#Read and annotate meme software
#Read and annotate meme code

Identification of similar motifs across multiple sequences can provide evidence for shared function, such as identification of kinase substrates based on similarities in phosphorylation site sequence, or characterizing DNA elements based on shared transcription factor binding sequences. Short, repeated sequences, or “motifs” represent functional units within biopolymers that can act as interaction surfaces, create structure, or contribute to enzymatic activity. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.īiopolymers, such as DNA and protein, perform varying functions based on their primary sequence. from the National Institute of General Medical Sciences of the NIH ( ). from the American Cancer Society ( ), and in part by Grant R35-GM128851 to D.J.M. įunding: This work was supported in part by Research Scholar Grant RSG-17-164-01-DDC to D.J.M.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The manuscript source code, raw data, and instructions to reproduce all analysis can be found at /snystrom/memes_paper/ Data used in this manuscript can be found on GEO under GSE141738 at the following address. Received: ApAccepted: SeptemPublished: September 27, 2021Ĭopyright: © 2021 Nystrom, McKay. PLoS Comput Biol 17(9):Įditor: Mihaela Pertea, Johns Hopkins University, UNITED STATES By providing an interface between the MEME Suite and R, memes allows for improved motif analysis workflows and easy access to a wide range of existing data visualization tools, further expanding the utility of MEME Suite tools.Ĭitation: Nystrom SL, McKay DJ (2021) Memes: A motif analysis environment in R using tools from the MEME Suite.

#Read and annotate meme software

Here, we describe a new piece of software named “memes” that connects MEME Suite tools to R, the statistical analysis environment. The MEME Suite is a widely used compilation of tools used for identifying and analyzing motifs found in biological sequences. For these reasons, motif analysis has become a key step in determining the function of biopolymers and in elucidating their roles in biological networks. Motifs can serve as binding sites between molecules, they can influence the structure of molecules, and they can contribute to enzymatic activities. Functional properties of biopolymers are determined by short, recurring stretches of subunits known as motifs. For example, nucleotides are the subunits of DNA and RNA, and amino acids are the subunits of proteins.

#Read and annotate meme code

memes is available as a Bioconductor package at, and the source code can be found at /snystrom/memes.īiologically active molecules such as DNA, RNA, and proteins are polymers composed of repeated subunits. Finally, memes provides data visualization capabilities to facilitate communication of results. In addition to interfacing with popular MEME Suite tools, memes leverages existing R/Bioconductor data structures to store the multidimensional data returned by MEME Suite tools for rapid data access and manipulation. memes provides a novel “data aware” interface to these tools, enabling rapid and complex discriminative motif analysis workflows.

Here we present memes, an R package that provides a seamless R interface to a selection of popular MEME Suite tools. The MEME Suite is a widely used toolkit for comprehensive analysis of biopolymer motifs however, these tools are poorly integrated within popular analysis frameworks like the R/Bioconductor project, creating barriers to their use. Identification of biopolymer motifs represents a key step in the analysis of biological sequences.