Project description:Methods: RNA-sequencing was performed on matched samples obtained across several different gene expression measurement methods including: (a) fresh-frozen (FF) RNA samples by mRNA-seq, Ribo-zero and DSN and (b) FFPE samples by Ribo-zero and DSN. We also assayed the matched samples with Agilent microarray. RNA-seq data was compared on the rRNA-removal efficiency, genome profile, library complexity, coverage uniformity and quantitative cosinstency across protocols and with microarray data. Results: Compared to mRNA-seq, Ribo-zero provides equivalent percentage of rRNA component, genome-based mapped reads, and consistent quantification of transcripts. Moreover, Ribo-zero and DSN protocols achieve concordant transcript profiling in FFPE samples, and provide substantially more information on non-poly(A) RNA, which cannot be captured by mRNA-seq. Therefore, our study provides evidence that RNA-sequencing can generate accurate and reproducible transcript quantification using FFPE tissues.
Project description:Methods: RNA-sequencing was performed on matched samples obtained across several different gene expression measurement methods including: (a) fresh-frozen (FF) RNA samples by mRNA-seq, Ribo-zero and DSN and (b) FFPE samples by Ribo-zero and DSN. We also assayed the matched samples with Agilent microarray. RNA-seq data was compared on the rRNA-removal efficiency, genome profile, library complexity, coverage uniformity and quantitative cosinstency across protocols and with microarray data. Results: Compared to mRNA-seq, Ribo-zero provides equivalent percentage of rRNA component, genome-based mapped reads, and consistent quantification of transcripts. Moreover, Ribo-zero and DSN protocols achieve concordant transcript profiling in FFPE samples, and provide substantially more information on non-poly(A) RNA, which cannot be captured by mRNA-seq. Therefore, our study provides evidence that RNA-sequencing can generate accurate and reproducible transcript quantification using FFPE tissues. mRNA profile of 11 breast tumors were assayed by Agilent microarray, and by RNA-sequencing on libraries including: (a) fresh-frozen (FF) RNA samples by mRNA-seq, Ribo-zero and DSN and (b) FFPE samples by Ribo-zero and DSN, using Illunia HiSeq2000 2x50bp. RNA-Seq raw data is to be made available through dbGaP (controlled access) due to patient privacy concerns: http://www.ncbi.nlm.nih.gov/gap/?term=phs000676
Project description:Purpose : The goal of this study was to use RNA Seq to explore the correlation of gene expression of a collection of clinical P. aeruginosa isolates to various phenotypes, such as antimicrobial resistance, biofilm formation or virulence Methods : mRNA profiles were generated for Pseudomonas aerugionsa clinical samples derived from various geographical locations by deep sequencing. The removal of ribosomal RNA was performed using the Ribo-Zero Bacteria Kit (Illumina) and cDNA libraries were generated with the ScriptSeq v2 Kit (Illumina) . The samples were sequenced in single end mode on an Illumina HiSeq 2500 device and mRNA reads were trimmed and mapped to the NC_008463.1 (PA14) reference genome from NCBI using Stampy pipeline with defaut settings.
Project description:Purpose : The goal of this study was to use RNA-seq to compare transcriptional profiles under biofilm conditions with planktonic growth and explore the correlation of gene expression of a collection of clinical P. aeruginosa isolates to various phenotypes, such as biofilm structure or virulence. Methods : mRNA profiles were generated for Pseudomonas aeruginosa clinical samples derived from various geographical locations by deep sequencing. The removal of ribosomal RNA was performed using the Ribo-Zero Bacteria Kit (Illumina) and cDNA libraries were generated with the ScriptSeq v2 Kit (Illumina). The samples were sequenced in single end mode on an Illumina HiSeq 2500 device or paired end mode on an Illumina Novaseq 6000. mRNA reads were trimmed and mapped to the NC_008463.1 (PA14) reference genome from NCBI using bowtie2 with default settings.
Project description:RNA-sequencing (RNA-Seq) protocols and bioinformatic pipelines are designed to streamline downstream analyses on sequences believed to be the most important. Here, we have challenged this dogma by preserving ribosomal RNA (rRNA) in our samples and by lowering the minimal RNA size window of our small RNA-Seq analyses to 8 nt