Monthly Archives: April 2017


Discovering sparse transcription factor codes for cell states and state transitions during development

Our next meeting will be at 3:00 on April 28th, 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.

Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships.

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


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.