An optimized kit-free method for making strand-specific deep sequencing libraries from RNA fragments
ABSTRACT: Deep sequencing of strand-specific cDNA libraries is now a ubiquitous tool for identifying and quantifying RNAs in diverse sample types. The accuracy of conclusions drawn from these analyses depends on precise and quantitative conversion of the RNA sample into a DNA library suitable for sequencing. Here, we describe an optimized method of preparing strand-specific RNA deep sequencing libraries from small RNAs, variably sized RNA fragments obtained from ribonucleoprotein particle footprinting experiments or fragmentation of long RNAs. Our approach works across a wide range of input amounts (400 pg to 200 ng), is easy to follow and produces a library in 2–3 days at relatively low reagent cost, all while giving the user complete control over every step. Because all enzymatic reactions were optimized and driven to apparent completion, sequence diversity and species abundance in the input sample are well preserved. Overall design: Deep sequencing libraries from either a randomized RNA oligo or an equimolar miRNA mix were analyzed for evenness of capture.
Project description:Deep sequencing of strand-specific cDNA libraries is now a ubiquitous tool for identifying and quantifying RNAs in diverse sample types. The accuracy of conclusions drawn from these analyses depends on precise and quantitative conversion of the RNA sample into a DNA library suitable for sequencing. Here, we describe an optimized method of preparing strand-specific RNA deep sequencing libraries from small RNAs, variably sized RNA fragments obtained from ribonucleoprotein particle footprinting experiments or fragmentation of long RNAs. Our approach works across a wide range of input amounts (400 pg to 200 ng), is easy to follow and produces a library in 2–3 days at relatively low reagent cost, all while giving the user complete control over every step. Because all enzymatic reactions were optimized and driven to apparent completion, sequence diversity and species abundance in the input sample are well preserved. Deep sequencing libraries from either a randomized RNA oligo or an equimolar miRNA mix were analyzed for evenness of capture.
Project description:Purpose: To determine the optimal RNA extraction and library generation protocols for mouse hippocampal tissue acquired by laser capture microdissection (LCM). Methods: Hippocampal subregion CA2 was captured from eight micron fresh frozen brain sections from AMIGO2 EGFP mice. Total RNA was extracted using the PicoPure (LCM standard) and QIAGEN micro RNeasy RNA extraction kits. RNA quantity and quality was assessed using a Bioanalzyer. Resulting RNA was used to generate cDNA libraries using NuGEN or SMARTer low input RNA-Seq library kits. We compared the effects of RNA quality and library generation approach to determine the methods that detected the greatest number of genes/exons with even coverage using minimal rounds of PCR. Results: We determined that the QIAGEN RNA extraction kit resulted in far superior RNA compared to the Picopure RNA extraction kit. We also found the NuGEN library generation kit that depletes ribosomal RNA towards the end of the protocol, led to higher cDNA library yields that required fewer rounds of PCR while providing even gene coverage and detection. Overall design: RNA from laser-captured tissue from mouse hippocampus was extracted using two low input methods generating a lower quality RNA sample (RIN 7, PicoPure) and higher quality sample (RIN 9, QIAGEN). Each sample was used to prepare cDNA using two commerically available low-input library generation methods (Clontech or NuGEN). The effect of RNA quality and library generation method were compared. The effect of shearing cDNA and PCR amplification were also tested for libraries made with the QIAGEN extracted RNA and NuGEN library kits (3 libraries, 16, 18 or 20 cycles of PCR). The libraries were multiplexed and run on the Illumina NextSeq500 instrument.
Project description:Sub-genomewide shRNA libraries were constructed using the current RNAi consortium constructs as well as using the DSIR (siRNA algoirthm) and a novel shRNA specific algorithm (shERWOOD). All libraries were placed into mir30 expression vectors. The shERWOOD libraries were also placed in a vector harboring an optimized mir cassette (ultramir). Each library was screened using the pancreatic cell line A385. A concensus set of essential genes identified as the set for which two shRNAs depleted in each of the libries. For these genes, a great percentage of shERWOOD seletected shRNA depleted. In addition the placement of shERWOOD selected constructs into ultramir scaffoled increased the rate of shRNA depletion for essential genes further. Purpose: shRNA screens were carried out using various library construction strategies to identify the strategy that provides the best shRNA screening results. Method: Libraries were constructed using the TRC shRNA set as well as shRNAs identified using the DSIR and shERWOOD algorithms. shRNA libraries were cloned into mir30 expression vectors. shERWOOD shRNAs were also cloned into an expression vector harboring an optimized microRNA scaffold termed ultramir. Each resultant library was screened using the pancreatic cell line A385. Each library was analyzed separately to identify a set of genes where at least two shRNAs depleted. These gene sets were intersected to develop a set of essential genes. Results: The shERWOOD shRNA libraries provided the highest number depleting shRNAs for each essential gene. Further these shRNAs depleted to a greater extent than did the shRNAs from the other libraries. When shERWOOD libraries were placed into the ultramir cassette a greater number of shRNAs per essential gene depleted.
Project description:Small RNAs, including microRNAs and their targets, as well as phased secondary siRNAs, were characterized in the soybean genome by deep sequencing of small RNA libraries from a wide range of tissues. The mRNA targets of many of these small RNAs were also validated from many of the same tissues using PARE (Parallel Analysis of RNA Ends) libraries. Overall design: Small RNA profiling was done by Illumina TruSeq sample preparation followed by high-throughput sequencing using an Illumina HiSeq 2500 at the Delaware Biotechnology Institute. PARE analysis used library construction approaches described in the journal Methods (PubMed ID: 23810899).
Project description:Deep sequencing of transcriptomes allows quantitative and qualitative analysis of many RNA species in a sample, with parallel comparison of expression levels, splicing variants, natural antisense transcripts, RNA editing and transcriptional start and stop sites the ideal goal. By computational modeling, we show how libraries of multiple insert sizes combined with strand-specific, paired-end (SS-PE) sequencing can increase the information gained on alternative splicing, especially in higher eukaryotes. Despite the benefits of gaining SS-PE data with paired ends of varying distance, the standard Illumina protocol allows only non-strand-specific, paired-end sequencing with a single insert size. Here, we modify the Illumina RNA ligation protocol to allow SS-PE sequencing by using a custom pre-adenylated 3’ adaptor. We generate parallel libraries with differing insert sizes to aid deconvolution of alternative splicing events and to characterize the extent and distribution of natural antisense transcription in C. elegans. Despite stringent requirements for detection of alternative splicing, our data increases the number of intron retention and exon skipping events annotated in the Wormbase genome annotations by 127 % and 121 %, respectively. We show that parallel libraries with a range of insert sizes increase transcriptomic information gained by sequencing and that by current established benchmarks our protocol gives competitive results with respect to library quality. Sequencing of mRNA from C. elegans with libraries of four differing insert sizes
Project description:To investigate the contribution of small non-coding RNAs, including microRNAs, to stress response in C. elegans, we examined their expression changes using Illumina deep-sequencing technology. Stress conditions we examined include heat shock, Pseudomonas aeruginosa (PA-14) and L1 arrest. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Total RNAs were purified from wild-type young adult animals that were exposed to each stress condition, and used for cDNA library preparation for small RNAs. As for the L1 arrest sample, first wild-type embryos were collected and cultured without a food source in M9 buffer for 48 hrs with rotation, and then RNAs purified from L1 animals in a developmentally arrested state were used for the library preparation. These cDNA libraries established were sequenced with Illumina Genome Analyzer II.
Project description:Strand-specific massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline for the comparison of library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in complexity, strand-specificity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in any organism. Examination of 11 different strand-specific RNA-Seq libraries from 7 distinct methods; also 2 control non-strand-specific RNA-Seq libraries. To assess the performance of each strand-specific library in digital expression profiling, we compared them to reference expression measurements estimated from expression profiles using competitive hybridization of a mid-log RNA sample vs. genomic DNA using Agilent arrays.
Project description:Small RNAs (sRNAs) play important roles in plants encountering stress environments. However, limited research has been conducted on the sRNAs involved in plant wound responses. To identify potential roles for the wounding-related sRNAs, sRNA deep sequencing was used. After leaves were wounded for 0.5 hour, total RNAs from unwounded and wounded leaves were isolated for sRNA library construction. The Illumina platform was used to sequence sRNA libraries. About 12 million sequence reads were obtained for each sample. Overall design: Examination of sweet potato leaves with either unwound or wound treatment
Project description:Exosomes are small membrane vesicles of endocytic origin secreted by most cells, and contain a wealthy cargo of protein and RNA species that can modulate recipient cells’ behaviors and may be used as biomarkers for diagnosis of human diseases. They have been found in blood and are valuable sources for biomarkers due to selective cargo loading and resemblance to their parental cells. The goal of this study is to identify circRNA, lncRNA and mRNA profiles in human blood by high-throughput RNA sequencing (RNA-seq). 1-4 ml plasma or serum were used to extract exosomal RNAs by exoRNeasy Serum/Plasma Maxi kit (Qiagen). The exosomal RNAs were further treated with DNAse I and subjected to ribosome minus low-input RNAseq library preparation. The libraries were sequenced by Illumina Hiseq platform. Overall design: Human blood exosomal RNAs were generated by deep sequencing using Illumina Hiseq.