|
| Status |
Public on Jul 01, 2018 |
| Title |
KO_Alkb_Treated_smRNA_Input_repeat1 |
| Sample type |
SRA |
| |
|
| Source name |
embryonic stem cells
|
| Organism |
Mus musculus |
| Characteristics |
genotype/variation: Mettl1 knockout
differentiation: undifferentiated
|
| Treatment protocol |
Mettl1 gene knockout was mediated by CRISPR/cas9.
|
| Growth protocol |
Mouse embryonic stem cells R1/E were cultured in LIF/serum medium. Cells were grown in a 5% CO2 cell culture incubator at 37℃.
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Total RNA samples were isolated with Trizol following the manufacturer’s instructions. small RNAs (<200nt) were first purified using the mirVana miRNA Isolation Kit (Thermo Fisher Scientific).
Libraries were prepared according to Illumina's instructions accompanying the TrueSeq Stranded mRNA library preparation kit, TruSeq Ribo Profile (Mammalian) Library Prep Kit and the NEBNext® Small RNA Library Prep Set.
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NextSeq 500 |
| |
|
| Description |
small RNA-Seq
m7G_MeRIP-Seq_reads.txt
|
| Data processing |
For RNA-Seq data analysis, after adapter trimming and low quality sequence filtering (Q30), the clean reads were mapped to mouse reference genome (mm10) using Tophat2. The raw counts of reads mapping to the gencode genes (v19) were computed by HTSeq with ‘union’ overlap mode. The raw counts were then normalized as Reads per kilo base per million mapped reads (RPKM) using edegR.
For Ribo-Seq data analysis, Adapter sequences were trimmed and low quality sequences (Q30) were discarded using trim_galore. The sequences with length of at least 25nt were further aligned to mouse rRNA and tRNA index constructed from Ensembl (Release 91) and GtRNAdb databases , respectively. The remaining reads were aligned to the UCSC canonical known gene transcripts of the mouse reference genome (mm10) using bowtie with a maximum one mismatch allowed.
For m7G-MeRIP-Seq and TRAC-Seq, after adaptor trimming and quality filtering, the clean sequencing data were first mapped to the tRNA sequences using Bowtie2. Then read count for each tRNA was calculated using the method in (Cozen et al., 2015). The read counts of tRNA are normalized as reads per million (RPM) by the total number of small RNA reads that are matched to tRNAs in each sample.
Genome_build: mm10
Supplementary_files_format_and_content: tab-delimited text file showing the normalized reads
|
| |
|
| Submission date |
Apr 04, 2018 |
| Last update date |
Jul 01, 2018 |
| Contact name |
Shuibin Lin |
| E-mail(s) |
shuibin.lin@childrens.harvard.edu
|
| Phone |
6173550204
|
| Organization name |
Boston Childrens Hospital
|
| Street address |
1 Blackfan Circle
|
| City |
Boston |
| ZIP/Postal code |
02115 |
| Country |
USA |
| |
|
| Platform ID |
GPL19057 |
| Series (1) |
| GSE112670 |
The m7G tRNA methylome regulates embryonic stem cell self-renewal and differentiation |
|
| Relations |
| BioSample |
SAMN08863431 |
| SRA |
SRX3881576 |
