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Function-based identification of mammalian enhancers using site-specific integration.

ABSTRACT: The accurate and comprehensive identification of functional regulatory sequences in mammalian genomes remains a major challenge. Here we describe site-specific integration fluorescence-activated cell sorting followed by sequencing (SIF-seq), an unbiased, medium-throughput functional assay for the discovery of distant-acting enhancers. Targeted single-copy genomic integration into pluripotent cells, reporter assays and flow cytometry are coupled with high-throughput DNA sequencing to enable parallel screening of large numbers of DNA sequences. By functionally interrogating >500 kilobases (kb) of mouse and human sequence in mouse embryonic stem cells for enhancer activity we identified enhancers at pluripotency loci including NANOG. In in vitro-differentiated cardiomyocytes and neural progenitor cells, we identified cardiac enhancers and neuronal enhancers, respectively. SIF-seq is a powerful and flexible method for de novo functional identification of mammalian enhancers in a potentially wide variety of cell types.

SUBMITTER: Dickel DE

PROVIDER: S-EPMC4008384 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Function-based identification of mammalian enhancers using site-specific integration.

Dickel Diane E DE Zhu Yiwen Y Nord Alex S AS Wylie John N JN Akiyama Jennifer A JA Afzal Veena V Plajzer-Frick Ingrid I Kirkpatrick Aileen A Göttgens Berthold B Bruneau Benoit G BG Visel Axel A Pennacchio Len A LA

Nature methods 20140323 5

The accurate and comprehensive identification of functional regulatory sequences in mammalian genomes remains a major challenge. Here we describe site-specific integration fluorescence-activated cell sorting followed by sequencing (SIF-seq), an unbiased, medium-throughput functional assay for the discovery of distant-acting enhancers. Targeted single-copy genomic integration into pluripotent cells, reporter assays and flow cytometry are coupled with high-throughput DNA sequencing to enable paral ...[more]

PMID: 24658141

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Project description:BackgroundEukaryotic gene regulation is a complex process comprising the dynamic interaction of enhancers and promoters in order to activate gene expression. In recent years, research in regulatory genomics has contributed to a better understanding of the characteristics of promoter elements and for most sequenced model organism genomes there exist comprehensive and reliable promoter annotations. For enhancers, however, a reliable description of their characteristics and location has so far proven to be elusive. With the development of high-throughput methods such as ChIP-seq, large amounts of data about epigenetic conditions have become available, and many existing methods use the information on chromatin accessibility or histone modifications to train classifiers in order to segment the genome into functional groups such as enhancers and promoters. However, these methods often do not consider prior biological knowledge about enhancers such as their diverse lengths or molecular structure.ResultsWe developed enhancer HMM (eHMM), a supervised hidden Markov model designed to learn the molecular structure of promoters and enhancers. Both consist of a central stretch of accessible DNA flanked by nucleosomes with distinct histone modification patterns. We evaluated the performance of eHMM within and across cell types and developmental stages and found that eHMM successfully predicts enhancers with high precision and recall comparable to state-of-the-art methods, and consistently outperforms those in terms of accuracy and resolution.ConclusionseHMM predicts active enhancers based on data from chromatin accessibility assays and a minimal set of histone modification ChIP-seq experiments. In comparison to other 'black box' methods its parameters are easy to interpret. eHMM can be used as a stand-alone tool for enhancer prediction without the need for additional training or a tuning of parameters. The high spatial precision of enhancer predictions gives valuable targets for potential knockout experiments or downstream analyses such as motif search.

Dataset Information

Function-based identification of mammalian enhancers using site-specific integration.

Publications

Function-based identification of mammalian enhancers using site-specific integration.

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