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| Status |
Public on Nov 04, 2015 |
| Title |
Mouse neural stem cell_2 |
| Sample type |
SRA |
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| Source name |
cerebral cortex
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| Organism |
Mus musculus |
| Characteristics |
cell line: CD-1
tissue: cerebral cortex
cell type: Mouse neural stem cell
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| Growth protocol |
Mouse neural stem cells were cultured and passaged every 2 days as monolayers in MEM/F12 medium containing Glutamax, non-essential amino acids, B27, N2 supplement, 20 ng/ml EGF, and 20 ng/ml FGF. Cells were dissociated using Accutase (Invitrogen) and seeded onto poly-d-lysine-coated plates or dishes.
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| Extracted molecule |
polyA RNA |
| Extraction protocol |
Total RNA was extracted using the miRNeasy kit (Qiagen) under manufactor’s protocols. The quality was accessed by Bioanalyzer. Samples with high RNA integrity number (RIN) (>8) were used for library construction. 100ng total RNAs were used for each sequencing library.
RNA samples were polyA selected and paired-end sequencing libraries were constructed using TruSeq RNA Sample Prep Kit as described in the TruSeq RNA Sample Preparation V2 Guide (Illumina).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 2000 |
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| Data processing |
We mapped the paired-end reads to the mouse reference genome (UCSC version mm10) using TopHat (version 2.0.11) and Bowtie2 (version 2.2.2). Cufflinks (version 1.3.0) was used to assemble transcripts from genomic read alignments. TopHat was run using default settings, except using the option “-G mm10_genes.gtf” (mm10_genes.gtf is an annotation file within the Illumina iGenome package. Cufflinks was run using default settings without any annotation file to identify novel transcripts. All transcripts were then merged using Cuffmerge and then compared to a reference annotation file (mm10_genes.gtf) using Cuffcompare. Transcripts not matching any reference transcripts were given a class code ‘u’ (to designate them as unknown intergenic transcripts) by Cuffcompare and the considered as ‘novel’ transcripts not present in the reference file. The resulting .gtf was then filtered and only multi-exonic transcripts were retained.
In order to generate a comprehensive lncRNA annotation for downstream analysis, we surveyed available lncRNA annotations in the public domain, including lncRNAdb, GENCODE, Ensembl, and UCSC RefSeq genes. For lncRNAdb, the transcript structures were obtained using the BLAT program when only sequence information was available. We also added several recently published RNA-Seq datasets related to brain or CNS development to our analyses, including RNA-Seq data from subventricular zone (SVZ) tissue and olfactory bulb (OB), among others. To combine lncRNA annotations generated using RNA-Seq, we merged lncRNA transcripts identified from the same loci that were not annotated by GENCODE, or any other canonical lncRNA database, into single transcripts because distinguishing full-length isoforms from partially reconstructed fragments is not always possible without further experimental evidence, especially when sequencing depths vary among different RNA-Seq datasets. Subsequently, we combined lncRNA annotations from all sources into one non-redundant annotation file. Because there are overlaps between various annotation resources, a procedure was implemented to eliminate redundancy. Among these annotation resources, lncRNAdb contains the most detailed information for ~100 lncRNAs collected from the literature. When transcripts from different sources overlapped, we retained the gene information (gene_id, gene_name etc.) for the loci in the following order of priority: 1) lncRNAdb, 2) GENCODE, 3) RefSeq, 4) Ensembl, or 5) annotated by RNA-Seq experiments. We further combined the resulting lncRNA annotation .gtf with iGenome mm10 gtf annotation and removed any redundancy.
To quantify transcript expression, we re-ran Cufflink with option –G and supplied the comprehensive .gtf. The multi-read correction and bias correction algorithms in the Cufflinks package were enabled (option: -u -b). Expression level estimation was reported as Fragments per Kilobase of transcript sequence per Million mapped fragment (FPKM) values together with confidence intervals. Transcripts with an FPKM > 0, FPKM_conf_lo (lower bound of confidence interval) > 0, and status = OK were considered as detected with confidence.
To facilitate downstream analysis (such as fold change, enrichment analysis, and other procedures), a previously calculated threshold of FPKM = 0.1 was adopted. Any FPKM < 0.1 were set to 0.1 for fold enrichment calculations in order to avoid ratio inflation.
Genome_build: mm10
Supplementary_files_format_and_content: Excel file include FPKM values for each Sample
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| Submission date |
Nov 03, 2015 |
| Last update date |
May 15, 2019 |
| Contact name |
Xiaomin Dong |
| E-mail(s) |
xiaomin.dong@yahoo.com
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| Organization name |
UT health
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| Department |
Department of neurosurgery
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| Lab |
Dr.Jiaqian Wu
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| Street address |
1825 Pressler Street
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| City |
houston |
| State/province |
Texas |
| ZIP/Postal code |
77030 |
| Country |
USA |
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| Platform ID |
GPL13112 |
| Series (2) |
| GSE74643 |
Comprehensive Identification of Long Non-coding RNAs in Purified Cell Types from the Brain Reveals Functional LncRNA in OPC Fate Determination (RNA-Seq of mouse neural stem cells) |
| GSE74648 |
Comprehensive Identification of Long Non-coding RNAs in Purified Cell Types from the Brain Reveals Functional LncRNA in OPC Fate Determination |
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| Relations |
| BioSample |
SAMN04233137 |
| SRA |
SRX1411155 |
| 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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