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  1. ...Multitissue single-nucleus RNA-seq reveals cell type–specific regulatory patterns of alternative polyadenylation in pigs Qiuhan Wen1,2, Zhen Wang1, Qi Bao1, Tianli Ding1, Haihan Zhang3, Jianbo Li4, Zhuang Liu5, Jieping Huang2 and Guoqiang Yi1,6,7 1Shenzhen Branch, Guangdong Laboratory of Lingnan...
  2. ...by regulating gene expression, RNA metabolism, and translation. Their dysregulation contributes to the development of human diseases, including cancer. 3-methylcytidine (m3C) primarily occurs in transfer RNA, where it regulates translation, stem cell pluripotency, and mitochondrial function. m3C has also been...
  3. ...,154 cells from all samples passing quality control (QC) for both RNA-seq and ATAC-seq were integrated, clustered, and annotated into 14 cell types (Fig. 2A; Supplemental Fig. S1A). The clustering distinguished nuclei of different cell types by their global pattern of gene expression (for the full list...
  4. ..., as well as in humans and viruses (Figs. 1⇑–3, 7). Lastly, our models revealed hidden TISs based on mRNA sequences across monocots and dicots, thereby improving gene annotation in plants (Figs. 6, 7). The translation of small or nonAUG TIS-initiated ORFs can expand proteome diversity and produce proteins...
  5. ...-methyladenosine (m6A) modifications in native mRNA. We used human and mouse cells with known genetic variants to assign the allelic origin of each mRNA molecule combined with a supervised machine learning model to detect read-level m6A modification ratios. Our analyses reveal the importance of sequences adjacent...
  6. ...and identify therapeutic opportunities. Here, we develop a cell type–specific regulatory atlas of the human middle temporal gyrus via leveraging single-nucleus RNA-seq (1,197,032 nuclei) and ATAC-seq (740,875 nuclei) data sets from 84 donors across four stages of AD neuropathological change (ADNC). We observe...
  7. ...expression (P-value = 4.4 × 10−40), RNA-induced silencing complex (RISC) (P-value = 9.2 × 10−38), DNA-binding transcription factor activity (P-value = 1.6 × 10−13), and structural component of ribosome (P-value = 1.9 × 10−10) (Supplemental Fig. S16).View larger version: In this window In a new window Figure...
  8. ...batch annotation module built on integrated data; a feature extraction module that captures contextual properties of sSNVs across DNA, RNA, and protein levels; and an analysis module that investigates potential biological mechanisms affected by sSNVs. Fourth, SynMall also continuously curates...
  9. ...regulatory 113 elements across vertebrates. 114 Results 115 In this study we examine the modulation of gene expression, chromatin accessibility and 116 histone modifications in Atlantic salmon following stimulation using poly I:C, a mimic of 117 infection with an RNA virus. A major transcriptomic response...
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  10. ...) for supervised link prediction in GRN inference, leveraging scRNA-seq data and existing regulatory information to predict latent TF–gene interactions. Similarly, Grace (Wang et al. 2024a) integrates structural causal models with graph neural networks to infer both GRNs and gene causality from scRNA-seq data...
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