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Sample GSM2866585

Query DataSets for GSM2866585

Status

Public on Feb 09, 2018

Title

Primed HC11 Rep 1 (P)

Sample type

SRA

Source name

Mouse HC11 cell line

Organism

Mus musculus

Characteristics

strain: COMMA-1D Mouse (Balb/c)
tissue: Mammary Epithelial cells
cell line: HC11
genotype: wild type

Treatment protocol

Embryonic stem cells (R1 cells) were harvested after they were grown on feeder cells with LIF, serum and 2i inhibitors till 50-60% confluency, undifferentiated mammary epithelial cells (HC11) were grown till they reach confluency in presence of EGF and Insulin, where as primed cells were harvested after treatment with hydrocortisone along with insulin and were grown for 48 hours prior to RNA isolation. Prolactin stage cells were harvested 72 hours after the primed cells were treated with fresh medium containing hydrocortisone, insulin and prolactin.

Growth protocol

Embryonic stem cells (R1 Cells) were grown on feeder cells with Lif, serum and 2i inhibitors till 50-60% confluency. HC11 normal cells were grown in RPMI1640-GlutaMAXTM complete medium with stage specific treatment as given below.

Extracted molecule

total RNA

Extraction protocol

Total RNA was extracted from ESC, Normal, Primed and Prolactin samples in replicates by using TRIzol reagent according to manufacturer protocol, rRNA was removed by using RiboZero kit (Cat No#MRZH11124).
Library was prepared by using NEBNext® Ultra™ RNA Library Prep Kit for Illumina (E7530S)

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 2500

Data processing

FastqQC to check quality of the data and to remove adapters from sequencing reads, it also removes reads containing N > 10% (N represents the base cannot be determined)and reads containing low quality (Qscore<= 5) base which is over 50% of the total base.
Paired end senquencing reads were mapped to the reference genome by using TopHat2 tool. Mismatch parameter was kept as 2 and remaining all parameters were set as default.
Gene expression level is measured by transcript abundance. The greater the abundance, the higher is the gene expression level. In our RNA-seq analysis, the gene expression level is estimated by counting the reads that map to genes or exons. Read count is not only proportional to the actual gene expression level, but is also proportional to the gene length and the sequencing depth. In order for the gene expression levels estimated from different genes and experiments to be comparable, the FPKM is used. In RNA-seq, FPKM, short for the expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs-sequenced, is the most common method of estimating gene expression levels, which takes into account the effects of both sequencing depth and gene length on-counting of fragments (Trapnell, Cole, et al., 2010).
The correlation between samples is an important indicator for testing the reliability of the experiment. The closer the correlation coefficient is to 1, the greater the similarity of the samples. ENCODE suggests that the square of the Pearson correlation coefficient should be larger than 0.92, under ideal experimental conditions
After gene expression abundance we performed differential gene expression by using DESeq2 (Anders et al, 2016). The differential gene expression analysis contains three steps: 1) Read counts Normalization; 2) Model dependent p-value estimation; negative binomial distribution 3)FDR value estimation based on multiple hypothesis testing.
Genome_build: UCSC mm10 mouse genome assembly

Submission date

Nov 28, 2017

Last update date

May 15, 2019

Contact name

Sreenivasulu Kurukuti

E-mail(s)

skurukuti@gmail.com

Organization name

University of Hyderabad

Department

Animal Biology