MEPP
MEPP quantifies motif enrichment in a position-dependent manner to characterize positional dependencies of regulatory DNA motifs relative to biological landmarks such as transcription start sites (TSS) and transcription factor (TF) binding motifs.
Key Features:
- Positional Enrichment Profiling: Generates a detailed positional profile of motif enrichment relative to landmarks such as transcription start sites (TSS) and other transcription factor (TF) binding motifs.
- Distance-Dependent Analysis: Analyzes spatial relationships between motifs and anchor points to reveal how motif positioning influences TF binding and chromatin state.
- Bias Correction: Corrects for lower-order nucleotide biases to distinguish genuine enrichment from sequence-composition artifacts.
Scientific Applications:
- Transcription initiation and TF binding analysis: Identifies sequence positions where motif presence correlates with transcriptional initiation or transcription factor binding.
- Interpretation of transcriptional activity and chromatin structure data: Aids interpretation of experimental data on transcriptional activity and chromatin structure by highlighting positional dependencies of binding site function.
- Regulatory mechanism exploration and hypothesis generation: Supports hypothesis generation and exploration of positional regulatory mechanisms involving TF interactions.
Methodology:
Integrates positional information into motif enrichment analysis to assess motif presence within DNA sequences and infer functional dependencies from the spatial arrangement of motifs relative to biologically meaningful features.
Topics
Details
- License:
- MIT
- Cost:
- Free of charge
- Tool Type:
- command-line tool
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Python
- Added:
- 12/27/2022
- Last Updated:
- 11/24/2024
Operations
Data Inputs & Outputs
Sequence motif discovery
Outputs
Publications
Delos Santos NP, Duttke S, Heinz S, Benner C. MEPP: more transparent motif enrichment by profiling positional correlations. NAR Genomics and Bioinformatics. 2022;4(4). doi:10.1093/nargab/lqac075. PMID:36267125. PMCID:PMC9575187.