Ph.D. Student, Computational Biology & BioinformaticsDuke University
A Bayesian Model for Nucleosome Positioning Using DNase-seq Data
As fundamental structural units of the chromatin, nucleosomes are involved in virtually all aspects of genome function. Different methods have been developed to map genome-wide nucleosome positions, including MNase-seq and a recent chemical method requiring genetically engineered cells. However, these methods are either low resolution and prone to enzymatic sequence bias or require genetically modified cells. The DNase I enzyme has been used to probe nucleosome structure since the 1960s, but in the current high throughput sequencing era, DNase-seq has mainly been used to study regulatory sequences known as DNase hypersensitive sites. This thesis shows that DNase-seq data is also very informative about nucleosome positioning. The distinctive oscillatory DNase I cutting patterns on nucleosomal DNA are shown and discussed. Based on these patterns, a Bayes factor is proposed to be used for distinguishing nucleosomal and non-nucleosomal genome positions. The results show that this approach is highly sensitive and specific. A Bayesian method that simulates the data generation process and can provide more interpretable results is further developed based on the Bayes factor investigations. Preliminary results on a test genomic region show that the Bayesian model works well in identifying nucleosome positioning. Estimated posterior distributions also agree with some known biological observations from external data. Taken together, methods developed in this thesis show that DNase-seq can be used to identify nucleosome positioning, adding great value to this widely utilized protocol.