|
| Status |
Public on Jun 23, 2020 |
| Title |
Doc12_neg_rep1 (RNA-seq) |
| Sample type |
SRA |
| |
|
| Source name |
ID3neg_Docile_day12
|
| Organism |
Mus musculus |
| Characteristics |
Sex: Male
tissue: spleen
cell type: ID3GFP- P14 T cells
infection: LCMV Docile
time: day12
|
| Treatment protocol |
N/A
|
| Growth protocol |
N/A
|
| Extracted molecule |
total RNA |
| Extraction protocol |
RNA extraction from sorted P14 T cells was performed following the manufacturer’s protocol using the RNAeasy Plus Mini Kit (Qiagen)
Libraries were generated following the manufacturer's protocol using the TruSeq RNA Library Preparation Kit (Illumina, San Diego, USA)
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NextSeq 500 |
| |
|
| Description |
Doc12n1
|
| Data processing |
The libraries were aligned to the GRCm38/mm10 genome using Subread-1.6.4; only uniquely mapped read-pairs were allowed. The mapped read-pairs in each library were assigned to the NCBI RefSeq gene annotation for mm10 (build 38.1) by using featureCounts.
Genes were excluded from downstream analysis if they failed to achieve a CPM (counts per million mapped reads) value of at least 0.5 in 2 libraries. Counts were quantile normalized, precision weighted with the “voom” function of the limma package and converted to log2 counts per kilobase per million(log2 FKPM).
A linear model was fitted to each gene, and empirical Bayes moderated t-statistics were used to assess differences in expression. Genes were called differentially expressed if they achieved a false discovery rate of 0.10 or less.
Genome_build: mm10
Supplementary_files_format_and_content: tab-delimited text files include normalized log2-FPKM values for each library. The supplementary file, Supp_Raw_Counts.txt, includes raw read counts for genes in all the libraries.
|
| |
|
| Submission date |
Dec 28, 2019 |
| Last update date |
Jun 26, 2020 |
| Contact name |
Wei Shi |
| E-mail(s) |
wei.shi@onjcri.org.au
|
| Organization name |
Olivia Newton-John Cancer Research Institute
|
| Lab |
Bioinformatics and Cancer Genomics
|
| Street address |
Level 5, ONJ Cancer Centre, 145 Studley Rd
|
| City |
Heidelberg |
| State/province |
Victoria |
| ZIP/Postal code |
3084 |
| Country |
Australia |
| |
|
| Platform ID |
GPL19057 |
| Series (2) |
| GSE142686 |
Early precursor T cells establish and propagate T cell exhaustion in chronic infection [RNA-seq] |
| GSE142687 |
Early precursor T cells establish and propagate T cell exhaustion in chronic infection |
|
| Relations |
| BioSample |
SAMN13696242 |
| SRA |
SRX7473591 |
