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Sample GSM1030552 Query DataSets for GSM1030552
Status Public on Jan 07, 2013
Title Post-diff mRNA-seq (C2C12.CNTL cells)
Sample type SRA
Source name C2C12.CNTL mouse cells
Organism Mus musculus
Characteristics cell type: C2C12.CNTL cells
Growth protocol Cell culture protocol: C3H10T1/2, C2C12 cells were cultured in high glucose DMEM with 10% fetal bovine serum at 10% CO2.
Adipogenesis protocol: For adipogenesis, cells were grown to confluence in medium containing 10% fetal bovine serum. At confluence, cells were exposed to induction medium containing dexamethasone (1uM), IBMX(0.1mM), insulin(5ug/ml) and rosiglitazone(1uM), and 10% FBS. 3 days later, cells were further cultured in DMEM containing insulin (5ug/ml) and rosiglitazone (1uM) until they were ready for collection.
Extracted molecule polyA RNA
Extraction protocol Total RNA from cultured cells or mouse tissues was isolated using QIAGEN RNeasy Plus mini columns according to the manufacturer’s instructions (Qiagen, MA).
mRNA-seq libraries preparation and deep sequencing: Total RNAs were extracted from C3H10T1/2 cells and adipocytes, differentiated shTAF7L-C3H10T1/2 cells, differentiated C2C12.CNTL and C2C12.TAF7L cells by RNeasy Plus Mini Kit (Qiagen), 8ug of each sample was used to purify mRNA and subsequently converted into to mRNA-seq library using mRNA-Seq Sample Prep Kit (Illumina).
Preparation of the ChIP-seq and mRNA-seq libraries on the DNA samples of the immunoprecipitation from antibodies and IgGs and mRNA extracted from different cells precisely followed the instructions from Illumina (Illumina Inc.), qualities of the libraries were assessed by 2100 Bioanalyzer and then subjected to ultra-high throughput sequencing.
Library strategy RNA-Seq
Library source transcriptomic
Library selection cDNA
Instrument model Illumina HiSeq 2000
Description Post-diff mRNA-seq (C2C12 cells)
Sample name: HY-RS5
Data processing Peak calling methods: To accurately identify significant binding positions across the genome, we incorporated two peak calling methods, as described previously (May et al., 2012). First, we used MACS (Zhang et al., 2008) (version 1.4, with default settings except: --nomodel --shiftsize=110 --pvalue=1e-2 --mfold=10,10000 --slocal=2000 --llocal=20000), with an FDR significant threshold of 0.05 (using IgG ChIP as a control). Second, we applied the Grizzly peak fitting algorithm (Harrison et al., 2011), which uses a model-based iterative approach to accurately identify multiple binding loci at every enriched region. Peaks were then associated with the nearest TSS (using gene annotation from UCSC, version mm9), and classified as promoter peaks (up to 500bp from start site), proximal enhancer peaks (larger than 500bp and less than 5Kb), distal enhancer peaks (less than 50 Kb from TSS), or none (further away than any start site). Peaks are summarized in the following format: (1) position (2) chromosome (3) from position (4) to position (5) max coverage (6) total coverage (7) peaks (8) their respective heights (9) threshold for stopping criterion. Note: There are some runs with no or very few peaks called.
Digital gene expression of mRNA-seq: The reads were than mapped to the mouse transcriptome (created using UCSC table browser, version mm9, on February 2012), using TopHat (Trapnell et al., 2009), version v1.4.0., using default parameters. We then applied cufflinks (Trapnell et al., 2010), version v1.3.0, using the default parameters except: --max-mle-iterations 1, to estimate the digital expression levels at each transcript. Due to the high number of sequence reads, these steps were done in seven batches of 25 million reads each, per sample.
Genome_build: UCSC mm9 [MGSCv37]
Supplementary_files_format_and_content: Peak calling output files (.xls; intersection of MACS and Grizzly Peak fitting algorithms).
Supplementary_files_format_and_content: Adipose_mRNA_ChIP.xlsx and Adipose_mRNA_ChIP.txt are large tables summarizing ChIP binding at promoters, proximal or remote for every data/gene, as well as gene expression levels and ratios. These two files are mostly identical: the binary excel format is ideal for visualizations and the text version suitable for automated parsing. The files were generated using my MATLAB programs, then curated manually.
Submission date Nov 02, 2012
Last update date May 15, 2019
Contact name Tommy Kaplan
Organization name Hebrew University
Department School of Computer Science and Engineering
Street address Givat Ram Campus
City Jerusalem
ZIP/Postal code 91904
Country Israel
Platform ID GPL13112
Series (1)
GSE41937 Dual Functions of TAF7L in Adipocyte Differentiation
Reanalyzed by GSE80797
SRA SRX202805
BioSample SAMN01798308

Supplementary data files not provided
SRA Run SelectorHelp
Raw data are available in SRA
Processed data are available on Series record

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