datasets that consists of multi-omics kinetic clustering and multi-layer network-based analysis. This frame-work was successfully applied to two case studies with different experimental designs and omics data collected. The first case studied transcriptomic and proteomic changes during cell cycle in human HeLa cells, while the second focused on maize transcriptomic and metabolomic response to aphid feeding. Propagation analysis on multi-layer networks identifies regulatory mechanisms and function prediction for both case studies. Our framework has led to the identification of new multi-layer interactions involved in key biological functions that cannot be revealed with single omics analysis and interplay in the kinetics that could help identify novel biological mechanisms.

systems biology↗