SEA version 3.0: a comprehensive extension and update of the Super-Enhancer archive

Chen, Chuangeng; Zhou, Dianshuang; Gu, Yue; Wang, Cong; Zhang, Mengyan; Lin, Xiangyu; Xing, Jie; Wang, Hongli; Zhang, Yan

doi:10.1093/nar/gkz1028

Cited by 51 publications

(52 citation statements)

References 23 publications

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“…(F) Genomic coordinates of common histone modifications in HeLa cells were determined from ENCODE ChIP-seq data. Coordinates of “super enhancers” in HeLa cells were extracted from the SEA 3.0 database [ 71 ]. Frequency of integration within 1 kb of these locations was calculated.…”

Section: Resultsmentioning

confidence: 99%

“…Genomic locations of RNA polymerase II binding and histone modifications was extracted from ENCODE data sets (Pol II: ENCFF246QVY; H3K27Ac: ENCFF113QJM; H3K9me3: ENCFF712ATO; H3K36me3: ENCFF864ZXP; H3K4me3: ENCFF862LUQ). Genomic coordinates of “super enhancers” in HeLa cells were extracted from the SEA 3.0 database [ 71 ]. Distance of integration to nearest feature was calculated using BedTools [ 72 ].…”

Section: Methodsmentioning

confidence: 99%

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Mutations altering acetylated residues in the CTD of HIV-1 integrase cause defects in proviral transcription at early times after integration of viral DNA

Winans

Goff

2020

PLoS Pathog

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The central function of the retroviral integrase protein (IN) is to catalyze the integration of viral DNA into the host genome to form the provirus. The IN protein has also been reported to play a role in a number of other processes throughout the retroviral life cycle such as reverse transcription, nuclear import and particle morphogenesis. Studies have shown that HIV-1 IN is subject to multiple post-translational modifications (PTMs) including acetylation, phosphorylation and SUMOylation. However, the importance of these modifications during infection has been contentious. In this study we attempt to clarify the role of acetylation of HIV-1 IN during the retroviral life cycle. We show that conservative mutation of the known acetylated lysine residues has only a modest effect on reverse transcription and proviral integration efficiency in vivo. However, we observe a large defect in successful expression of proviral genes at early times after infection by an acetylation-deficient IN mutant that cannot be explained by delayed integration dynamics. We demonstrate that the difference between the expression of proviruses integrated by an acetylation mutant and WT IN is likely not due to altered integration site distribution but rather directly due to a lower rate of transcription. Further, the effect of the IN mutation on proviral gene expression is independent of the Tat protein or the LTR promoter. At early times after integration when the transcription defect is observed, the LTRs of proviruses integrated by the mutant IN have altered histone modifications as well as reduced IN protein occupancy. Over time as the transcription defect in the mutant virus diminishes, histone modifications on the WT and mutant proviral LTRs reach comparable levels. These results highlight an unexpected role for the IN protein in regulating proviral transcription at early times post-integration.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mutations altering acetylated residues in the CTD of HIV-1 integrase cause defects in proviral transcription at early times after integration of viral DNA

Winans

Goff

2020

PLoS Pathog

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“…On the other hand, as a single histone mark is not a reliable CRM predictor, a great deal of efforts have been made to predict CRMs based on multiple histone marks and chromatin accessibility (CA) data from the same cell/tissue types using various machine-learning methods, including hidden Markov models(Ernst & Kellis, 2012), dynamic Bayesian networks(Hoffman et al, 2013), time-delay neural networks(Firpi, Ucar, & Tan, 2010), random forest(Rajagopal et al, 2013), and support vector machines (SVMs)(Kleftogiannis, Kalnis, & Bajic, 2015). Many enhancer databases have also been created either by combining results of multiple such methods(Ashoor, Kleftogiannis, Radovanovic, & Bajic, 2015; Fishilevich et al, 2017; Zerbino, Wilder, Johnson, Juettemann, & Flicek, 2015), or by identifying overlapping regions of CA and histone mark tracks in the same cell/tissue types(Chen et al, 2020; Cheneby, Gheorghe, Artufel, Mathelier, & Ballester, 2018; Gao & Qian, 2020; Kang et al, 2019; G. Zhang et al, 2018). In particular, most recently, the ENCODE phase 3 consortium(Moore et al, 2020) identified 926,535 candidate cis -regulatory elements (cCREs) based on overlaps between millions of DNase I hypersensitivity sites (DHSs)(Thurman et al, 2012) and transposase accessible sites (TASs)(Buenrostro, Giresi, Zaba, Chang, & Greenleaf, 2013), active promoter histone mark H3K4me3(Aday, Zhu, Lakshmanan, Wang, & Lawson, 2011) peaks, active enhancer mark H3K27ac(Creyghton et al, 2010) peaks, and insulator mark CTCT(Kim et al, 2007) peaks in a large number of cell/tissue types.…”

Section: Introductionmentioning

confidence: 99%

Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

2020

Preprint

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Running title: A map of cis-regulatory modules in the human genome ABSTRACT Annotating all cis-regulatory modules (CRMs) in genomes is essential to understand genome functions, however, it remains an uncompleted task despite great progresses made since the development of ChIPseq techniques. As a continued effort, we developed a new algorithm dePCRM2 for predicting CRMs and constituent transcription factor (TF) binding sites (TFBSs) by integrating numerous TF ChIP-seq datasets based on a new ultra-fast, accurate motif-finding algorithm and an efficient combinatory motif pattern discovery method. dePCRM2 partitions genome regions covered by extended binding peaks in the datasets into a CRM candidates (CRMCs) set and a non-CRMCs set, and predicts CRMs and constituent TFBSs by evaluating each CRMC using a novel score. Applying dePCRM2 to 6,092 datasets covering 77.47% of the human genome, we predicted 201 unique TF binding motif families and 1,404,973 CRMCs.Intriguingly, the CRMCs are under stronger evolutionary constraints than the non-CRMCs, and the higher a CRMC can score, the stronger evolutionary constraint it receives and the more likely it is a full-length enhancer. When evaluated on functionally validated VISTA enhancers and causal ClinVar mutants, dePCRM2 achieves 97.43~100.00% sensitivity at p-value ≤ 0.05. dePCRM2 also largely outperforms existing methods in sensitivity and specificity, as well as by the evaluation of evolution constraints.Based on our predictions and evolutionary behaviors of the genome, we estimated that about 21.95% and 54.87% of the genome might code for TFBSs and CRMs, respectively, for which we predicted 80.21%. CRMs, rather than coding sequences (CDSs), that mainly account for inter-species divergence and intraspecies diversity (

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“…More than 80,000 super-enhancers (combined numbers for the mouse and human genome) can be accessed through the online dbSuper database [28]. An updated version of the Super Enhancer Archive (SEA 3.0) provides another entry point [29], but this database was unfortunately offline when we were drafting this manuscript (Supplementary Table 1).…”

Section: Identifying Putative Regulatory Elementsmentioning

confidence: 99%

“…[28] http://sea.edbc.org SEA version 3.0 was updated in 2019 and promises to be a comprehensive resource that stores predicted super-enhancers and enhancers from 11 different species and more than 200 types of cells, tissues and diseases. [29] https://tabula-muris-senis.ds.czbiohub.org/ A large compendium of single cell transcriptome data from the model organism Mus musculus that contains scRNAseq datasets of 23 organs and tissues, including the mammary gland at 6 different timepoints (1 month, 3 months, 18 months, 21 months, 24 months, 30 months). This online dataset explicitly includes stromal cells and other cell types from the supportive tissue (e.g.…”

Section: Tool Description Referencementioning

confidence: 99%

How to use online tools to generate new hypotheses for mammary gland biology research: a case study forWnt7b

Grift

Heijmans

Amerongen

2020

Preprint

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An increasing number of ‘-omics’ datasets, generated by labs all across the world, are becoming available. They contain a wealth of data that are largely unexplored. Not every scientist, however, will have access to the required resources and expertise to analyze such data from scratch. Luckily, a growing number of investigators is dedicating their time and effort to the development of user friendly, online applications that allow researchers to use and investigate these datasets. Here, we will illustrate the usefulness of such an approach.Using regulation of Wnt7b as an example, we will highlight a selection of accessible tools and resources that are available to researchers in the area of mammary gland biology. We show how they can be used for in silico analyses of gene regulatory mechanisms, resulting in new hypotheses and providing leads for experimental follow up. We also call out to the mammary gland community to join forces in a coordinated effort to generate and share additional tissue-specific ‘-omics’ datasets and thereby expand the in silico toolbox.

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SEA version 3.0: a comprehensive extension and update of the Super-Enhancer archive

Cited by 51 publications

References 23 publications

Mutations altering acetylated residues in the CTD of HIV-1 integrase cause defects in proviral transcription at early times after integration of viral DNA

Mutations altering acetylated residues in the CTD of HIV-1 integrase cause defects in proviral transcription at early times after integration of viral DNA

Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

How to use online tools to generate new hypotheses for mammary gland biology research: a case study forWnt7b

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