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| Status |
Public on Jan 20, 2020 |
| Title |
JS181209 |
| Sample type |
SRA |
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| Source name |
Mouse lung adenocarcinoma (K-rasLA2-G12D/+; p53LSL/LSL; Rosa26-CreERT2+, KPR) cells
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| Organism |
Mus musculus |
| Characteristics |
cell type: lung adenocarcinoma (K-rasLA2-G12D/+; p53LSL/LSL; Rosa26-CreERT2+, KPR) cells
infected with: control sgRNA (gCon)
treatment: +Tamoxifen
s4u feed: No
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| Treatment protocol |
Tamoxifen-treated cells had media spiked to a final concentration of 0.5 µM tamoxifen and untreated (no tamoxifen) cells had media spiked with vehicle control. s4U-treated cells had their media spiked with s4U to a final concentration of 100 µM for total RNA-seq or 1 mM for transient-transcriptome RNA-seq. Untreated (no s4U) cells had their media left unsupplemented. Cells were incubated with s4U for 1h or 5m for total RNA-seq or transient-transcriptome RNA-seq, respectively.
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| Growth protocol |
KPR cells, previously infected with with an sgRNA targeting the PVT1 p53RE or a nontargeting control, were grown in Dulbecco’s Modified Essential Media supplemented with 10% fetal bovine serum (Gibco), 0.1 mM nonessential amino acids, 2 mM L-glutamine, and Pen/Strep (50 U/ml). All cell cultures were maintained at 37℃ in a humidified incubator with 5% carbon dioxide.
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| Extracted molecule |
total RNA |
| Extraction protocol |
RNA was harvested with Trizol reagent, followed by chloroform extraction and ethanol precipitation. Genomic DNA was depleted using Turbo DNAse, followed by cleanup with RNAclean beads. RNA for transient-transciptome RNA-seq was enriched with MTS-biotin and all RNA was treated with oxidative-nucleophilic-aromatic-substitution chemistry as described in Schofield et al. Nature Methods 2018.
Libraries were generated following standard protocols using the SMARTer Stranded Total RNA-Seq Kit-Pico Input Mammalian, V2 (Takara Bio USA, cat. 634413).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
Reads filtered for number of T-to-C mutations (0-5+) and strandedness (min/pos)
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| Data processing |
Reads were filtered for unique sequences using FastUniq
Reads were trimmed using cutadapt (--minimum-length=20)
Reads were aligned to the GRCm38 genome using HISAT2 aligned with default parameters and --mp 4,2 .
Reads were processed using Picard tools, including FixMateInformation, SortSam and BuildBamIndex
Reads were filtered for those that aligned uniquely (flag: 83/163, 99/147), with MAPQ ≥ 2, and without insertions.
HTSeq-count was used to identify mapped reads in UCSC transcripts (union mode)
T-to-C mutations were identified using Rsamtools and a custom R script (available upon request). Only mutations at positions with a base quality score of greater than 45 that were at least three nt from the end of the read were counted. Reads were excluded where there were greater than five T-to-C mutations and these mutations did not account for at least one third of the observed mutations (NM tag).
SNP sites were filtered from our data in two ways: 1, bcftools was used to identify T-to-C SNP sites in control samples, and these sites were excluded from analysis; 2, sites exhibiting high T-to-C mutation rates in non-s4U treated controls were excluded from analysis.
To examine the distribution of reads with each minimum number of T-to-C mutations, bam files were filtered using Picard tools.
STAR aligner was used to make genome-coverage tracks (inputAlignmentsFromBam mode, outWigType bedGraph), and tracks were normalized using DESeq2 (estimateSizeFactors).
Tracks were converted to binary format using IGVtools (toTDF).
Genome_build: GRCm38
Supplementary_files_format_and_content: bedgraph
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| Submission date |
Dec 30, 2019 |
| Last update date |
Jan 21, 2020 |
| Contact name |
Matthew Simon |
| E-mail(s) |
matthew.simon@yale.edu
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| Organization name |
Yale University
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| Department |
MBB
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| Lab |
Simon Lab
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| Street address |
West Campus, 100 West Campus Drive, Ste MIC312A
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| City |
Orange |
| State/province |
CT |
| ZIP/Postal code |
06477 |
| Country |
USA |
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| Platform ID |
GPL24247 |
| Series (2) |
| GSE142772 |
p53 activates the long noncoding RNA Pvt1b to inhibit Myc and suppress tumorigenesis |
| GSE143204 |
p53 activates the long noncoding RNA Pvt1b to inhibit Myc and suppress tumorigenesis |
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| Relations |
| BioSample |
SAMN13701939 |
| SRA |
SRX7480750 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM4239979_JS181209.TC.0.min.bedGraph.gz |
66.9 Mb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.0.pos.bedGraph.gz |
70.8 Mb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.1.min.bedGraph.gz |
5.2 Mb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.1.pos.bedGraph.gz |
5.6 Mb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.2.min.bedGraph.gz |
211.5 Kb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.2.pos.bedGraph.gz |
244.3 Kb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.3.min.bedGraph.gz |
11.2 Kb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.3.pos.bedGraph.gz |
16.2 Kb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.4.min.bedGraph.gz |
1.8 Kb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.4.pos.bedGraph.gz |
2.3 Kb |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.5.min.bedGraph.gz |
478 b |
(ftp)(http) |
BEDGRAPH |
| GSM4239979_JS181209.TC.5.pos.bedGraph.gz |
664 b |
(ftp)(http) |
BEDGRAPH |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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