Dataset Information

Genome wide analysis reveals Zic3 interaction with distal regulatory elements of stage specific developmental genes in zebrafish.

ABSTRACT: Zic3 regulates early embryonic patterning in vertebrates. Loss of Zic3 function is known to disrupt gastrulation, left-right patterning, and neurogenesis. However, molecular events downstream of this transcription factor are poorly characterized. Here we use the zebrafish as a model to study the developmental role of Zic3 in vivo, by applying a combination of two powerful genomics approaches--ChIP-seq and microarray. Besides confirming direct regulation of previously implicated Zic3 targets of the Nodal and canonical Wnt pathways, analysis of gastrula stage embryos uncovered a number of novel candidate target genes, among which were members of the non-canonical Wnt pathway and the neural pre-pattern genes. A similar analysis in zic3-expressing cells obtained by FACS at segmentation stage revealed a dramatic shift in Zic3 binding site locations and identified an entirely distinct set of target genes associated with later developmental functions such as neural development. We demonstrate cis-regulation of several of these target genes by Zic3 using in vivo enhancer assay. Analysis of Zic3 binding sites revealed a distribution biased towards distal intergenic regions, indicative of a long distance regulatory mechanism; some of these binding sites are highly conserved during evolution and act as functional enhancers. This demonstrated that Zic3 regulation of developmental genes is achieved predominantly through long distance regulatory mechanism and revealed that developmental transitions could be accompanied by dramatic changes in regulatory landscape.

SUBMITTER: Winata CL

PROVIDER: S-EPMC3814314 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Genome wide analysis reveals Zic3 interaction with distal regulatory elements of stage specific developmental genes in zebrafish.

Winata Cecilia L CL Kondrychyn Igor I Kumar Vibhor V Srinivasan Kandhadayar G KG Orlov Yuriy Y Ravishankar Ashwini A Prabhakar Shyam S Stanton Lawrence W LW Korzh Vladimir V Mathavan Sinnakaruppan S

PLoS genetics 20131031 10

Zic3 regulates early embryonic patterning in vertebrates. Loss of Zic3 function is known to disrupt gastrulation, left-right patterning, and neurogenesis. However, molecular events downstream of this transcription factor are poorly characterized. Here we use the zebrafish as a model to study the developmental role of Zic3 in vivo, by applying a combination of two powerful genomics approaches--ChIP-seq and microarray. Besides confirming direct regulation of previously implicated Zic3 targets of t ...[more]

PMID: 24204288

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Project description:BackgroundThe data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).ResultsWe develop a suite of ML models, under the banner AIKYATAN, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-'a-vis the competition. Specifically, AIKYATAN-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.ConclusionsOur exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our AIKYATAN suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Dataset Information

Genome wide analysis reveals Zic3 interaction with distal regulatory elements of stage specific developmental genes in zebrafish.

Publications

Genome wide analysis reveals Zic3 interaction with distal regulatory elements of stage specific developmental genes in zebrafish.

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