Background: Genome‐wide mapping of protein‐DNA interactions has been widely used toinvestigate biological functions of the genome. An important question is to what extent suchinteractions are regulated at the DNA sequence level. However, current investigation ishampered by the lack of computational methods for systematic evaluating sequence specificity.Results: We present a simple, unbiased quantitative measure for DNA sequence specificitycalled the Motif Independent Measure (MIM). By analyzing both simulated and realexperimental data, we found that the MIM measure can be used to detect sequence specificityindependent of presence of transcription factor (TF) binding motifs. We also found that thelevel of specificity associated with H3K4me1 target sequences is highly cell‐type specific andhighest in embryonic stem (ES) cells. We predicted H3K4me1 target sequences by using the Nscoremodel and found that the prediction accuracy is indeed high in ES cells.Conclusions: Our method provides a unified framework for quantifying DNA sequencespecificity and serves as a guide for development of sequence‐based prediction models.
|Numero di pagine||23|
|Stato di pubblicazione||Published - 2011|
All Science Journal Classification (ASJC) codes
- Structural Biology
- Molecular Biology
- Computer Science Applications
- Applied Mathematics