|
| Status |
Public on Dec 31, 2022 |
| Title |
WT SETMAR 3 |
| Sample type |
SRA |
| |
|
| Source name |
HEK293T cells
|
| Organism |
Homo sapiens |
| Characteristics |
vector: pFLAG-CMV4-SETMAR(wt)
cell line: HEK293T
|
| Treatment protocol |
Cells were seeded into 6-well plates, 500,000 cells per well, and grown to ≈60% confluency and were then transiently transfected with either empty vector plasmid or plasmid containing wild type SETMAR. DNA (2 µg) was transfected with 2 µl of 2 µg/ml polyethyleneimine per well.
|
| Growth protocol |
HEK293T cells were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin in an incubator at 37°C and 5% CO2.
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Cells were harvested 48 hours post-transfection and RNA was isolated using the QIAGEN RNeasy Mini Kit. DNaseI digestion was performed immediately following RNA isolation using PureLink DNase according to manufacturer instructions, and samples were purified using the QIAGEN RNeasy Mini Kit.
500 nanograms of RNA per sample were then used to prepare a single-indexed strand-specific cDNA library using the TruSeq Stranded mRNA Library Prep Kit (Illumina). The resulting libraries were assessed for quantity and size distribution using Qubit and Agilent 2100 Bioanalyzer.
200 pM pooled libraries were utilized per flowcell for clustering amplification on cBot using HiSeq 3000/4000 PE Cluster Kit and sequenced with 2×75bp paired-end configuration on HiSeq4000 (Illumina) using HiSeq 3000/4000 PE SBS Kit. A Phred quality score (Q score) was used to measure the quality of sequencing. More than 90% of the sequencing reads reached Q30 (99.9% base call accuracy). Sequencing data were first assessed using FastQC for quality control.
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 4000 |
| |
|
| Description |
HEK293T cells transfected with vector containing full length wild type SETMAR
Georgiadis_RNAseq_STAR_featureCoutns_edgeR_20170418_WTvsVoid.xlxs
|
| Data processing |
All sequenced libraries were then mapped to the human genome (UCSC hg19) using STAR RNA-seq aligner with the following parameter: “--outSAMmapqUnique 60”.
The reads distribution across the genome was assessed using bamutils (from ngsutils). Uniquely mapped sequencing reads were assigned to hg19 refGene genes using featureCounts (from subread) with the following parameters: “-s 2 –p –Q 10”.
Quality control of sequencing and mapping results were summarized using MultiQC. Genes with read count per million (CPM) < 1 in more than 4 of the samples were removed. The data were normalized using the TMM (trimmed mean of M values) method.
Differential expression analysis was performed using edgeR.
False discovery rate (FDR) was computed from p-values using the Benjamini-Hochberg procedure.
Genome_build: UCSC hg19
Supplementary_files_format_and_content: Excel spreadsheet of differential expression analysis from edgeR listing all expressed genes with false discovery rate, p-value, log2fold change, log2(CountsPerMillion) for each replicate, and raw counts for each replicate
|
| |
|
| Submission date |
Aug 24, 2017 |
| Last update date |
Dec 31, 2022 |
| Contact name |
Millie Georgiadis |
| E-mail(s) |
mgeorgia@iu.edu
|
| Organization name |
Indiana University School of Medicine
|
| Department |
Biochemistry and Molecular Biology
|
| Lab |
Dr. Millie Georgiadis
|
| Street address |
635 Barnhill Drive
|
| City |
Indianapolis |
| State/province |
Indiana |
| ZIP/Postal code |
46202 |
| Country |
USA |
| |
|
| Platform ID |
GPL20301 |
| Series (1) |
| GSE103076 |
Structural and Genome-wide Analysis Support a Role for SETMAR in Transcriptional Regulation |
|
| Relations |
| SRA |
SRX3130863 |
| BioSample |
SAMN07559510 |
