Роман байесовского Изменение точки Алгоритм всему геному анализ различных типов данных ChIPseq
Summary
Наши байесовского Точка изменения (BCP) алгоритм основывается на состоянии самой современной прогресс в области моделирования изменения точки через скрытых Марковских моделей и применяет их к иммунопреципитации хроматина секвенирования (ChIPseq) анализ данных. BCP хорошо работает как в широком и точечные типов данных, но превосходит точно идентифицировать надежные, воспроизводимые острова диффузного гистонов обогащения.
Abstract
ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.
Protocol
Representative Results
Discussion
Мы задались целью разработать модель для анализа ChIPseq данных, которые могут идентифицировать как точечные и диффузные структуры данных, одинаково хорошо. До сих пор регионы обогащения, в частности, диффузные регионов, которые отражают предполагает ожидание больших размеров остров, бы?…
Divulgations
The authors have nothing to disclose.
Acknowledgements
STARR основу премии (MQZ), NIH грант ES017166 (MQZ), NSF гранта DMS0906593 (HX).
Materials
| Name of the reagent | Company | Catalogue number | Comments (optional) |
| Linux-based workstation |
References
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