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| Status |
Public on Nov 23, 2023 |
| Title |
SHAM3 lnc |
| Sample type |
SRA |
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| Source name |
Diabetic rats after sham operation
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| Organism |
Rattus norvegicus |
| Characteristics |
strain: wistar
tissue: liver
group: Diabetic rats after sham operation
genotype: wild type
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| Treatment protocol |
Then the eligible animals were randomly divided into two body weight-matched groups (SG and SHAM) .
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| Growth protocol |
Cohorts of rats used in the study received a 60 kcal% saturated high-fat diet (HFD) (60% of calories as fat) for four weeks and followed a single intraperitoneal injection with streptozotocin (35 mg/kg in sodium citrate buffer) after 16h of fasting to induce DM. Three days after the STZ administration, rats with fasting blood glucose consistently exceeded 16.7 mmol/L were considered as promising diabetic model.
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| Extracted molecule |
total RNA |
| Extraction protocol |
Total RNA was extracted using Trizol reagent (Invitrogen, CA, USA) following the manufacturer's procedure. The total RNA quantity and purity were analysis of Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent, CA, USA) with RIN number >7.0. Approximately 5 ug of total RNA was used to deplete ribosomal RNA according to the manuscript of the Epicentre Ribo-Zero Gold Kit (Illumina, San Diego, USA). Following purification, the poly(A)- or poly(A)+ RNA fractions is fragmented into small pieces using divalent cations under elevated temperature. Then the cleaved RNA fragments were reverse-transcribed to create the final cDNA library in accordance with the protocol for the mRNA-Seq sample preparation kit (Illumina, San Diego, USA), the average insert size for the paired-end libraries was 300 bp (±50 bp). And then we performed the paired-end sequencing on an Illumina Hiseq 4000 at the (lc-bio, China) following the vendor's recommended protocol.
RNA libraries were prepared for sequencing using standard Illumina protocols
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| Library strategy |
ncRNA-Seq |
| Library source |
transcriptomic |
| Library selection |
size fractionation |
| Instrument model |
Illumina HiSeq 4000 |
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| Data processing |
cutadapt-1.9 (cutadapt.readthedocs.io/en/stable/)was used to remove the reads that contained adapter contamination, low quality bases and undetermined bases. Then sequence quality was verified using FastQC v0.10.1 (www.bioinformatics.babraham.ac.uk/projects/fastqc/). We used hisat2-2.0.4 (ccb.jhu.edu/software/hisat2/)to map reads to the genome, Ensembl v96 (command line: hisat2 -1 R1.fastq.gz -2 R2.fastq.gz -S mapped.sam).
The mapped reads of each sample were assembled using stringtie-1.3.4(ccb.jhu.edu/software/stringtie/) with default parameters (command line: stringtie -p 2 -G genome.gtf -o output.gtf -l mapped.bam). Then, all transcriptomes from Samples were merged to reconstruct a comprehensive transcriptome using gffcompare (github.com/gpertea/gffcompare/). After the final transcriptome was generated, StringTie was used to perform expression level for mRNAs by calculating FPKM (FPKM = [total_exon_fragments / mapped_reads(millions) × exon_length(kb)]).
Transcripts that overlapped with known mRNAs and transcripts shorter than 200 bp were discarded. Then we utilized CPC0.9-r2 (cpc2.cbi.pku.edu.cn/) with default parameters(cpc2 -i novel.fa -o cpc2.out) and CNCI2.0 (wwww.bioinfo.org/software/cnci) with default parameters (CNCI.py -f novel.fa -o CNCI.result -p 1 -m ve -g novel.gtf -d genome.fa) to predict transcripts with coding potential. All transcripts with CPC score <-1 and CNCI score <0 were removed and remained transcripts were considered as lncRNAs.
Genome_build: Rnor_6.0.96
Supplementary_files_format_and_content: tab-delimited text files include FPKM values for each Sample
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| Submission date |
Nov 23, 2020 |
| Last update date |
Nov 23, 2023 |
| Contact name |
Linchuan Li |
| E-mail(s) |
linchuanlee@hotmail.com
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| Organization name |
Shandong University
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| Street address |
16766# Jingshi Road
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| City |
Jinan |
| State/province |
Shandong |
| ZIP/Postal code |
250000 |
| Country |
China |
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| Platform ID |
GPL22396 |
| Series (2) |
| GSE162018 |
Next generation sequencing of long non-coding RNAs in diabetes mellitus rats after sleeve gastrectomy |
| GSE162022 |
Next generation sequencing of circular and lnc RNAs in diabetes mellitus rats after sleeve gastrectomy |
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| Relations |
| BioSample |
SAMN16874460 |
| SRA |
SRX9557018 |
| Supplementary data files not provided |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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