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Improved regulatory element prediction based on tissue-specific local epigenomic signatures.

ABSTRACT: Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulatory element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared with existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types. REPTILE is available at https://github.com/yupenghe/REPTILE/.

SUBMITTER: He Y

PROVIDER: S-EPMC5338528 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Improved regulatory element prediction based on tissue-specific local epigenomic signatures.

He Yupeng Y Gorkin David U DU Dickel Diane E DE Nery Joseph R JR Castanon Rosa G RG Lee Ah Young AY Shen Yin Y Visel Axel A Pennacchio Len A LA Ren Bing B Ecker Joseph R JR

Proceedings of the National Academy of Sciences of the United States of America 20170213 9

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulatory element prediction based on tissue-specific local epigenet ...[more]

PMID: 28193886

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Project description:BackgroundDisulfide bonds are traditionally considered to play only structural roles. In recent years, increasing evidence suggests that the disulfide proteome is made up of structural disulfides and reversible disulfides. Unlike structural disulfides, reversible disulfides are usually of important functional roles and may serve as redox switches. Interestingly, only specific disulfide bonds are reversible while others are not. However, whether reversible disulfides can be predicted based on structural information remains largely unknown.MethodsIn this study, two datasets with both types of disulfides were compiled using independent approaches. By comparison of various features extracted from the local structural signatures, we identified several features that differ significantly between reversible and structural disulfides, including disulfide bond length, along with the number, amino acid composition, secondary structure and physical-chemical properties of surrounding amino acids. A SVM-based classifier was developed for predicting reversible disulfides. RESULTS: By 10-fold cross-validation, the model achieved accuracy of 0.750, sensitivity of 0.352, specificity of 0.953, MCC of 0.405 and AUC of 0.751 using the RevSS_PDB dataset. The robustness was further validated by using RevSS_RedoxDB as independent testing dataset. This model was applied to proteins with known structures in the PDB database. The results show that one third of the predicted reversible disulfide containing proteins are well-known redox enzymes, while the remaining are non-enzyme proteins. Given that reversible disulfides are frequently reported from functionally important non-enzyme proteins such as transcription factors, the predictions may provide valuable candidates of novel reversible disulfides for further experimental investigation.ConclusionsThis study provides the first comparative analysis between the reversible and the structural disulfides. Distinct features remarkably different between these two groups of disulfides were identified, and a SVM-based classifier for predicting reversible disulfides was developed accordingly. A web server named RevssPred can be accessed freely from: http://biocomputer.bio.cuhk.edu.hk/RevssPred .

Project description:MotivationAnnotating a given genomic locus or a set of genomic loci is an important yet challenging task. This is especially true for the non-coding part of the genome which is enormous yet poorly understood. Since gene set enrichment analyses have demonstrated to be effective approach to annotate a set of genes, the same idea can be extended to explore the enrichment of functional elements or features in a set of genomic intervals to reveal potential functional connections.ResultsIn this study, we describe a novel computational strategy named loci2path that takes advantage of the newly emerged, genome-wide and tissue-specific expression quantitative trait loci (eQTL) information to help annotate a set of genomic intervals in terms of transcription regulation. By checking the presence or the absence of millions of eQTLs in a set of input genomic intervals, combined with grouping eQTLs by the pathways or gene sets that their target genes belong to, loci2path build a bridge connecting genomic intervals to functional pathways and pre-defined biological-meaningful gene sets, revealing potential for regulatory connection. Our method enjoys two key advantages over existing methods: first, we no longer rely on proximity to link a locus to a gene which has shown to be unreliable; second, eQTL allows us to provide the regulatory annotation under the context of specific tissue types. To demonstrate its utilities, we apply loci2path on sets of genomic intervals harboring disease-associated variants as query. Using 1 702 612 eQTLs discovered by the Genotype-Tissue Expression (GTEx) project across 44 tissues and 6320 pathways or gene sets cataloged in MSigDB as annotation resource, our method successfully identifies highly relevant biological pathways and revealed disease mechanisms for psoriasis and other immune-related diseases. Tissue specificity analysis of associated eQTLs provide additional evidence of the distinct roles of different tissues played in the disease mechanisms.Availability and implementationloci2path is published as an open source Bioconductor package, and it is available at http://bioconductor.org/packages/release/bioc/html/loci2path.html.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Improved regulatory element prediction based on tissue-specific local epigenomic signatures.

Publications

Improved regulatory element prediction based on tissue-specific local epigenomic signatures.

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