Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Next Generation Sequencing Facilitates Quantitative Analysis of antidepressants stressed E. coli K-12 MG1655 Transcriptomes

ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of antidepressants stressed E. coli K-12 MG1655 Transcriptomes

PROVIDER: PRJNA832584 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR18936500_1.fastq.gz	Fastqsanger.gz
	SRR18936500_2.fastq.gz	Fastqsanger.gz
	SRR18936501_1.fastq.gz	Fastqsanger.gz
	SRR18936501_2.fastq.gz	Fastqsanger.gz
	SRR18936502_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 96

Similar Datasets

Next Generation Sequencing Facilitates Quantitative Analysis of antidepressants stressed E. coli K-12 MG1655 Transcriptomes

Project description:The goals of this study are to use Next-generation sequencing (NGS) to detect bacterial mRNA profiles of wild-type E. coli K-12 MG1655 and its mutants, and their mRNA response under the exposure of five antidepressants, including sertraline, duloxetine, bupropion, escitalopram and agomelatine. The concentrations were 50 mg/L for the treatment. For sertraline, in addition to 50 mg/L, 1 mg/L was also applied. The group without dosing antidepressants was the control group. Each concentration was conducted in triplicate. By comparing the mRNA profiles of experimental groups and control group, the effects of these five antidepressants on transcriptional levels can be revealed. Illumina HiSeq 2500 was applied. The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli MG1655 reference genome (NC_000913.3) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.

2023-03-01 | GSE201666 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of original and mutant E. coli K-12 MG1655 Transcriptomes

Project description:The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of original E. coli K-12 MG1655 and fluoxetine induced E. coli mutants in response to 100 mg/L fluoxetine for 8 h, in triplicate, using Illumina HiSeq 2500.The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli reference genome (NC_000913) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.

2020-12-18 | GSE108190 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and mutant E. coli K-12 MG1655 Transcriptomes

Project description:The goals of this study are to use Next-generation sequencing (NGS) to detect bacterial mRNA profiles of wild-type E. coli K-12 MG1655 and triclosan induced E. coli mutants in response to 0.2 mg/L triclosan for 8 h, in triplicate, using Illumina HiSeq 2500.The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli reference genome (NC_000913) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.

2018-12-01 | GSE107048 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of original and mutant E. coli K-12 MG1655 Transcriptomes

Project description:Next Generation Sequencing Facilitates Quantitative Analysis of original and mutant E. coli K-12 MG1655 Transcriptomes

| PRJNA422780 | ENA

Proteomic analysis of E. coli K-12 MG1655 with the treatment of antidepressants

Project description:Proteomics analyses were conducted to reveal bacterial responses towards antidepressants on translational level.

2023-10-24 | PXD033554 | Pride

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and mutant E. coli K-12 MG1655 Transcriptomes

Project description:Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and mutant E. coli K-12 MG1655 Transcriptomes

| PRJNA418956 | ENA

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and disinfectants stressed Acinetobacter baylyi ADP1 Transcriptomes

Project description:Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and disinfectants stressed Acinetobacter baylyi ADP1 Transcriptomes

| PRJNA595840 | ENA

Expression profiling of E. coli K-12 MG1655

Project description:Previously unpublished expression data from E. coli K-12 MG1655 involved in the creation of a large RNAseq database.

2019-12-17 | GSE122211 | GEO

RNA-seq of Escherichia coli K-12 MG1655 and Stx2 phage lysogens

Project description:Here, we investigated the impact of Stx2 phage carriage on Escherichia coli (E. coli) K-12 MG1655 host gene expression. Using quantitative RNA-seq analysis, we compared the transcriptome of naïve MG1655 and the lysogens carrying the Stx2 phage of the 2011 E. coli O104:H4 outbreak strain or of the E. coli O157:H7 strain PA8, which share high degree of sequence similarity.

2019-06-17 | GSE126710 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and non-antibiotic pharmaceuticals stressed Acinetobacter baylyi ADP1 Transcriptomes

Project description:Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and non-antibiotic pharmaceuticals stressed Acinetobacter baylyi ADP1 Transcriptomes

| PRJNA595841 | ENA

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data