Genome-scale “-omics” measurements are challenging to benchmark due to the enormous variety of unique biological molecules involved. Mixtures of previously-characterized samples can be used to benchmark repeatability and reproducibility using component proportions as truth for the measurement. We describe and evaluate experiments characterizing the performance of RNA-sequencing (RNA-Seq) measurements, and discuss cases where mixtures can serve as effective process controls.We apply a linear model to total RNA mixture samples in RNA-seq experiments. This model provides a context for performance benchmarking. The parameters of the model fit to experimental results can be evaluated to assess bias and variability of the measurement of a mixture. A linear model describes the behavior of mixture expression measures and provides a context for performance benchmarking. Residuals from fitting the model to experimental data can be used as a metric for evaluating the effect that an individual step in an experimental process has on the linear response function and precision of the underlying measurement while identifying signals affected by interference from other sources. Effective benchmarking requires well-defined mixtures, which for RNA-Seq requires knowledge of the post-enrichment ‘target RNA’ content of the individual total RNA components. We demonstrate and evaluate an experimental method suitable for use in genome-scale process control and lay out a method utilizing spike-in controls to determine enriched RNA content of total RNA in samples.Genome-scale process controls can be derived from mixtures. These controls relate prior knowledge of individual components to a complex mixture, allowing assessment of measurement performance. The target RNA fraction accounts for differential selection of RNA out of variable total RNA samples. Spike-in controls can be utilized to measure this relationship between target RNA content and input total RNA. Our mixture analysis method also enables estimation of the proportions of an unknown mixture, even when component-specific markers are not previously known, whenever pure components are measured alongside the mixture.The online version of this article (doi:10.1186/s12864-015-1912-7) contains supplementary material, which is available to authorized users. Measurement assurance for genome-scale measurements is challenged by the impracticality of creating a sample containing known quantities of tens of thousands of components, such as the RNA transcripts measured in an RNA-seq experiment. Deep sequencing of cellular RNA can generate vast quantities of gene expression information, yet measurement biases have been identified at nearly every step of the library preparation process –4.As RNA-sequencing expression data expands from discovery into clinical applications, the sources and magnitudes of bias and variability must be carefully understood and quantified. The basic units of expression in sequencing, such as transcripts per million reads (TPM) or fragments per kilobase per million reads (FPKM), are still undergoing revision , 6. EvenIdeally, a measurement process is linear and possesses a known precision. A linear measurement process shows an increase in signal proportional to an increase in the object being measured. It is also helpful if measured signal is additive, arising only from a single source. Precision consists of repeatability and reproducibility, defined as the degree of closeness in multiple measurements made by a single user and the closeness between multiple labs, respectively. We show that mixtures can demonstrate that a measurement’s response function is linear and of high specificity  while measuring its variability and precision. Properly constructed mixture samples can be used to correct for systematic measurement errors, provide ongoing monitoring of performance, serve as a tool for interlaboratory comparison, and create a context for evaluating batch effects, protocols, and informatic analyses.There are two known approaches to creating useful genome-scale standards. One is the creation of a limited number of external spike-in controls, such as those designed by the External RNA Controls Consortium (ERCC), which were created for microarrays and have been applied to next-gen sequencing –11. A seMixtures can serve as a test that applies to each of the tens of thousands of transcripts in a sample’s transcriptome. Linearity of the measurement response can be demonstrated based on the fundamental understanding that a mixture is a linear combination of its components. Previous work with mixtures in microarrays –14 utiliUsing known mixture compositions, predicted values can be calculated based on the assumption that the measurement response is linear. Deviation of the observed values from the model-predicted value is an indication of bias in the measurement. Systematic biases could be introduced by sample preparation, signal processing, interference from related or mis-annotated genes, or sampling variation. Signal arising from off-target molecules, such as a closely related transcript, can cause false positive results and result in a lowered specificity. Mixture samples can provide information about the measurement sensitivity, specificity, repeatability, reproducibility, dynamic range, and limit of detection.Determining the relative contributions to gene expression of individual components within mixtures of biological states has received some attention in clinical research, where biopsies and other patient samples are often mixtures. The process of resolving gene expression signals introduced by each individual component of a mixture –23 has bNone of these methods corrects for RNA enrichment. Different cell types express different total amounts of RNA of varying types, confounding estimates of cell type proportion made based on the quantification of total RNA . Others We assess linear response, specificity, and accuracy of genome-scale measurements using mixtures. In the process, we demonstrate that linear models can be used to separate these mixtures into the proper components. We were mindful that while our mixtures were of total RNA, the sequencing process enriches for RNA subclasses, and that differential enrichment is an important factor when interpreting results. We anticipate that a mixture-based approach to measurement assurance is highly generalizable to many types of mixtures and can be extended to the wide variety of genome-scale measurements, including but not limited to proteomic and metabolomic measurements.To assess measurement parameters of genome-scale transcriptome data, we analyzed two RNA-seq experiments measuring synthetic mixtures of commercially available human total RNA samples Fig.  13, 14,,14, 34. In a second experiment, which we call BLM, we generated multiple libraries of two mixtures (BLM-1 and BLM-2) composed of total RNA isolated from human brain , liver (L), and muscle (M) tissue were measured for this study. These two mixtures were made with component proportions of 1B:1L:2M and 1B:2L:1M. The total RNA of each individual tissue were also sequenced as single component samples to provide an expression signature for each tissue. ERCC spike-in control RNAs  preparedThese mixtures were designed to have a defined expression signal ratio between them. For example, if the measurement response were linear and unbiased, the signal in the SEQC-C sample would be exactly 1/4 the signal of SEQC-B plus 3/4 the signal from SEQC-A due to the design of the mixture. However, these total RNA mixtures went through RNA-seq library preparation by polyA selection, which purposely removes certain classes of RNA, such as ribosomal RNA, from the sample. As the resulting sequence data comes from only the selected subset, the fraction of which can be different from sample to sample, a correction for this differential enrichment must be applied to accurately reflect the experimental process and allow the model to return the designed ratios of expression between mixtures  is composed of a number of named components C , with each component comprising a proportion of the mixture. ΦC. i,M is the expression signal arising from a particular gene/transcript i in mixture M.We observed that mixture expression is a linear combination of the component samples and the mixture proportions of each component. Equation 1 describes the relationship between signal in the mixtures and signal in the constituent samples. A mixture This study uses four mixtures of the same general form:C is defined as the fraction of measured RNA per unit total RNA in component C.These mixtures were made of total RNA, while the expression signal (sequencing reads) arises only from the enriched RNA. As the fraction of the total RNA mass that matches the enrichment criteria varies between cell types, the enrichment of total RNA introduces a bias. Additional file Including this factor, the BLM1 mixture equation becomes Eq. There are a few approaches that have been described to measure ρ. One study directly measured the post-selection RNA content between SEQC-A and SEQC-B samples . AnotherThe ρ factor can be determined using spiked-in RNA  as samplC = ρA*.75+ ρB*.25 and ρD = ρA*.25+ ρB*.75 hold true to within 5 % of ρA, indicating that the enriched RNA content of a mixture is a linear combination of the enriched RNA content of its components. Additionally, solving the system of BLM equations only for the enrichment fractions  yields very similar enrichment fractions to those calculated from spiked-in RNA, leading us to be confident in these measurements.Figure The target RNA fraction ρ is a property of an individual RNA sample and can be affected by any sample manipulation - chief among them the polyA selection step in sample preparation. For replicates within a single polyA-selected SEQC experimental run, the ρ of a mix varies slightly, likely due to fluctuations in efficiency of polyA selection. (Additional file BLM were recalculated for the BLM mixtures BLM-1 and BLM-2. The ρ values and the sequencing expression data Xi were used to solve for the mixture proportions ΦBLM by linear regression to the mixture equation. Figure i for each transcript. Figure BLM values at which residuals were minimized for the two mixtures for each replicate sample in each laboratory. Estimates of the three component proportions in the two mixtures are consistent with the designed 25:25:50 and 25:50:25 proportions in the two BLM mixtures. Figure To demonstrate the accuracy of this analytical framework of mixtures, the mixture proportions ΦIn studies by the SEQC , differeFigure Mixtures of biological samples can be useful as process controls for measurements with linear response functions. A mixture can be treated as linear combination of its components. Two experimental datasets with known mixture parameters were used to test the linearity of RNA-seq measurements. In RNA-seq, the enrichment of the total RNA mixture components by polyA selection must be accounted for, as the sequencing experiment measures only the RNA which passes this enrichment filter.Mixtures with either known or unknown proportions can be analyzed. If mixture proportion information is known a priori, genome-scale data can be used as a process control to test the repeatability and sensitivity of measurements by comparing observed and expected measures. Alternatively, if the mixture proportions are an unknown and desired parameter, expression measures from the mixture in combination with the single components can be used to experimentally determine the mixture proportions. This application can be valuable in un-mixing biological mixtures, including clinical mixtures, cell cultures, and xenografts –29. WhilMixtures can provide measurement process assurance to a sequencing experiment. Using mixture samples alongside pure samples, one can demonstrate the reproducibility and sensitivity of genome-scale RNA, protein, as well as metabolite measurements. The main goal of this type of mixture analysis is to create a known ratio value by which the measurement characteristics of an experiment can be assessed. While an experiment’s measurement of this known ratio is not sufficient to prove the validity of the measurement, it is a necessary condition, and any deviations are indicative of bias.We demonstrate process control usage of mixtures by comparing the nine SEQC sites. Figure While we demonstrate mixture analysis with two specific samples, the analysis is generalizable to any number or type of mixture components. Any mixture split into known individual components can be measured in this way. For example, a clinical researcher may have three samples of interest from healthy, chronically diseased and acutely diseased sources. A mixture of these three cell types would provide confidence in the measurements made on the three samples individually by verifying the repeatability of that measurement. It can also provide a benchmark sample to assess comparability over space and time. These mixtures can detect biases introduced by batch effects, operator effects, sample mislabeling, and technical artifacts while evaluating the variability of the measurement. Mixture samples with known proportions can help determine experimental reproducibility and discover technical artifacts introduced by the measurement process by comparison of the expected to observed proportions.With this analytical model, end users and core facilities can use known mixtures as a process control to track changes in measurement quality whenever changes to the experimental process are made. By including a predefined mixture, cross-sample comparisons can be made to demonstrate the internal consistency of measurements made using any new experimental technique, kit, or downstream analysis tool. In this way, there is some assurance that changes in experimental protocol have not affected measurement reproducibility. Residuals from modeled counts can be used as a metric to evaluate the magnitude of effect an experimental process has on the linearity and precision of underlying measurements.In addition to gaining an understanding of the measurement process using the benchmarking workflow, unknown samples can be collected and studied to determine the relative proportion of known components. Proportions of components can be determined even in the absence of any type-specific markers, given measurable differences in expression between the cell types.Resolving the composition of mixtures has proven useful in determining the purity of cell lines or proportions of heterogeneous cells, in identifying interesting cellular contaminants such as partially differentiated cells, and understanding clinical samples containing mixed cell types. In contrast to approaches using transgene expression , the mixIn addition to providing limit of detection and cross-experiment comparison characterizations of a dataset, spike-in controls can be used in mixture samples to determine the enriched RNA fraction of cells. Enrichment fraction is a critical parameter for comparing samples that do not have identical total RNA content. This is most relevant to cells with variable global expression , includiWe demonstrate that the ERCC controls can be used as an estimator of enriched RNA content within samples. Of note, the SEQC study  results There are many methods used to determine component gene expression profiles from mixture samples. To the best of our knowledge, our method is the only one that accounts for RNA enrichment as calculated via spike-in controls. When comparing samples of variable RNA content, bias arises when that variability is not accounted for. We describe a straightforward method for measuring the enrichment of target RNA in RNA-seq samples using spike-in RNA. We show that enrichment-corrected deconvolution of two mixture datasets returns the best approximation of known mixture proportions Figs.  and 5, dPrevious methods used to determine the composition of RNA-seq mixtures make inaccurate estimates of mixture proportion in the BLM sample where the enrichment fractions vary substantially between mixture components. These methods are nearer to true values in the SEQC sample, where the RNA content difference between samples is less significant, but all estimates are improved by incorporating enrichment fraction measurement (Additional file Control mixtures most easily demonstrate that an experimental process is linear and internally consistent, and can track the changes in variability over time. A first experiment with a new process should utilize these controls to demonstrate the reproducibility of measurements between single component and mixture samples. Subsequently, changes to the process can be evaluated by comparing the model residuals before and after the change. For example, a lab interested in changing from a total RNA measurement to a messenger RNA measurement may wish to evaluate if this change had any effect on sequencing output. The change in the sum of residuals between these two different experiments would allow a global comparison, while the change in residuals of individual genes may highlight a set of genes, which become inconsistently biased between experiments. Table Although mean mixture proportion values returned from a linear combination of mixture components approximate the nominal mixture proportion in both measured samples, the increased variability of the muscle estimate in the BLM mixture (error bars, Fig. Our estimation of targeted RNA fraction is imperfect; an assumption of the model we built is that the enrichment proportion is constant between replicates of the same sample. Additional file We demonstrate the linear response function and specificity of RNA-sequencing measurements using mixtures of biological samples. We recommend the use of such mixtures as benchmarks to characterize the repeatability and reproducibility of experiments. Spike-in controls can be used to calculate the measured RNA content of total RNA mixtures, compensating for biases introduced by polyA enrichment or similar RNA enrichment techniques. Our method creates a framework for using mixtures in measurement process control and corrects for biases introduced by RNA selection. Correction for differential enrichment improves the accuracy of mixture proportion determination in RNA-seq experiments.Benchmarking genome-scale measurements using mixed samples will remain useful even after the era of short-read sequencing is over. Answering the biological question of “what types of cells are in the mixture I’m sequencing?” requires more information than even a perfect transcriptome reconstruction could provide. The biological and measurement value added by mixed samples are demonstrated here to be platform-independent. We anticipate that mixtures can provide the same measurement assurance to protein and metabolite measurements. Confidence in the reproducibility of measurement and understanding the components in complex biological samples will always be a staple of quality science.For the BLM experiment, Human Brain Reference RNA, Human Liver Total RNA, and Human Skeletal Muscle Total RNA were purchased from Ambion. Human RNA tissues were purchased from Ambion. Ambion certifies that all human derived materials have been prepared from tissue obtained with consent from a fully informed donor or a member of the donor’s family.This purified RNA was quantified by absorbance on a NanoDrop 1000, mixed in the specified proportions, then spiked with ERCC RNA transcribed from NIST SRM 2374. For Illumina sequencing, the Illumina TruSeq protocol was followed. HiSeq runs generated 100 + 100 bp paired-end reads. Solid 5500 sequencing followed the Life Technologies Whole Transcriptome protocol, yielding 75 + 35 bp paired-end reads. Spike-in composition and amounts are included in the data submission to ENA.BLM gene counts were based on raw count data quantified using HTSeqCounts  based onhttp://github.com/usnistgov/mixtureprocesscontrol as a supplemental file to clarify this procedure.The relative abundance of components in unknown mixtures were calculated by first observing the mean target RNA fraction for the neat components across replicates. The count data in the mixture was set as the response, predicted by the count data from the individual components modified by the enrichment fraction, as based on the mixture equations. An example R script ‘generalmixturesolver’ is provided at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE47774].The SEQC data is available from GEO GSE47774. [http://www.ebi.ac.uk/ena/data/view/PRJEB8231].The BLM data is available from the European Nucleotide Archive, PRJEB8231. [https://github.com/usnistgov/mixtureprocesscontrol.Figure code, count tables, and example scripts are available on No ethics approval was required from an ethics committee for the study.


Here we describe a dynamic programming approach for model-free sequence comparison that incorporates high-throughput chemical probing data. Based on SHAPE probing data alone, ribosomal RNAs (rRNAs) from three diverse organisms – the eubacteria E. coli and C. difficile and the archeon H. volcanii – could be aligned with accuracies comparable to alignments based on actual sequence identity. When both base sequence identity and chemical probing reactivities were considered together, accuracies improved further. Derived sequence alignments and chemical probing data from protein-free RNAs were then used as pseudo-free energy constraints to model consensus secondary structures for the 16S and 23S rRNAs. There are critical differences between these experimentally-informed models and currently accepted models, including in the functionally important neck and decoding regions of the 16S rRNA. We infer that the 16S rRNA has evolved to undergo large-scale changes in base pairing as part of ribosome function. As high-quality RNA probing data become widely available, structurally-informed sequence alignment will become broadly useful for de novo motif and function discovery.Discovery and characterization of functional RNA structures remains challenging due to deficiencies in  Despite the clear functional importance of structure in RNA molecules, it remains very difficult to correctly identify and annotate similar RNA structures because their sequences are often poorly conserved even for RNAs that form very similar higher-order structures. A solution is to use a metric that identifies structural motifs but this, too, is difficult because RNA structure modeling based on sequence alone is generally not very accurate. In this work, we use SHAPE chemical probing to obtain model-free information about RNA structure and then exploit this information to align RNAs by sequence. We show for ribosomal RNAs that sequence alignments based on SHAPE experimental information alone are as accurate as those that actually use sequence information. In addition, we identify regions in the 16S ribosomal RNA that form conserved secondary structures that are different from currently accepted models. These differences, rather than being errors, reveal sites of conformational flexibility that may underlie mechanistic functions in ribosome assembly and translation regulation. We anticipate that structure-informed sequence alignment and structure modeling will become broadly useful tools in RNA function analysis. RNA is a central participant in gene expression and regulation . Howeverab initio or concurrent structure prediction. RNA chemical probing is structurally robust and is not limited by the current, relatively poor, understanding of RNA structure. The SHAPE structure probing approach . Th. Th14]. E. coli to C. difficile and to H. volcanii, respectively; whereas, conventional sequence identity-based alignments using the Needle algorithm had sensitivities of 84% and 72%, respectively. For 23S rRNA, SHAPE-based alignments performed well but not quite at the level of sequence-based alignment  and SIVcpz strain MB897  aligned with a sensitivity of 97% relative to extensively manually curated and hand-annotated alignments (see following companion article in this issue).Pairwise SHAPE-based alignments were also used to generate multiple sequence alignments . AlignmeBoth sequence comparisons and SHAPE data have been successfully used to direct RNA secondary structure modeling . Given tThe SHAPE-defined sequence alignments were incorporated as arguments for secondary structure modeling using the RNAalifold algorithm in the Vienna RNA package –22. RNAaE. coli 16S, 70%; C. difficile 16S, 64%; H. volcanii 16S, 39%; E. coli 23S, 46%). This suggests that these nucleotides are weakly base paired or form alternate structures under the experimental conditions used in this work.Structure models were generated for 16S and 23S ribosomal RNAs  Figs. ThE. coli and C. difficile 16S rRNA predictions improved  undergo a conformational change during conversion of the 30S subunit from an inactive to active conformation  and are  and h44 inset). Given that these alternate structures occur in and near the codon-anticodon decoding site, we propose that these structures form a switch involving low energy base-pairing rearrangements important for regulation of translation.Strikingly, all regions that structure-directed alignment suggests form alternate structures in the 16S rRNA are located close to each other in three-dimensional space, in the neck of the intact small ribosomal subunit , inset. The strategies developed here allow for highly accurate, structure-informed alignments of large  and likeE. coli RNA (DH5α strain) was prepared as described . The supernatant was discarded, and the pellet was allowed to air-dry for 5 minutes. To lyse the cells, 1 mL TRIsure (Bioline) was added to the pellet. The resultant mixture was incubated at room temperature for 5 minutes. The mixture was then transferred to a vial containing 250 μL 0.1-mm glass beads. Cells were lysed using a bead beater over two 90 second pulses, with cells held on ice between pulses. The resultant mixture was extracted with 200 μL chloroform, and the aqueous layer was extracted three times with phenol (pH 8.0):chloroform:isoamyl alcohol (25:24:1), followed by three extractions with chloroform. The RNA-containing solution was exchanged for folding buffer (50 mM Hepes (pH 8.0), 200 mM potassium acetate (pH 8.0), and 5 mM MgCl2) using a pre-equilibrated gel filtration column .S medium  at 37°C d 5% H2)  to an ODH. volcanii cells (strain DS70) were grown to an OD600 of 0.8 and collected by centrifugation . Cells were lysed by incubation in low salt solution . Following lysis, this solution was extracted three times with phenol (pH 8.0):chloroform:isoamyl alcohol (25:24:1), followed by three extractions with chloroform. The RNA-containing solution was exchanged for folding buffer  using a pre-equilibrated gel filtration column .Growth medium was prepared by bringing 600 ml 30% salt solution , 5 g bacteriological peptone , and 1 g yeast extract  to 1 L with deionized water. 2] was added to a concentrated RNA solution  to a final volume of 90 μL. The RNA solution was incubated at 37°C for 30 minutes. Following incubation, 10 μL DMSO (no-modification control) or 10 μL 100 mM 1M7 in DMSO (native 1M7-modified sample) was added to the RNA solution. The RNA solution was then incubated at 37°C for 3 minutes. For the denatured control, 25 μL 4× denaturing control buffer  and 50 μL deionized formamide were added to a concentrated RNA solution , and deionized water was added to a final volume of 90 μL. This solution was held at 95°C for 1 minute, and then 10 μL 100 mM 1M7 in DMSO was added; the combined solution was incubated at 95°C for 1 minute.Determination of SHAPE reactivity by SHAPE-MaP employs three experiment conditions: chemical modification of native RNA, chemical modification of denatured RNA, and a no-modification control. All chemical modifications were performed using 1-methyl-7-nitroisatoic anhydride (1M7) . Chemica2], incubated at 94°C for 4 minutes, and then transferred immediately to ice. Fragmented RNA was purified using a G-25 column (GE) with elution into 1× TE . Following fragmentation, reverse transcription was performed using a 20-μL aliquot of fragmented RNA and 2 μL random DNA nonamers (200 ng/μL). The solution was incubated at 65°C for 5 minutes and then placed on ice. To this solution, 7 μL reaction buffer , 4 μL 60 mM MnCl2, and 5 μL water were added. The solution was pre-incubated at 25°C for 2 minutes prior to adding 2 μL Superscript II (Invitrogen). The reaction was incubated at 25°C for 10 minutes, 42°C for 180 minutes, and 70°C for 15 minutes. Following reverse transcription, the RNA was purified using a G-25 column (GE) with elution into 1× TE.After modification, all three samples were purified by affinity chromatography  with elution into 22 μL buffer. To prepare sequencing libraries, the purified RNA samples were first fragmented; 20 μL of RNA solution was combined with 30 μL fragmentation buffer [250 mM Tris (pH 8.3), 375 mM KCl, 15 mM MgClThe cDNA was converted to a double-stranded DNA library with Illumina platform-specific sequence tags. First, 40 μL of the purified reverse transcription product was used in an 80-μL second-strand synthesis reaction . The product of the second-strand synthesis reaction was purified  and eluted into 12 μL elution buffer. A 10-μL aliquot of the purified DNA solution was then used in a 50-μL end repair reaction . Following end repair, the DNA was purified  and eluted into a final volume of 30 μL 1× TE.To incorporate Illumina platform-specific sequence tags, a dA-tailing reaction was used to incorporate a single-nucleotide overhang at the 3′ ends of the double-stranded DNA. A 15-μL aliquot of purified DNA from the end repair step was used in a 20-μL dA-tailing reaction . Illumina sequences were incorporated using a ligation step with Illumina iAdapters (prepared in house). Immediately following completion of the dA-tailing reaction, 7.5 μL of 5× reaction buffer , 2.5 μL 125 nM DNA adapter, 3.75 μL Quick T4 DNA Ligase (New England Biolabs), and 3.75 μL water were added to the dA-tailing reaction mix. The ligation reaction was then incubated at 20°C for 15 minutes. The ligation reaction was purified twice  with final elution into 20 μL 10 mM Tris (pH 8.0).Illumina libraries were prepared using emulsion PCR , 34. TheThe concentrations of sequencing samples were determined by Qubit High Sensitivity DNA fluorescence assays (Life Technologies) and High Sensitivity DNA Bioanalyzer assays (Agilent). Each sample was diluted to 2 nM and pooled. The pooled library was sequenced using an Illumina MiSeq (300 cycles—PE kit). Sequences were aligned and mutation events counted using the SHAPE-MaP analysis pipeline . SHAPE rx and y began with declaration of matrices D, P, and Q. Each matrix had dimensions m by n, where m and n were the lengths of sequences x and y plus 1, respectively. Considering alignment of (x0…xi) and (y0…yj), Di,j corresponds to the score associated with alignment, Pi,j corresponds to the score associated with alignment that ends with a gap in x, and Qi,j corresponds to the score of alignment that ends with a gap in y. To initialize each matrix, D0,0 was set to 0, Di,0 was set to GOP + i × GEP, and D0,j was set to GOP + j × GEP, where GOP and GEP are the gap opening penalty and gap extension penalty, respectively; P0,j and Qi,0 were set to arbitrarily large negative numbers. Every other cell in the matrix was populated by the following recursion, where s describes a pair-wise comparison score, and xi and yj are the SHAPE values of each sequence at i-th and j-th positions:m and b:b in the recursion, where x'i and y'j were the base identities at positions i and j in sequences x and y, respectively:b is described by the following equation with parameters MATCH and MISMATCH.SHAPE-based alignment was based on the Gotoh algorithm with affine gap penalties . SHAPE-btion see  is descri,j, representing the 3′-most position of the alignment. The next position in the alignment was found using the following comparison: if Di,j was equal to sum of Di-1,j-1 and the score of the pairwise comparison between xi-1 and yj-1, the next position was aligned nucleotides xi-1 and yi-1; if Di,j was equal to Pi,j, the next position was a gap in sequence y; if Di,j was equal to Qi,j, the next position was a gap in sequence x. The trace-back operation was finished when a position was encountered where i = 0 or j = 0.Following population of the matrices by recursion, a trace-back operation was used to find the optimal alignment. The trace-back operation began at position m and b, base-identity scoring parameters MATCH and MISMATCH, and gap penalty parameters GOP and GEP were optimized by exhaustive search over the 16S and 23S rRNA aligned sequence pairs. The best parameter set was then selected based on the average sensitivity across all alignments. In experiments considering only SHAPE values, m = -2, b = 2, GOP = -5, and GEP = -0.25. Scoring function parameters were preserved across SHAPE-only and combined SHAPE and base identity alignments, but GOP and GEP parameters were reoptimized for alignments considering both SHAPE reactivity and base identity. When both SHAPE and base identity were considered, m = -2, b = 2, GOP = -6, GEP = -1, MATCH = 2, and MISMATCH = -2.The SHAPE scoring parameters E. coli and C. difficile and E. coli and H. volcanii were obtained for both 16S and 23S rRNAs. RNA sequence alignments generated in this work were then evaluated by comparison to these alignments. Sensitivities were calculated as the percentage of matched nucleotides in the CRW alignments found in a given alignment.From multiple sequence alignments on the CRW, pairwise alignments between Multiple sequence alignments were generated using T-Coffee . First, To establish a base-line for comparison, sequence-only secondary structure predictions were performed with RNAfold from the Vienna RNA package with a maximum base paring distance of 600 nucleotides . Secondahttp://www.tbi.univie.ac.at/RNA.Multiple sequence alignments were used as input for RNAalifold from the Vienna RNA package . SHAPE dx and y and any base pair in the predicted model at positions xʹ and yʹ were required to meet the following criterion:Secondary structure models were evaluated by calculating sensitivity (sens) as the percentage of base pairs from the CRW covariation model found in predicted structures and by calculating positive predictive values (ppv) as the percentage of predicted pairs found in the covariation model. It should be emphasized that these reference structures are themselves experimental models, and base pairs in these models may show slight local rearrangements in terms of base-pairing partners , 37. To S1 FigE. coli nucleotides are colored by their SHAPE reactivities .This model was constrained by 23S rRNA consensus base pairs based on a SHAPE-based sequence alignment. Predicted pairs that exactly match the accepted covariation model  are show(TIF)Click here for additional data file.S2 FigFull legend is given in (TIF)Click here for additional data file.S1 Dataset(ZIP)Click here for additional data file.S1 Software(ZIP)Click here for additional data file.


Ribonuclease L (RNase L) is a metal-ion–independent endoribonuclease associated with antiviral and antibacterial defense, cancer and lifespan. Despite the biological significance of RNase L, the RNAs cleaved by this enzyme are poorly defined. In this study, we used deep sequencing methods to reveal the frequency and location of RNase L cleavage sites within host and viral RNAs. To make cDNA libraries, we exploited the 2′, 3′-cyclic phosphate at the end of RNA fragments produced by RNase L and other metal-ion–independent endoribonucleases. We optimized and validated 2′, 3′-cyclic phosphate cDNA synthesis and Illumina sequencing methods using viral RNAs cleaved with purified RNase L, viral RNAs cleaved with purified RNase A and RNA from uninfected and poliovirus-infected HeLa cells. Using these methods, we identified (i) discrete regions of hepatitis C virus and poliovirus RNA genomes that were profoundly susceptible to RNase L and other single-strand specific endoribonucleases, (ii) RNase L-dependent and RNase L-independent cleavage sites within ribosomal RNAs (rRNAs) and (iii) 2′, 3′-cyclic phosphates at the ends of 5S rRNA and U6 snRNA. Monitoring the frequency and location of metal-ion–independent endoribonuclease cleavage sites within host and viral RNAs reveals, in part, how these enzymes contribute to health and disease. Endoribonucleases include metal-ion–dependent enzymes and metal-ion–independent enzymes . Metal-ix5’A(2’p5’A)n; x = 1-3; n ≥ 2] (Ribonuclease L (RNase L) is a well-studied endoribonuclease associated with lifespan , cancer ; n ≥ 2] ,35, the ; n ≥ 2] . Charact; n ≥ 2]  and RNas; n ≥ 2]  cleavageHepatitis C virus (HCV), which is restricted by the 2-5A/RNase L pathway in tissue culture cells , is ableArabidopsis thaliana tRNA ligase, an enzyme that specifically recognizes RNA fragments with 2′, 3′-cyclic phosphate termini  (PV (Type 1 Mahoney) and HCV (genotype 1a) RNAs were used in this study. A plasmid encoding PV cDNA  were purified as previously described , RNase A (0.5 nM), or RNase L (25 nM), and 2-5A (25 nM). These reaction conditions were optimized empirically to give limited and increasing amounts of viral RNA cleavage over time. RNase A, even at relatively low concentrations, cleaved the viral RNAs readily . Higher concentrations of RNase L (25 nM) were needed for optimal enzymatic activity, due in part to the required dimerization of RNase L following 2-5A binding  or a dominant-negative R667A mutant form of RNase L (M25 HeLa cells) were grown as previously described  in Dulbe6 cells per 35 mm well). Following 1 h of virus adsorption at room temperature, the inoculum was removed and replaced with 2 ml of DMEM containing fetal bovine serum, pen-strep and G418. Mock infections were performed using phosphate buffered saline without virus. Mock-infected and PV-infected cells were incubated at 37°C. At the indicated times , RNA was isolated from the cells using guanidine thiocyanate disruption , P:C:I extraction and ethanol precipitation. Host and viral RNAs in each sample were separated by agarose gel electrophoresis and visualized using ethidium bromide and UV light. PV was isolated from a parallel set of cells and quantified by plaque assays to monitor the magnitude and kinetics of virus replication (Cells were infected with 10 plaque forming units (PFU) per cell of PV diluted in phosphate buffered saline  were purified as previously described , A. thaliana tRNA ligase (0.75 µM), Riboguard (Epicenter) at 1U per µl and RNA from in vitro reactions  or RNA from cells . Saturating RNA linker concentrations (20 µM), in excess to RNA fragments with 2′, 3′-cyclic phosphates, maximized ligation efficiencies (data not shown). After incubation at 37°C for 1 h, RNAs were purified using P:C:I extraction and ethanol precipitation. GlycoBlue (Ambion) was included in all ethanol precipitation steps associated with the preparation of cDNA libraries. The RNAs were fragmented for 10 min at 70°C using 1X Fragmentation Reagents (Ambion), followed by an ethanol precipitation. Because a 2′-phosphate persists at the junction between RNA fragments and RNA linkers, fragmented RNAs were dephosphorylated at 30°C for 1 h in reactions (20 µl of volume) containing Tpt1 buffer , NAD+ (10 mM) and Tpt1 (4.5 µM). These RNAs were P:C:I extracted and ethanol precipitated.escribed . An RNA Following the ligation and Tpt1 reactions, RNAs were resuspended in denaturing sample buffer , heat denatured (10 min at 65°C) and fractioned on a 6% urea-polyacrylamide gel. RNAs in the gel were visualized using SYBR-Gold (Invitrogen) and blue light transillumination. RNAs 100–500 nt in length were excised from the gel, crushed into a slurry with 0.3 M sodium acetate and incubated at 42°C for 2 h. RNA was purified from polyacrylamide by running samples through DTR cartridges (EdgeBio) followed by ethanol precipitation.Superscript III (Invitrogen) and a DNA primer (5′-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC GCT C-3′) were incubated with the gel-purified RNAs to make cDNA (per the manufacturer’s instructions). Exonuclease I (0.5 U) and recombinant shrimp alkaline phosphatase (0.25 U) (USB/Affymetrix) were added to each RT reaction, followed by additional incubation at 37°C for 30 min, to remove excess DNA primer and dNTPs. RT reactions were then heated to 95°C to inactivate the enzymes. RNAs were hydrolyzed using 1 N NaOH treatment at 100°C for 10 min and neutralized with 1 N HCl. cDNA was P:C:I extracted and ethanol precipitated.2 and 1 mM DTT). Linked cDNAs were P:C:I extracted and ethanol precipitated. Linked cDNAs were resuspended in formamide sample buffer and fractioned on a 6% urea-polyacrylamide gel. cDNAs >150 bases long were gel purified and resuspended in 30 µl of water.A DNA linker was attached to the 3′-end of the cDNA in reactions (10 µl of volume) containing 1 µM miRNA cloning linker 1 (5′-rApp-CTG TAG GCA CCA TCA AT-ddC-3′ [Integrated DNA Technologies]), 10 units of T4 RNA ligase I (New England Biolabs) and T4 RNA ligase buffer  was used to amplify cDNA libraries before Illumina sequencing. PCR reactions (50 µl) contained 1 U Phusion High Fidelity DNA polymerase (New England Biolabs), HF or GC Phusion buffer, 0.4 mM dNTPs, 5–8 µl of cDNA template, 0.8 µM forward indexed primer (5′-CAA GCA GAA GAC GGC ATA CGA GAT XXX XXX GTG ACT GGA GTT CAT TGA TGG TGC CTA CAG-3′ where XXX XXX = a distinct 6-base index sequence used to distinguish one cDNA library from another when mixing multiple libraries into one Illumina sequencing run), 0.8 µM reverse primer (5′-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC GCT CTT CCG ATC T-3′) and 1.0–4.6% DMSO. Thermal cycling conditions were an initial 98°C for 30 s, 28–30 cycles of , a final extension at 72°C for 10 min and a 4°C hold.PCR products 250–1000 bp in length were gel purified from a 6% nondenaturing polyacrylamide gel and resuspended in 10 mM Tris–HCl [pH 8.5] with 0.1% Tween-20. The amounts of DNA in each library were determined using the Qubit fluorometer (Invitrogen). Five to seven DNA libraries were combined for multiplexed sequencing for a combined total of 10 nM of DNA for each Illumina sequencing run. A multiplexing index read primer (5′-CTG TAG GCA CCA TCA ATG AAC TCC AGT CAC-3′) was used to identify the library after sequencing on the MiSeq or GAIIx .Illumina sequence data were trimmed of residual linker sequences and then aligned to the following sequences using Bowtie  with a 5′-end of each cDNA read was plotted against the nucleotide position of each genome using R  and are accessible through GEO Series accession number GSE52489 .RNase L, RNase A and other metal-ion–independent endoribonucleases target single-stranded regions of RNA, leaving 2′, 3′-cyclic phosphates at the end of RNA fragments . We explInitially, we used viral RNAs and purified endoribonucleases to optimize and validate the 2′, 3′-cyclic phosphate cDNA synthesis and Illumina sequencing methods. HCV and PV RNAs were incubated for 0–20 min in reactions containing RNase L and 2-5A or RNase A, followed by agarose gel electrophoresis to characterize the RNA fragments . RNase LSupplemantary Tables S1 and S2 and Supplementary Figures S2–S5). RNase L and RNase A cleavage sites in the viral RNAs were extremely reproducible across RNA samples from independent time points .The viral RNAs from each time point shown in Supplementary Figures S4 and S5). A peak of cleavage was evident in the E2 gene of HCV RNA, with especially large amounts of RNase L cleavage at UA1755, UA4422 and UU4465 and especially large amounts of RNase A cleavage at UC1671, UC1682 and CU1754 . As highlighted in Supplementary Figure S5, UA961 was the most frequent RNase L cleavage site within region 1 (1108 UMI reads after 5 min of cleavage), UA1803 was the most frequent RNase L cleavage site within region 2 (4223 UMI reads after 5 min of cleavage) and UU3102 was the most frequent RNase L cleavage site within region 3 (5347 UMI reads after 5 min of cleavage). Intriguingly, these regions of PV RNA encode portions of the capsid proteins recognized by neutralizing antibodies .RNase L cleaved HCV and PV RNAs predominantly at UpN dinucleotides (UA and UU > UG), with prominent amounts of cleavage at distinct locations in the viral RNAs . Pyrimidd CU1754 A. Three Supplementary Figures S2 and S4 for HCV RNA/Supplementary Figures S3 and S5 for PV RNA). This is most clearly presented in Supplementary Figure S4, focusing on four prominent RNase L cleavage sites in HCV RNA. The number of UMI-corrected cDNA reads at UA1755, UA4422, UU4465 and UU4466 increased coordinate with the time of incubation with RNase L . Note that UU4465 and UU4466 are adjacent cleavage sites, where cleavage at UU4465 would preclude detectable cDNA reads at UU4466. These data suggest that viral RNAs were partially digested, even after 20 min of incubation with RNase L, and that prominent cleavage sites detected at early times did not disappear after more prolonged periods of incubation.Consistent with increasing amounts of viral RNA cleavage over time, the number of UMI-corrected cDNA reads at prominent cleavage sites in HCV and PV RNAs increased coordinate with the time of incubation with RNase L . cDNA reads corresponding to PV RNA increased from undetectable levels at early times after infection to 5% of the cDNA at 6 hpa in W12 HeLa cells and 26.1% of the cDNA at 6 hpa in M25 HeLa cells (Supplementary Table S3). Abundant amounts of cDNA in each sample aligned to rRNAs (28S/18S/5.8S/5S rRNAs) and U6 snRNA (Supplemantary Table S3). cDNA reads corresponding to host mRNAs and other nonribosomal RNAs were present in each sample, ranging from 2.6% of all cDNA from PV-infected W12 HeLa cells at 2 hpa to 16.3% of cDNA in mock-infected W12 HeLa cells at 8 hpa.cDNA libraries were prepared and sequenced using the RNAs from HeLa cells  and in vivo . Surprisingly, there were no other obvious RNase L-dependent cleavage sites in PV RNA isolated from W12 HeLa cells. The increased frequency of UA and UU dinucleotides in cDNAs from W12 HeLa cells at 6 and 8 hpa was consistent with the activation of RNase L at these times . 28S rRNA was cleaved in an RNase L-independent fashion at specific sites, most frequently at AG409, GU432, CA1699, UG2055, GC2083, GA2093, GU2097, AA2396, UG2427, AU4512 and UA4729 . There were no obvious RNase L-dependent cleavage sites in 28S rRNA under the conditions of this experiment. Interestingly, expansion segments of 28S rRNA were largely resistant to cleavage . The most common cleavage sites identified in 18S rRNA include CG130, AG225, CC502, 541UU, 743UU, AC1403, CC1419, GU1432, UC1433, GU1462, UU1463, AU1477, UA1506, AG1507 and UU1721 (Supplementary Table S5). UU541 and UU743 were clearly RNase L-dependent cleavage sites based on their increased frequency in PV-infected W12 HeLa cells at 6 and 8 hpa and the low frequency of cleavage at these sites in RNA isolated from uninfected and PV-infected M25 HeLa cells (Supplementary Table S5).18S rRNA was cleaved reproducibly at specific sites, including two RNase L-dependent cleavage sites , which had relatively few 2′, 3′-cyclic phosphates relative to other rRNAs (Supplementary Table S3). The most common cleavage sites identified in 5.8S rRNA include cleavage at A80, A108, C109, U110, U111, C113, A137, U142 and U156 (Supplementary Table S6). Notably, 2′, 3′-cyclic phosphates were frequently detected at the 3′-end of mature 5.8S rRNA .Reproducible distribution of RNase L-independent cleavage sites was exhibited by 5.8S rRNA , was exhibited by 5S rRNA, which consistently had large amounts of 2′, 3′-cyclic phosphate-dependent cDNA relative to other RNAs from HeLa cells (Supplementary Table S3).A striking distribution of RNase L-independent cleavage sites, with the majority of signal at the 3′-end of mature 5S rRNA , as well as the tertiary structure of 80S ribosomes (The location of these RNase L-dependent and RNase L-independent endoribonuclease cleavage sites were mapped onto the secondary structures of rRNAs (ibosomes  27). RN. RNSupplibosomes A and B. ibosomes C. RNase ibosomes D. Cleavaibosomes D.FigureSupplementary Table S3). The U6 snRNA cDNA reads in our libraries align almost exclusively to the 3′-end of mature U6 snRNA . U6 snRNA, an RNA polymerase III (Pol III) transcript, is posttranscriptionally modified by a nuclease encoded by gene C16orf57, which leaves 2′, 3′-cyclic phosphates at the end of mature U6 snRNAs . Overall, these methods reliably and reproducibly reveal the location and relative frequency of 2′, 3′-cyclic phosphates in host and viral RNAs. These cDNA synthesis and Illumina sequencing methods did not detect RNA fragments corresponding to RNase P or RNase MRP activity, consistent with the specificity of the method for RNA fragments with 2′, 3′-cyclic phosphates.RNase L, like other metal-ion–independent endonucleases, is expected to produce RNA fragments with 2′, 3′-cyclic phosphate termini . Our datSupplementary Figure S15), a cellular enzyme known to leave cyclic phosphates at the end of mature U6 snRNA in cells . Thus, a metal binding pocket at this site in 28S rRNA, under physiologic conditions within cells, may lead to rRNA cleavage. Carefully controlled experimental variables are needed to attribute specific cleavage sites to particular endonucleases or to metal binding pockets.Several lines of evidence suggest that cyclic phosphates within host and viral RNAs were due to metal-ion–independent endonucleolytic cleavage of RNA within cells rather than cleavage during RNA processing or within linker reactions. Cyclic phosphates were not frequent in viral RNA transcripts isolated from control reactions lacking endonucleases, confirming the absence of endonuclease activity within RNA linker reactions or other steps of RNA processing. The integrity of host and viral RNAs purified from cells was confirmed by agarose gel electrophoresis B, host ain cells . Nonethein cells . Thus, ireported . This isin vitro . We entertained two hypotheses as we considered these data. First, PV RNA may assume a secondary and tertiary structure that renders some portions of the genome particularly susceptible to single-strand–specific endoribonucleases while rendering other regions resistant . Second, antigenic variation in the capsid proteins, as a consequence of antibody selection, could render portions of the capsid genes more sensitive to cleavage by single-strand–specific endoribonucleases, due to the counter-selective forces of antigenic variation and resistance to endoribonucleases . The predicted secondary structures of HCV and PV RNAs do explain much of the data . In addition, atomic force microscopy of HCV and PV RNA genomes reveals globular structures, where some regions of the viral RNA genomes might be more exposed to endoribonucleases than others . A complete understanding of the discrete distribution of endoribonuclease cleavage sites in PV RNA awaits further investigation.Endoribonuclease cleavage sites across PV RNA genomes have not been previously described. The striking distribution of RNase L and RNase A cleavage sites in PV RNA B, with pin vitro B and in . Unlike many studies, which actively exclude rRNAs from deep sequencing experiments to enrich for other RNA populations, we intentionally retained rRNAs in our study to identify RNase L-dependent and RNase L-independent cleavage sites in rRNAs. Although we did not detect RNase L-dependent cleavage sites in 28S rRNA under the conditions of our experiment, others have reported RNase L-dependent cleavage of 28S rRNA . Each of the sites mapped to single-stranded regions of rRNA , and most were located at the surface of 40S and 60S ribosomal subunits  and to the reduced frequency of Illumina cDNA reads in GC-rich regions of RNA  are distant from the mRNA decoding channel , is analogous to an oxidative stress-induced rRNA cleavage site identified in yeast , and almost all of the 5S rRNA signal was associated with the 3′-end of mature 5S rRNA . The 3′-ends of 5.8S and 5S rRNAs are on the surface of 60S ribosomal subunits .U6 snRNA is a Pol III transcript modified posttranscriptionally by an exonuclease that leaves 2′, 3′-cyclic phosphates at the end of mature U6 snRNAs . The abuWe used 2′, 3′-cyclic phosphate cDNA synthesis and Illumina sequencing to identify and quantify metal-ion–independent endoribonuclease cleavage sites in host and viral RNAs. With these methods, we identified discrete regions of HCV and PV RNA genomes that were profoundly susceptible to RNase L and other single-strand specific endoribonucleases, RNase L-dependent and RNase L-independent cleavage sites within rRNAs, and 2′, 3′-cyclic phosphates at the ends of 5S rRNA and U6 snRNA. We expect these methods will prove useful in the characterization of other metal-ion–independent endoribonucleases, like the NendoU of arteriviruses and coronaviruses whose RNA substrates remain unknown , and theSupplementary Data are available at NAR Online.Mucosal and Vaccine Research Colorado (MAVRC), Golfers Against Cancer, the University of Colorado Cancer Center, the March of Dimes [Basil O’Connor Starter Scholar Award5-FY10-478 to J.R.H.]; a Damon Runyon-Rachleff Innovation Award [DRR-17-12 to J.R.H.]; the American Cancer Society [RSG-13-216-01-DMC to J.R.H.]; and the National Institutes of Health (NIH) [CA044059 to R.H.S. and AI042189 to D.J.B.]. Funding for open access charge: NIH.Conflict of interest statement. None declared.


Pimephales promelas) tissues  using the Qiagen RNeasy® Plus Mini, Qiagen RNeasy® Plus Universal, Promega Maxwell® 16 LEV simplyRNA, Ambion MagMAX™-96 and Promega SimplyRNA HT kits. Kit performance was evaluated via measures of RNA quantity  and quality , presence of gDNA).This study compared the performance of five commercially available kits in extracting total RNA from small eukaryotic tissue samples (<15 mg). Total RNA was isolated from fathead minnow , while the RNA recovered via the MagMAX™ kit tended to exhibit a high degree of degradation .Each kit was generally capable of extracting the amount of RNA required for most downstream gene expression applications suggesting that RNA yield is unlikely to be a limiting factor for any of the kits evaluated. However, differences in the quality of RNA extracted via each of the kits indicate that these kits may differ in their ability to yield RNA acceptable for some applications. Overall, the findings of this study demonstrate that there are practical differences between commercially available RNA extraction kits that should be taken into account when selecting extraction methods to be used for isolating RNA designated for gene expression analysis.The online version of this article (doi:10.1186/s12896-014-0094-8) contains supplementary material, which is available to authorized users. Previous studies have shown that extraction of high-quality RNA from tissue samples is a key step in the collection of accurate and reliable gene expression data via real-time quantitative PCR (qPCR), hybridization microarrays and next generation sequencing (NGS) technologies (RNA-Seq) -6. RecovRNA-Seq) -9. SeverRNA-Seq) ; howeverRNA-Seq) .e.g., gDNA eliminator solution) extractions and solid-phase extractions . Several methods are available for the isolation of RNA [e.g., phenol-chloroform, TRIzol, etc.) or solid-phase extraction using silica-based membrane or paramagnetic bead technologies. Though many RNA isolation kits require manual operation, kits designed for use with liquid-handling robots are becoming more readily available due to an increased demand for high-throughput assays.Although there are differences in the technologies that commercially-available kits employ to isolate total RNA from tissue samples, each uses the same general approach: 1) cell lysis and inhibition of ribonuclease (RNase) activity, 2) removal of DNA, and 3) isolation of total RNA (of which 1-5% is mRNA). Most kits require that tissues be homogenized in a buffer containing detergents and an RNase denaturing agent to lyse cells and inhibit RNase activity. Removal of genomic DNA (gDNA) most often involves the treatment of samples with DNase I enzyme; however, some kits utilize non-enzymatic methods, including liquid-liquid . This ratio estimates the degree of contamination resulting from protein and other contaminants  and total RNA samples with values ≥1.8 are considered “pure” and acceptable for downstream molecular analyses [In selecting an RNA isolation kit, not only must the ability of the kit to recover the required amount of total RNA be taken into consideration, but also the ability of the kit to recover RNA meeting specific quality criteria. Total RNA samples to be used in gene expression analysis should be: 1) free of proteins and contaminants that can inhibit downstream molecular analyses, 2) intact and free of nucleases (that can lead to subsequent degradation) and 3) free from gDNA contamination . The deganalyses . More reanalyses  and can analyses ,19,20; hanalyses . With reanalyses . RT(−) sPimephales promelas) tissues  using the following kits: 1) RNeasy® Plus Mini Kit , 2) RNeasy® Plus Universal Kit (Qiagen), 3) Maxwell® 16 LEV simplyRNA Purification Kit , 4) MagMAX™-96 Total RNA Isolation Kit  and 5) SimplyRNA HT System . The quantity and quality of RNA extracted from each of these kits was compared in an effort to determine which kits were most appropriate for low- and high-throughput RNA extractions of small tissues.The goal of this study was to compare the performance of several commercially available manual and automated RNA extraction kits to isolate total RNA from small amounts of vertebrate tissue samples (average mass of <20 mg). To accomplish this goal, total RNA was isolated from fathead minnow  and whole larvae  were also sampled for analysis. All tissues were weighed immediately upon collection and masses are reported in Table p >0.13 in all cases; Table For each of the kits tested, tissues were homogenized in the manufacturer supplied homogenization/lysis buffer with a Misonix Microson XL-2000 Ultrasonic Cell Disruptor . The specifications of the Plus Mini, Plus Universal, Maxwell® and SimplyRNA HT kits were such that tissues did not require trimming prior to homogenization to achieve manufacturer suggested tissue amounts. However, the mass of blood, kidney and larvae samples designated for processing using the MagMAX™ kit were often greater than the recommended tissue amount of 5 mg. To account for this, the blood and kidneys samples were trimmed (while frozen) to achieve masses ≤5 mg. Given that the cellular composition of larvae is far from homogeneous and that the mass of larvae was just above the recommended mass of 5 mg, larvae were left intact. For each tissue type, statistical analysis was conducted to determine if there were differences in the masses of tissues designated for extraction via each of the kits. Tissues that were trimmed for use with the MagMAX™ kit were excluded from this analysis. For each tissue type, statistical analysis confirmed that there were no significant differences in the amount of starting material processed by each kit . Total RNA concentrations were converted to total amounts of RNA extracted (μg) to account for differences in the required elution volumes  samples . Becausefree kit  per manufacturer protocol to determine whether additional DNase treatment was needed to supplement the DNA removal procedures utilized by each of the kits. Because the quantity of RNA extracted from embryos was less than the amount required for use with the DNA-free kit, RNA samples from embryos were pooled prior to treatment and only pooled samples were used in subsequent analysis.Each of the RNA samples selected for this analysis was subjected to a second DNase treatment via Invitrogen’s DNA-First-strand cDNA was synthesized for each of the RNA samples selected for the additional DNase treatment, as well as the corresponding total RNA samples prior to the additional treatment. The iScript cDNA Synthesis Kit  was utilized for first-strand cDNA synthesis. Briefly, reactions were carried out via a PTC-100 thermal cycler  with 0.1 μg total RNA in 7.5 μl nuclease-free water, 2 μl iScript reaction mix and 0.5 μl iScript reverse transcriptase. The resulting cDNA was diluted with nuclease-free water to a final volume of 40 μL.Following first-strand synthesis, the cDNA samples  were shipped on dry ice to Texas Christian University  where qPCR was utilized to quantify the expression of glutathione S-transferase (GST). All qPCR reactions were performed in triplicate using a CFX Connect real-time PCR detection system managed by CFX Manager Software version 3.0 (Bio-Rad). Each 10 μl reaction contained 0.4 μL cDNA, 5 μL iQ SYBR-Green supermix (Bio-Rad) and 300 nM of forward and reverse primer. The GST primers were designed using CLC Genomics Workbench 6.8.4 , synthesized by Integrated DNA Technologies , and had the following sequences: forward 5′-GGGCATTGGTGATCTAAC-3′ and reverse 5′-TTTTCAAACACAGGGAGG-3′. All reactions were carried out in white shell/clear well PCR plates (Bio-Rad) and the thermal cycling program consisted of an activation step  followed by 40 cycles of denaturing  and annealing . The threshold cycle (Ct) values were determined for each of the selected samples with and without the additional DNase treatment and in corresponding RT (−) samples.i.e., total RNA amount and mass-adjusted total RNA amount) and quality  of RNA extracted using each kit were evaluated via one-way analysis of variance  followed by Tukey’s multiple comparisons tests. In cases where the assumption of homogeneity of variances was not met, a Welch ANOVA followed by Wilcoxon multiple comparisons tests was conducted. Kit precision was evaluated by testing for unequal variances (Levene’s test) between each of the RNA quantity and quality parameters measured and through the calculation of coefficients of variation (%CV). Statistical significance was declared at p <0.05.For each tissue type, significant differences in the quantity . For example, there were significant differences in amount of RNA recovered from blood, kidney and larvae by each kit . However, the average amount of total RNA recovered from these tissues (regardless of the kit used) exceeded the minimum amount considered sufficient for most downstream molecular applications, suggesting a lack of practical difference between the kits with regard to RNA yield from these tissue types. Likewise, there were instances where statistical differences in RNA quality were not necessarily indicative of practical differences in RNA quality. This was particularly true for A260:A280 ratios. For each tissue, there were significant differences in the A260:A280 ratios of RNA recovered by each of the kits . However, for all but embryos, the average A260:A280 ratios of total RNA samples were ≥1.8  of nucleic acid in these samples [While some of our results are tissue-dependent, a few general trends are particularly noteworthy with regard to the quantity and quality of RNA recovered by each of the kits. First, for each tissue (except embryos), the amount of RNA recovered from each sample was ≥1 μg, regardless of the kit used indicating that each of the kits evaluated was capable of recovering sufficient amounts of total RNA for analysis via qPCR or NGS technologies Figure , Table 3 samples , rather i.e., least degraded) total RNA as evidenced by the fact that the majority of samples extracted via these two kits (regardless of tissue type) had average RIN values ≥7  was greater than that from single G. salaris, providing evidence that sample pooling can be utilized to optimize RNA isolation.For single embryos, the amount of RNA recovered (regardless of the kit utilized) was relatively low in comparison to other tissues and ranged from an average of 0.21 to 1.38 μg. The Plus Universal and MagMAX™ kits were capable of extracting ≥1 μg total RNA, an amount often suitable for qPCR and NGS analysis. However, there were differences in the quality of RNA obtained using these kits. Specifically, the Plus Universal kit produced moderately degraded RNA (average RIN =5.5), while the MagMAX™ kit yielded highly degraded RNA (average RIN =3.8). Assuming that NGS analysis requires RNA with RIN values ≥7, these results suggest that neither the Plus Universal nor the MagMAX™ kit produces RNA from single embryos that is suitable for NGS experiments. Given that moderately degraded RNA can often produce successful qPCR results, it is possible that embryonic RNA extracted via the Plus Universal kit may be acceptable for qPCR analysis. The suitability of the Plus Mini, Maxwell® and SimplyRNA HT kits for embryonic RNA extractions appears to limited by the quantity of RNA yielded (<1 μg for each kit), not the quality of RNA yielded (RNA ≥7 for RNA extracted from the Plus Mini and Maxwell® kits). Given this, coupled with the fact that extractions were performed on single embryos, it is possible that the performance of these kits could be improved by pooling embryos prior to extraction. Fromm  found thi.e., sample type, quantitation method, amplicon length, etc.).When determining whether an RNA sample is acceptable for downstream gene expression analysis, the quantity and quality of RNA are generally the primary factors considered. In order to evaluate the performance of each kit in its ability to produce RNA suitable for qPCR, NGS and microarray analysis, we established minimum RNA quantity and quality criteria specific to each type of analysis Table . With re260:A280 ratios ≥1.8 and RIN ≥5 from blood, spleen, kidney and larvae indicating that RNA extracted from these tissues via these kits is acceptable for qPCR. Likewise, the SimplyRNA HT kit extracted RNA meeting the quantity and quality criteria for qPCR analysis from spleen, kidney and larvae; though in our hands, the ability of this kit to extract suitable RNA from blood samples was less consistent. The quality of RNA extracted from the larval samples via the MagMAX™ kit met the minimum quality criteria for qPCR . However, none of the RNA samples extracted from blood, spleens and kidneys met these criteria suggesting that the MagMAX™ kit may be limited in its ability to extract intact RNA.Embryos aside, the Plus Universal and MagMAX™ kits generally yielded higher amounts of RNA per mg of tissue than the other kits, while the SimplyRNA HT kit consistently yielded the lowest amount of RNA. Despite significant differences in the quantity of RNA recovered by each kit, the amount of RNA recovered from each tissue sample was ≥1 μg, independent of the kit used - with the exception of blood extracted via the SimplyRNA HT kit (average yield of 1.74 ± 1.7 μg). Our results indicate that each of the kits tested is likely to extract quantities of total RNA sufficient for most qPCR. A closer examination of the quality of RNA extracted using each of these kits, revealed that the Plus Mini, Plus Universal and Maxwell® kits recovered RNA with AWhereas each of the kits except the MagMAX™, were capable of isolating RNA meeting the criteria for qPCR analysis, only the Plus Mini and Maxwell® kits were consistent in their ability to isolate RNA meeting the assumed criteria for microarray analysis. Nearly all of the RNA extracted from blood, spleen, kidney and larvae samples by these two kits contained RNA of acceptable quantity  and quality . Likewise, the SimplyRNA HT had similar performance characteristics for kidney and larvae samples with the majority of samples fulfilling the criteria for microarray and NGS analysis. However, this kit was less consistent in its ability to extract RNA from blood and spleen as the majority of the blood and spleen RNA samples extracted with this kit did not meet the minimum quantity requirements for microarray analysis. Furthermore, the majority of spleen samples isolated with this kit did not fulfill the quality requirements for microarray or NGS analysis. This indicates that the SimplyRNA HT may not be sufficient for extracting blood and spleen samples for use in microarray analysis; although, it is possible that increasing the amount of starting material could potentially overcome the low yield observed for the SimplyRNA HT blood and spleen samples. It should also be noted that these inconsistencies in the quantity of RNA recovered via the SimplyRNA HT kit may be an artifact of this kit being in the beta test stage (at time of assay) and not being quite fully optimized for our BiomekFX platform – to our knowledge, we were among the first groups outside of Promega to evaluate this kit. The Plus Universal kit consistently produced RNA samples of sufficient quantity; however, none of the samples extracted with this kit had RIN values ≥7 suggesting that the RNA recovered via this kit is subject to fragmentation and may be less than optimal for microarray and NGS applications.i.e., DNase I treatment) is effective for the tissue types evaluated here. Similarly, the Plus Mini and Plus Universal kits offered near complete removal of DNA from most of the samples indicating that the DNA removal procedure utilized by these kits  is sufficient for most tissues. In contrast, the majority of the MagMAX™ samples evaluated for gDNA contamination tested positive, indicating that this kit is inconsistent in its ability to effectively remove DNA. It should be noted that an additional DNAse I treatment was effective in removing DNA from all of the samples that tested positive for gDNA contamination. Thus, the performance of kits with seemingly ineffective gDNA removal capabilities can be easily augmented via additional DNA removal procedures.Expression analysis of samples prior to the additional DNase treatment showed the RT (−) reactions conducted with samples extracted via the Maxwell® and SimplyRNA HT kits were gDNA free  [Some downstream molecular procedures do require samples that are almost completely free of gDNA contamination (etected) ; howeveretected) . A compap =0.004). An examination of the %CV for the amount of RNA recovered by each kit showed that the Plus Universal Kit and Maxwell® kits were the most consistent in their ability to isolate RNA . In contrast, the Plus Mini, MagMAX™ and SimplyRNA HT kits were less consistent (%CV >25 in each case). Given that many of the embryo RNA samples contained amounts of RNA near the minimum amount required for qPCR and NGS analysis , the precision of these kits is of particular importance. This is borne out when comparing the Plus Mini and Plus Universal kits, as the average amount of embryonic RNA isolated by each of these kits was >1 μg . Despite these differences in variance, the %CV associated with the A260:A280 ratios for each of these tissues extracted by each of the kits was ≤3% in all but one case, suggesting acceptable consistency in the ability of each of the kits to isolate “pure” RNA. The one exception to this appears to be the SimplyRNA HT kit, as the %CV associated with the A260:A280 ratios for blood RNA samples was 10.6%. This in combination with the fact that half of the blood samples extracted via this kit had A260:A280 ratios <1.8 indicates that the SimplyRNA HT kit lacked precision in isolating “pure” RNA from blood samples. However, it is plausible that the higher variance in A260:A280 ratios for blood RNA samples isolated with the SimplyRNA HT kit is related to the relatively low amount of RNA yielded from these samples, as low A260:A280 ratios can result from low concentrations of RNA [With regard to RNA quality, there were differences in the variance associated with the As of RNA .p <0.04 in each case), except for larvae. This result suggests that the kits differed with regard to their consistency in extracting intact RNA. An examination of %CVs revealed that the Plus Mini, Plus Universal, and Maxwell® kits offered the highest degree of consistency regardless of tissue type, as the %CVs were < ~10. The MagMAX™ and SimplyRNA HT kits offered suitable consistency for spleen and larvae samples (%CV <10); however, these kits lacked consistency for blood, kidney and embryo samples (%CV >25 in each case). Overall, these findings suggest that the Plus Mini, Plus Universal, and Maxwell® kits offer more precision in isolating intact RNA than the MagMAX™ and SimplyRNA HT kits. The reduced precision associated with the MagMAX™ and SimplyRNA HT kits may be related to the performance of the kits themselves. Alternatively, it is possible that the observed lack of consistency was related to the fact that both of these kits were utilized in conjunction with the BiomekFX robot. Thus, the possibility that the performance of the robot introduced an additional source of variation cannot be excluded.Significant differences in the variance associated with RIN values of RNA obtained via the kits were noted for each tissue type . The amount of time required for RNA extractions via these kits was not specifically measured in this study. However, Qiagen reports that the amount of time required for the Plus Mini kit is 25 m  and we e


Snake venoms contain many proteinaceous toxins that can cause severe pathology and mortality in snakebite victims. Interestingly, mRNA encoding such toxins can be recovered directly from venom, although yields are low and quality is unknown. It also remains unclear whether such RNA contains information about toxin isoforms and whether it is representative of mRNA recovered from conventional sources, such as the venom gland. Answering these questions will address the feasibility of using venom-derived RNA for future research relevant to biomedical and antivenom applications.Venom was extracted from several species of snake, including both members of the Viperidae and Elapidae, and either lyophilized or immediately added to TRIzol reagent. TRIzol-treated venom was incubated at a range of temperatures (4–37°C) for a range of durations (0–48 hours), followed by subsequent RNA isolation and assessments of RNA quantity and quality. Subsequently, full-length toxin transcripts were targeted for PCR amplification and Sanger sequencing. TRIzol-treated venom yielded total RNA of greater quantity and quality than lyophilized venom, and with quality comparable to venom gland-derived RNA. Full-length sequences from multiple Viperidae and Elapidae toxin families were successfully PCR amplified from TRIzol-treated venom RNA. We demonstrated that venom can be stored in TRIzol for 48 hours at 4–19°C, and 8 hours at 37°C, at minimal cost to RNA quality, and found that venom RNA encoded multiple toxin isoforms that seemed homologous (98–99% identity) to those found in the venom gland.The non-invasive experimental modifications we propose will facilitate the future investigation of venom composition by using venom as an alternative source to venom gland tissue for RNA-based studies, thus obviating the undesirable need to sacrifice snakes for such research purposes. In addition, they expand research horizons to rare, endangered or protected snake species and provide more flexibility to performing fieldwork on venomous snakes in tropical conditions. Venomous snakes use venom proteins to capture their prey. When a human is bitten, these proteins cause severe pathology and often lead to death, disability or other serious complications. Understanding the composition of snake venom is essential not only for developing more effective snakebite treatments but also for acquiring a better understanding of the biology that underpins this extraordinary group of animals. Currently, to determine the genes expressing these venom proteins requires sacrificing the snake and isolation of mRNA from the venom gland. This is undesirable for ethical reasons and limits the scope of this type of research. Encouragingly, we and others have recently demonstrated that mRNA encoding venom proteins can be obtained from lyophilized snake venom. In this study we show that venom RNA in these samples is in fact considerably degraded. We report a protocol to improve the yield of undegraded RNA (immediate addition of TRIzol reagent to the venom). We also explored the limitations of this new protocol, in terms of time and temperature applicability. We then showed that considerable isoform diversity can be captured from snake venom RNA, and show that the isoforms identified are identical to those found in the venom gland of sacrificed snakes. Our findings provide protocols enabling researchers working in a range of fields to considerably expand the scope of their research because venom composition data can now be reliably acquired from venom which in turn can be obtained from live snakes without harming the animals. Venomous snakes are highly proficient predators that occupy almost all natural habitats: from desert to tropical jungle and all terrestrial environments within this range, as well as marine and freshwater bodies ,2. The eEach venomous snake species/genus has evolved a distinct repertoire of venom proteins to achieve their predatory objective . Thus, tShaw’s group in Belfast were the first to document that mRNA encoding toxins can be recovered from the venom or skin secretions of several species ,25, and All snakes were maintained in individual cages within the temperature, humidity and light-controlled environment of the herpetarium of the Alistair Reid Venom Research Unit, Liverpool School of Tropical Medicine. The facility and its protocols for the expert husbandry of the snakes are regularly inspected and approved by the UK Home Office and the LSTM Animal Welfare and Ethical Review Board.Bitis arietans), (ii) a single, captive bred, adult specimen of the intermediate shield-nose snake (Aspidelaps scutatus intermedius), (iii) six Angolan coral snakes (Aspidelaps lubricus cowlesi), (iv) four cape coral snakes (Aspidelaps lubricus lubricus) and (v) a single monocled cobra (Naja kaouthia). The number of snakes used in this study was the maximum possible given the limited availability of these animals in our facility. We also used data from a separate study wherein venom glands were dissected from terminally-anaesthetised specimens of B. arietans, and immediately snap frozen in liquid nitrogen.Venom was extracted and pooled from (i) ten, wild-caught, Nigerian puff adders  after 8, 24 or 48 hours. A sample of venom was also conventionally lyophilized and stored at 4°C until required. Each aliquot equated to 500 μL of liquid venom . All other venom samples had TRIzol added at a ratio of 5:1 and were processed using the TRIzol Plus RNA Purification System. All samples were DNase treated on column during the TRIzol Plus purification system protocol  and total RNA was eluted in 30 μL nuclease free water (QIAGEN).To assess the potential benefit of mRNA-stabilising reagents, we added TRIzol reagent (Life Technologies) to the venoms at a ratio of 5:1 as soon after venom extraction as safety permitted. RNA was isolated from the venom using the TRIzol Plus RNA Purification System following the manufacturer’s instructions (unless stated otherwise). To assess the effect of storage time and temperature on venom RNA stability, a sample of the Bitis arietans venom gland tissue (272 mg) was immediately homogenised under liquid nitrogen using a pestle and mortar and then a TissueRuptor (QIAGEN). Samples were DNase treated  and total RNA was extracted and eluted in 30 μL nuclease free water (QIAGEN) using the TRIzol Plus RNA Purification System protocol. Venom gland transcriptomes were sequenced, assembled and annotated as previously described .LYO) or TRIzol treated (VTRZ) venom samples (including after variant storage temperature and times), was compared using a Bioanalyzer . The Bioanalyzer uses the entire electrophoretic trace (electropherogram) of the RNA sample including the presence/absence of degradation products in a sample concentration-independent manner. We added 1 μL of each total RNA sample into Agilent RNA 6000 Nano/Pico Chips (following the manufacturer’s instructions) and then inserted them into the Agilent 5000 Bioanalyzer for analysis. The quality of the two ribosomal RNA peaks are used as a reliable indicator of the integrity of the other RNA molecules. Calculations are based upon quantitating the amount of fluorescent dye intercalated within the RNA, thereby efficiently discounting contaminants such as phenol (a common carry over from RNA isolation preparations) that can vitiate the accuracy of standard UV-spectrometry. Agilent’s RNA 2100 Expert software was used to calculate total RNA concentration and assign a RNA integrity number (RIN), which range from 1–10; with 10 being completely undegraded, and 1 being completely degraded RNA [The quality and quantity of RNA isolated from conventionally lyophilized  primers and processed it into single-stranded cDNA using the Superscript III first-strand synthesis system (Invitrogen). Each single-stranded cDNA sample was treated with RNase H to eliminate contaminating RNA. This process provided a stable and reproducible copy of each venom/venom gland RNA preparation.B. arietans serine proteases (SP), snake venom metalloproteinases (SVMPs), L-amino acid oxidases (LAO), C-type lectins (CTL), vascular endothelial growth factors (VEGF) and aminopeptidases (AMP) were designed (B. arietans transcriptome data [Primers complimentary to genes encoding designed  and syntome data .2 (PLA2), CTLs, SVMPs and Kunitz-type serine protease inhibitors (KSPI) (Aspidelaps spp. (the objective of the experiment). We therefore decided to pool the venom RNA of N. kaouthia (that has known venom gland gene isoform diversity) with that from A. s. intermedius, A. l. cowlesi, and A. l. lubricus, thereby ensuring the presence of multiple gene targets for PCR amplification. Furthermore, since there are no published genes encoding Aspidelaps 3FTXs, PLA2s, CTLs, SVMPs or KSPIs, our primers were designed upon conserved sequences flanking the open reading frames of published sequences from several species of the genus Naja (cobras), Walterinnesia aegyptia (desert black cobra) and Ophiophagus hannah (king cobra). These snakes are the closest phylogenetic relatives of the genus Aspidelaps [To assess whether mRNA recovered from venom includes representation of transcripts encoding multiple diverse isoforms, we generated primers complementary to the three-finger neurotoxin (3FTX)  gene fams (KSPI) . Howeverpidelaps . To maxi2O (QIAGEN) to a final volume of 50 μL. PCR experiments were performed using a Techne TC-512 thermal cycler. Touchdown PCR conditions were as follows: Initial denaturation at 94°C for 3 min. Denaturing at 94°C, 30 sec; annealing temperature gradient 66–50°C, 1 min (-1.6°C/cycle); extension at 72°C, 1 min; 10 cycles. Denaturing at 94°C, 30 sec; annealing at 50°C, 1 min; extension at 72°C, 1 min; 25 cycles. Final extension at 72°C for 10 min. Amplicons were visualised on a 1% agarose in TBE gel, and analysed using the Bio-Rad Gel Doc System and Image Lab Software (Bio-Rad).One μL of venom cDNA was added to 5 μL 10 x PCR buffer, 5 μL 2 mM dNTP mixture (Fisher Scientific), 2.5 μL 10 μM forward primer, 2.5 μL 10 μM reverse primer, 1 μL DreamTaq polymerase (Life Technologies) and 33 μL nuclease-free HBitis arietans amplicons, generated from highly stringent, gene-specific primers for SVMP, LAO, SP, VEGF and AMP, were submitted for Sanger sequencing . The amplicons from the pooled elapid venoms were generated with less species- and gene-specific primers and therefore were likely to contain divergent sequences. Since we sought to capture this diversity, we purified amplicons from each PCR reaction from the agarose gel using the Promega Wizard Kit (Promega), cloned these into pCR2.1-TOPO vectors (Life Technologies), transformed chemically competent E. coli  to amplify the recombinant plasmids, and submitted the purified plasmids  for sequencing as above. This was also completed for the B. arietans venom CTL amplicons.Sequencing files were trimmed of vector sequence and base calls with <30 PHRED scores using SeqTrace , translaLYO) because most venom collections, including the samples used in all previous publications on venom mRNA, are/will be prepared in this manner (extracted venom is frozen for 3+ hours and then lyophilized into a dehydrated powder). The RIN number of VLYO was 4.6  had an RIN number of 7.7  was substantially greater than that from the same amount of VLYO sample (5 ng). Nevertheless, these quantities of venom RNA are 1000-fold less than that obtained from venom gland tissue (35 μg from 272 mg of tissue). Current Next Generation Sequencing (NGS) technologies routinely require greater than 1 μg total RNA as a starting point. Thus, although the total RNA obtained from VTRZ is of sufficient quality, there is insufficient quantity of total RNA for standard (non-PCR amplified) transcriptome NGS.Consistent with the RNA quality analysis, the total yield of RNA  obtainedTRZ, but they provide no indication as to the quality/protein-encoding validity of the underlying mRNA. To investigate this, we designed primers to PCR-amplify full-length transcripts encoding B. arietans venom proteins from the VLYO and VTRZ samples. We selected toxin sequence templates that we had verified as differing with respect to transcript size and proportional representation [The Bioanalyzer results suggest that total RNA is largely undegraded in Ventation , i.e. SVB. arietans VLYO and VTRZ cDNA  and for different durations  than those used above (4°C and 0 hours) to determine if the TRIzol-treatment protocol was suited to recovering RNA from venoms collected under field conditions.Having shown the benefit of TRIzol-treating venom to acquire undegraded full-length toxin transcripts, we next explored the effects of storing VTRZ after storage at all time and temperature conditions tested show distinct ribosomal peaks (LYO (red) samples. The quantity of total RNA recovered from VTRZ samples decreased with extended storage periods and for all storage temperatures  different sequencing techniques, (ii) from venom glands and (iii) from Viperidae venoms, we compared PCR-derived B. arietans CTL genes obtained from VTRZ RNA to those obtained from venom gland samples (a) after PCR amplification, (b) as Expressed Sequence Tags (EST) from a cDNA library, and (c) as NGS sequences. As anticipated, the VTRZ CTL sequences included analogues (98–99% nucleotide sequence identity) of the isoforms recovered from venom gland and by whatever sequence protocol used  was in fact extensively degraded. We therefore modified the experimental approach, by omitting the lyophilization step and instead treating venom with TRIzol immediately after extraction (VTRZ). We show here that the complete coding regions of the transcripts can be PCR amplified from both VLYO and VTRZ samples  at minimal cost to RNA degradation Figs . FinallyLYO, but as the incubation time increased the yield of total RNA decreased, although the quality remained stable. This is important, particularly for the collection of venom during fieldwork, where access to adequate storage conditions or laboratory access for sample processing are often very limited. Our study has identified the value of TRIzol processing of snake venom. This therefore suggests that other similar products may also have comparable scientific benefits, and will require the similar protocol-standardising analysis as performed here with TRIzol.That RNA quality remains high, irrespective of an increase in temperature or time, likely explains why the majority of transcripts were successfully PCR amplified. These results suggest that the incubation of venom in TRIzol helps to reduce degradation of RNA, including mRNA, but that it comes at a duration-to-yield cost. For example, incubation of venom in TRIzol at 19°C for all durations resulted in a greater yield of high quality RNA than was possible from VTRZ over VLYO .Considering the fact that snake venom contains a diverse array of destructive nucleases and phosphodiesterases  and is aLYO Figs  and 2 suB. arietans venom and venom gland . The pooling of venom sourced from many snakes of the same species would reduce this limitation, but would of course impart its own set of limitations on subsequent analysis, particularly given reports of intra-specific variation in venom composition –15. RNA S1 Table(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.