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GEO help: Mouse over screen elements for information. |
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| Status |
Public on Feb 19, 2024 |
| Title |
C2C12, 72 hours after differentiation, biological replicate3 |
| Sample type |
SRA |
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| Source name |
mouse myoblast cells
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| Organism |
Mus musculus |
| Characteristics |
treatment: 72 hours after differentiation
treatment label: D72h
cell line: C2C12
genotype: wild type
molecule subtype: total RNA, remove rRNA
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| Treatment protocol |
HepG2 cells were treated with 500ng/ml ADR or 500μM CoCl2 for 24h before harvesting; 2ug/ml poly (I:C) were transfected into HepG2 with lipo2000 and then the cells were cultured for 12h before harvesting. C2C12 cells grow up to 80% confluence, and then the culture medium was changed to the differentiation medium which is consisted of DMEM with 2% horse serum.
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| Growth protocol |
HepG2 and U-87 MG were cultured in MEM supplemented with 10% FBS, 1% penicillin-streptomycin at 37 °C in a humidified. HEK293T and C2C12 cells were clutured in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin at 37 °C in a humidified atmosphere with 95% air and 5% CO2.
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| Extracted molecule |
total RNA |
| Extraction protocol |
RNA was harvested using Trizol reagent. 2ug total RNA was used to prepare the sequencing libraries.
RNA libraries were prepared for sequencing using standard Illumina or nanopore protocols
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
HiSeq X Ten |
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| Description |
Discover the full-length non-capped RNAs of the mammalian transcriptome
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| Data processing |
NGS raw data gained from Illumina HiSeqX10 with 150 bp paired end reads, TGS raw data gained from MinION nanopore with single end reads
For Illumina sequencing data, Cutadapt (v2.8) (Martin, 2011) was firstly used to cut the sequencing adapters of the paired-end reads with the following parameters: cutadapt -a AGATCGGAAGAGCACACGTCTG -A AGATCGGAAGAGCGTCGT -m 15 -e 0.15. The NAP-seq specific 5’-adapter (AAGCAGTGGTATCAACGCAGAGT) and 3’-adapter (AGTCGTAGTAAGTCTGTGCTCG) which marked the boundary of the napRNAs were subsequently moved by our programme flClipAdapter with the following parameters: -l 15 -e 0.1 -c 6. Finally, the reads were mapped to the reference genome (hg38 or mm10) with STAR (Dobin et al., 2013) software with the following parameters: --genomeLoad NoSharedMemory --limitBAMsortRAM 60000000000 --alignEndsType EndToEnd --outFilterType BySJout --outFilterMultimapScoreRange 0 --outFilterMultimapNmax 20 --outFilterMismatchNmax 10 --outFilterMismatchNoverLmax 0.05 --outFilterScoreMin 0 --outFilterScoreMinOverLread 0 --outFilterMatchNmin 20 --outFilterMatchNminOverLread 0.8 --seedSearchStartLmax 15 --seedSearchStartLmaxOverLread 1 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 --alignSJoverhangMin 20 --alignSJDBoverhangMin 10 --outSAMtype BAM Unsorted --outSAMmode Full --outSAMattributes All --outSAMunmapped None --outSAMorder Paired --outSAMprimaryFlag AllBestScore --outSAMreadID Standard --outReadsUnmapped Fastx --limitOutSJcollapsed 5000000 --alignEndsProtrude 150 ConcordantPair --readFilesCommand zcat . For nanopore sequencing data, Cutadapt (v2.8) was used to cut the NAP-seq specific adapters with the following parameters: -j 16 -g AAGCAGTGGTATCAACGCAGAGT -a AGTCGTAGTAAGTCTGTGCTCG -m 20 -e 0.3 -O 10, and then use Cutadapt again to move any possible adapters with the following parameters: -j 16 -g CGAGCACAGACTTACTACGACT -a ACTCTGCGTTGATACCACTGCTT -m 20 -e 0.3 -O 10, and we just retained the read that contained the NAP-seq specific adapters. Finally, thr reads were mapped to the reference genome (hg38) with minimap2 (Li, 2018) with the following parameters: --junc-bed hg38.gencode.v30.geneAnno.bed -t 16 -k15 -w5 --splice -g2000 -G200k -A2 -B4 -O4,96 -E2,0 -C18 -z400,200 -ub --end-bonus=18 --junc-bonus=18 --splice-flank=yes -ub --sam-hit-only --secondary=no -a
To identify pronounced high-confidence napRNAs, we firstly assembled the continuous reads to the contigs, and then calculated the numbers of start reads containing specific 5’-adapter (startReadNum) and end reads containing specific 3’-adapter (endReadNum). We reasoned that the startReadNum and endReadNum of a candidate napRNA should be significantly higher over the sequence upstream and downstream, while the coverage of the contigs should also be significantly higher over the regions around. Finally, we developed the computational software napSeeker to calculate the numbers of start reads containing specific 5’-adapter (startReadNum) and end reads containing specific 3’-adapter (endReadNum). We calculated the fold change between startReadNum and numbers of reads containing specific 5’-adapter within 100nt upstream and downstream (startFC), in a similar way, the fold change between endReadNum and the numbers of reads containing specific 3’-adapter upstream and downstream within 100nt (endFC). Next, we calculated the fold change between coverage of the contigs and the regions within 20nt upstream/downstream (up20ntFC/down20ntFC). A high-confidence napRNA had to meet the following criteria: (1) startReadNum, endReadNum ≥ 7; (2) startFold, endFold ≥ 2. (2) up20ntFold, down20ntFold ≥ 2. (4) length≥100. What’s more, the candidate napRNAs had to express in at least 2 samples of all the human (or mouse) samples. Finally, we only retained the napRNA with the summary counts ≥ 20. These stringent parameters allowed us to identify the highest-confidence candidate napRNAs.
Genome_build: hg38, mm10
Supplementary_files_format_and_content: bigwig files were generated using self-developed software; Scores represent Reads per million mapped reads (RPM).
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| Submission date |
Dec 27, 2021 |
| Last update date |
Feb 19, 2024 |
| Contact name |
Jian-Hua Yang |
| E-mail(s) |
[email protected]
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| Phone |
86-20-84112517
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| Organization name |
Sun Yat-sen University
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| Department |
School of Life Sciences
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| Street address |
No. 135, Xingang Xi Road
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| City |
Guangzhou |
| ZIP/Postal code |
510275 |
| Country |
China |
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| Platform ID |
GPL21273 |
| Series (1) |
| GSE192632 |
NAP-seq Reveals Novel Classes of Structured Noncoding RNAs with Regulatory Functions |
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| Relations |
| BioSample |
SAMN24433668 |
| SRA |
SRX13509532 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM5753603_C2C12_D72h_rep3.rpm.minus.coverage.bw |
980.3 Mb |
(ftp)(http) |
BW |
| GSM5753603_C2C12_D72h_rep3.rpm.minus.end.bw |
4.5 Mb |
(ftp)(http) |
BW |
| GSM5753603_C2C12_D72h_rep3.rpm.minus.start.bw |
6.1 Mb |
(ftp)(http) |
BW |
| GSM5753603_C2C12_D72h_rep3.rpm.plus.coverage.bw |
980.5 Mb |
(ftp)(http) |
BW |
| GSM5753603_C2C12_D72h_rep3.rpm.plus.end.bw |
4.6 Mb |
(ftp)(http) |
BW |
| GSM5753603_C2C12_D72h_rep3.rpm.plus.start.bw |
6.2 Mb |
(ftp)(http) |
BW |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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