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Sample GSM2100580

Query DataSets for GSM2100580

Status

Public on Feb 06, 2019

Title

Hets3

Sample type

SRA

Source name

Dorsal telencephalon

Organism

Mus musculus

Characteristics

strain: B6:129SF2/J
tissue: Dorsal telencephalon
developmental stage: Newborn (P0)
genotype: TCF4 heterozygote

Extracted molecule

total RNA

Extraction protocol

Brains was removed from newborn pups. Dorsal telencephalons were dissected out and snap frozen on dry ice. Total RNA were extracted with QIAGEN RNeasy plus mini kit (CAT#:74134). The quality and concentration of RNA samples were measured with Agilen 2100 Bioanalyzer and Qubit 2.0 Fluorometer.
Libraries were constructed with NEBnext Ultra RNA library prep kit from Illumina.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 4000

Data processing

Illumina Casava1.8 software used for basecalling.
Raw reads were filtered to exclude adapters, low quality reads and reads containing N (if more than 10%). Obtained clean reads in each sample were more than 96%.
Reference genome and gene model annotation files were downloaded from genome website browser (NCBI/UCSC/Ensembl) directly. Indexes of the reference genome were built using Bowtie v2.0.6 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.9. Bowtie uses a BWT(Burrows-Wheeler Transformer) algorithm for mapping reads to the genome and Tophat can generate a database of splice junctions based on the gene model annotation file and thus achieve a better mapping result than other non-splice mapping tools.
HTSeq v0.6.1 was used to count the read numbers mapped of each gene. And then RPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. RPKM, Reads Per Kilobase of exon model per Million mapped reads, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels (Mortazavi et al., 2008
(For DESeq with biological replicates) Differential expression analysis between two conditions/groups (three biological replicates per condition) was performed using the DESeq R package (1.10.1). DESeq provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the False Discovery Rate(FDR). Genes with an adjusted P-value <0.05 found by DESeq were assigned as differentially expressed.
Genome_build: mm10
Supplementary_files_format_and_content: text files include fpkm values or read counts for each sample

Submission date

Mar 28, 2016

Last update date

May 15, 2019

Contact name

Hong Li

E-mail(s)

hong.li@yale.edu

Organization name

Yale University

Department

Neuroscience