Open Access
Integrative analysis of 111 reference human epigenomes
Anshul Kundaje,Wouter Meuleman,Jason Ernst,Angela Yen,Pouya Kheradpour,Zhizhuo Zhang,Jianrong Wang,Lucas D. Ward,Abhishek Sarkar,Gerald Quon,Matthew L. Eaton,Yi-Chieh Wu,Andreas R. Pfenning,Xinchen Wang,Melina Claussnitzer,Yaping Liu,Mukul S. Bansal,Soheil Feizi-Khankandi,Ah Ram Kim,Richard C Sallari,Nicholas A Sinnott-Armstrong,Laurie A. Boyer,Elizabeta Gjoneska,Li-Huei Tsai,Manolis Kellis +24 more
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TLDR
In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.Abstract:
The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.read more
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Exposure to Phthalate, an Endocrine Disrupting Chemical, Alters the First Trimester Placental Methylome and Transcriptome in Women
N. M. Grindler,Lauren A. Vanderlinden,Rajendiran Karthikraj,Kurunthachalam Kannan,Stephanie B. Teal,Alex J. Polotsky,Theresa L. Powell,Ivana V. Yang,Thomas Jansson +8 more
TL;DR: These findings are consistent with the model that phthalates impact placental function by modulating the expression of critical placental genes through epigenetic regulation.
Journal ArticleDOI
Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks.
TL;DR: This model, termed Xpresso, more than doubles the accuracy of alternative sequence-based models and isolates rules as predictive as models relying on chromatic immunoprecipitation sequencing data, and its residuals can be used to quantify the influence of enhancers, heterochromatic domains, and microRNAs.
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Cyclin A2/E1 activation defines a hepatocellular carcinoma subclass with a rearrangement signature of replication stress
Quentin Bayard,Léa Meunier,Camille Peneau,Victor Renault,Jayendra Shinde,Jean-Charles Nault,Iadh Mami,Gabrielle Couchy,Giuliana Amaddeo,Emmanuel Tubacher,Delphine Bacq,Vincent Meyer,Tiziana La Bella,Audrey Debaillon-Vesque,Paulette Bioulac-Sage,Olivier Seror,Jean-Frédéric Blanc,Julien Calderaro,Jean-François Deleuze,Sandrine Imbeaud,Jessica Zucman-Rossi,Eric Letouzé +21 more
TL;DR: A new poor prognosis HCC entity and a rearrangement signature related to replication stress are revealed and pan-cancer analysis reveals a similar signature in BRCA1-mutated breast and ovarian cancers.
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Archaic Adaptive Introgression in TBX15/WARS2
Fernando Racimo,David Gokhman,Matteo Fumagalli,Amy Ko,Torben Hansen,Ida Moltke,Anders Albrechtsen,Liran Carmel,Emilia Huerta-Sanchez,Rasmus Nielsen,Rasmus Nielsen +10 more
TL;DR: It is reported that selection in the region with the second most extreme signal of positive selection in Greenlandic Inuit favored a deeply divergent haplotype that is closely related to the sequence in the Denisovan genome, and was likely introgressed from an archaic population.
Journal ArticleDOI
PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
TL;DR: This work developed a deep learning-based algorithmic framework named PEDLA, which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues, and demonstrated that PEDla framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction.
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