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      High-Resolution Epigenomic Atlas of Human Embryonic Craniofacial Development

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          SUMMARY

          Defects in patterning during human embryonic development frequently result in craniofacial abnormalities. The gene regulatory programs that build the craniofacial complex are likely controlled by information located between genes and within intronic sequences. However, systematic identification of regulatory sequences important for forming the human face has not been performed. Here, we describe comprehensive epigenomic annotations from human embryonic craniofacial tissues and systematic comparisons with multiple tissues and cell types. We identified thousands of tissue-specific craniofacial regulatory sequences and likely causal regions for rare craniofacial abnormalities. We demonstrate significant enrichment of common variants associated with orofacial clefting in enhancers active early in embryonic development, while those associated with normal facial variation are enriched near the end of the embryonic period. These data are provided in easily accessible formats for both craniofacial researchers and clinicians to aid future experimental design and interpretation of noncoding variation in those affected by craniofacial abnormalities.

          In Brief

          Wilderman et al. report the global identification of gene regulatory sequences active in early human craniofacial development. Systematic comparisons with over 120 different human tissues and cell types reveal shared and craniofacial-specific enhancers. Craniofacial enhancers are enriched with genetic associations for both orofacial clefting risk and face shape.

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          Most cited references60

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          Identifying ChIP-seq enrichment using MACS.

          Model-based analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modification from ChIP-seq data. MACS consists of four steps: removing redundant reads, adjusting read position, calculating peak enrichment and estimating the empirical false discovery rate (FDR). In this protocol, we provide a detailed demonstration of how to install MACS and how to use it to analyze three common types of ChIP-seq data sets with different characteristics: the sequence-specific transcription factor FoxA1, the histone modification mark H3K4me3 with sharp enrichment and the H3K36me3 mark with broad enrichment. We also explain how to interpret and visualize the results of MACS analyses. The algorithm requires ∼3 GB of RAM and 1.5 h of computing time to analyze a ChIP-seq data set containing 30 million reads, an estimate that increases with sequence coverage. MACS is open source and is available from http://liulab.dfci.harvard.edu/MACS/.
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            ChIP-seq accurately predicts tissue-specific activity of enhancers.

            A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover because they are scattered among the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here we present the results of chromatin immunoprecipitation with the enhancer-associated protein p300 followed by massively parallel sequencing, and map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases demonstrated reproducible enhancer activity in the tissues that were predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities, and suggest that such data sets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.
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              Unsupervised pattern discovery in human chromatin structure through genomic segmentation.

              We trained Segway, a dynamic Bayesian network method, simultaneously on chromatin data from multiple experiments, including positions of histone modifications, transcription-factor binding and open chromatin, all derived from a human chronic myeloid leukemia cell line. In an unsupervised fashion, we identified patterns associated with transcription start sites, gene ends, enhancers, transcriptional regulator CTCF-binding regions and repressed regions. Software and genome browser tracks are at http://noble.gs.washington.edu/proj/segway/.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                7 May 2018
                01 May 2018
                23 May 2018
                : 23
                : 5
                : 1581-1597
                Affiliations
                [1 ]Graduate Program in Genetics and Developmenta Biology, UConn Health, Farmington, CT 06030, USA
                [2 ]Department of Genetics and Genome Sciences, UConn Health, Farmington, CT 06030, USA
                [3 ]Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
                [4 ]Kavli Institute for Neuroscience, Yale University, New Haven, CT 06520, USA
                [5 ]Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
                Author notes
                [* ]Correspondence: cotney@ 123456uchc.edu
                [6]

                Lead Contact

                Article
                NIHMS965685
                10.1016/j.celrep.2018.03.129
                5965702
                29719267
                f8577a8b-abad-49de-abbd-42df9c800fb7

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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