Translate this page to:
In JoVE (1)
Other Publications (2)
Articles by Yifan Mo in JoVE
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Haipeng Xing1, Willey Liao1,2, Yifan Mo1,2, Michael Q. Zhang2,3
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
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
PLoS Computational Biology. Jul, 2012 | Pubmed ID: 22844240
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.
Nature. Nov, 2012 | Pubmed ID: 23135404
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.