Biology subjects

Evelo, C. T.

Publications and source records attributed to Evelo, C. T..

4 recordsLinked to original sources

bioRxiv2026

User-friendly transcriptomic data analysis with ArrayAnalysis

Transcriptomic profiling has become a cornerstone of modern biomedical research. To make transcriptomic analyses accessible to a broader scientific community, specifically including researchers with limited bioinformatics expertise, we introduced ArrayAnalysis in 2013 as a user-friendly web-based application for microarray data analysis. We now present a major update (https://arrayanalysis.org), introducing a strongly interactive platform that facilitates the dedicated exploration and analysis of both microarray and RNA-seq data, and allows for the generation of publication-ready outputs. Users can perform key analysis steps, including data pre-processing and quality control, differential expression analysis, and gene set analysis, via a sequential, interactive workflow. At each step, the application provides interactive visualizations accompanied by information pages to support interpretation. Users can dynamically adjust figure layouts and colour palettes and export figures as vector graphics and high-resolution raster images. For non-expert users, ArrayAnalysis offers step-by-step guidance to support correct usage and facilitate learning, while for experienced bioinformaticians, it provides a streamlined and flexible workflow ideal for large-scale analyses requiring efficient and consistent processing. ArrayAnalysis is available both as a web application and for local deployment as a desktop application, Docker image, or R package, making it suitable for diverse computational environments, user groups, and analytical purposes. Together, ArrayAnalysis empowers a broad community of biomedical researchers to unlock the full potential of transcriptomic data. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/738193v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1072123org.highwire.dtl.DTLVardef@11095e0org.highwire.dtl.DTLVardef@1dfaee7org.highwire.dtl.DTLVardef@53d31e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Collaborative network analysis for the interpretation of transcriptomics data in rare diseases, an application to Huntington's disease

BackgroundRare diseases may affect the quality of life of patients and in some cases be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms that can cause disease. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenicity mechanisms from multiple perspectives. ResultsWe analyzed a Huntingtons disease (HD) transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in HD and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation. ConclusionsIn summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.

bioinformatics↗

bioRxiv2023

Molecular Adverse Outcome Pathways: towards the implementation of transcriptomics data in risk assessments

Adverse Outcome Pathways (AOPs) are designed to provide mechanistic insights into toxicological processes after exposure to a stressor and facilitate the replacement of animal studies with in vitro testing systems. Starting with a Molecular Initiating Event (MIE) and ending with an Adverse Outcome, the sequence of Key Events (KEs) spans across biological levels, but the majority of KEs in AOP-Wiki describe molecular and cellular processes. That suggests that established transcriptome-wide studies can be used to validate or measure a multitude of KEs simultaneously and be a goldmine of useful information about cellular responses. Currently, in toxicology, omics technologies are not widely applied in risk assessments of chemicals because of their complexity and lack of consensus on aspects such as standardization, analysis and interpretation. Given their value in hypothesis generation and ability to provide a vast amount of information about a biological response, the challenge lies in the acceptance of using transcriptomics data in regulatory risk assessments. We here introduce molecular AOPs to define the connections between KEs of the AOP-Wiki and curated biological pathways in WikiPathways, thereby providing a new method for the analysis and interpretation of transcriptomics data to identify KE activation. To study this, we performed case studies on liver steatosis and mitochondrial complex I inhibition, for which molecular AOPs were developed and public transcriptomics datasets were selected. Upon extension of the molecular AOP networks in Cytoscape, we mapped and analysed transcriptomics data, and calculated an enrichment score for individual KEs. Further interpretation of the data was done through the visualisation of the data on the specific molecular pathways. Two molecular AOPs were developed and KEs were linked to the appropriate molecular pathways, allowing a detailed exploration of molecular processes with the selected transcrip-tomics datasets. This has shown us that we can verify the activation of specific MIEs and KEs, and assess progression across the AOP in the steatosis case study through variables in exposure time and dose. These case studies have shown that transcriptomics data can be used for identifying the potential activation of KEs. However, it is also clear that extensive datasets are required to fully test the capabilities of molecular AOPs, and the process of linking molecular pathways and KEs can be challenging, not always allowing one-to-one mapping. While proven valuable to analyse and understand transcriptomic data, pathways linked to KEs appear to show inconsistent levels of activation and should be looked into and refined. More case studies are required to optimize the approaches used for the development and use of molecular AOPs with transcriptomics datasets.

bioinformatics↗

bioRxiv2020

ComMet: A method for comparing metabolic states in genome-scale metabolic models

Being comprehensive knowledge bases of cellular metabolism, Genome-scale metabolic models (GEMs) serve as mathematical tools for studying cellular flux states in various organisms. However, analysis of large-scale (human) GEMs, still presents considerable challenges with respect to objective selection and reaction flux constraints. In this study, we introduce a model-based method, ComMet (Comparison of Metabolic states), for comprehensive analysis of large metabolic flux spaces and comparison of various metabolic states. ComMet allows (a) an in-depth characterisation of achievable flux states, (b) comparison of flux spaces from several conditions of interest and (c) identification and visualization of metabolically distinct network modules. As a proof-of-principle, we employed ComMet to extract the biochemical differences in the human adipocyte network (iAdipocytes1809) arising due to unlimited/blocked uptake of branched-chain amino acids. Our study opens avenues for exploring several metabolic conditions of interest in both microbe and human models. ComMet is open-source and is available at https://github.com/macsbio/commet.

systems biology↗