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Sample GSM1403301

Query DataSets for GSM1403301

Status

Public on Apr 08, 2015

Title

mRNA_seq_shRNA_control rep1

Sample type

SRA

Source name

WT ES cells

Organism

Mus musculus

Characteristics

cell type: ZHBTc4 ES cells
time after treatment: 4 days post infection
treatment: pLKO control lentivirus

Extracted molecule

polyA RNA

Extraction protocol

For mRNA-sequencing, we obtained total RNAs from each sample and then poly(A) mRNA isolation from total RNA (5 µg) and fragmentation were performed using the Illumina Truseq RNA Sample Prep Kit Ver. 2 with poly-T oligo-attached magnetic beads, according to the manufacturer’s instructions. Reverse transcription of RNA fragments was performed using Superscript II reverse transcriptase (Life Technologies). For ChIP-sequencing, extracts containing DNA fragments with an average size of 400bp were immmunoprecipitated by using antibodies against GFP (control) or Pontin

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 2500

Description

mRNA-sequencing
Normalized_counts_2.txt

Data processing

Illumina CASAVA1.8.2 software used for basecalling.
For both sequencing data, we first removed adapter sequences (TrueSeq universal and index adapters) and then trimmed the ends of the adapter-free reads for which PHRED scores were lower than 20 using the cutadapter software (Martin, 2011). Remaining reads were then aligned to the mouse reference genome (NCBIM 37) using TopHat aligner (Trapnell et al., 2009) for mRNA-sequencing and using Bowtie2 (Langmead and Salzberg, 2012) for ChIP-sequencing. For the mRNA-sequencing data, considering the variations in individual genomes and presence of multiple gene copies, we used two mismatches in a read and allowed the reads to be aligned in up to 10 different locations, which are default options in the TopHat aligner. After the alignment, we counted the number of reads mapped to gene features (GTF file of NCBIM 37) using HTSeq (Van Verk et al., 2013). To reduce the technical variations across the samples, we normalized the read counts using the TMM method (Robinson and Oshlack, 2010) that uses RNA compositions and library sizes between the samples provided by edgeR package (Robinson et al., 2010) in R. For the ChIP-sequencing data, among the aligned reads, only the reads uniquely aligned and with MAPQ scores larger than 5 were used for further analysis.
Genome_build: ENSEMBL NCBIM37
Supplementary_files_format_and_content: tab-delimited text files including normalized read counts for each sample (mRNA-sequencing)
Supplementary_files_format_and_content: BED files including the aligned reads to reference genome (ChIP-sequencing)

Submission date

Jun 03, 2014

Last update date

May 15, 2019

Contact name

Bhin Jin-hyuk

E-mail(s)

bynjh007@postech.ac.kr

Organization name

POSTECH

Department

Department of Chemical engineering