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Next Generation Sequencing Facilitates Quantitative Analysis of E.coli, Wild Type and ΔrecA Transcriptomes

ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of E.coli, Wild Type and ΔrecA Transcriptomes

PROVIDER: PRJNA743685 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR15039461_1.fastq.gz	Fastqsanger.gz
	SRR15039461_2.fastq.gz	Fastqsanger.gz
	SRR15039462_1.fastq.gz	Fastqsanger.gz
	SRR15039462_2.fastq.gz	Fastqsanger.gz
	SRR15039463_1.fastq.gz	Fastqsanger.gz

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

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Next Generation Sequencing Facilitates Quantitative Analysis of E.coli, Wild Type and ΔrecA Transcriptomes

Project description:Purpose:Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived E.coli transcriptome profiling (RNA-seq) of Wild type and ΔrecA with or without antibiotics treatment. Methods:libraries with different indices were multiplexed and loaded on an Illumina HiSeq instrument, Sequencing was carried out using a 2x150bp paired-end (PE) configuration, image analysis and base calling were conducted by the HiSeq Control Software (HCS) + OLB + GAPipeline-1.6 (Illumina) on the HiSeq instrument. Main data analysis include Quality Control(Cutadapt), Mapping(Bowtie2 (v2.2.6)),Expression analysis(HTSeq (v0.6.1p1)), Differential expression analysis (DESeq2 Bioconductor package). Results:Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the E.coli genome, The treatment of ampicillin affected the transcriptomic profile of either in the wild type or the ΔrecA strain, compared with that of untreated control cells, with changes to the expression of 4373 and 4286 coding sequences, respectively. Analysis of only the genes with a log2 fold change (log2FC) of ≥ ± 2 showed that 161 and 248 genes were differentially expressed in the ampicillin-treated wild type and ΔrecA strains, respectively. Conclusions:Our study represents the detailed analysis of E.coli transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

2021-07-06 | GSE179434 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of Solanum lycopersicum bud and flower Transcriptomes

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Next Generation Sequencing Facilitates Quantitative Analysis of Retinoblastoma Transcriptomes

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Next Generation Sequencing Facilitates Quantitative Analysis of CHAA Transcriptomes

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Next Generation Sequencing Facilitates Quantitative Analysis of IBP Transcriptomes

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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 oncogene induced transcriptomes

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Next Generation Sequencing Facilitates Quantitative Analysis of B cell Transcriptomes

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