We used TissueEnrich [ 48 ] to carry out tissue-specific gene enrichment analysis, using all of the tissue-specific genes from the mouse ENCODE dataset and default parameters. The enrichment was calculated by using the hypergeometric test. Placentas were microdissected from e9. Three placentas were combined per biological replicate, and three biological replicates were carried out. Chromatin isolation was carried out as previously described in [ 84 ] except that the final nuclear lysis was performed in 0.

Primer sequences and efficiency values, calculated after testing dilutions of input DNA, are listed in Additional file 2 : Table S7. Randomization analysis was performed to determine the significance of specific ontology terms within subnetworks or gene groups. We generated 10, gene sets that matched the size of the group or subnetwork of interest. The p value is equivalent to the number of random sets that have a lower FDR than our original term, divided by 10, The pairwise comparison of genes in each group for all tissues Additional file 1 : Figure S6 was calculated by comparing the genes of the same group between each pair of tissues.

The Mann—Whitney U test was used to check whether the set of overlap counts was significantly different between groups. NuScore was run using the 2cv5 human template and default parameters. Recon was also run with the default parameters. Nucleosome position scores for each position were averaged across all sequences within a particular group. Similar averaging was also done on the deformation energy across all sequences within a group. To determine whether the mean nucleosome position score was significantly different between the tested groups, we used the Mann—Whitney U test.

Gene Ontology. Genomic Regions Enrichment of Annotations Tool. University of California, Santa Cruz. AJ contributed to tissue-specific gene enrichment and other analysis. RRS and GT conceived of experiments and contributed to the study design, interpretation of results, and writing the manuscript. All authors have read and approved the final manuscript.

Data supporting the conclusion of this article are available in the GEO repository, under the data accession GSE The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Research Open Access. Combined analysis of dissimilar promoter accessibility and gene expression profiles identifies tissue-specific genes and actively repressed networks. Abstract Background The assay for transposase-accessible chromatin ATAC-seq is a powerful method to examine chromatin accessibility.

Results We started by assaying the open chromatin landscape in the mid-gestation placenta, when the fetal vasculature has started developing. Conclusions Within the placenta, we identified an active placenta-specific gene network as well as a repressed neuronal network. Identifying accessible regions in the mid-gestation mouse placenta To define the chromatin landscape in mouse placenta at e9. Because ATAC-seq is infrequently carried out in whole tissue, we first assessed data quality. Therefore, we calculated the correlation of promoter accessibility with gene expression.

Gene expression was measured using transcripts per million TPM , calculated from previously published e9. It is likely that a higher correlation is typically not observed because accessible regions are not always associated with gene activity.

## High Energy Physics - Phenomenology

They can also be associated with gene repression or genes that are poised to become active [ 23 — 25 ]. Although some aspects of this correlation have been investigated, the majority of studies have not fully explored the relationship between ATAC-seq and RNA-seq data, especially with respect to genes that have low accessibility and a high level of expression.

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We found that the majority of genes had medium—low accessibility and medium—low expression MA—ME , and the second largest group genes had high accessibility and high expression HA—HE Fig. To determine the biological functions associated with these groups, we carried out a functional enrichment analysis using the Genomic Regions Enrichment of Annotation Tool GREAT [ 26 ]. As expected, we found clear distinctions between the biological processes enriched in each group.

These findings are in agreement with previous studies.

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One such study, carried out in human T-helper cells, found that genes with accessible promoters and high expression were enriched for housekeeping functions, whereas those with inaccessible promoters were enriched for olfactory terms [ 27 ]. A more recent study also found that genes with accessible promoters in three different types of hematopoietic stem cells HSCs were enriched for terms related to regulating the cell cycle and DNA damage and repair [ 28 ].

Previous studies have identified regions of high accessibility that are also marked by a histone modification associated with gene repression, H3K27me3 [ 23 , 34 ]. Therefore, we hypothesized that HA—ME genes may be actively repressed in the placenta. To identify potential transcription factors that may repress HA—ME genes, we scored motifs for transcription factors expressed in e9. Three motifs were significantly enriched in HA—ME gene promoters relative to a background set of all gene promoters from each of the other three groups Fig.

The first enriched motif was for Sp2, a transcription factor that can repress cholesterol synthesis genes [ 36 ] and can form a repression complex with Klf6 to repress Mmp9 [ 37 ]. The second enriched motif was for Setdb1, a histone methyltransferase that trimethylates lysine 9 on histone H3. Setdb1 is known to interact with Oct4 in embryonic stem cells ESCs to repress genes involved in differentiation [ 38 , 39 ].

Interestingly, the most highly connected genes in this subnetwork were Fzd4, Egf, and Syt1, of which Fzd4 and Egf are each known to be involved in neuronal differentiation [ 44 — 47 ]. We included genes that have a known role in neuronal differentiation, as well as those that have no known role in this process. A large number of genes were found to have medium—low promoter accessibility and high expression Fig. Since the GO terms were related to, but not specific to, placental functions, we also used TissueEnrich to perform tissue-specific gene enrichment analysis on the MA—HE group, which showed strong enrichment for placenta-specific genes Fig.

Based on the results from the placenta data, we predicted that sensory perception genes would be enriched in the MA—ME group in tissues and cells not associated with such functions and that genes important for general cellular functionality would be enriched in the HA—HE group of all of the cell types. We compared the MA—ME genes between all nine of the datasets including e9. A similar analysis of HA—HE genes again showed high gene overlap between all datasets, with a median pairwise overlap value of Mouse data curation Mouse data were compiled from previous studies published until Dec.

Integration of ATAC-seq and RNA-seq Genes were defined as highly accessible HA if the maximum number of overlapping ATAC-seq reads within their promoter accessibility was higher than the 70th percentile of the data, and were defined as having medium—low accessibility MA if the coverage was below the 50th percentile. Motif enrichment analysis The library of position weight matrices used to score binding sites in promoters was obtained by curating data from multiple resources described in [ 35 ]. Tissue-specific gene enrichment analysis We used TissueEnrich [ 48 ] to carry out tissue-specific gene enrichment analysis, using all of the tissue-specific genes from the mouse ENCODE dataset and default parameters.

Randomization analysis Randomization analysis was performed to determine the significance of specific ontology terms within subnetworks or gene groups. Comparison of gene groups between tissues The pairwise comparison of genes in each group for all tissues Additional file 1 : Figure S6 was calculated by comparing the genes of the same group between each pair of tissues. Competing interests The authors declare that they have no competing interests.

Availability of data and materials Data supporting the conclusion of this article are available in the GEO repository, under the data accession GSE Consent for publication Not applicable. Placental development: lessons from mouse mutants. Nat Rev Genet.

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## [] A combined analysis of $K N(\overline{K}N)$ scattering in the Regge realm

Genomic evolution of the placenta using co-option and duplication and divergence. Genome Res. Comparative analysis of mouse and human placentae across gestation reveals species-specific regulators of placental development. Changes in the enhancer landscape during early placental development uncover a trophoblast invasion gene-enhancer network.

Histone H3K27ac separates active from poised enhancers and predicts developmental state. Integrative analysis of reference human epigenomes.

Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. MORC1 represses transposable elements in the mouse male germline.

Nat Commun. BMC Genom. Chromatin state dynamics during blood formation. Molecular transitions in early progenitors during human cord blood hematopoiesis. Mol Syst Biol. The dynamic landscape of open chromatin during human cortical neurogenesis. IL signaling remodels adipose chromatin architecture to limit thermogenesis and energy expenditure. Housekeeping and tissue-specific genes in mouse tissues.