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| Status |
Public on Jun 14, 2024 |
| Title |
R2_HepG2_replicate1 |
| Sample type |
SRA |
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| Source name |
HepG2
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| Organism |
Homo sapiens |
| Characteristics |
cell line: HepG2
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| Treatment protocol |
For HepG2 libraries, 600k (R1 libraries) or 1 million (R2) cells were seeded in 6-well plates or 6 cm plates respectively. 24 hours later, cells were transfected with Lipofectamine 3000 (Invitrogen L3000001) and 5 (R1) or 10 (R2) ug of library DNA following the manufacturer’s instructions. For R2, media was replaced 6 hours later. Cells were then grown for 48 hours after transfection. For K562 libraries, 1.5 million K562 cells per replicate were electroporated with 10 ug DNA using the Neon Transfection System (Invitrogen MPK5000), transferred to 6-well plates (R1) or 6 cm plates (R2) with RPMI + FBS (no Pen/Strep), and incubated for 48 hours.
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| Growth protocol |
HepG2 cells were obtained from ATCC (HB-8065) and cultured in EMEM (ATCC 30-2003) + 10% FBS + 1% Pen/Strep at 37°C and 5% CO2. K562 cells were obtained from ATCC (CCL-243) and cultured in RPMI (Gibco 11875093) + 10% FBS + 1% Pen/Strep at 37°C and 5% CO2.
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| Extracted molecule |
polyA RNA |
| Extraction protocol |
Total RNA was extracted from cells 48h after transfection using the Monarch total RNA miniprep kit (NEB T2010S). For DNA libraries, barcode region of plasmid was extracted via PCR.
mRNA was isolated from total RNA extracts using the magnetic mRNA Isolation Kit (NEB S1550S) then reverse transcribed with the Maxima H Minus Reverse Transcriptase (Thermo EP0753) using a UMI-containing RT primer. Plasmid DNA was prepared with the same UMI-containing primer.
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
NextSeq 550 |
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| Description |
R2_processed_data.csv
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| Data processing |
Sequence reads were trimmed for 3' adaptor sequence/low-quality sequencing using cutadapt package. (parameters: maximum error rate = 0.8, minimum length = 10, maximum length = 10, no_indels)
UMIs from trimmed sequences were collapsed using starcode (parameters: seq_trim=0, umi_len=10)
Number of unique UMIs per barcode sequence calculated, and UMI counts summed across all barcodes corresponding to the same enhancer, using a custom Python script
Assembly: n/a, synthetic DNA used
Supplementary files format and content: csv containing enhancer sequence, barcode sequences for this enhancer, and unnormalized read counts for each library
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| Submission date |
Jun 04, 2024 |
| Last update date |
Jun 14, 2024 |
| Contact name |
Christopher Yin |
| E-mail(s) |
c8yin@uw.edu
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| Organization name |
University of Washington
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| Department |
Electrical & Computer Engineering
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| Lab |
Seelig
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| Street address |
3946 W Stevens Wy NE
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| City |
Seattle |
| State/province |
WA |
| ZIP/Postal code |
98105 |
| Country |
USA |
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| Platform ID |
GPL21697 |
| Series (1) |
| GSE269036 |
Iterative deep learning-design of human enhancers exploits condensed sequence grammar to achieve cell type-specificity [RNA-Seq] |
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| Relations |
| BioSample |
SAMN41673192 |
| SRA |
SRX24794969 |
| Supplementary data files not provided |
SRA Run Selector |
| Raw data are available in SRA |
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