Dataset Information

Diverse functions of distal regulatory elements at the IFNG locus.

ABSTRACT: Previous studies have identified multiple conserved noncoding sequences (CNS) at the mouse Ifng locus sufficient for enhancer activity in cell-based assays. These studies do not directly address biology of the human IFNG locus in a genomic setting. IFNG enhancers may be functionally redundant or each may be functionally unique. We test the hypothesis that each IFNG enhancer has a unique necessary function using a bacterial artificial chromosome transgenic model. We find that CNS-30, CNS-4, and CNS+20 are required at distinct stages of Th1 differentiation, whereas CNS-16 has a repressive role in Th1 and Th2 cells. CNS+20 is required for IFN-? expression by memory Th1 cells and NKT cells. CNS-4 is required for IFN-? expression by effector Th1 cells. In contrast, CNS-16, CNS-4, and CNS+20 are each partially required for human IFN-? expression by NK cells. Thus, IFNG CNS enhancers have redundant necessary functions in NK cells but unique necessary functions in Th cells. These results also demonstrate that distinct CNSs are required to transcribe IFNG at each stage of the Th1 differentiation pathway.

SUBMITTER: Collins PL

PROVIDER: S-EPMC3273639 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Diverse functions of distal regulatory elements at the IFNG locus.

Collins Patrick L PL Henderson Melodie A MA Aune Thomas M TM

Journal of immunology (Baltimore, Md. : 1950) 20120113 4

Previous studies have identified multiple conserved noncoding sequences (CNS) at the mouse Ifng locus sufficient for enhancer activity in cell-based assays. These studies do not directly address biology of the human IFNG locus in a genomic setting. IFNG enhancers may be functionally redundant or each may be functionally unique. We test the hypothesis that each IFNG enhancer has a unique necessary function using a bacterial artificial chromosome transgenic model. We find that CNS-30, CNS-4, and C ...[more]

PMID: 22246629

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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

Diverse functions of distal regulatory elements at the IFNG locus.

Publications

Diverse functions of distal regulatory elements at the IFNG locus.

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