Beteiligte:
Li, Zhijian;
Nagai, James S;
Kuppe, Christoph;
Kramann, Rafael;
Costa, Ivan G
Erschienen:
Oxford University Press (OUP), 2023
Erschienen in:Bioinformatics Advances
Sprache:
Englisch
DOI:
10.1093/bioadv/vbad003
ISSN:
2635-0041
Entstehung:
Anmerkungen:
Beschreibung:
<jats:title>Abstract</jats:title>
<jats:sec>
<jats:title>Summary</jats:title>
<jats:p>The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory network inference including modalities integration, trajectory analysis, enhancer-to-promoter association, network analysis and visualization. This enables to study the complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease-driven cellular remodeling. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Availability and implementation</jats:title>
<jats:p>scMEGA is implemented in R, released under the MIT license and available from https://github.com/CostaLab/scMEGA. Tutorials are available from https://costalab.github.io/scMEGA.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Supplementary information</jats:title>
<jats:p>Supplementary data are available at Bioinformatics Advances online.</jats:p>
</jats:sec>