DNA and protein sequences digitally store information about biological function in a complex code that is not yet fully understood. The fundamental unit of this code is the sequence motif, which is defined as a small, recurring DNA or protein sequence pattern. A DNA motif might be involved, for example, in turning on or off the transcription of a gene in response to environmental cues. A protein motif might encode the properties of the binding site that allows the protein to carry out its function. The MEME Suite of motif-based sequence analysis software builds statistical models of DNA and protein motifs, allowing biologists to discover novel motifs, to search for new instances of known motifs, and to compare motifs to one another. This proposal continues to develop and maintain the MEME Suite, which is in regular use by biologists around the world.
The aims of this work are five-fold: (1) to increase the accessiblity, usability and interoperability of the MEME Suite, (2) to expand the MEME Suite to handle epigenetic data regarding histone modifications, methylation, nucleosome positioning and DNaseI hypersensitive sites, (3) to integrate a variety of existing motif-based software tools into the MEME Suite, (4) to augment the algorithms used by the MEME Suite with proven enhancements, and (5) to continue to improve our user support services.
This project will improve existing, widely used software that enables biologists to understand how DNA and protein sequences encode information about biological function. Identifying and accurately char- acterizing functional sequence motifs allows scientists to understand how genes are turned on and off and how proteins carry out their functions in the cell. Such knowledge is fundamental to any model of the basic molecular mechanisms of the cell, and in particular, for molecular-scale models of disease processes.
|Grant, Charles E; Johnson, James; Bailey, Timothy L et al. (2016) MCAST: scanning for cis-regulatory motif clusters. Bioinformatics 32:1217-9|
|Bailey, Timothy L; Johnson, James; Grant, Charles E et al. (2015) The MEME Suite. Nucleic Acids Res 43:W39-49|
|Ma, Wenxiu; Noble, William S; Bailey, Timothy L (2014) Motif-based analysis of large nucleotide data sets using MEME-ChIP. Nat Protoc 9:1428-50|
|Tanaka, Emi; Bailey, Timothy L; Keich, Uri (2014) Improving MEME via a two-tiered significance analysis. Bioinformatics 30:1965-73|
|Lesluyes, Tom; Johnson, James; Machanick, Philip et al. (2014) Differential motif enrichment analysis of paired ChIP-seq experiments. BMC Genomics 15:752|
|Cuellar-Partida, Gabriel; Buske, Fabian A; McLeay, Robert C et al. (2012) Epigenetic priors for identifying active transcription factor binding sites. Bioinformatics 28:56-62|
|Bailey, Timothy L; Machanick, Philip (2012) Inferring direct DNA binding from ChIP-seq. Nucleic Acids Res 40:e128|
|Wang, Jie; Zhuang, Jiali; Iyer, Sowmya et al. (2012) Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res 22:1798-812|