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Sample GSM5560872

Query DataSets for GSM5560872

Status

Public on Sep 08, 2021

Title

IBP4（control group）

Sample type

SRA

Source name

Marine Sediment

Organism

Pseudoalteromonas sp. GCY

Characteristics

cell type: Marine bacteria
treatment: untreated

Extracted molecule

total RNA

Extraction protocol

strain GCY were centrifuged and collected, flash frozen on dry ice, and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit (Cat#FC-122-1001) was used with 1 ug of total RNA for the construction of sequencing libraries.
RNA libraries were prepared for sequencing using standard Illumina protocols

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 2000

Description

Control_1

Data processing

The library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. Illumina Casava1.7 software used for basecalling.
Quality control.Raw data (raw reads) of fastq format were firstly processed through in-house perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. Reference genome and gene model annotation files were downloaded from genome website directly. Both building index of reference genome and aligning clean reads to reference genome were used Bowtie2-2.2.3.
Quantification of gene expression level.HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. FPKM, expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced, 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.
Differential expression analysis.Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq R package (1.18.0). 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. Genes with an adjusted P-value <0.05 found by DESeq were assigned as differentially expressed.Prior to differential gene expression analysis, for each sequenced library, the read counts were adjusted by edgeR program package through one scaling normalized factor. Differential expression analysis of two conditions was performed using the DEGSeq R package (1.20.0). The P values were adjusted using the Benjamini & Hochberg method. Corrected P-value of 0.005 and log2 (Fold change) of 1 were set as the threshold for significantly differential expression.
Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the GOseq R package, in which gene length bias was corrected. GO terms with corrected Pvalue less than 0.05 were considered significantly enriched by differential expressed genes.We used KOBAS software to test the statistical enrichment of differential expression genes in KEGG pathways. For the SNP analysis, Picard-tools (v1.96) and samtools (v 0.1.18) were used to sort, mark duplicated reads and reorder the bam alignment results of each sample. GATK2 software was used to perform SNP calling.
Genome_build: mm8
Supplementary_files_format_and_content: tab-delimited text files include readcount values for each Sample ...

Submission date

Sep 07, 2021

Last update date

Sep 08, 2021

Contact name

Shuo Wu

E-mail(s)

ws403361019@126.com

Organization name

Dalian university of technology

Street address

Linggong Road No.2