monaLisa: an R/Bioconductor package for identifying regulatory motifs

Dania Machlab; Lukas Burger; Charlotte Soneson; Filippo M Rijli; Dirk Schübeler; Michael B Stadler

doi:10.1093/bioinformatics/btac102

monaLisa: an R/Bioconductor package for identifying regulatory motifs

Bioinformatics. 2022 Apr 28;38(9):2624-2625. doi: 10.1093/bioinformatics/btac102.

Authors

Dania Machlab^{1

2

3}, Lukas Burger^{1

2}, Charlotte Soneson^{1

2}, Filippo M Rijli^{1

3}, Dirk Schübeler^{1

3}, Michael B Stadler^{1

2

3}

Affiliations

¹ Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
² SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
³ Faculty of Science, University of Basel, Basel, Switzerland.

Abstract

Summary: Proteins binding to specific nucleotide sequences, such as transcription factors, play key roles in the regulation of gene expression. Their binding can be indirectly observed via associated changes in transcription, chromatin accessibility, DNA methylation and histone modifications. Identifying candidate factors that are responsible for these observed experimental changes is critical to understand the underlying biological processes. Here, we present monaLisa, an R/Bioconductor package that implements approaches to identify relevant transcription factors from experimental data. The package can be easily integrated with other Bioconductor packages and enables seamless motif analyses without any software dependencies outside of R.

Availability and implementation: monaLisa is implemented in R and available on Bioconductor at https://bioconductor.org/packages/monaLisa with the development version hosted on GitHub at https://github.com/fmicompbio/monaLisa.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Software*
Transcription Factors*

Substances

Transcription Factors