P278
BiomiX, a user-friendly bioinformatic tool for automatized multiomics data analysis and integration.
IPERI C. 1, FERNÁNDEZ-OCHOA Á. 2, PERS J. 1, BARTUREN G. 3,4, ALARCÓN-RIQUELME M. 3,5, CORNEC D. 1, BORDON A. 1, JAMIN C. 1,6
1 Lymphocytes B, Autoimmunité et Immunothérapies - UMR 1227, Brest, France; 2 Department of Analytical Chemistry, University of Granada, Granada, Spain; 3 GENYO, Centre for Genomics and Oncological Research Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Granada, Spain; 4 Department of Genetics, Faculty of Sciences, University of Granada, Granada, Spain; 5 Institute for Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; 6 Laboratoire d’Immunologie et Immunothérapie, CHU de Brest, Brest, France
1. Objective
High-throughput technology in health and biological sciences boosted the amount of information obtainable from samples. The increased dependency on these technologies revealed how data analysis represents the bottleneck step in both time and in bioinformatics skilled users. BiomiX tool offers an efficient and time-saving pipeline to analyze -omics data singularly and integrate multi-omics data from the same patients.
2.Methods
BiomiX was developed from the European PRECISESADS database1, including overlapping data of whole blood and sorted immune cells, transcriptomics, plasma and urine metabolomics, and whole blood methylomics from 363 SLE patients and 508 controls (CTRLs). Transcriptomics data were analysed through a differential gene expression (DGE) analysis using DESeq package, while plasma and urine metabolomics peaks changes were quantified and statistically tested. Peaks annotation was performed automatically by comparing m/z, retention time and spectra stored in public databases. Methylomics analysis was performed by ChAMP R package. Common sources of variations among the -omics were identified by Multi-Omics Factor Analysis (MOFA) integration.
3.Results
Biomix carried out analyses highlighting the most relevant features for each -omics, including statistical results and report figures. To facilitate interpretation, files ready for pathway analysis tools such as EnrichR, GSEA and MetaboAnalyst are generated. A panel of genes can be also used by BiomiX to define subgroups of patients and compare them to CTRLs (e.g. SLE IFN-α positive patients by 26 IFN-α genes). To ease the MOFA factors interpretation, BiomiX automatically selects the MOFA factors statistically relevant in discriminate condition and control, providing the correlation of each factor with clinical features and articles where the top contributors to the factor appear simultaneously in the text (e.g genes, peak, cpg_island).
4.Conclusions
This user-friendly tool, based on R is compatible with Linux and Microsoft OS and aims to make accessible the multi-omics analysis for users not experts in bioinformatics.