BiomiX, a user-friendly bioinformatic tool for democratized analysis and integration of multiomics data

被引：0

作者：

Iperi, Cristian ^{[1
]}

Fernandez-Ochoa, Alvaro ^{[2
]}

Barturen, Guillermo ^{[3
,4
]}

Pers, Jacques-Olivier ^{[5
]}

Foulquier, Nathan ^{[5
]}

Bettacchioli, Eleonore ^{[5
]}

Alarcon-Riquelme, Marta ^{[3
,6
]}

PRECISESADS Flow Cytometry Study Grp, Divi

PRECISESADS Clinical Consortium, Anne

Cornec, Divi ^{[5
]}

Bordron, Anne ^{[1
]}

Jamin, Christophe ^{[5
]}

机构：

[1] Univ Brest, LBAI, UMR1227, Inserm, Brest, France

[2] Univ Granada, Dept Analyt Chem, Granada, Spain

[3] Univ Granada, GENYO, Ctr Genom & Oncol Res Pfizer, Andalusian Reg Govt,PTS Granada, Granada, Spain

[4] Univ Granada, Fac Sci, Dept Genet, Granada, Spain

[5] Univ Brest, CHU Brest, INSERM, LBAI,UMR1227,Lab Immunol, Brest, France

[6] Karolinska Inst, Inst Environm Med, S-17169 Stockholm, Sweden

来源：

BMC BIOINFORMATICS | 2025年 / 26卷 / 01期

关键词：

Multiomics; Data integration; User-friendly; MOFA; CHRONIC LYMPHOCYTIC-LEUKEMIA; BIOCONDUCTOR PACKAGE; OMICS DATA; IDENTIFICATION; MUTATIONS; DISCOVERY;

D O I：

10.1186/s12859-024-06022-y

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

BackgroundInterpreting biological system changes requires interpreting vast amounts of multi-omics data. While user-friendly tools exist for single-omics analysis, integrating multiple omics still requires bioinformatics expertise, limiting accessibility for the broader scientific community.ResultsBiomiX tackles the bottleneck in high-throughput omics data analysis, enabling efficient and integrated analysis of multiomics data obtained from two cohorts. BiomiX incorporates diverse omics data, using DESeq2/Limma packages for transcriptomics, and quantifying metabolomics peak differences, evaluated via the Wilcoxon test with the False Discovery Rate correction. The metabolomics annotation for Liquid Chromatography-Mass Spectrometry untargeted metabolomics is additionally supported using the mass-to-charge ratio in the CEU Mass Mediator database and fragmentation spectra in the TidyMass package. Methylomics analysis is performed using the ChAMP R package. Finally, Multi-Omics Factor Analysis (MOFA) integration identifies shared sources of variation across omics data. BiomiX also generates statistics, report figures and integrates EnrichR and GSEA for biological process exploration and subgroup analysis based on user-defined gene panels enhancing condition subtyping. BiomiX fine-tunes MOFA models, to optimize factors number selection, distinguishing between cohorts and providing tools to interpret discriminative MOFA factors. The interpretation relies on innovative bibliography research on Pubmed, which provides the articles most related to the discriminant factor contributors. Furthermore, discriminant MOFA factors are correlated with clinical data, and the top contributing pathways are explored, all with the aim of guiding the user in factor interpretation.ConclusionsThe analysis of single-omics and multi-omics integration in a standalone tool, along with MOFA implementation and its interpretability via literature, represents significant progress in the multi-omics field in line with the "Findable, Accessible, Interoperable, and Reusable" data principles. BiomiX offers a wide range of parameters and interactive data visualization, allowing for personalized analysis tailored to user needs. This R-based, user-friendly tool is compatible with multiple operating systems and aims to make multi-omics analysis accessible to non-experts in bioinformatics.

引用

页数：25