Nonparametric Tests for Differential Histone Enrichment with ChIP-Seq Data

Inaccessible LCG Promoters Act as Safeguards to Restrict T Cell Development to Appropriate Notch Signaling Environments

T cell development is restricted to the thymus and is dependent on high levels of Notch signaling induced within the thymic microenvironment. To understand Notch function in thymic restriction, we investigated the basis for target gene selectivity in response to quantitative differences in Notch signal strength, focusing on the chromatin architecture of genes essential for T cell differentiation. We find that high Notch signal strength is required to activate promoters of known targets essential for T cell commitment, including Il2ra, Cd3ε, and Rag1, which feature low CpG content (LCG) and DNA inaccessibility in hematopoietic stem progenitor cells. Our findings suggest that promoter DNA inaccessibility at LCG T lineage genes provides robust protection against stochastic activation in inappropriate Notch signaling contexts, limiting T cell development to the thymus.

Unified Analysis of Multiple ChIP-Seq Datasets

High-throughput sequencing technologies are increasingly used in molecular cell biology to assess genome-wide chromatin dynamics of proteins bound to DNA, through techniques such as chromatin immunoprecipitation sequencing (ChIP-seq). These techniques often rely on an analysis strategy based on identifying genomic regions with increased sequencing signal to infer the binding location or chemical modifications of proteins bound to DNA. Peak calling within individual samples has been well described, however relatively little attention has been devoted to the merging of replicate samples, and the cross-comparison of many samples. Here, we present a generalized strategy to enable the unification of ChIP-seq datasets, enabling enhanced cross-comparison of binding patterns. The strategy works by merging peak data between different (even unrelated) samples, and then using a local background to recalculate enrichment. This strategy redefines the peaks within each experiment, allowing for more accurate cross-comparison of datasets.