Searching journal content for articles similar to Shivram et al. 29 (2): 184.

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  1. ...differential gene expression for six major cell types intensified at severe ADNC. Integrating peak-to-gene linkages and motif enrichment analyses, we reconstruct transcription factor (TF)–target gene networks across six major brain cell types. By integrating -wide association study (GWAS) loci with cell type...
  2. ..., interacting physiological systems. An increasing number of studies indicate that, beyond the traditionally emphasized hypothalamic–pituitary–gonadal (HPG) axis, metabolic and immune tissues are integral components of the regulatory network controlling laying performance. For instance, the liver sustains high...
  3. ...expression in biological systems. At the core of these networks lies the inference of gene regulation, which reflects the binding of transcription factors (TFs) to specific DNA sequences to either activate or repress target gene expression. This regulation directs crucial cellular processes...
  4. ...promoter and 103 enhancer regions (Wang et al., 2008; Creyghton et al., 2010). In contrast, H3K27me3 is a 104 repressive mark associated with polycomb-mediated transcriptional repression and gene 105 silencing (Bernstein et al., 2006; Barski et al., 2007), typically associated with regulatory 106 regions...
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  5. ...-type independent. Gene set enrichment analysis (GSEA) reveals 28 that transcripts encoding proteins belonging to the same pathway often show similar 29 turnover dynamics. Furthermore, transcript isoforms exhibit distinct turnover profiles, 30 suggesting that RNA turnover plays an important role in regulating m...
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  6. ...expectations for when differential gene expression should matter. Here, we collated existing data into a gene-regulatory network (GRN) and performed developmental transcriptomics across different environmental conditions, genetic backgrounds, and mutants to assess the regulatory logic of mouth-form plasticity...
  7. ...outcomes, there must also be consistent regulation of such genes by cis-regulatory modules (CRMs). CRMs consist of clusters of transcription factor binding sites (TFBSs) that regulate the expression of target genes through collective or competitive binding of operative transcription factors (TFs). However...
  8. .... Domain-adaptive neural networks improve cross-species prediction of transcription factor binding. Genome Res 32: 512–523. doi:10.1101/gr.275394.121 ↵de Boer CG, Taipale J. 2024. Hold out the : a roadmap to solving the cis-regulatory code. Nature 625: 41–50. doi:10.1038/s41586-023-06661-w ↵Dey KK, van de...
  9. ...2 leads to loss of transcriptional repression. Our results reveal a novel function of Y1P-dependent PROMPTs in mediating PRC2 recruitment to chromatin and RNA Pol II promoter pausing in response to DNA damage.RNA polymerase II (RNA Pol II) is an essential enzyme that catalyzes the transcription...
  10. ...that are grouped by TF family and are often enriched in proximal or distal regulatory regions. For instance, SP1-like TFs activate or repress basal transcription by usually binding to the GC box (GGGCGG) or GT/CACC box in the promoter region of many genes. CTCF-like TFs not only function as transcriptional...
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