Memes: A motif analysis environment in R using tools from the MEME Suite

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Biopolymer motif identification is crucial for biological sequence analysis.
• Existing MEME Suite tools lack seamless integration with R/Bioconductor, hindering usability.
• Barriers exist in applying powerful motif analysis tools within popular R-based frameworks.

Purpose of the Study:

• To present memes, an R package providing a unified interface to MEME Suite tools.
• To enable "data aware" motif analysis workflows within R.
• To facilitate rapid data access, manipulation, and visualization of motif analysis results.

Main Methods:

• Developed an R package named memes.
• Created a seamless R interface to selected MEME Suite tools.
• Leveraged R/Bioconductor data structures for multidimensional data handling.
• Integrated data visualization capabilities for results communication.

Main Results:

• memes provides a "data aware" interface for discriminative motif analysis.
• Facilitates rapid and complex motif analysis workflows in R.
• Enables efficient storage and manipulation of MEME Suite output using R/Bioconductor objects.
• Offers integrated visualization of motif analysis findings.

Conclusions:

• memes enhances the accessibility and utility of MEME Suite tools for R users.
• Streamlines biopolymer motif discovery and analysis within the R/Bioconductor ecosystem.
• Promotes more efficient and integrated biological sequence analysis workflows.