CpG island mediated linear and spatial gene partitioning (other data sets)
Ontology highlight
ABSTRACT: In order to elucidate the general rules for gene localization and regulation mediated by CpG islands, we reanalyzed diverse published data of DNaseI-seq, MeDIP-seq, LMNB1-DamID-seq, PolII ChIA-pet, and Hi-C analysis.
Project description:In order to study the gene expression change upon transcription factor (MYOD1, SPI1) induction, we reanalyzed published gene expression microarray data. Raw data were preprocessed and normalized with the Robust Multi-array Average (RMA)(Irizarry et al., 2003) method.
Project description:In order to elucidate the general rules for gene localization and regulation mediated by CpG islands, we reanalyzed published ChIP-seq data of CXXC domain, H3K9me3, KDM2A, SUV39H1, ATF4, MYBL1, MYOD1, SPI1, and CTCF. Raw data were downloaded from Sequence Read Archive (SRA) in National Center for Biotechnology Information (NCBI) database. FASTQ files were extracted with the SRA Toolkit version 2.5.5 and aligned using Bowtie 2.2.5 onto the mouse and human genome (mm9 and hg19, respectively). For the identification of factor binding sites, model-based analysis for ChIP-seq peak caller (MACS 1.4.2) was used with a p-value cutoff of 1e-5.
Project description:We reanalyzed published RNA-seq data to study 1) the genomic landscapes near surrounding regions of transcriptional start sites with regard to the gene expression activities and 2) the gene expression change upon transcription factor (MYBL1, ATF4) depletion. Raw data were downloaded from Sequence Read Archive (SRA) in National Center for Biotechnology Information (NCBI) database. FASTQ files were extracted with the SRA Toolkit version 2.5.5 and aligned using STAR 2.4.2 onto the mouse and human genome (mm9 and hg19, respectively). Gene expression was calculated as RPKM values using rpkmforgenes.py (Ramsköld et al., 2009).
Project description:In order to test the global effects of CpG island-centered gene regulation on global gene expression profile, pA+ RNA-seq data of diverse tissues and cell lines were gathered and profiled. All available mouse poly-A positive RNA-seq data (3,818 samples) were summarized and downloaded at May, 5th, 2015. Among them, excluding single cell RNA-seq or experiments whose expression verified gene counts are small (less than 5,000 genes with RPKM 0.5 or higher), 1,524 high quality RNA-seq data were used. Raw data were downloaded from Sequence Read Archive (SRA) in National Center for Biotechnology Information (NCBI) database. FASTQ files were extracted with the SRA Toolkit version 2.5.5 and aligned using STAR 2.4.2 onto the mouse and human genome (mm9 and hg19, respectively). Gene expression was calculated as RPKM values using rpkmforgenes.py (Ramsköld et al., 2009).
Project description:Comparison of DamID profiles and LAD patterning across cell types reveals regions of variable LADs LmnB1 interactions at the nuclear periphery of C57Bl/6 pro-B cells and fibroblasts were identified by DamID in duplicate for both pro-B and fibroblasts (4 samples total). For each sample, Dam-LmnB1/Dam-only Log2 ratios of chromosomes 11 and 12 were calculated, lifted over to mm9, duplicates were normalized and averaged together.
Project description:To probe the functional consequences of nuclear lamina genome association, we made LMNA, LBR, and double LMNA/LBR K562 knockout lines. We first examined the genome interaction changes with nulcear lamina using LMNB1 DamID seq and further assessed the functional consequences of these changes using RNA-seq and repli-seq.