IBD, colon cancer, microbiome and COVID-19 were obtained from the NCBI website (Supplementary TableS6). Differentially expressed genes (DEGs) between any two diets (p-adj ≤ 0.05) were identified in the RNA-seq data and displayed in the respective heatmaps, generated using the Pheatmap ...
Single-cell RNA-seq coupled to a new functional annotation approach identifies distinct functional states of Th17 cells and the underlying molecular mechanisms that regulate their pathogenicity. Jellert T. Gaublomme,N Yosef,Y Lee,... - 《Cell》 被引量: 143发表: 2015年 加载更多来源...
For single-cell RNA-sequencing (scRNA-seq), cells were individually sorted into 8-Strip PCR tubes containing the lysis buffer. For bulk RNA-seq, 100 cells (P100) were sorted into one PCR tube as a biological replicates. The batch information for cell sorting was included in Online-only Tabl...
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RNA-Seq data showed that enzymes involved in DNA methylation (Dnmt1, Dnmt3a,b) and demethylation (Tet1,2,3, Gadd45a) are enriched in the dorsal compared to the ventral pole of the DG (Supplementary Fig.2O). To examine mCG, mCH, and hmC with single base resolution genome-wide, we ...
We identified 696 poly(A)- genes by bulk RNA sequencing, of which around 30 % could be recovered in a single cell by SUPeR-seq (for details, see Additional file 3). When merged the SUPeR-seq data of the seven single cell samples together, over 50 % of these 696 genes could be ...
To update the database this year, we retrieved all new lncRNA–target relationships from papers published from 1 August 2014 to 30 April 2018 and RNA-seq datasets before and after knockdown or overexpression of a specific lncRNA. LncRNA2Target database v2.0 provides a web interface through ...
Schuübeler from which released data were used in the analyses and to D. Schübeler lab for providing unpublished RNA-seq data. The authors are grateful to T. Ye, and S. Le Gras for bioinformatics support; the IGBMC NGS platform for data generation; P. Eberling for generation of peptides...
Gene functionality is closely connected to its expression specificity across tissues and cell types. RNA-Seq is a powerful quantitative tool to explore genome wide expression. The aim of this study is to provide a comprehensive RNA-Seq dataset across the
Single-cell RNA-seq data preprocessing Raw reads from scRNA-seq were processed using the Cell Ranger software suite (v2.2.0). Briefly, reformatted reads were mapped to the mouse reference genome (GRCm38) with the Ensembl GRCm38.91 GTF file. For each replicate, a gene-by-cell count matrix...