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Sample GSM5482244

Query DataSets for GSM5482244

Status

Public on Oct 06, 2021

Title

SC1810_rep1

Sample type

SRA

Source name

bacterial

Organism

Actinobacillus pleuropneumoniae

Characteristics

genotype: wild type

Growth protocol

cultured in TSB medium (with 0.01% NAD and 5% bovine serum) for 8 h at 37 °C

Extracted molecule

total RNA

Extraction protocol

RNA was harvested using the Total RNA Isolation System (Promega, Madison, WI, USA) according to the manufacturer’s instructions. RNA degradation and contamination were monitored on 1% agarose gels, and RNA purity (OD260/280) was detected using a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). RNA concentration and integrity were assessed using a Qubit® RNA Assay Kit in Qubit® 2.0 Fluorometer (Life Technology, CA, USA) and RNA Nano 6000 Assay Kit of Bioanalyzer 2100 system (Agilent Technologies, CA, USA), respectively.
RNA libraries were prepared for sequencing using standard Illumina protocols

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Data processing

The clustering of the index-coded samples was performed on a cBot Cluster Generation System using Novaseq 6000 PE Cluster Kit (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Novaseq 6000 platform and 150bp paired-end reads were generated.
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. At the same time, Q20, Q30 and GC content the clean data were calculated. All the downstream analyses were based on the clean data with high quality.
The index of the reference genome was built using Bowtie v2.0.6 and paired-end clean reads were aligned to the reference genome using TopHat v2.1.1. Differential expression analysis of two conditions/groups (three biological replicates per condition) was performed using the DESeq R package (v1.42.0), which provides statistical routines for determining differential expression in digital gene expression data using a model based on a negative binomial distribution.
The resulting P-values were adjusted using Benjamini and Hochberg’s approach for controlling the false discovery rate (FDR, q-value).
Genome_build: SUB9215048
Supplementary_files_format_and_content: tab-delimited text files include FPKM values for each Sample …
Supplementary_files_format_and_content: Matrix table with raw gene counts for every gene and every sample

Submission date

Jul 28, 2021

Last update date

Oct 06, 2021

Contact name

Bowen Zheng

E-mail(s)

zhengbowen@stu.sicau.edu.cn

Organization name

Sichuan

Department

Sichuan Agricultural University