Project description:Deep Sequencing of protein-coding and non-protein-coding RNAs from mouse differentiated embryonic stem cells and 14.5dpc mouse fetal head Analysis of Ribominus RNA from mouse differentiated embryonic stem cells and 14.5dpc mouse fetal head There are no processed data files for GSM566806-GSM566811. There are no fastq raw data files for GSM566812 and GSM566813 since these samples are the combined reads from all sequence lanes.
Project description:Purpose: The goal of this study was to compare NGS-derived keratinocytes transcriptome profiling (RNA-seq) between non lesional and lesional derived keratinocytes of hailey-Hailey disease patients. Methods: kerationocytes gene profiles of lesional and non-lesional derived keratinocytes were generated by deep sequencing, in triplicate, using Illumina HIseq2000. The sequence reads that passed quality filters were analyzed at gene level with the following methods: STAR for the alignment; HTSeq for generating raw read counts; edgeR for normalization and differential expression analysis. QRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 45 million sequence reads per sample to the human genome (hg19). Hierarchical clustering of gene expressions clearly showed separation between non-lesional and lesional keratinocytes. A total of 1,453 differentially expressed genes at FDR<0.05 were identified, uncovering several genes that may contribute to Hailey-Hailey manifestation function. Conclusions: Our study represents the first detailed analysis of keratinocyte gene expression profile, generated by RNA-seq technology. We conclude that RNA-seq gene based characterization of HHD-keratinocytes would expedite genetic network analyses and permit the dissection of complex biological functions.
Project description:Purpose: The goal of this study is to compare endothelial small RNA transcriptome to identify the target of OASL under basal or stimulated conditions by utilizing miRNA-seq. Methods: Endothelial miRNA profilies of siCTL or siOASL transfected HUVECs were generated by illumina sequencing method, in duplicate. After sequencing, the raw sequence reads are filtered based on quality. The adapter sequences are also trimmed off the raw sequence reads. rRNA removed reads are sequentially aligned to reference genome (GRCh38) and miRNA prediction is performed by miRDeep2. Results: We identified known miRNA in species (miRDeep2) in the HUVECs transfected with siCTL or siOASL. The expression profile of mature miRNA is used to analyze differentially expressed miRNA(DE miRNA). Conclusions: Our study represents the first analysis of endothelial miRNA profiles affected by OASL knockdown with biologic replicates.
Project description:Purpose: The goals of this study are designed to study the alteration of transcriptome profiling (RNA-seq) in CP5322-treated and non-treated cells. The drug was a herbal small molecular, which was designed as a HDAC inhibitor, called CP5322. TK1 was the group of CP5322-treated, and CK1 was the group of control. Methods: The mRNA profiles of drug-treated and non-treated cells were generated by next genenration sequencing, using Illumina platform. The clustering of the index-coded samples was performed on a cBot cluster generation system using HiSeq PE Cluster Kit v4-cBot-HS (Illumina) according to the manufacturer's instructions. After cluster generation, the libraries were sequenced on an Illumina platform and 150 bp paired-end reads were generated, which was called Raw Reads. And the low quality sequences and adaptor contamination were removed.Data processing was completed to obtain high-quality sequences (Clean Reads), and all subsequent analyses were based on Clean Reads. Results: Using the data analysis workflow, we mapped about 46 million sequence reads per sample to the human genome (h19) and identified 23 thousand transcripts in the drug-treated and non-treated cells. And the mapping rate was about 97%. RNA-seq data showed a significant alteration when the Kasumi-1 cells were treated by drug. Conclusions: The RNA-Seq data provided us a priori evidence for further research.
Project description:A cDNA library was constructed by Novogene (CA, USA) using a Small RNA Sample Pre Kit, and Illumina sequencing was conducted according to company workflow, using 20 million reads. Raw data were filtered for quality as determined by reads with a quality score > 5, reads containing N < 10%, no 5' primer contaminants, and reads with a 3' primer and insert tag. The 3' primer sequence was trimmed and reads with a poly A/T/G/C were removed
Project description:The goals of this study are to use Next-generation sequencing (NGS) to detect bacterial mRNA profiles of wild-type A.baylyi ADP1, and its 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 A.baylyi ADP1 reference genome (NC_005966.1), using SeqAlto (version 0.5). Cufflinks (version 2.2.1) 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.