Large-scale determination and characterization of cell type-specific regulatory elements in the human genome

AbstractHistone modifications have been widely elucidated to play vital roles in gene regulation and cell identity. The Roadmap Epigenomics Consortium generated a reference catalogue of several key histone modifications across >100s of human cell types and tissues. Decoding these epigenomes into functional regulatory elements is a challenging task in computational biology. To this end, we adopted a differential chromatin modification analysis framework to comprehensively determine and characterize cell type-specific regulatory elements (CSREs) and their histone modification codes in the human epigenomes of five histone modifications across 127 tissues or cell types. The CSREs show significant relevance with cell type-specific biological functions and diseases and cell identity. Clustering of CSREs with their specificity signals reveals diverse histone codes, demonstrating the diversity of functional roles of CSREs within the same cell or tissue. Last but not least, dynamics of CSREs from close cell types or tissues can give a detailed view of developmental processes such as normal tissue development and cancer occurrence.

Download Full-text

Incorporating Gene Expression in Genome-wide Prediction of Chromatin Accessibility via Deep Learning

10.1101/610642 ◽

2019 ◽

Author(s):

Qiao Liu ◽

Wing Hung Wong ◽

Rui Jiang

Keyword(s):

Deep Learning ◽

Human Genome ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Superior Performance ◽

Specific Cell ◽

Cell Type ◽

Transcriptome Profile ◽

Cell Type Specific

AbstractRegulatory elements (REs) in human genome are major sites of non-coding transcription which lack adequate interpretation. Although computational approaches have been complementing high-throughput biological experiments towards the annotation of the human genome, it remains a big challenge to systematically and accurately characterize REs in the context of a specific cell type. To address this problem, we proposed DeepCAGE, an deep learning framework that incorporates transcriptome profile of human transcription factors (TFs) for accurately predicting the activities of cell type-specific REs. Our approach automatically learns the regulatory code of input DNA sequence incorporated with cell type-specific TFs expression. In a series of systematic comparison with existing methods, we show the superior performance of our model in not only the classification of accessible regions, but also the regression of DNase-seq signals. A typical scenario of usage for our method is to predict the activities of REs in novel cell types, especially where the chromatin accessibility data is not available. To sum up, our study provides a fascinating insight into disclosing complex regulatory mechanism by integrating transcriptome profile of human TFs.

Download Full-text

A probabilistic framework to dissect functional cell-type-specific regulatory elements and risk loci underlying the genetics of complex traits

10.1101/059345 ◽

2016 ◽

Cited By ~ 4

Author(s):

Yue Li ◽

Jose Davila-Velderrain ◽

Manolis Kellis

Keyword(s):

Complex Traits ◽

Large Scale ◽

Learning Algorithm ◽

Cell Types ◽

Regulatory Elements ◽

Probabilistic Framework ◽

Cell Type ◽

Tissue Specific ◽

Functional Variants ◽

Cell Type Specific

AbstractDissecting the physiological circuitry underlying diverse human complex traits associated with heritable common mutations is an ongoing effort. The primary challenge involves identifying the relevant cell types and the causal variants among the vast majority of the associated mutations in the noncoding regions. To address this challenge, we developed an efficient probabilistic framework. First, we propose a sparse group-guided learning algorithm to infer cell-type-specific enrichments. Second, we propose a fine-mapping Bayesian model that incorporates as Bayesian priors the sparse enrichments to infer risk variants. Using the proposed framework to analyze 32 complex human traits revealed meaningful tissue-specific epigenomic enrichments indicative of the relevant disease pathologies. The prioritized variants exhibit prominent tissue-specific epigenomic signatures and significant enrichments for eQTL and conserved elements. Together, we demonstrate the general benefits of the proposed integrative framework in elucidating meaningful tissue-specific epigenomic elements from large-scale correlated annotations and the implicated functional variants for future experimental interrogation.

Download Full-text

Universal annotation of the human genome through integration of over a thousand epigenomic datasets

10.1101/2020.11.17.387134 ◽

2020 ◽

Author(s):

Ha Vu ◽

Jason Ernst

Keyword(s):

Human Genome ◽

Large Scale ◽

Cell Types ◽

Chromatin State ◽

Open Chromatin ◽

Cell Type ◽

Chromatin States ◽

Modeling Approach ◽

Cell Type Specific ◽

Stack Model

AbstractGenome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative ‘stacked modeling’ approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges. In this paper, using a version of ChromHMM enhanced for large-scale applications, we applied the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, denoted the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we used in characterizing each state. Compared to cell-type-specific annotations, the full-stack annotation directly differentiates constitutive from cell-type-specific activity and is more predictive of locations of external genomic annotations. Overall, the full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing cell-type-specific annotations for studying the non-coding human genome.

Download Full-text

Characterization of the cell type-specific determinant in the genome of minute virus of mice.

Journal of Virology ◽

10.1128/jvi.62.2.552-557.1988 ◽

1988 ◽

Vol 62 (2) ◽

pp. 552-557 ◽

Cited By ~ 28

Author(s):

J P Antonietti ◽

R Sahli ◽

P Beard ◽

B Hirt

Keyword(s):

Cell Type ◽

Minute Virus Of Mice ◽

Cell Type Specific ◽

Minute Virus ◽

Specific Determinant

Download Full-text

Connectivity characterization of the mouse basolateral amygdalar complex

Nature Communications ◽

10.1038/s41467-021-22915-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Houri Hintiryan ◽

Ian Bowman ◽

David L. Johnson ◽

Laura Korobkova ◽

Muye Zhu ◽

...

Keyword(s):

Granular Cell ◽

Cell Types ◽

Projection Neurons ◽

Cell Type ◽

Connectivity Map ◽

Analysis Techniques ◽

Domain Specific ◽

Cell Type Specific ◽

Unique Domain

AbstractThe basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks.

Download Full-text