Project description:RNA-Seq is an effective method to study the transcriptome, but can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations, or cadavers. Recent studies have proposed several methods for RNA-Seq of low quality and/or low quantity samples, but their relative merits have not been systematically analyzed. Here, we compare five such methods using a comprehensive set of metrics, relevant to applications such as transcriptome annotation, transcript discovery, and gene expression. Using a single human RNA sample, we constructed and deeply sequenced 10 libraries with these methods and two control libraries. We find that the RNase H method performed best for low quality RNA, and can even effectively replace oligo (dT) based methods for standard RNA-Seq. SMART and NuGEN had distinct strengths for low quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development.
Project description:RNA-Seq is an effective method to study the transcriptome, but can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations, or cadavers. Recent studies have proposed several methods for RNA-Seq of low quality and/or low quantity samples, but their relative merits have not been systematically analyzed. Here, we compare five such methods using a comprehensive set of metrics, relevant to applications such as transcriptome annotation, transcript discovery, and gene expression. Using a single human RNA sample, we constructed and deeply sequenced 10 libraries with these methods and two control libraries. We find that the RNase H method performed best for low quality RNA, and can even effectively replace oligo (dT) based methods for standard RNA-Seq. SMART and NuGEN had distinct strengths for low quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development. Examination of 9 different RNA-Seq libraries starting from total RNA from 5 distinct methods; also 3 control RNA-Seq libraries
Project description:High-throughput RNA-sequencing has now become the gold standard method for whole-transcriptome gene expression analysis. It is widely used in a number of applications studying various transcriptomes of cells and tissues. It is also being increasingly considered for a number of clinical applications, including expression profiling for diagnostics or alternative transcripts detection. However, RNA sequencing can be challenging in some situations, for instance due to low input quantities or degraded RNA samples. Several protocols have been proposed to overcome some of these challenges, and many are available as commercial kits. Here we perform a comprehensive testing of three recent commercial technologies for RNA-seq library preparation (Truseq, Smarter and Smarter Ultra-Low) on human reference tissue preparations, for standard (1ug), low (100 and 10 ng) and ultra-low (< 1 ng) input quantities, and for mRNA and total RNA, stranded or unstranded. We analyze the results using read quality and alignments metrics, gene detection and differential gene expression metrics. Overall, we show that the Truseq kit performs well at 100 ng input quantity, while the Smarter kit shows degraded performances for 100 and 10 ng input quantities, and that the Smarter Ultra-Low kit performs quite well for input quantities < 1 ng. All the results are discussed in details, and we provide guidelines for the selection of a RNA-seq library preparation kits by biologists.
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
Project description:Background: The main bottleneck for genomic studies of tumors is the limited availability of fresh frozen (FF) samples collected from patients, coupled with comprehensive long-term clinical follow-up. This shortage could be alleviated by using existing large archives of routinely obtained and stored Formalin-Fixed Paraffin-Embedded (FFPE) tissues. However, since these samples are partially degraded, their RNA sequencing is technically challenging. Results: In an effort to establish a reliable and practical procedure, we compared three protocols for RNA sequencing using pairs of FF and FFPE samples, both taken from the same breast tumor. In contrast to previous studies, we compared the expression profiles obtained from the two matched sample types, using the same protocol for both. Three protocols were tested on low initial amounts of RNA, as little as 100 ng, to represent the possibly limited availability of clinical samples. For two of the three protocols tested, poly(A) selection (mRNA-seq) and ribosomal-depletion, the total gene expression profiles of matched FF and FFPE pairs were highly correlated. For both protocols, differential gene expression between two FFPE samples was in agreement with their matched FF samples. Notably, although expression levels of FFPE samples by mRNA-seq were mainly represented by the 3'-end of the transcript, they yielded very similar results to those obtained by ribosomal-depletion protocol, which produces uniform coverage across the transcript. Further, focusing on clinically relevant genes, we showed that the high correlation between expression levels persists at higher resolutions. Conclusions: Using the poly(A) protocol for FFPE exhibited, unexpectedly, similar efficiency to the ribosomal-depletion protocol, with the latter requiring much higher (2-3 fold) sequencing depth to compensate for the relative low fraction of reads mapped to the transcriptome. The results indicate that standard poly(A)-based RNA sequencing of archived FFPE samples is a reliable and cost-effective alternative for measuring mRNA-seq on FF samples. Expression profiling of FFPE samples by mRNA-seq can facilitate much needed extensive retrospective clinical genomic studies.
Project description:Next generation RNA-Sequencing (RNA-seq) is a flexible approach that can be applied to e.g. global quantification of transcript expression, the characterization of RNA structure such as splicing patterns and profiling of expressed mutations. Many RNA-seq protocols require up to microgram levels of total RNA input amounts to generate high quality data, and thus remain impractical for the limited starting material amounts typically obtained from rare cell populations, such as those from early developmental stages or from laser micro-dissected clinical samples. Here, we present an assessment of the contemporary ribosomal RNA depletion-based protocols, and identify those that are suitable for inputs as low as 1-10 ng of intact total RNA and 100-500 ng of partially degraded RNA from formalin-fixed paraffin-embedded tissues.
Project description:Next-generation sequencing (NGS) technology applications like RNA-sequencing (RNA-seq) have dramatically expanded the potential for novel genomics discoveries, but the proliferation of various platforms and protocols for RNA-seq has created a need for reference data sets to help gauge the performance characteristics of these disparate methods. Here we describe the results of the ABRF-NGS Study on RNA-seq, which leverages replicate experiments across multiple sites using two reference RNA standards tested with four protocols (polyA selected, ribo-depleted, size selected, and degraded RNA), and examined across five NGS platforms (Illumina’s HiSeqs, Life Technologies’ Personal Genome Machine and Proton, Roche 454 GS FLX, and Pacific Biosciences RS). These results show high (R2 >0.9) intra-platform consistency across test sites, high inter-platform concordance (R2 >0.8) for transcriptome profiling, and a large set of novel splice junctions observed across all platforms. Also, we observe that protocols using ribosomal RNA depletion can both salvage degraded RNA samples and also be readily compared to polyA-enriched fractions. These data provide a broad foundation for standardization, evaluation and improvement of RNA-seq methods.