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BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization.

Authors :
Taverna F
Goveia J
Karakach TK
Khan S
Rohlenova K
Treps L
Subramanian A
Schoonjans L
Dewerchin M
Eelen G
Carmeliet P
Source :
Nucleic acids research [Nucleic Acids Res] 2020 Jul 02; Vol. 48 (W1), pp. W385-W394.
Publication Year :
2020

Abstract

The amount of biological data, generated with (single cell) omics technologies, is rapidly increasing, thereby exacerbating bottlenecks in the data analysis and interpretation of omics experiments. Data mining platforms that facilitate non-bioinformatician experimental scientists to analyze a wide range of experimental designs and data types can alleviate such bottlenecks, aiding in the exploration of (newly generated or publicly available) omics datasets. Here, we present BIOMEX, a browser-based software, designed to facilitate the Biological Interpretation Of Multi-omics EXperiments by bench scientists. BIOMEX integrates state-of-the-art statistical tools and field-tested algorithms into a flexible but well-defined workflow that accommodates metabolomics, transcriptomics, proteomics, mass cytometry and single cell data from different platforms and organisms. The BIOMEX workflow is accompanied by a manual and video tutorials that provide the necessary background to navigate the interface and get acquainted with the employed methods. BIOMEX guides the user through omics-tailored analyses, such as data pretreatment and normalization, dimensionality reduction, differential and enrichment analysis, pathway mapping, clustering, marker analysis, trajectory inference, meta-analysis and others. BIOMEX is fully interactive, allowing users to easily change parameters and generate customized plots exportable as high-quality publication-ready figures. BIOMEX is open source and freely available at https://www.vibcancer.be/software-tools/biomex.<br /> (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Details

Language :
English
ISSN :
1362-4962
Volume :
48
Issue :
W1
Database :
MEDLINE
Journal :
Nucleic acids research
Publication Type :
Academic Journal
Accession number :
32392297
Full Text :
https://doi.org/10.1093/nar/gkaa332