EAGLE: An algorithm that utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions

AbstractLong-range regulation by distal enhancers is crucial for many biological processes. The existing methods for enhancer-target gene prediction often require many genomic features. This makes them difficult to be applied to many cell types, in which the relevant datasets are not always available. Here, we design a tool EAGLE, an enhancer and gene learning ensemble method for identification of Enhancer-Gene (EG) interactions. Unlike existing tools, EAGLE used only six features derived from the genomic features of enhancers and gene expression datasets. Cross-validation revealed that EAGLE outperformed other existing methods. Enrichment analyses on special transcriptional factors, epigenetic modifications, and eQTLs demonstrated that EAGLE could distinguish the interacting pairs from non- interacting ones. Finally, EAGLE was applied to mouse and human genomes and identified 7,680,203 and 7,437,255 EG interactions involving 31,375 and 43,724 genes, 138,547 and 177,062 enhancers across 89 and 110 tissue/cell types in mouse and human, respectively. The obtained interactions are accessible through an interactive database enhanceratlas.org. The EAGLE method is available at https://github.com/EvansGao/EAGLE and the predicted datasets are available in http://www.enhanceratlas.org/.Author summaryEnhancers are DNA sequences that interact with promoters and activate target genes. Since enhancers often located far from the target genes and the nearest genes are not always the targets of the enhancers, the prediction of enhancer-target gene relationships is a big challenge. Although a few computational tools are designed for the prediction of enhancer-target genes, it’s difficult to apply them in most tissue/cell types due to a lack of enough genomic datasets. Here we proposed a new method, EAGLE, which utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions. Comparing with other existing tools, EAGLE displayed a better performance in the 10-fold cross-validation and cross-sample test. Moreover, the predictions by EAGLE were validated by other independent evidence such as the enrichment of relevant transcriptional factors, epigenetic modifications, and eQTLs.Finally, we integrated the enhancer-target relationships obtained from human and mouse genomes into an interactive database EnhancerAtlas, http://www.enhanceratlas.org/.

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Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

10.1101/354944 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xuran Wang ◽

Jihwan Park ◽

Katalin Susztak ◽

Nancy R. Zhang ◽

Mingyao Li

Keyword(s):

Single Cell ◽

Cell Types ◽

Cellular Heterogeneity ◽

Tissue Cell ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Cell Type Specific ◽

Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

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Histone modification profiles are predictive for tissue/cell-type specific expression of both protein-coding and microRNA genes

BMC Bioinformatics ◽

10.1186/1471-2105-12-155 ◽

2011 ◽

Vol 12 (1) ◽

Cited By ~ 24

Author(s):

Zhihua Zhang ◽

Michael Q Zhang

Keyword(s):

Histone Modification ◽

Tissue Cell ◽

Cell Type ◽

Specific Expression ◽

Protein Coding ◽

Cell Type Specific Expression ◽

Cell Type Specific ◽

Microrna Genes

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Recapitulation of prostate tissue cell type-specific transcriptomes by an in vivo primary prostate tissue xenograft model

PLoS ONE ◽

10.1371/journal.pone.0233899 ◽

2020 ◽

Vol 15 (6) ◽

pp. e0233899

Author(s):

Nelson T. Gross ◽

Jianmin Wang ◽

Michael V. Fiandalo ◽

Eduardo Cortes Gomez ◽

Anica Watts ◽

...

Keyword(s):

Xenograft Model ◽

Prostate Tissue ◽

Tissue Cell ◽

Cell Type ◽

Cell Type Specific

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Mouse Aging Cell Atlas Analysis Reveals Global and Cell Type Specific Aging Signatures Revision 1

10.1101/2019.12.23.887604 ◽

2019 ◽

Author(s):

Martin Jinye Zhang ◽

Angela Oliveira Pisco ◽

Spyros Darmanis ◽

James Zou

Keyword(s):

Cell Types ◽

Tissue Cell ◽

Rna Seq ◽

Cell Type ◽

Cellular Processes ◽

Age Related ◽

Cell Type Specific ◽

Global Aging ◽

Mammalian System ◽

Aging Cell

ABSTRACTAging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq dataset to systematically characterize gene expression changes during aging across diverse cell types in the mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types also change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. Scoring cells based on these shared aging genes allowed us to contrast the aging status of different tissues and cell types from a transcriptomic perspective. In addition, we identified genes that exhibit age-related expression changes specific to each functional category of tissue-cell types. All together, our analyses provide one of the most comprehensive and systematic characterizations of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.

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Purifying selection on noncoding deletions of human regulatory elements detected using their cellular pleiotropy

10.1101/2020.05.19.105205 ◽

2020 ◽

Author(s):

David W. Radke ◽

Jae Hoon Sul ◽

Daniel J. Balick ◽

Sebastian Akle ◽

Robert C. Green ◽

...

Keyword(s):

Specific Activity ◽

Purifying Selection ◽

Cell Types ◽

Ctcf Binding ◽

Tissue Cell ◽

Cellular Activity ◽

Loss Of Function ◽

Cell Type ◽

Genomic Deletions ◽

Cell Type Specific

Genomic deletions provide a powerful loss-of-function model in non-coding regions to assess the role of purifying selection on human noncoding genetic variation. Regulatory element function is char-acterized by non-uniform tissue/cell-type activity, necessarily linking the study of fitness consequences from regulatory variants to their corresponding cellular activity. We used deletions from the 1000 Genomes Project (1000GP) and a callset we generated from genomes of participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) in order to examine whether purifying selection preserves noncoding sites of chromatin accessibility (DHS), histone modification (enhancer, transcribed, polycomb-repressed, heterochromatin), and topologically associated domain loops (TAD-loops). To examine this in a cellular activity-aware manner, we developed a statistical method, Pleiotropy Ratio Score (PlyRS), which calculates a correlation-adjusted count of “cellular pleiotropy” for each noncoding base-pair by analyzing shared regulatory annotations across tissues/cell-types. Comparing real deletion PlyRS values to simulations in a length-matched framework and using genomic covariates in analyses, we found that purifying selection acts to preserve both DHS and enhancer sites, as evident by both depletion of deletions overlapping these annotations and a shift in the allele frequency spectrum of overlapping deletions towards rare alleles. However, we did not find evidence of purifying selection for transcribed, polycomb-repressed, or heterochromatin sites. Additionally, we found evidence that purifying selection is acting on TAD-loop boundary integrity by preserving co-localized CTCF binding sites. Notably, at regions of DHS, enhancer, and CTCF within TAD-loop boundaries we found evidence that both sites of tissue/cell-type-specific activity and sites of cellularly pleiotropic activity are preserved by selection.Significance StatementWe used natural genomic deletions as a loss-of-function model to assess the role of purifying selection in preserving human noncoding regulatory sites. We examined this in a cellular activity-aware manner through development of a statistical method, Pleiotropy Ratio Score (PlyRS), which calculates an adjusted count of “cellular pleiotropy” for each noncoding basepair by analyzing correlations from shared regulatory annotations across tissues/cell-types. By comparing real deletion PlyRS values to simulations, we found that purifying selection acts to preserve both DHS and enhancer sites and TAD-loop boundary integrity by preserving co-localized CTCF binding sites. Notably, we found evidence at these regulatory regions that both sites of tissue/cell-type-specific activity and sites of cellularly pleiotropic activity are preserved by selection.

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