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| Status |
Public on Apr 08, 2017 |
| Title |
TRZT22_2; ZT22, total RNA, rep2 |
| Sample type |
SRA |
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| Source name |
kidney
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| Organism |
Mus musculus |
| Characteristics |
strain: C57BL/6JRj
Sex: male
age: 11-12 weeks
timepoint: ZT22
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| Extracted molecule |
total RNA |
| Extraction protocol |
Kidneys were homogenized in 3 volumes of lysis buffer (150 mM NaCl, 20 mM Tris-HCl pH7.4, 5 mM MgCl2, 5 mM DTT, 100 μg ml-1 cycloheximide, 1% Triton X-100, 0.5% Sodium deoxycholate, complete EDTA-free protease inhibitors (Roche) and 40 U ml-1 RNasin plus (Promega)) using a Teflon homogenizer. Lysates were incubated for 10 min on ice and cleared by centrifugation at 1000 x g, 4°C for 3 min in a tabletop centrifuge. Supernatants were flash-frozen and stored in liquid nitrogen. Lysates of two mice per timepoint were pooled. Total RNA-seq libraries were generated from the lysate pool. For ribosome profiling libraries, lysate pools (15 OD260) were digested with 650 U RNase I (Ambion) for 45 min at room temperature and passed over size exclusion columns for ribosome purification prior to RNA extraction and library preparation.
Sequencing libraries for total RNA and ribosome protected RNA were generated using Ribo-Zero (Epicentre) and ARTseq ribosome profiling kits (Epicentre).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 2500 |
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| Description |
total RNA
total RNA
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| Data processing |
Base-calling: Casava 1.82
Adapter trimming: Adapter sequences were removed using cutadapt utility with following options: -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC --match-read-wildcards -m 6
Insert size filtering: Trimmed read sequences were filtered by their size using an in-house Python script with following inclusive ranges: [26,35] for footprints, [21,60] for total RNA
Alignment: Trimmed and filtered insert sequences were mapped sequentially to following databases: mouse rRNA, human rRNA, mt-tRNA, mouse tRNA, mouse cDNA (Ensembl.v.75) and mouse genome (GRCm38.p2). All but last one were mapped using bowtie (version 2.2.1) using following parameters: -p 2 -L 15 -k 20 –no-unal. Mapping against genomic sequence was performed using tophat (v2.0.11) with following parameters: --transcriptome-index=Mmusculus.GRCm38.75.dna.ensembl_data -p 2. After each alignment, only reads that were not aligned were used in the following mapping. For further analysis (processed data files), only alignments against mouse cDNA were used. Separately from this alignment strategy, each query set was also directly aligned against mouse genome using tophat with similar parameters (“global tophat”).
Post-alignment filtering: For each query sequence, only alignments with maximum score were kept. The output of “global tophat” alignment was used to estimate expressed transcript models using cufflinks (v2.2.1) with following parameters: --GTF Mus_musculus.GRCm38.75.gtf --frag-len-mean 37 --frag-len-std-dev 8 --compatible-hits-norm --multi-read-correct --upper-quartile-norm --frag-bias-correct Mmusculus.GRCm38.75.dna.ensembl.fa -p 3. Transcript FPKM estimates from all samples were merged using cuffcompare with following parameters: -r Mus_musculus.GRCm38.75.gtf -R -V. Resulting FPKM tracking information was parsed with an in-house Python script to filter out transcripts which were not found to have an FPKM > 0.1, a LOW95 > 0.05 and a FMI > 2.0 in at least 3 samples. A database of expressed transcripts was used in further analysis.
Abundance measurement: mRNA and RPF (ribosome protected fragment) levels were estimated per locus. An in-house Python script was used to count uniquely (by gene) mapped reads within each annotation feature (5'UTR, CDS, 3'UTR). For multi-isoform loci, preference order used was CDS, 5'UTR, 3'UTR.
Normalization: Read counts of total RNA and RPF were normalized with upper quantile method of R package edgeR. Prior to normalization, transcripts which did not have at least 10 counts in at least one fourth 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.p2 (mm10)
Supplementary_files_format_and_content: rpkm files were generated with R; gene IDs are ENSEMBL-IDS
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| Submission date |
May 10, 2016 |
| Last update date |
May 15, 2019 |
| Contact name |
David Gatfield |
| E-mail(s) |
[email protected]
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| Organization name |
University of Lausanne
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| Department |
Center for Integrative Genomics
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| Street address |
Genopode
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| City |
Lausanne |
| ZIP/Postal code |
1015 |
| Country |
Switzerland |
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| Platform ID |
GPL17021 |
| Series (1) |
| GSE81283 |
Translational contributions to tissue-specificity in rhythmic and constitutive gene expression |
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| Relations |
| BioSample |
SAMN04967914 |
| SRA |
SRX1754821 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM2149149_Kidney_TRZT22_2.cds.rpkm.txt.gz |
151.5 Kb |
(ftp)(http) |
TXT |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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