JoVE   
You do not have subscription access to articles in this section. Learn more about access.

  JoVE Biology

  
You do not have subscription access to articles in this section. Learn more about access.

  JoVE Neuroscience

  
You do not have subscription access to articles in this section. Learn more about access.

  JoVE Immunology and Infection

  
You do not have subscription access to articles in this section. Learn more about access.

  JoVE Clinical and Translational Medicine

  
You do not have subscription access to articles in this section. Learn more about access.

  JoVE Bioengineering

  
You do not have subscription access to articles in this section. Learn more about access.

  JoVE Applied Physics

  
You do not have subscription access to articles in this section. Learn more about access.

  JoVE Chemistry

  
You do not have subscription access to articles in this section. Learn more about access.

  JoVE Behavior

  
You do not have subscription access to articles in this section. Learn more about access.

  JoVE Environment

|   

JoVE Science Education

General Laboratory Techniques

You do not have subscription access to videos in this collection. Learn more about access.

Basic Methods in Cellular and Molecular Biology

You do not have subscription access to videos in this collection. Learn more about access.

Model Organisms I

You do not have subscription access to videos in this collection. Learn more about access.

Model Organisms II

You do not have subscription access to videos in this collection. Learn more about access.

Essentials of
Neuroscience

You do not have subscription access to videos in this collection. Learn more about access.

Essentials of Developmental Biology

You have subscription access to videos in this collection through your user account.

In JoVE (1)

Other Publications (2)

Articles by Yifan Mo in JoVE

 JoVE Biology

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

1Department of Applied Mathematics & Statistics, Stony Brook University, 2Computational Biology and Bioinformatics, Cold Spring Harbor Laboratory, 3Department of Molecular and Cell Biology, University of Texas at Dallas


JoVE 4273

Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.

Other articles by Yifan Mo on PubMed

Genome-wide Localization of Protein-DNA Binding and Histone Modification by a Bayesian Change-point Method with ChIP-seq Data

Next-generation sequencing (NGS) technologies have matured considerably since their introduction and a focus has been placed on developing sophisticated analytical tools to deal with the amassing volumes of data. Chromatin immunoprecipitation sequencing (ChIP-seq), a major application of NGS, is a widely adopted technique for examining protein-DNA interactions and is commonly used to investigate epigenetic signatures of diffuse histone marks. These datasets have notoriously high variance and subtle levels of enrichment across large expanses, making them exceedingly difficult to define. Windows-based, heuristic models and finite-state hidden Markov models (HMMs) have been used with some success in analyzing ChIP-seq data but with lingering limitations. To improve the ability to detect broad regions of enrichment, we developed a stochastic Bayesian Change-Point (BCP) method, which addresses some of these unresolved issues. BCP makes use of recent advances in infinite-state HMMs by obtaining explicit formulas for posterior means of read densities. These posterior means can be used to categorize the genome into enriched and unenriched segments, as is customarily done, or examined for more detailed relationships since the underlying subpeaks are preserved rather than simplified into a binary classification. BCP performs a near exhaustive search of all possible change points between different posterior means at high-resolution to minimize the subjectivity of window sizes and is computationally efficient, due to a speed-up algorithm and the explicit formulas it employs. In the absence of a well-established "gold standard" for diffuse histone mark enrichment, we corroborated BCP's island detection accuracy and reproducibility using various forms of empirical evidence. We show that BCP is especially suited for analysis of diffuse histone ChIP-seq data but also effective in analyzing punctate transcription factor ChIP datasets, making it widely applicable for numerous experiment types.

Novel Foxo1-dependent Transcriptional Programs Control T(reg) Cell Function

Regulatory T (T(reg)) cells, characterized by expression of the transcription factor forkhead box P3 (Foxp3), maintain immune homeostasis by suppressing self-destructive immune responses. Foxp3 operates as a late-acting differentiation factor controlling T(reg) cell homeostasis and function, whereas the early T(reg)-cell-lineage commitment is regulated by the Akt kinase and the forkhead box O (Foxo) family of transcription factors. However, whether Foxo proteins act beyond the T(reg)-cell-commitment stage to control T(reg) cell homeostasis and function remains largely unexplored. Here we show that Foxo1 is a pivotal regulator of T(reg )cell function. T(reg) cells express high amounts of Foxo1 and display reduced T-cell-receptor-induced Akt activation, Foxo1 phosphorylation and Foxo1 nuclear exclusion. Mice with T(reg)-cell-specific deletion of Foxo1 develop a fatal inflammatory disorder similar in severity to that seen in Foxp3-deficient mice, but without the loss of T(reg) cells. Genome-wide analysis of Foxo1 binding sites reveals ~300 Foxo1-bound target genes, including the pro-inflammatory cytokine Ifng, that do not seem to be directly regulated by Foxp3. These findings show that the evolutionarily ancient Akt-Foxo1 signalling module controls a novel genetic program indispensable for T(reg) cell function.

Waiting
simple hit counter