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Sample GSM7923224 Query DataSets for GSM7923224
Status Public on Jul 23, 2024
Title Tet_TKO_NSC_RNAseq_rep1
Sample type SRA
 
Source name Neural stem cells
Organism Mus musculus
Characteristics cell type: Neural stem cells
Sex: Male
strain: V6.5 mixed 129/C57BL/6
genotype: Tet triple knockout
Treatment protocol For CUT&Tag, ESC and NSCs were crosslinked, bound to Con-A beads, incubated with primary antibody and treated with pre-loaded pA-Tn5 adapter complex.
Growth protocol ESCs preplated and cultured on gelatin in serum/LIF for 24 hours before harvest for high-throughput sequencing. NSCs were generated following the protocol of Okabe et al. (1996) as modified by Lee et al. (2000), passaged once, and then harvested for high-throughput sequencing.
Extracted molecule total RNA
Extraction protocol For RNA-seq, RNA was extracted from ESCs and NSCs using Qiagen RNeasy Mini kit. For WGBS, DNA was extracted from ESCs and NSCs using Zymo Quick-DNA Miniprep Kit.
RNA-seq libraries were prepared by Novogene (https://en.novogene.com/). WGBS libraries were prepared by BGI Genomics (https://en.genomics.cn/). CUT&Tag libraries were prepared by NEBNext HiFi 2x PCR Master mix.
 
Library strategy RNA-Seq
Library source transcriptomic
Library selection cDNA
Instrument model Illumina NovaSeq 6000
 
Description RNAseq_TKOvsWT_NSC_DESeq2_normalizedCounts.csv
M9_Ntkc2
Data processing For RNA-seq, libraries were sequenced using the Illumina Novoseq 6000 platform. Adaptor and low-quality trimming were performed with trim galore (v0.6.5, https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Clean reads were mapped to Mus musculus reference genome mm10 using STAR (v2.7.3a) with default parameters. Counts were extracted from mapped reads with featureCounts using the –largestOverlap parameter. DESeq2 (v1.40.2) was used to identify differentially expressed genes (FDR < 0.05, fold change > 2) from raw counts using the standard package documentation
For WGBS, paired-end sequencing on a DNBseq platform was performed at BGI Genomics. Raw read filtering was performed by BGI Genomics using SOAPnuke with the parameters -n 0.001 -l 20 -q 0.4 –adaMR 0.25 –ada_trim –polyX 50 to remove adaptors and filter out low-quality reads. Clean reads were mapped to mm10 using Bismark (v0.22.3) with default parameters. Read deduplication was performed using deduplicate_bismark and cytosine methylation levels were extracted with bismark_methylation_extractor. BAM files were balanced to the sample with the lowest number of reads with SAMtools (v1.9)
For CUT&Tag, libraries were sequenced on an Illumina NextSeq 500 platform at the Einstein Epigenomics Shared Facility. Adaptor and low-quality trimming were performed with trim galore. Clean reads were mapped to mm10 using bowtie293 using options -local -sensitive -very-sensitive-local --no-unal -no-mixed -no-discordant -phred33 -I 10 -X 700. Duplicate reads were removed with Picard tools (v2.26.10, https://broadinstitute.github.io/picard/).
Samtools was used to convert SAM to BAM, sort and index corresponding BAM files. Bigwig files were generated using bamCoverage from deepTools, and bigwig files were analyzed using plotProfile and plotHeatmap. Peaks annotation were implemented by R package "ChIPseeker".
Assembly: mm10
Supplementary files format and content: bigwig
Supplementary files format and content: normalized gene counts
Supplementary files format and content: bedgraph
 
Submission date Nov 29, 2023
Last update date Jul 23, 2024
Contact name Meelad M Dawlaty
E-mail(s) meelad.dawlaty@einsteinmed.org
Phone 718-678-1224
Organization name Albert Einstein College of Medicine
Department Genetics
Lab Dawlaty Lab
Street address 1301 Morris Park Ave, Price 419, Bronx
City New York
State/province New York
ZIP/Postal code 10461
Country USA
 
Platform ID GPL24247
Series (1)
GSE248933 Transcriptomic and epigenomic profiling of Tet TKO neural stem cells to study NSC specification and multipotency
Relations
BioSample SAMN38502432
SRA SRX22683111

Supplementary data files not provided
SRA Run SelectorHelp
Raw data are available in SRA

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