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 Pseudomonas aeruginosa PAO1 Transcriptomes under AgNP and AgNR exposure

ABSTRACT: The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of Pseudomonas aeruginosa PAO1 in response to 0, 1, 20 and 25 mg/L AgNPs or 0, 1,30 and 300 mg/L AgNRs for 2 h, 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.

ORGANISM(S): Pseudomonas aeruginosa PAO1

PROVIDER: GSE139104 | GEO | 2020/10/18

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Next Generation Sequencing Facilitates Quantitative Analysis of Acinetobacter baylyi ADP1 Transcriptomes under heavy metal-based NPs and ions exposure

Project description:The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of Acinetobacter baylyi ADP1 in response to 0, 1 mg/L AgNPs or Ag ions, 1 mg/L CuO NPs or Cu ions, 10 mg/L ZnO NPs or Zn ions for 2 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.

2020-10-23 | GSE139295 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of E.coli DH5a Transcriptomes under triclosan exposure

Project description:The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of E.coli DH5a in response to 0, 20ug/L and 2 mg/L triclosan for 2 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.

2021-01-12 | GSE143554 | 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 E. coli K-12 LE392, P. putida KT2440, and RP4 plasmid Transcriptomes under triclosan exposure

Project description:The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of E. coli K-12 LE392, P. putida KT2440 and IncPα RP4 plasmid in response to 0, 0.02, 20 and 2000 μg/L triclosan for 2 h, in duplicate, 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.

2019-04-01 | GSE112451 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of E. coli K-12 LE392, P. putida KT2440, and RP4 plasmid Transcriptomes under AgNP and silver ion exposure

Project description:The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of E. coli K-12 LE392, P. putida KT2440 and IncPα RP4 plasmid in response to 0 and 1 μg/L AgNPs or silver ion for 2 h, in duplicate, 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.

2020-02-01 | GSE128246 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and carbamazepine-stressed E. coli K-12 LE392, P. putida KT2440, and RP4 plasmid 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 LE392, P. putida KT2440 and IncPα RP4 plasmid, and their mRNA response under the exposure of antiepileptic drug carbamazepine. Three concentrations of carbamazepine were applied, which were 0.05 mg/L, 10.0 mg/L and 50.0 mg/L (refer to low, medium and high, respectively). The group without dosing carbamazepine was the control group. Each concentration was conducted in triplicate. By comparing the mRNA profiles of experimental groups and control group, the effects of carbamazepine 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 reference genome (NC_000913), P.putida reference genome (NC_002947), and IncPα plasmid reference genome (NC_00) 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.

2019-01-23 | GSE112064 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of E. coli K-12 LE392, P. putida KT2440, and Acinetobacter baylyi ADP1 Transcriptomes under various antidepressant exposure

Project description:The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of E. coli K-12 LE392, P. putida KT2440 and Acinetobacter baylyi ADP1 in response to various antidepressant concentrations for 2 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), Pseudomonas putida KT2440 genome (NCBI:txid160488) and Acinetobacter baylyi ADP1 genome (NCBI:txid62977) 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.

2022-08-03 | GSE181900 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and non-antibiotic pharmaceuticals stressed E. coli K-12 LE392, P. putida KT2440, and RP4 plasmid Transcriptomes [experiment 1]

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 LE392, P. putida KT2440 and IncPα RP4 plasmid, and their mRNA response under the exposure of six non-antibiotic pharmaceuticals, including ibuprofen, naproxen, gemfibrozil, diclofenac, propanolol, and iopromide. The concentrations were 0.5 mg/L for ibuprofen, naproxen, gemfibrozil, diclofenac, propanolol, and 1.0 mg/L for iopromide. The group without dosing pharmaceutical 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 six non-antibiotic pharmaceuticals 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 reference genome (NC_000913), P.putida reference genome (NC_002947), and IncPα plasmid reference genome (NC_00) 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.

2021-09-01 | GSE130562 | GEO

Project description:The goals of this study are to use Next-generation sequencing (NGS) to detect bacterial mRNA profiles of E. coli K-12 LE392, P. putida KT2440 and IncPα RP4 plasmid, and their mRNA response under the exposure of CuO NPs and Cu2+. The concentrations were 5 μmol/L for CuO NPs and Cu2+. The group without dosing CuO NPs or Cu2+ 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 CuO NPs and Cu2+ 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 reference genome (NC_000913), P.putida reference genome (NC_002947), and IncPα plasmid reference genome (NC_00) 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.

2022-05-15 | GSE131228 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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