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| Status |
Public on May 19, 2014 |
| Title |
Dcr21 |
| Sample type |
SRA |
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| Source name |
liver
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| Organism |
Mus musculus |
| Characteristics |
genotype: Dicer knockout
tissue: liver
timepoint: ZT8
age: 3-6 months
genetic background: C57BL/6 x 129SvEv x 129S2/SvPas
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| Treatment protocol |
Mice were i.p. injected with tamoxifen for 5 consecutive days (total 2 mg), then entrained to light-dark cycles for one month with free access to food/water and sacrificed at 4-hour intervals around-the-clock
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| Extracted molecule |
total RNA |
| Extraction protocol |
Total RNA was prepared as in Gatfield et al., (Genes Dev., 23:1313, 2009)
Dnase-treated total RNAs were subjected to rRNA depletion using Ribo-Zero Magnetic Kit (Human/Mouse/Rat, Epicentre). The resulting rRNA-depleted RNA samples were then used to prepare random-primed cDNA libraries using the Illumina Tru-Seq RNA Sample Preparation Kit (Illumina) according to the manufacturer's recommendations
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 2500 |
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| Description |
rRNA-depleted total RNA
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| Data processing |
Base-calling: Casava 1.8.2
QC: Sequencing runs were only further processed, if their quality related statistics were within three standard deviations of the mean of all runs. The five quality related statistics used were the percentage of clusters passed filtering (%PF clusters), mean quality score (PF clusters), percentage of reads aligned, mean alignment score and percentage of alignment error.
QC: Presence of adapter sequences in the datasets were checked with cutadapt utility. It was not necessary to trim reads to remove the adapter sequences.
Pre-alignment: Sequences were first mapped to local databases of mouse rRNA (Ensembl and NCBI) and rodent-specific repeat sequences (RepBase Update version 18.10) using bowtie2 version 2.1.0 with default alignment parameters. Mapped reads were tagged to be filtered in later steps of the analysis.
Alignment: All reads were mapped to Genome Reference Consortium GRCm38 (mm10) version of mouse reference genome sequence using tophat version 1.4.1 with known mm10 transcripts provided via the –transcriptome-index option.
Filtering: Only non-rRNA, non-repeat reads that mapped uniquely to the mouse genome were selected for further analysis.
Abundance measurement: Expression levels for mRNA and pre-mRNA were estimated per locus. An in-house Python script was used to count the reads mapped within each annotation feature in a similar way as implemented in htseq-count utility software against modified gene models (filtered, flattened, provided as processed data file: Mmusculus_v1.1_flatten.gtf). Only reads which can be unambiguously identified as either exonic (continuous or spliced) or intronic for a single locus were counted towards the mRNA or pre-mRNA counts of that locus, respectively.
Pre-normalization: Mappable and countable mRNA and pre-mRNA lengths (in bps) for each locus were calculated by means of generating all possible 100-bp long reads in silico (faux reads) for each transcript type and counting the faux reads through identical mapping and counting work flow used for real experimental reads. These lengths were later used in RPKM calculations.
Normalization: Based the extend of differential expression and the presence of highly-expressed genes, read counts of mRNA and pre-mRNA datasets were normalized with upper quantile and TMM normalization methods, respectively. Prior to normalization, transcripts which did not have at least 10 counts in at least one third of the samples were removed from the datasets.
Normalization: RPKM values were calculated as the number of counted reads per 1000 mappable and countable bases per geometric mean of normalized read counts per million.
Genome_build: Genome Reference Consortium GRCm38 (mm10)
Supplementary_files_format_and_content: rpkm files were generated with R; gene IDs are ENSEMBL-IDS
Supplementary_files_format_and_content: GTF file were generated with a series of in-house Python scripts; briefly unexpressed transcript models were filtered out, some new models were constructed with Cufflinks 2.0.2 and all models were flattened per gene.
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| Submission date |
May 05, 2014 |
| Last update date |
May 15, 2019 |
| Contact name |
David Gatfield |
| E-mail(s) |
david.gatfield@unil.ch
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| Phone |
+41 21 692 39 94
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| Organization name |
University of Lausanne
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| Department |
CIG
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| Street address |
UNIL CIG
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| City |
Lasuanne |
| ZIP/Postal code |
1015 |
| Country |
Switzerland |
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| Platform ID |
GPL17021 |
| Series (1) |
| GSE57313 |
MicroRNAs Shape Circadian Hepatic Gene Expression on a Transcriptome-Wide Scale |
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| Relations |
| BioSample |
SAMN02744464 |
| SRA |
SRX533204 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM1379409_Dcr21_mRNA.rpkm.gz |
163.5 Kb |
(ftp)(http) |
RPKM |
| GSM1379409_Dcr21_premRNA.rpkm.gz |
154.4 Kb |
(ftp)(http) |
RPKM |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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