|
| Status |
Public on Dec 04, 2014 |
| Title |
RNA-seq siCont ActD 4h (biological replicate #2) |
| Sample type |
SRA |
| |
|
| Source name |
RS2_siCont-4h
|
| Organism |
Homo sapiens |
| Characteristics |
cell line: HeLa
cell type: human cervical cancer cell line
|
| Treatment protocol |
HeLa cells were transfected twice with 40 nM of siRNAs using Lipofectamine 2000 (Invitrogen). To block transcription, HeLa cells were treated with actinomycin D (Sigma, 4 μg/ml).
|
| Growth protocol |
HeLa cells were maintained in DMEM (Welgene) supplemented with 10% fetal bovine serum (Welgene).
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Total RNAs were extracted by TRIzol (Invitrogen), and the quality was checked by Bioanalyzer 2100 (Agilent).
The same amount of control poly(A) spike-in (Affymetrix, #900433) was mixed with each sample with the same amount of total RNA, and subsequently rRNA was depleted by Ribo-Zero (Epicentre). RNA-seq libraries were constructed using Illumina TruSeq RNA sample preparation kit v2.
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 2500 |
| |
|
| Description |
RNA-seq for HeLa total RNA 4h after actinomycin D treatment to control knockdown cells
|
| Data processing |
The sequence reads were aligned to the spike-in RNA sequences (GenBank accessions M24537.1, X04603.1, L38424.1, and X17013.1) by Bowtie2 (Langmead and Salzberg, 2012) using options “--end-to-end --sensitive -k 4”.
Scaling factor of a sample was calculated as geometric mean of ratios for valid spike-ins between geometric mean of read counts in all samples and read counts in the sample.
Spike-in RNAs with insufficient read counts (< 500) in any sample were excluded from the calculation. The reads not aligned to any spike-in RNAs were aligned to UCSC hg19 genome assembly using STAR (Dobin et al., 2013) with an option “--outFilterScoreMin 3” and splicing junction annotations generated from the NCBI RefSeq and the UCSC knownGene. The reduced RefSeq transcript set for non-overlapping representation was prepared as previously described (Chang et al., 2014b).
Reads mapped to each transcript were counted using BEDTools multicov (Quinlan and Hall, 2010) with default options. The read counts were scaled by the factors calculated from spike-in read counts. Transcripts not detected with enough reads (< 1,000 normalized reads in the siControl 0 hr sample) were removed to reduce noises.
Genome_build: hg19
Supplementary_files_format_and_content: A spreadsheet file contain normalized read counts by spike-in scaling; RS2-normalized-readcount.csv is for biological replicate #2 samples.
|
| |
|
| Submission date |
Sep 26, 2014 |
| Last update date |
May 15, 2019 |
| Contact name |
Hyeshik Chang |
| E-mail(s) |
hyeshik@snu.ac.kr
|
| Organization name |
Seoul National University
|
| Department |
School of Biological Sciences
|
| Lab |
Hyeshik Chang Lab
|
| Street address |
Building 203 Room 525, School of Biological Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu
|
| City |
Seoul |
| State/province |
South Korea |
| ZIP/Postal code |
08826 |
| Country |
South Korea |
| |
|
| Platform ID |
GPL16791 |
| Series (1) |
| GSE59626 |
Uridylation by TUT4 and TUT7 marks mRNA for degradation [RNA-Seq] |
|
| Relations |
| BioSample |
SAMN03083488 |
| SRA |
SRX712888 |
