Biology subjects

Bodein, A.

Publications and source records attributed to Bodein, A..

3 recordsLinked to original sources

bioRxiv2024

Comprehensive benchmark of integrative strategies for analyzing microbiome-metabolome relationships

BackgroundThe exponential growth of high-throughput sequencing technologies was an incredible opportunity for researchers to combine various -omics within computational frameworks. Among these, metagenomics and metabolomics data have gained an increasing interest due to their involvement in many complex diseases. However, currently, no standard seems to emerge for jointly integrating both microbiome and metabolome datasets within statistical models. ResultsThus, in this paper we comprehensively benchmarked nineteen different integrative methods to untangle the complex relationships between microorganisms and metabolites. Methods evaluated in this paper cover most of the researchers goals such as global associations, data summarization, individual associations, and feature selection. Through an extensive and realistic simulation we identified best methods across questions commonly encountered by researchers. We applied the most promising methods in an application to real gut microbial datasets, unraveling complementary biological processes involved between the two omics. We also provided practical guidelines for practitioners tailored to specific scientific questions and data types. ConclusionIn summary, our work paves the way toward establishing research standards when mutually analyzing metagenomics and metabolomics data, building foundations for future methodological developments.

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↗

bioRxiv2019

A generic multivariate framework for the integration of microbiome longitudinal studies with other data types

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g. sparsity, compositional).\n\nWe propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial communities data, and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modelling, to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

bioinformatics↗