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.
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.
Project description:Rapidly increased studies by third-generation sequencing [Pacific Biosciences (Pacbio) and Oxford Nanopore Technologies (ONT)] have been used in all kinds of research areas. Among them, the plant full-length single-molecule transcriptome studies were most used by Pacbio while ONT was rarely used. Therefore, in this study, we developed ONT RNA-sequencing methods in plants. We performed a detailed evaluation of reads from Pacbio and Nanopore PCR cDNA (ONT Pc) sequencing in plants (Arabidopsis), including the characteristics of raw data and identification of transcripts. We aimed to provide a valuable reference for applications of ONT in plant transcriptome analysis.
Project description:The specificity of the RNA-CASing process was analysed by Next-Generation Sequencing. Therfor small RNAs were isolated from purified proteins of Escherichia coli and subjected to Illumina sequencing or nanopore sequencing.
Project description:We sequenced DNA from a bulk of Col x Ler F2 hybrid plants (WT and recq4) using Nanopore long-read sequencing and identified crossover sites with COmapper. For nanopore sequencing of gDNA from 1,000 pooled seedlings, 10-day-old seedlings were ground in liquid nitrogen using a mortar and pestle. The ground tissue was resuspended in four volumes of CTAB buffer (1% [w/v] CTAB, 50 mM Tris-HCl pH 8.0, 0.7 M NaCl, 10 mM EDTA) and incubated at 65°C for 30 min. Following chloroform extraction, isopropanol precipitation and removal of RNAs as above, the gDNA pellet was resuspended in 150 μl TE (10 mM Tris-HCl pH 8.0, 0.1 mM EDTA) buffer and gDNA was quantified using a Qubit dsDNA Broad Range assay kit (Thermo Fisher, Q32853). Nine micrograms of gDNA from pollen or seedlings was used to construct a nanopore long-read sequencing library using a Ligation Sequencing Kit V14 (Nanopore, SQK-LSK114). The libraries were sequenced using a PromethION platform (BGI, Hong Kong).
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.
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.
Project description:Using Nanopore sequencing, our study has revealed a close correlation between genomic methylation levels and antibiotic resistance rates in Acinetobacter Baumannii. Specifically, the combined genome-wide DNA methylome and transcriptome analysis revealed the first epigenetic-based antibiotic-resistance mechanism in A. baumannii. Our findings suggest that the precise location of methylation sites along the chromosome could provide new diagnostic markers and drug targets to improve the management of multidrug-resistant A. baumannii infections.