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| Status |
Public on Dec 22, 2012 |
| Title |
Frog liver |
| Sample type |
SRA |
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| Source name |
Frog_liver_pooled
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| Organism |
Xenopus tropicalis |
| Characteristics |
gender: mixed
age: adult
tissue: liver
molecule subtype: poly-A enriched RNA
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| Extracted molecule |
total RNA |
| Extraction protocol |
About 20 μg of each tissue was homogenized in 700 μl QIAzol Lysis Reagent (Qiagen) using ceramic beads (Precellys). RNA was extracted according to manufacturer’s recommendations. Briefly, 140 μl of chloroform was added to the homogenate. After phase separation, 450 μl of isopropanol was added to the upper, aqueous phase. The yield and quality of the total RNA were monitored by spectrophotometry at 260, 280, and 230 nm using a Bioanalyzer Eukaryote Total RNA Nano Series II chip (Agilent).
10 μg extracted total RNA was DNase-treated (Turbo DNase, Ambion) and polyadenylated RNA was enriched twice from total RNA using polyATtract mRNA isolation system IV (Promega). RNA was reversed transcribed and converted into double-stranded cDNA (SuperScript cDNA synthesis kit, Invitrogen), sheared by sonication followed by end-repairment, A-tailing, paired end adapter (Illumina) ligation and, prior to PCR amplification, cDNA was UNG-treated to maintain strand-specificity. cDNA was amplified by 15 cycles of PCR and size selected (200-300 bp).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 2000 |
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| Description |
Sample 17
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| Data processing |
Full genomic sequences for the analyzed species were downloaded from the UCSC Genome Browser database. Full transcriptomic sequences for all species were downloaded from Ensembl. For each gene, a canonical transcript was selected for gene expression analysis based on the hierarchy derived from the BioMart associated transcript names. For the cases in which such information was not available, the longest protein-coding transcript was selected as the gene representative.
For each species, we assembled all canonical transcript (as defined above) sequences. In order to calculate the effective number of unique mappable positions in each transcript (i.e. the effective length) we performed the following steps. For each read length k, we extracted the L-k+1 (L being the transcript length) k-mer sequences from each canonical transcript and then aligned the full set of k-mers against the respective genome using Bowtie, allowing for a maximum of two mismatches. k-mers with no or one unique genomic alignment were then likewise aligned back to the canonical transcriptome. For each transcript, the number of such k-mers having a unique transcriptomic alignment was determined. This corresponds to the transcript’s effective number of unique mappable positions for k-mer mRNA-Seq reads. For each sample, the corresponding mRNA-Seq data were aligned against the respective genome using Bowtie, allowing for a maximum of two mismatches. Reads with one unique genomic alignment were then aligned against the canonical transcriptome and, for each transcript, the number of reads with one unique transcriptomic alignment were counted. Gene expression levels were determined as reads per thousand mappable positions of target transcript sequence per million of reads, where the reads uniquely align to the analyzed transcriptome. This procedure for estimating gene expression levels is a corrected version of the widely used RPKM (reads per kilobase of target transcript sequence per million of total reads) metric, and is referred to as “cRPKM” [Labbe RM et al. Stem Cells (2012)].
Genome_build: mm9, galGal3, anoCar2, xenTro2, tetNig2
Supplementary_files_format_and_content: Tab-delimited text files include cRPKM values (as defined above), as well as the mappability and the total read count for each canonical transcript, for each Sample. To generate bigWig files we aligned the mRNA-Seq data against the respective genomes using TopHat; genomeCoverageBed (part of the BEDTools suite) was run on the resulting BAM files for the quantification of genomic coverage, outputting bedGraph files; bedGraphToBigWig (dowloaded from UCSC) was then used to generate bigWig files from the bedGraph files.
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| Submission date |
Oct 04, 2012 |
| Last update date |
May 15, 2019 |
| Contact name |
Nuno L Barbosa-Morais |
| E-mail(s) |
nuno.barbosa.morais@utoronto.ca
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| Phone |
4169784633
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| Fax |
4169465545
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| URL |
http://www.utoronto.ca/intron/barbosa-morais.html
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| Organization name |
University of Toronto
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| Department |
The Donnelly Centre
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| Lab |
Blencowe
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| Street address |
160 College Street, Room 908
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| City |
Toronto |
| State/province |
ON |
| ZIP/Postal code |
M5S 3E1 |
| Country |
Canada |
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| Platform ID |
GPL15472 |
| Series (1) |
| GSE41338 |
The evolutionary landscape of alternative splicing in vertebrate species |
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| Relations |
| SRA |
SRX191165 |
| BioSample |
SAMN01758127 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM1015166_Frog_liver.GE.transC.txt.gz |
260.7 Kb |
(ftp)(http) |
TXT |
| GSM1015166_Frog_liver.bw |
66.5 Mb |
(ftp)(http) |
BW |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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