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| Status |
Public on Mar 18, 2019 |
| Title |
NPAI-control (71571) |
| Sample type |
SRA |
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| Source name |
Implanted glioma brain tumor
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| Organism |
Mus musculus |
| Characteristics |
tissue: Glioma brain tumor
genotype: NRAS-V12; shP53; shATRX; IDH1-R132H
treatment: Control
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| Treatment protocol |
After 7 days of cell implantation, the animals were treated with 2 Gy of ionizing radiation (IR) per day during e days for a total of 10 Gy (IR).
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| Growth protocol |
Brain tumors were generated in BL6 mice by implantation of 50.000 tumor neurospheres expression IDH1-R132H or control WT.
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| Extracted molecule |
total RNA |
| Extraction protocol |
RNA was harvested using the RNEasy-Plus Kit (Qiagen;74136) and assessed for quality using the TapeStation (Agilent). Samples with RNA Integrity Number of 8 or greater were rRNA depleted using
Ribo Minus (Invitrogen;K1550-04).
rRNA-depleted samples were prepared using the TruSeq mRNA SamplePrep v2 kit (Illumina;RS-122-2001, RS-122-2002). The entire fraction of 0.1-3 ug of rRNA depleted total RNA was fragmented and
copied into first-strand cDNA using reverse transcriptase and random primers. cDNA 3‘-ends were adenylated and adapters were ligated. One of the adapters had a 6-nucleotide barcode that was unique
for each sample, allowing us to multiplex in a HiSeq flow cell (Illumina). Products were purified and enriched by PCR to create the final cDNA library. Libraries were checked for quality and quantity by TapeStation
(Agilent) and qPCR using a library quantification kit for Illumina platforms (Kapa Biosystems;KK4835). They were clustered on the cBot (illumina) and sequenced 24 samples per lane on 6 lanes of a 50-cycle
single end HiSeq 4000. HiSeq Control Software version 3.3.52 was used according to manufacturer’s protocols. Demultiplexing and Fastq file generation was done using bcl2fastq version 2.17.1.14.
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 4000 |
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| Description |
NRAS activated; TP53 knockdown; ATR knockdown; IDH1-R132H expression
Glioma implanted mouse model
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| Data processing |
[QC] We checked the quality of the raw reads data for each sample using FastQC (version 0.11.3) to identify features of the data that may indicate quality problems (e.g. low quality scores, over-represented sequences, inappropriate GC content, etc.).
[Aligment] We used the software package Tuxedo Suite for alignment, differential expression analysis, and post-analysis diagnostics. Briefly, we aligned reads to the reference transcriptome including both mRNAs and lncRNSs (UCSC mm10) using TopHat (version 2.0.3) and Bowtie (version 2.2.1). We used default parameter settings for alignment, with the exception of: “--b2-very-sensitive” telling the software to spend extra time searching for valid alignments.
[QC] We used FastQC for a second round of quality control (post-alignment), to ensure that only high quality data would be input to expression quantitation and differential expression analysis.
[Normalization and Quantitation] We used Cufflinks/CuffDiff (version 2.2.1) for expression quantitation, normalization, and differential expression analysis, using UCSC mm10.fa as the reference genome sequence. For this analysis, we used parameter settings: “--multi-read-correct” to adjust expression calculations for reads that map in more than one locus, as well as “--compatible-hits-norm” and “--upper-quartile –norm” for normalization of expression values. We generated diagnostic plots using the CummeRbund package.
[Differential Expression] We used locally developed scripts to format and annotate the differential expression data output from CuffDiff. Briefly, we identified genes and transcripts as being differentially expressed based on three criteria: test status = “OK”, FDR < 0.05 and fold change ≥ ± 1.5.
[Annotation] We annotated genes and isoforms with NCBI Entrez GeneIDs and text descriptions. We further annotated differentially expressed genes with Gene Ontology (GO) terms using NCBI annotation.
[Enrichment Testing] We used DAVID (version 6.7) for enrichment analysis of the set of differentially expressed genes to identify significantly enriched functional categories.
Genome_build: MM10
Supplementary_files_format_and_content: matrix of mapped read counts for each gene for each sample
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| Submission date |
Feb 16, 2017 |
| Last update date |
May 15, 2019 |
| Contact name |
Richard C McEachin |
| E-mail(s) |
mceachin@umich.edu
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| Organization name |
University of Michigan
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| Department |
DCM&B
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| Street address |
2800 Plymouth Road
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| City |
Ann Arbor |
| State/province |
United States |
| ZIP/Postal code |
48109 |
| Country |
USA |
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| Platform ID |
GPL21103 |
| Series (2) |
| GSE94975 |
Differential gene expression in IDH1-R132H Low Grade Glioma animal brain tumors brain in response to 10 Gy of radiation |
| GSE94976 |
IDH1-R132H Low Grade Glioma animal brain tumors |
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| Relations |
| BioSample |
SAMN06341115 |
| SRA |
SRX2566780 |
| 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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