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Sample GSM5536440

Query DataSets for GSM5536440

Status

Public on Aug 28, 2021

Title

kpn_IMP3

Sample type

SRA

Source name

kpn_IMP

Organism

Klebsiella pneumoniae

Characteristics

strain: kpn0507
treatment: imipenem

Treatment protocol

kpn1,kpn2 and kpn3 were treated with PBS, kpn_IMP1, kpn_IMP2 and kpn_IMP3 were treated with Imipenem.

Growth protocol

Klebsiella pneumoniae strain (kpn0507) was cultured and amplified in lysogeny broth (LB) medium at 37°C for 5 hours

Extracted molecule

total RNA

Extraction protocol

RNA was extracted by RNAprep Pure Bacteria Kit (TIANGEN BIOTECH, BEIJING, Cat#DP430)
Total RNA was used as input material for the RNA sample preparations. mRNA was purified from total RNA using probes to remove rRNA. Fragmentation was carried out using divalent cations under elevated temperature in First Strand Synthesis Reaction Buffer(5X). First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase, then use RNaseH to degrade the RNA. And in the DNA polymerase I system, use dUTP to replace the dNTP of dTTP as the raw material to synthesize the second strand of cDNA. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3’ ends of DNA fragments, Adaptor with hairpin loop structure were ligated to prepare for hybridization. Then USER Enzyme was used to degrade the second strand of cDNA containing U, In order to select cDNA fragments of preferentially 370~420 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.

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 TruSeq PE Cluster Kit v3-cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated.
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 N base 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.
Reads mapping to the reference genome: 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.
Novel gene and gene structure analysing: Rockhopper was used to identify novel genes, operon, TSS, TTS and Cis-natural antisense transcripts. It can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes. Then, we extract upstream 700bp sequence of Transcription Start Site for predicting promoter using TDNN.(Time-DelayNeural Network); Predict UTR: According to the information of Transcription Start Site(Transcription terminal Site) and Translation start site(Translation terminal site), we extracted 5’UTR(3’UTR) sequences. Then, RBSfinder and TransTermH were used to predict SD sequence and terminator sequence respectively. Analysis of sRNA: Rockhopper was used to identify new intergenic region transcripts, and Blastx was compared with the nr library to annotate the newly predicted transgenic regions, and the unmarked transcripts were used as candidate non-coding sRNAs. RNA fold and Inta RNA were used to predict secondary structure and target gene respectively.
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 .
Genome_build: Klebsiella pneumoniae reference genome
Supplementary_files_format_and_content: gene_fpkm.txt includes FPKM values for each Sample

Submission date

Aug 25, 2021

Last update date

Aug 28, 2021

Contact name

Yanbing Ma

E-mail(s)

MolecularImmunology@imbcams.com.cn

Phone

+86 18768156085

Organization name

Chinese Academy of Medical Sciences & Peking Union Medical College