Can We Predict Gene Expression by Understanding Proximal Promoter Architecture?


Our next meeting will be at 3:00 on April 14th, in room 4160 of the Discovery building. Our Selected paper is Discovering sparse transcription factor codes for cell states and state transitions during development.
The abstract is as follows.

We review computational predictions of expression from the promoter architecture – the set of transcription factors that can bind the proximal promoter. We focus on spatial expression patterns in animals with complex body plans and many distinct tissue types. This field is ripe for change as functional genomics datasets accumulate for both expression and protein–DNA interactions. While there has been some success in predicting the breadth of expression (i.e., the fraction of tissue types a gene is expressed in), predicting tissue specificity remains challenging. We discuss how progress can be achieved through either machine learning or complementary combinatorial data mining. The likely impact of single-cell expression data is considered. Finally, we discuss the design of artificial promoters as a practical application.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.

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