Methods for standardized sequencing of nucleic acids and uses thereof

Abstract

To provide a method for determining the quantity of native nucleic acid targets, for providing reproducibility in measurement of nucleic acid copy number in samples, for a NGS method that reduces inter-experimental and inter-laboratory variation in measurement of nucleic acid copy number in samples.SOLUTION: Described is a method comprising measuring a proportional relationship of at least one native target sequencing event of at least one nucleic acid in a sample to the respective competitive internal amplification control (IAC) for that nucleic acid. Also described is a method where the at least one event comprises: an observation, a count and / or a read between the native target and its respective IAC.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application Serial No. 61 / 729,853, filed November 26, 2012, U.S. Provisional Application Serial No. 61 / 730,463, filed November 27, 2012, and U.S. Provisional Application Serial No. 61 / 784,394, filed March 14, 2013, the entire disclosures of which are expressly incorporated by reference into this specification.

[0002] STATEMENT REGARDING FEDERALLY FUNDED RESEARCH

[0002] This invention was made with United States government support under Grant Nos. CA138397 and HL108016 awarded by the National Institutes of Health. The United States government has certain rights in this invention.

[0003] The present invention relates to a method for standardized sequencing of nucleic acids and uses thereof. [Background technology]

[0004]

[0004] Identification of genetic information is becoming an important part of the information for diagnosis and treatment of many diseases. In order to make such diagnostic tools readily available, it is desirable that this identification is as efficient and inexpensive as possible. For diagnostic, medical, regulatory and ethical reasons, this identification should be as accurate as possible to exclude erroneous measurement results.

[0005]

[0005] In addition to the desire to obtain information on human genetic material, there is great interest in obtaining genetic information on, for example, mitochondria, pathogens and disease-causing organisms.

[0006]

[0006] One method for obtaining information is the Sanger sequencing method of genomic analysis. Other methods are becoming available that offer improved performance when compared to Sanger sequencing. These methods include short-range high-density parallel sequencing technology, Next Generation Sequencing (or NextGen or "NGS"), which samples RNA in biological samples. It attempts to provide a more comprehensive and accurate view than Ger sequencing.

[0007]

[0007] Next-generation sequencing (NGS) is useful in many clinical applications due to its automated and highly parallel analysis of nucleic acid templates. However, the limits of clinical questions that NGS can address are largely determined by: i) the upstream source of the nucleic acid template (e.g., human tissue, microbial samples, etc.), and ii) whether the clinically relevant biological variation in the nucleic acid template is greater than the technical variation (often introduced by variants such as sample preparation, sequencing, and / or data analysis workflows).

[0008]

[0008] Workflows for NGS library preparation vary widely, but can be broadly categorized into one of two approaches: 1) digestion or fragmentation of the nucleic acid sample followed by ligation to universal adapter sequences, or 2) PCR with target-specific primers that incorporate universal adapter sequences at their 5' ends. In both approaches, if the nucleic acid template is RNA, a reverse transcription step is used to generate the necessary DNA template for sequencing.

[0009] One concern with NGS is that these quantitative sequencing methods have high intra- and inter-laboratory variability. This problem therefore reduces the value of any results. This has hindered the use of these sequencing methods in molecular diagnostics.

[0010]

[0010] For example, non-systematic (irreproducible) biases (i.e., errors) are often inadvertently introduced during the preparation of sequencing libraries. These non-systematic biases are a major obstacle to the implementation of NGS as a reliable, efficient, routine measurement (quantification) of nucleic acid abundance in clinical settings.

[0011]

[0011] The most likely source of non-systematic bias (thus preventing inter-laboratory comparison of quantitative NGS data and therefore routine clinical use) results from problems arising from nucleic acid fragmentation, adapter ligation and PCR.

[0012]

[0012] Additionally, although not explicitly required, the FDA has issued guidance and industry recommendations that PCR-based in vitro diagnostic (IVD) devices should contain internal amplification controls (IACs) to control for interfering substances and to prove that negative results for a sample are not caused by inhibitors.

[0013]

[0013] In addition, to avoid random sampling errors and ensure reliable measurements, it is necessary to sequence (i.e., read) a sufficient number of copies of the analyte to be measured. One problem is that the range of transcript representation after library preparation often remains very high (typically more than one million-fold), which imposes high costs. This is because the transcript from each gene must be sequenced at least ten times (to ensure ten "reads"). To ensure ten reads for the least expressed gene, a gene that is expressed at a one-million-fold higher level needs to be read at least ten million times.

[0014]

[0014] Therefore, an NGS method that reduces inter-experiment and inter-laboratory variability in measuring nucleic acid copy numbers in samples would be extremely useful for both research and clinical applications. Summary of the Invention

[0015]

[0015] Described herein is a method for providing reproducibility in measuring nucleic acid copy numbers in a sample, the method comprising measuring the proportional relationship of at least one natural target sequencing event of at least one type of nucleic acid in the sample to a respective competitive internal amplification control (IAC) for that nucleic acid.

[0016]

[0016] Also described herein are methods, wherein the at least one event comprises: an observation, count and / or reading between the natural target and its respective IAC.

[0017] Also described herein are methods for controlling for non-systematic errors in the preparation of PCR-based NGS libraries that involve sharing identical priming sites with a natural nucleic acid template of interest to mimic the kinetics of the natural target in the PCR reaction, thus controlling for target-specific variation in PCR efficiency.

[0017]

[0018] Also described herein is the use of competitive IAC methods to provide convergence of target analyte representation in a sample while retaining quantitative information of the original representation of both low and high abundance targets, allowing quantitative measurement of the original representation with low numbers of sequencing reads.

[0018]

[0019] In certain embodiments, a method is described herein for determining the amount of a first nucleic acid, comprising the steps of: providing a series of serially diluted standardized mixtures comprising a competitor template for said first nucleic acid and a competitor template for a second nucleic acid present in a number of samples comprising said first nucleic acid; wherein said competitor templates are at known concentrations relative to each other; combining one of the samples with the first of said serially diluted standardized mixtures; coamplifying said first nucleic acid and said competitor template for said first nucleic acid to generate an amplification product thereof; obtaining a first relationship; said first relationship comprises comparing said amplification product of said first nucleic acid to said amplification product of said competitor template for said first nucleic acid; determining whether said first relationship is within the range of about 1:10 to about 10:1; If not, repeating said combining, co-amplifying, obtaining and determining steps with a second of said serially diluted standardized mixtures; coamplifying said second nucleic acid and said competitor template for said second nucleic acid to generate an amplification product thereof; obtaining a second relationship, said second relationship comparing said amplification product of said second nucleic acid to said amplification product of said competitor template for said second nucleic acid; and Compare said first and second relationships.

[0019]

[0020] In one embodiment, the method includes the steps of: comparing said amplification product of said first nucleic acid to said amplification product of said competitor template for said first nucleic acid; determining whether the first relationship is within the range of about 1:100 to about 100:1, or about 1:1000 to about 1000:1, or about 1:10,000 to about 10,000:1; If not, repeating said combining, co-amplifying, obtaining and determining steps with a second of said serially diluted standardized mixtures; coamplifying said second nucleic acid and said competitor template for said second nucleic acid to generate an amplification product thereof; obtaining a second relationship; The second relationship compares the amplification product of the second nucleic acid to the amplification product of the competitor template for the second nucleic acid; and Compare said first and second relationships.

[0020]

[0021] Further, in certain embodiments, the products from a series of co-amplification reactions in claim 2 are combined and amplified in a second round using primer pairs that recognize the respective NT and CT products from the first round of amplification and also have a gene-specific barcode primer and a universal primer at the 5' end to facilitate sequencing.

[0021]

[0022] Other systems, methods, features, and advantages of the invention will be or become apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims.

[0022]

[0023] This patent or application document may contain one or more drawings and / or one or more photographs executed in color. Copies of this patent or patent application publication with color drawing(s) and / or photograph(s) will be provided by the Office upon request and payment of the necessary fee. [Brief description of the drawings]

[0023] [Figure 1]

[0024] Figure 1: A549 gDNA titrated for ICA mixture. Graph showing titration of approximately equimolar mixtures of competitive internal amplification controls (IACs) compared to a constant amount of genomic DNA (gDNA) input of 100,000 copies into each multiplex PCR. Plotted on the X-axis are the initially estimated amounts of each IAC in the equimolar mixture. The Y-axis represents the observed frequency of sequencing events (reads) for each native template divided by the sum of the read frequencies for that native template and its respective competing IAC. [Figure 2-1]

[0025] 2A-2F: Graphs showing titration of a mixture of approximately equimolar competitive internal amplification controls (IACs) for: Fig. 2A) a constant input amount of 100,000 copies of genomic DNA (gDNA) into a multiplex PCR, or cDNA material of 11 ng of reverse transcribed RNA from SEQC samples into each multiplex PCR: Fig. 2B) A-RT1, Fig. 2C) A-RT2, Fig. 2D) B, Fig. 2E) C and Fig. 2F) D. Plotted on the X-axis is the initially estimated amount of each IAC in the equimolar mixture. The Y-axis represents the observed frequency of sequencing events (reads) divided by the sum of the read frequencies for the native template and its respective competing IAC. [Figure 2-2] Same as above [Figure 2-3] Same as above [Figure 2-4] Same as above [Figure 2-5] Same as above [Figure 2-6] Same as above [Diagram 3]

[0026] Figure 3: Graph showing the analysis of assay accuracy. Measured values ​​for SEQC sample C-RT1 were compared against expected values ​​based on measured values ​​of SEQC samples A and B (% difference). The percent difference between the predicted signal C' and the actual assay signal C was used as an indicator of relative assay accuracy (RA). The RA score ΔC for the target genes was defined as (C-C' / C'), respectively. The mean RA value (line), median quartiles (box), standard deviation (whiskers), and outliers are shown. The mean RA value was very close to the estimated value. Only certain assays have RAs that differ from the mean by more than 25%. [Figure 4-1]

[0027] Figure 4A: Graph showing differential expression (DE) between genes; i.e., gene targets (n=88) measured between samples SEQC A-RT1, B, C assessed for whether their DE change was 1.5-3.0, 2-3, 3-5, 5-10 fold, or greater than 10 fold. Control for false positive or negative changes was assessed by comparing SEQC A-RT1 to SEQC A-RT2. [Figure 4-2]

[0028] FIG. 4B: Table 1 shows summary statistics for differential expression in sample C compared to expected values ​​based on samples A and B. [Diagram 5]

[0029] Figure 5: Graph showing assay reproducibility between reverse transcriptions. Two reverse transcriptions (RT1 vs. RT2) of SEQC sample A were measured for expression of 119 gene targets that successfully passed the performance criteria in Figure 1. [Figure 6]

[0030] Figure 6: Graph showing the same data as in Figures 3 and 4. Expected measurements (x-axis) vs. observed measurements (y-axis) for sample C. Reverse transcription of SEQC sample C was measured for expression of 119 gene targets that successfully passed the performance criteria in Figure 1. Of the 119 gene targets, 88 had an R2>0.95 for the Hill equation curve fit to determine the equivalence point and concentration of each target (y-axis) (Figure 2). [Figure 7]

[0031] Figure 7: Graph showing convergence and increased uniformity of 97 gene targets from Figure 5. Plotted on the X-axis is the data from Figure 5 relative to the highest abundance template. On the Y-axis is the actual proportion of sequencing reads in which the gene target is relative to the highest sequenced template. [Figure 8]

[0032] Figure 8: Graph showing the ROC curve for detecting greater than 3-fold changes for RNA sequencing using the Illumina platform from Bullard et al. BMC Bioinformatics 2010, 11:94. [Figure 9]

[0033] Figure 9: Provides a schematic representation of the PCR master mix including a mixture of internal amplification controls (IAC). [Figure 10]

[0034] Figures 10-11: Graphs showing titration of the mixture of internal amplification controls to gDNA and SEQC cDNA. [Figure 11]

[0034] Figures 10-11: Graphs showing titration of a mixture of gDNA and internal amplification controls for SEQC cDNA. [Figure 12]

[0035] Figure 12: Graph showing sequencing (within site) of replicates of the same library preparation, where X-axis = 1.8 million sequencing reads and Y-axis = 3 million sequencing reads. [Figure 13]

[0036] FIG. 13: Graph showing distinct library preparations (within site) sequenced, where X-axis = 2.6 million sequencing reads and Y-axis = 4.8 million sequencing reads. [Figure 14-1]

[0037] 14A-14B: Graphs showing prediction of measurements for samples C and D based on measurements (within site) for samples A and B; where X-axis = 15.2 million sequencing reads and Y-axis = 4.9 million sequencing reads. [Figure 14-2] Same as above [Figure 15]

[0038] Figure 15: Graph showing inter-laboratory comparison of measurements (inter-site), i.e., separate library preparations sequenced at different sites (inter-site); here X-axis = 2.6 million sequencing reads and Y-axis = 400,000 sequencing reads. [Figure 16]

[0039] 16A-16B: Graphs showing receiver curves for accurately detecting fold changes based on FIG. 13 (Result 4), showing receiver curves for calling differential expression based on FIG. 14 -Result 4. [Figure 17-1]

[0040] Figure 17: Graph showing that PCR-driven library preparation converges native target concentration and reduces required read depth. [Figure 17-2] Same as above [Figure 18-1]

[0041] Figures 18A-18B: Standardized RNA sequencing (STARSEQ) workflow and data analysis. [Figure 18-2] Same as above [Figure 19]

[0042] 19A-19C: STARSEQ reduces oversampling without signal compression. [Figure 20]

[0043] Figures 20A-20B: STARSEQ reduces required sequencing reads by up to 10,000-fold. [Figure 21-1]

[0044] Figures 21A-21E: Performance of STARSEQ with ERCC reference material. [Figure 21-2] Same as above [Figure 21-3] Same as above [Figure 21-4] Same as above [Figure 21-5] Same as above [Figure 22-1]

[0045] Figures 22A-22F: Performance of STARSEQ with endogenous cDNA targets. [Figure 22-2] Same as above [Figure 22-3] Same as above [Figure 22-4] Same as above [Figure 22-5] Same as above [Figure 22-6] Same as above [Figure 23-1]

[0046] Figures 23A-23B: Cross-platform comparison of STARSEQ with TaqMan qPCR and Illumina RNA sequencing. [Figure 23-2] Same as above [Figure 24]

[0047] Figure 24: Plot of the difference between TaqMan and STARSEQ measurements. [Diagram 25]

[0048] Figure 25: Plot of the difference between Illumina RNA sequencing and STARSEQ measurements. [Figure 26]

[0049] Figure 26: Assay performance. [Figure 27]

[0050] Figure 27: STARSEQ "True Negatives" versus TaqMan and RNA Sequencing. [Figure 28]

[0051] Figure 28: Standard deviation of ERCC measurements. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024]

[0052] Throughout this disclosure, various publications, patents and published patent specifications are referenced by an identifying citation. The disclosures of these publications, patents and published patent specifications are hereby incorporated by reference into this disclosure in order to more fully describe the state of the art to which this invention pertains.

[0025]

[0053] The method for evaluating nucleic acid is described herein, and the application and business method of using such composition and method.Some aspects of the present disclosure relate to the improvement in Willey and Willey et al. US Patent No. 5,043,390; No. 5,639,606; No. 5,876,978 and No. 7,527,930, which are expressly incorporated herein by reference.

[0026]

[0054] Methods for evaluating nucleic acids

[0055] The method described herein is for evaluating the amount of nucleic acid in a sample.In some embodiments, the method allows for the measurement of small amounts of nucleic acid, for example, when nucleic acid is expressed in low amounts in specimen, when small amounts of nucleic acid remain intact, and / or when small amounts of specimen are provided.

[0027]

[0056] "Specimen," as used herein, may refer to material collected for analysis, such as a swab of culture, a pinch of tissue, a biopsy, a vial of bodily fluids, such as saliva, blood and / or urine, etc., taken from any biological entity for research, diagnostic or other purposes.

[0028]

[0057] Specimen may also refer to the amount typically collected in a biopsy, such as an endoscopic biopsy (using a brush and / or forceps), a needle aspiration biopsy (including fine needle aspiration biopsy), as well as the amount provided in a sorted cell population (e.g., a flow sorted cell population), and / or a microdissected material (e.g., laser capture microdissected tissue). For example, biopsies of suspected cancerous lesions in the lung, breast, prostate, thyroid, and pancreas are commonly performed by fine needle aspiration (FNA) biopsy, bone marrow is also obtained by biopsy, and tissues of the brain, developing embryo, and animal models can be obtained by laser capture microdissected samples.

[0029]

[0058] "Biological entity," as used herein, can refer to any entity that can harbor nucleic acids, including any species, e.g., viruses, cells, tissues, in vitro cultures, plants, animals, subjects participating in clinical trials, and / or subjects being diagnosed or treated for a disease or condition.

[0030]

[0059] "Sample," as used herein, may refer to the specimen material used in connection with a given assay, reaction, run, test, and / or experiment. For example, a sample may include an aliquot of a collected specimen material, up to and including all of the specimen. As used herein, the terms assay, reaction, run, test, and / or experiment may be used interchangeably.

[0031]

[0060] In some embodiments, a collected specimen can comprise less than about 100,000 cells, less than about 10,000 cells, less than about 5,000 cells, less than about 1,000 cells, less than about 500 cells, less than about 100 cells, less than about 50 cells, or less than about 10 cells.

[0032]

[0061] In some embodiments, assessing, evaluating, and / or measuring a nucleic acid includes, for example, measuring the level of expression of a gene. In some embodiments, providing a measure of the amount of a nucleic acid may refer to providing a measure of the amount of the nucleic acid in a specimen and / or sample to determine a level of the nucleic acid. In some embodiments, providing a measure of the amount may refer to detecting the presence or absence of a nucleic acid of interest. In some embodiments, providing a measure of the amount may refer to quantifying the amount of the nucleic acid, e.g., providing a measure of the concentration or amount of the nucleic acid present. In some embodiments, providing a measure of the amount of the nucleic acid may refer to counting the amount of the nucleic acid, e.g., indicating the number of molecules of the nucleic acid present in the sample. The "nucleic acid of interest" may be referred to as a "target" nucleic acid, and / or a "gene of interest," e.g., the gene being evaluated may be referred to as a target gene. The number of molecules of the nucleic acid may also be referred to as the number of copies of the nucleic acid in the specimen and / or sample.

[0033]

[0062] As used herein, "nucleic acid" can refer to polymeric forms of nucleotides and / or nucleotide-like molecules of any length. In certain embodiments, the nucleic acid can serve as a template for the synthesis of a complementary nucleic acid, for example, by base-complementary incorporation of nucleotide units. For example, the nucleic acid can include naturally occurring DNA, for example, genomic DNA; RNA, for example, mRNA, and / or synthetic molecules, including, but not limited to, cDNA and recombinant molecules produced in any manner. For example, the nucleic acid can be produced from chemical synthesis, reverse transcription, DNA replication, or a combination of these production methods. Linkages between the subunits can be provided by phosphates, phosphonates, phosphoramidates, phosphorothioates, etc., or by non-phosphate groups, such as, but not limited to, peptide-type bonds utilized in peptide nucleic acids (PNAs). The linking groups can be chiral or achiral. The polynucleotides can have any three-dimensional structure, including single-stranded, double-stranded, and triple-helical molecules, and can be, for example, DNA, RNA, or hybrid DNA / RNA molecules.

[0034]

[0063] Nucleotide-like molecules can refer to structural moieties that can act substantially like nucleotides, for example, exhibit base complementarity with one or more bases present in DNA or RNA, and / or are capable of base-complementary incorporation. The terms "polynucleotide", "polynucleotide molecule", "nucleic acid molecule", "polynucleotide sequence" and nucleic acid sequence can be used interchangeably herein with "nucleic acid". In some specific embodiments, the nucleic acid to be measured can include a sequence corresponding to a specific gene.

[0035]

[0064] In some embodiments, the collected specimen contains RNA to be measured, such as mRNA expressed in tissue culture. In some embodiments, the collected specimen contains DNA to be measured, such as cDNA reverse transcribed from a transcript. In some embodiments, the nucleic acid to be measured is provided in a heterogeneous mixture of other nucleic acid molecules.

[0036]

[0065] The term "natural template" as used herein can refer to a nucleic acid obtained directly or indirectly from a specimen that can serve as a template for amplification. For example, it can refer to a cDNA molecule corresponding to a gene whose expression is to be measured, where the cDNA is amplified and quantified.

[0037]

[0066] The term "primer" generally refers to a nucleic acid that can serve as a point of initiation of synthesis along a complementary strand when conditions are suitable for the synthesis of a primer extension product.

[0067] General description of the method

[0068] Preparation of a sequencing library involves some combination or all of the following steps: 1) fragmentation of nucleic acids; 2) in vivo cloning, which serves to join adjacent nucleic acid adapter sequences; 3) in vitro adapter ligation; 4) PCR-based adapter addition; and 5) ligation of a probe to capture the sequences by fill-in and circularization with a polymerase, with or without the ligation of a probe. A single molecule inversion probe type technology that uses an adaptor contained within the probe sequence.

[0038]

[0069] The definition of a "nucleic acid adapter" is that it can serve as any or all of the following: a) a recognition site for a sequencing primer, b) a barcode sequence of nucleotides to deconvolute samples prepared for sequencing during analysis, and c) a universal nucleic acid site that allows for the amplification of multiple templates, or even the addition of fusion tail sequences by amplification.

[0039]

[0070] The sequencing library prepared from one or more of steps 1-5 above is then analyzed on a sequencing instrument and a representative sampling of the library is sequenced. The number of times each unique nucleic acid target is observed is then counted and the relative ratio between the counts of each unique nucleic acid target is assessed. However, this relative ratio does not represent the true proportionality of abundance between each unique nucleic acid target in the original sample.

[0040]

[0071] The loss of the original representation is a technical artifact (e.g., error, bias) of steps 1 through 5. Furthermore, this error is non-systematic, i.e., not the same amount of bias, at least among the following errors: i) the steps of library preparation (1 through 5 above); ii) preparation of replicates of the sequencing library; iii) different replicate time points; iv) different technicians preparing the libraries; and / or v) preparing the libraries in different laboratories.

[0041]

[0072] This non-systematic error in the ratio of nucleic acids effectively targets errors (i-v), making any comparison of results between library preparations for the same sample error-prone, thus limiting the application of sequencing as a tool to cost-effectively as well as reliably measure the copy number of nucleic acids.

[0042]

[0073] One embodiment described herein is a method that utilizes a mixture of internal standard nucleic acid molecules of known numbers (i.e., abundance, concentration and / or amount) corresponding to unique nucleic acid targets (also defined as "natural targets" or NTs), which are to be mixed in the nucleic acid sample prior to preparation of a library for sequencing or prior to sequencing (if library preparation is not required).

[0043]

[0074] Each nucleic acid target is similar to its respective internal standard except for one or more changes to its nucleic acid sequence. These differences between the natural target and the internal standard are identifiable by sequencing and can include deletions, additions, or substitutions to the order or composition of nucleotides used.

[0044]

[0075] By introducing an internal standard into a sample of nucleic acid targets prior to library preparation, the non-systematic errors introduced by steps 1-5 (as well as sequencer instrument-specific biases) are experienced similarly by both the native and internal standard targets.

[0045]

[0076] At the end of sequencing, the ratio of sequencing events (i.e., observations, counts, reads) between the native targets and their respective internal standards is assessed along with the original number of internal standard nucleic acid molecules input into the sample prior to library preparation to quantitatively determine the original amount of each native target in the original sample prior to library preparation and sequencing.

[0046]

[0077] Thus, the inclusion of an internal standard controls for error and relative changes in the ratios between natural targets during steps 1-5 and subsequent sequencing, so the methods described herein ensure that low abundance natural targets are preferentially increased compared to high abundance natural targets during library preparation. This preferential amplification or enrichment can be exploited such that at the end of sequencing library preparation the relative ratio between each unique native target will converge towards equimolar (i.e. uniform) abundance in the library. This results in more equal coverage of sequencing depth between the native targets. And because the internal standards also experience preferential amplification or enrichment, this method allows the original amount of each native target in the original sample to be quantitatively determined prior to library preparation.

[0047]

[0078] In one limiting example, for every 10-fold reduction in the depth of the ratio between native targets, 10-fold fewer sequencing reads are required, thus achieving an approximate 10-fold reduction in direct sequencing costs.

[0048]

[0079] Thus, addition of a mixture of nucleic acid standards prior to sequencing library preparation (or prior to sequencing if library preparation is not required) provides accurate quantification of native targets by end-point sequencing.

[0049]

[0080] The use of standardized mixtures of nucleic acid internal standards allows for direct comparison of inter-laboratory results for nucleic acid molecular diagnostics and other quantitative sequencing results.

[0081] Also, in certain embodiments, the further addition of an internal standard allows for convergence of native target abundances, thus reducing the cost of direct sequencing by normalizing fold-proportion native target abundances towards each other.

[0050]

[0082] The inclusion of a mixture of internal standard nucleic acids of known quantity (i.e., abundance, concentration and / or number) during library preparation offers certain advantages. Because it may not be known which of steps 1-5 or the sequencing may introduce error, the present method reduces this bias, thus allowing comparison of results between libraries and between laboratories, while at the same time offering the ability to reduce the cost of direct sequencing due to convergence in the concentrations of nucleic acid targets.

[0051]

[0083] In certain embodiments, the methods described herein include a known number of internal standard molecules for each gene to be measured in the nucleic acid sample prior to sequencing or preparation of a library for sequencing.

[0052]

[0084] In addition, in certain embodiments, the preparation of a standardized mixture of internal standards can be used by multiple laboratories, thereby increasing the reliability of the measurement of each targeted gene and increasing the reproducibility of the measurement between experiments and between laboratories.Therefore, the measurement of copy number for each nucleic acid compared to its respective internal standard molecule of known copy number in the standardized mixture of internal standards, and the use of the same SMIS across multiple experiments and laboratories increases reliability and quality control by controlling for the variation introduced by the preparation of sequencing libraries.

[0053]

[0085] In certain embodiments, the methods described herein use gene-specific reverse transcription and / or PCR for library preparation for quantification by sequencing. In certain embodiments, PCR optimization allows multiplexing of 100, 300, 500, 1000, or more genes to generate sufficient PCR product of each target gene for quantification by sequencing. PCR optimization can result in 10-fold, 100-fold, 1000-fold, 10,000-fold, or greater convergence in transcript expression between the original genes, while maintaining the ability to quantify the original relative transcript expression by measuring each gene relative to its respective internal standard. Thus, including an internal standard of known copy number in the sample prior to library preparation (or prior to sequencing if library preparation is not required) allows for the control of subsequent changes in transcript expression. Here, it is possible to optimize the convergence between genes without losing information about the original representation. For example, in certain embodiments there may be greater than 1000-fold convergence, resulting in a reduction in the required number of "reads" from 10,000,000 to 10,000.

[0054]

[0086] In certain embodiments, where each chip for a typical next generation sequencer allows for 10 million reads, this result allows the number of samples / chip analyzed to be increased from 1 to 1,000. Currently, a chip for a typical sequencer costs $1,000, thereby reducing the cost of the chip / sample from about $1,000 to about $1.00.

[0055]

[0087] In addition, rare transcripts can be measured with statistical significance.For example, the copy number of the nucleic acid corresponding to gene transcript, for example copy number / cell, can be determined, where the gene is expressed at low copy number.Counting less than about 1,000 molecules can allow measurement of less than about 10 copy number / cell of at least 100 different gene transcripts in a small biological specimen.The method can measure and / or count less than about 10 copy number / cell of at least 100 different gene transcripts in a small biological specimen.

[0056]

[0088] Furthermore, in some embodiments, more measurements can be obtained from a given specimen and / or sample of a size typically used to measure low copy numbers of nucleic acids, e.g., corresponding to a single gene. For example, some embodiments can measure and / or count less than about 100 copies, less than about 50 copies, less than about 20 copies, less than about 10 copies, less than about 8 copies, less than about 5 copies, less than about 5 copies, less than about 20 copies, less than about 100 copies, less than about 8 copies, or less than about 5 copies per cell of at least about 20, at least about 50, at least about 80, at least about 100, at least about 120, at least about 150, or at least about 200 different nucleic acids in a sample, e.g., corresponding to different gene transcripts.

[0057]

[0089] The expressed substance can be endogenous to the biological entity, e.g., the transcription product of a gene naturally expressed in a given cell type, or the expressed substance to be measured can be of an exogenous nature, e.g., the method can be used to quantify transfected genes after gene therapy and / or reporter genes in transient transfection assays, e.g., to determine the efficiency of transfection.

[0058]

[0090] EXAMPLES

[0059]

[0091] The methods and aspects described herein are further defined in the following examples, in which all parts and percentages are by weight and degrees are Celsius unless otherwise stated. Specific aspects of the invention are defined in the examples herein. It should be understood that these examples, while illustrating preferred embodiments of the invention, are given by way of illustration only. From the discussion herein and these examples, one skilled in the art can ascertain the essential features of the invention, and can make various changes and modifications of the invention to adapt it to various uses and conditions without departing from the spirit and scope thereof.

[0060]

[0092] The PCR-based NGS library preparation method incorporates a competitive internal amplification control (IAC), which controls for most of the non-systematic errors introduced during NGS library preparation and allows for inter-laboratory comparison of quantitative NGS data.

[0061]

[0093] Thus, competitive IACs mimic the kinetics of the natural template in a PCR reaction, and thus control for non-systematic errors in PCR-based NGS library preparation by sharing identical priming sites with the natural nucleic acid template of interest to control for target-specific variations in PCR efficiency.

[0062]

[0094] In the methods described herein, the proportionality of the sequencing reads of a natural target to its respective competing IAC does not change during NGS library preparation because the competing IAC undergoes the same kinetics as the natural nucleic acid template.

[0063]

[0095] Furthermore, if the concentration of the competing IAC placed during sample preparation is known, it is now possible to accurately calculate the original abundance of the native nucleic acid molecule that was present at the start of the NGS library preparation.

[0064]

[0096] As an example, when multiple laboratories used the same mixture of competing IACs in multiple different studies, each of the multiple laboratories showed consistent results.

[0065]

[0097] Thus, the use of competitive IACs in PCR-based NGS library preparation enables cost-effective, highly multiplexed analysis of large numbers of nucleic acid targets across multiple samples with a high degree of accuracy and reproducibility.

[0066]

[0098] An additional benefit of the incorporation of competitive IACs is that protocols such as multiplex PCR that result in normalization (i.e., convergence) towards equimolar (i.e., uniform) concentrations of each native target can be performed using such methods.

[0067]

[0099] It should be understood that due to template concentration normalization, it is possible that abundance between natural nucleic acid templates may differ by more than 1 million times.In the past, the most highly expressed natural nucleic acid template would be unnecessarily oversampled and sequenced 10 million times to sequence the least expressed nucleic acid template (e.g., at least 10 times to accurately detect a 2-fold change (power=80%; type I error rate=0.05)).However, the use of the competitive IAC method described herein provides normalization in the expression of target analytes, and still retains the quantitative information of the original expression of both low and high abundance targets with low number of sequencing reads.Therefore, the reduction in oversampling of over-represented nucleic acid targets results in reduced costs and stochastic sampling errors associated with deep sequencing.

[0068]

[0100] Example 1

[0101] Competitive IA for NGS library preparation and subsequent measurement of nucleic acid abundance Multiplex PCR using C

[0102] Used in the FDA-funded Sequencing Quality Control (SEQC) program A reference material RNA titration pool (with nucleic acid abundances already measured by multiplex qPCR, microarray and NGS platforms under a variety of conditions) was obtained.

[0069]

[0103] NGS libraries were analyzed using primers and a competition with 150 gene targets. The PCR products were prepared from reference material that had been reverse transcribed using multiplex PCR in the presence of IAC.

[0104] The NGS library preparation was performed within individual test sites, between laboratories, The reproducibility of nucleic acid abundance measurements across different nucleic acid measurement platforms was also assessed using multiplex PCR with competitive IAC.

[0070]

[0105] Cost and time to market of multiplex PCR using competitive IAC for NGS library preparation The effectiveness and advantages of this method were compared to commonly used Illumina-based NGS library preparation protocols and Taqman qPCR for accurately measuring nucleic acid abundance in a clinical setting.

[0071]

[0106] Methods and Results

[0107] 1 for each of the 150 uniquely transcribed genes in the human genome Forward and reverse primers were designed corresponding to the 01 base pair region (i.e., amplicon). Each primer was designed to have a uniform melting temperature of 68°C. Each primer also contained a universal tail sequence that could be used for multi-template PCR, for example, in the addition of barcode and sequencing adapter sequences after the initial multiplex PCR. These primers were synthesized by Integrated DNA Technologies (IDT), combined in equimolar ratios, and diluted to a final working concentration of each primer of 50 nanomolar. A corresponding mixture of 150 competitive internal amplification controls (IACs), each 101 bases long, was synthesized by Integrated DNA Technologies (IDT). Each of the competitive IACs contained identical target-specific priming sites for their respective native nucleic acid template targets. Inside these identical forward and reverse priming sites, there was a 6-nucleotide substitution at an internal site in the sequence so that the competitive IACs could be distinguished from their corresponding native targets during post-sequencing data analysis.

[0072]

[0108] Each competing IAC was assayed at approximately equimolar concentrations relative to each other by IDT. The competing IACs were combined into a mixture. Because the mixture of competing IACs may not have been in a strict 1:1 ratio, the absolute abundance of each copy number of the competing IACs and their ratios relative to each other for each of the 150 competing IACs were determined by titration relative to a known amount of genomic DNA (gDNA) reference material. The genomic DNA reference material can serve as a normalization reagent because each of the unique genomic sequences is present in a 1:1 ratio to each other throughout the genome. Thus, any perceived difference in competing IAC concentration when titrated to gDNA is in fact indicative of a systematic difference in the ratios present between the competing IACs in the mixture. This systematic difference is determined by titration to a fixed amount of gDNA and is always applied to future calculations and measurements made with that particular lot or mixture of IACs (Figure 1).

[0073]

[0109] FIG. 1 shows the results of adding 100,000 copies of gD to each multiplex PCR. Titration of a mixture of internal amplification controls (IACs) compared to NA input is shown. Plotted on the Y-axis is the frequency or ratio of observed native reads divided by the sum of both the native reads and their respective competing IAC reads. Plotted on the X-axis is the initial estimated amount of each target in the approximately equimolar mixture of competing IACs. Ten dilutions ranging from 10,000,000 copies of each IAC (Log10 concentration = 0) to 1,000 (Log10 concentration = -2) copies were input into each of 10 reactions to generate the curves shown. Of the 150 designed primer sets, competing IACs, and their respective native targets, 119 were titrated using goodness of fit (R 2 >0.95). More than 95% of the competing IACs, when diluted to 100,000 copies (10,000,000 starting IAC copies diluted to 100,000 or Log10 concentration-2, i.e., 100-fold from 10,000,000), were within 10-fold of the predicted equivalence point: (native) / (native+IAC)=0.5. The new concentrations served as the actual concentrations for each of the 119 assays in the competing IAC mixture and served as the standard for absolute accuracy (i.e., true precision) μg.

[0074]

[0110] Therefore, the 15% IAC to a titrated mixture with a fixed amount of gDNA After testing 0 assays, 119 of the 150 assays had sufficient performance characteristics (Hill plot R 2 >0.95). These corrections were then applied to all future measurements made with this IAC mixture.

[0075]

[0111] The first step in the MAQC project, also known as the Sequencing Quality Control (SEQC) project, is Phase III generated four pools from two RNA sample types: Universal Human Reference RNA (UHRR) from Stratagene and Human Brain Reference RNA (HBRR) from Ambion. The four pools contained two reference RNA samples as well as two mixtures of the original samples: Sample A, 100% UHRR; Sample B, 100% HBRR; Sample C, 75% UHRR:25% HBRR; and D, 25% UHRR:75% HBRR. This combination of biologically distinct RNA sources and known dosing differences provided a method to assess the accuracy of the platform based on the differentially expressed genes detected. Ten (10) μg aliquots of these RNA pools were used for Samples A, B, C, and D.

[0076]

[0112] Each of the RNA titration pool reference materials (samples A, B, C and D) was reverse transcribed as described in Canales et al., 2006, except that Superscript III reverse transcriptase from Invitrogen was used instead of MMLV reverse transcriptase and 1 μg of RNA was loaded in each reverse transcription reaction. In addition, to determine the variance introduced into the sequencing library preparation by reverse transcription, sample A was reverse transcribed twice from two separate preparations of reverse transcription master mix.

[0077]

[0113] The reverse transcribed RNA titration pool of these five cDNAs (sample AR One (1) μL from each of A1, A-RT2, B, C and D) was spiked into one of 12 multiplex PCR reactions containing a serially diluted mixture of competitive internal amplification control (IAC) mixtures representing 150 targets. These 12 serial dilutions of competitive IAC mixtures ranged from 107 copies loaded down to 103. A total of 12 μL of each sample was consumed during the multiplex PCR, corresponding to a total of approximately 133 ng of RNA for each sample.

[0078]

[0114] Concentration of the natural substance for each gene target is equal to the competing IAC. The degree (i.e., equivalence point) was determined for each reverse transcribed reference material (samples SEQC A-RT1, A-RT2, B, C and D), which was determined using the Hill equation (Figure 2).

[0079]

[0115] The graph in Figure 2 shows a constant amount of 10 Shown is a titration of a mixture of internal amplification controls (IACs) compared to 0,000 copies of gDNA or 11 ng of reverse transcribed RNA cDNA material (samples SEQC A-RT1, A-RT2, B, C and D). Plotted on the Y-axis is the frequency or ratio of observed native reads divided by the sum of both native reads and their respective competing IAC reads. Plotted on the X-axis is the initial estimated amount of each target in the roughly equimolar mixture of competing IACs. Dilutions ranging from 10,000,000 copies of each IAC (Log10 concentration = 0) to 1,000 (Log10 concentration = -2) copies were input into each of 10 reactions to generate the above curves.

[0080]

[0116] Samples C and D represent known crossover doses between samples A and C. The accuracy of the platform for differentially expressed genes was evaluated (Figure 3). The values ​​measured for SEQC sample C-RT1 were compared (% difference) to the values ​​expected based on the measured values ​​of SEQC samples A and B. The predicted signal C' and the actual A The percentage difference between the assay signals C was used as an index of relative assay accuracy (RA). The RA score ΔC for each target gene was defined as (C-C' / C'). The distribution of the percentage difference from the expected RA score for each gene is shown in box plots for normalized qNGS (n=88) and normalized RT-PCR (n=201). The components of the box plot are as follows: horizontal line, median; box, interquartile range; whiskers, 1.5× interquartile range; black square, outliers.

[0081]

[0117] Gene targets measured among samples SEQC A-RT1, A-RT2, B, and C (n=88) were assessed for differential expression (DE) between genes (Figure 4A); changes in DE were 1.5-3.0, 2-3, 3-5, 5-10 fold, or greater than 10 fold. Control for false positive or negative changes was assessed by comparing SEQC A-RT1 to SEQC A-RT2. Summary statistics showing differential expression in sample C compared to expected values ​​based on samples A and B are shown in Figure 4B-Table 1.

[0082]

[0118] The reproducibility of the assay between reverse transcriptions is shown in Figure 5. Reverse transcription (RT1 vs. RT2) was measured for expression of 119 gene targets that successfully passed the performance criteria in Figure 1. Of the 119 gene targets, 97 exceeded R for Hill equation curve fitting to determine equivalence points and concentrations of each target. 2 >0.95 (Figure 2).

[0083]

[0119] The same data as in FIGS. 3 and 4 is shown in FIG. Expected measurements (x-axis) versus observed measurements (y-axis) in sample C. Reverse transcription of SEQC sample C was measured for expression of 119 gene targets that successfully passed the performance criteria in Figure 1. Of the 119 gene targets, 88 met R 0 for Hill equation curve fitting to determine equivalence points and concentrations of each target. 2 >0.95 (y-axis) (Figure 2).

[0084]

[0120] The convergence and increased uniformity of the 97 gene targets from Figure 5 is shown in Figure 7. Figure 3. Plotted on the x-axis is the data from Figure 5 relative to the highest abundance template. On the y-axis is the actual proportion of sequence reads for which the gene target is relative to the highest sequence template. Note that although measurements and accuracy are not compressed (Figures 4-6), 75% of gene targets have sequencing read abundances within 10-fold of each other; that is, the sequencing depth has decreased from roughly 1000-fold to 10-fold below. This corresponds to a 100-fold reduction in the cost of direct sequencing.

[0085]

[0121] The ROC curve for detecting a greater than 3-fold change for RNA sequencing using the Illumina platform from Bullard et al. BMC Bioinformatics 2010, 11:94 is shown in Figure 8. In comparison to Figure 4B, this ROC curve represents about 75% accuracy of RNA sequencing to detect a greater than 3-fold change. Meanwhile, the standardized qNGS described herein has an accuracy of greater than 97% (Figure 4B). It is noteworthy that the standardized qNGS method utilized 10 times sequencing depth to accurately detect a 3-fold change across a 1000-fold ratio difference between native targets. In contrast, traditional RNA sequencing would require 100 times more reads to reach a similar accuracy. In one example, 5 million sequencing reads were utilized to accurately quantify 97 genes using the standardized qNGS method. In comparison, traditional RNA sequencing would require well over 500 million reads for accurate quantification.

[0086]

[0122] Example 2

[0123] Quantitative alignment after PCR-driven library preparation using an internal standard mixture Column determination improves analytical performance and reduces costs.

[0087]

[0124] Major sources of technical variation during next generation sequencing (NGS) library preparation Nonsystematic biases introduced by NGS have hindered the application of NGS to measure nucleic acid abundance in clinical settings.

[0088]

[0125] The cost of current qPCR clinical diagnostics is driven by the cost of the chemistry they use (usually fluorescent). The number of assay targets is fixed and linearly related to the number of nucleic acid targets they are interrogating. Furthermore, each assay target requires a separate reaction vessel and numerous controls, which can be prohibitively expensive. These cumulative costs impede the emergence of more complex clinical diagnostics based on the measurement of multiple nucleic acid targets. More cost-effective alternatives for multiplexed nucleic acid target abundance measurement are not flexible in bringing novel assay targets online cost-effectively without perturbing existing gene panels, or amenable to quantitative data standardization and site-to-site reproducibility. Although NGS can amenable to cost-effective, highly multiplexed quantitative analysis of large numbers of patient samples and nucleic acid targets, there is a need for efficient methods to enable comparison of quantitative NGS results across sites and avoid the need for deep sequencing to accurately measure nucleic acid abundance.

[0089]

[0126] In this example, a competitive internal amplification control (IAC) mixture (i.e., an internal control) Our PCR-based NGS library preparation protocol, incorporating a standardized approach, controls for most of the biases introduced during NGS library preparation and enables clinical laboratories to provide cost-effective, moderately complex diagnostic panels from quantitative NGS data.

[0090]

[0127] Used in the FDA-funded Sequencing Quality Control (SEQC) program A reference material RNA titration pool was obtained (samples A, B, C, and D). SEQC project RNA samples C and D represent known cross-titrations between SEQC project RNA samples A and B, allowing SEQC expression values ​​to be compared to measured and expected expression values ​​to determine the accuracy of the method. Multiplex PCR with primers and competitive IACs for 150 gene targets was used to prepare NGS libraries from: 1) gDNA to test general analytical performance, and 2) cDNA from reverse transcribed SEQC reference material to determine accuracy.

[0091]

[0128] result:

[0129] gDNA mixed with a serially titrated competitive IAC mixture as input Using 10 6 The linear dynamic range is over an order of magnitude, 2 = 0.995 (0.993-0.997; 95% CI). The correlation coefficient between the predicted and observed values ​​for sample C was R 2 = 0.96, and the correlation coefficient of predicted vs. observed values ​​for sample D is R 2 = 0.94, and the accuracy of detecting a 3-fold change as determined by the ROC curve was 97% (95-99%; 95% CI). The intersite correlation coefficient for measurements based on only 400,000 sequencing reads was approximately 10% of the abundance between the natural targets. 5 R over a linear dynamic range of orders of magnitude 2 =0.92.

[0092]

[0130] The methods described herein are directed to eliminating non-targeted sequences introduced during NGS library preparation. Overcoming a significant source of systematic bias, which enables reproducible inter-laboratory and inter-platform quantitative NGS results, as well as a clear path to regulatory approval for clinical diagnostic applications.

[0093]

[0131] The method described herein (NGS with internal amplification controls (IACs)) includes: Provides intra- and inter-site reproducibility of quantitative next-generation sequencing (NGS) data. The methods described herein address the need for deep sequencing by converging the number of reads required to adequately sequence both rare and high abundance nucleic acid targets. Thus, the cost of direct sequencing is also reduced.

[0094]

[0132] Figure 9 shows the PCR master mix containing a mixture of internal amplification controls (IACs). A schematic diagram is provided. The IAC serves as an inter-library, inter-site reference. The IAC is stable for long periods of time (e.g., years). The IAC mixture controls for PCR bias and is present at known concentrations. The target-specific primer mixture includes hundreds of targets per reaction. The target-specific primer contains a universal tail.

[0095]

[0133] 10A-10B show internal amplification of gDNA and SEQC cDNA. 1 is a graph showing titration of a control mixture. The plot is in the form of a dose-response curve for inhibition of an enzyme system. Taq polymerase is the enzyme. The inhibitor is the concentration of a competitive internal amplification control (IAC). The dose-response is measured as the ratio of sequencing reads observed for the native genomic DNA (gDNA) or native complementary DNA (cDNA) target to the sum of the native and IAC sequencing reads. The gDNA plot represents 119 of the 150 designed gene targets (approximately 80% assay design success rate). The average correlation coefficient for the fit of a three-parameter fixed slope Hill equation to each of the 119 assays is R 2 =0.995 (0.993-0.997; 95%CI).

[0096]

[0134] The average IC50 (50% inhibitory concentration) was 10 4.98 and the input gDA N copy number is 10 5Thus, titration of the mixture of internal amplification controls provides true, not relative, accuracy of copy number determination of a complex mixture of nucleic acids.

[0097]

[0135] In Figures 10-11 (Result 1), the cDNA plot shows 119 activation ( These represent 110 of the 10 (working) gene target assays. Nine (9) assays had insufficient read depth for at least one sequencing read for both the native target as well as the internal amplification control. The average IC50 (50% inhibitory concentration) was determined for each nucleic acid target under a variety of conditions in SEQC Samples A, B, C, and D and used in subsequent examples; comparisons of results were performed for the following:

[0136] Figure 12 (Result 2) shows the sequencing of replicates (within-site) of the same library preparation. Shown is a graph where X-axis = 1.8 million sequencing reads and Y-axis = 3 million sequencing reads.

[0098]

[0137] Figure 13 (Result 3) shows the sequenced separate library preparations (within the site). Shown is where X-axis = 2.6 million sequencing reads and Y-axis = 4.8 million sequencing reads.

[0099]

[0138] Figures 14A-14B (Result 4) are based on measurements (within site) of samples A and B. 4 shows predicted values ​​for measurements of samples C and D, where X-axis = 15.2 million sequencing reads and Y-axis = 4.9 million sequencing reads.

[0100]

[0139] Figure 15 (Result 5) shows the results of separate library preparations sequenced at different sites. Figure 1 shows inter-laboratory comparison (between sites) of measurements made (between sites) where X-axis = 2.6 million sequencing reads and Y-axis = 0.4 million sequencing reads.

[0101]

[0140] Figures 16A-16B (Result 6) accurately plot the fold change based on Figure 13 (Result 4). Shown is the receiver curve for detecting, which shows the receiver curve for calling differential expression based on Figure 14-Result 4.

[0102]

[0141] Figure 17 (Result 7) shows that PCR-driven library preparation reduces the number of reads required. Convergence to native target concentration with decreasing depth. PCR-driven library preparation. Convergence of native template amplicon concentrations during preparation reduces the number of sequencing reads to adequately sequence all targets. Internal amplification controls provide a necessary reference point at the beginning of PCR-driven sequencing library preparation to accurately measure each nucleic acid target despite convergence in template concentrations (see Figures 12-16 - Results 2-6). In this example, direct sequencing depth is reduced 1000-fold and all targets are within 100-fold of each other.

[0103] It should also be understood that it is within the intended scope of this disclosure that the methods described herein include the use of intermediate complexity clinical panels based on PCR-driven NGS library preparation with internal amplification controls.Non-limiting examples include panels for: lung cancer risk test (15 genes); lung cancer diagnostic test (4 genes); lung cancer chemoresistance test (20 genes), and BCR-ABL fusion transcript test (2 genes).

[0104]

[0142] Example 3

[0143] Standardized RNA Sequencing (STARSEQ)

[0144] Standardized RNA sequencing (STARSEQ) was performed using two separate reference materials. The assay was performed using: 1) genomic DNA (gDNA) derived from blood of phenotypically normal individuals at the University of Toledo Medical Center (UTMC) (723 de-identified samples) following a protocol approved by the UTMC Institutional Review Board, and 2) four reference RNA samples (A, B, C, and D) provided by the FDA-funded Sequencing Quality Control (SEQC) project (formerly the MAQC Consortium). Sample A consisted of universal human reference RNA obtained from Stratagene. Sample B consisted of human brain reference RNA obtained from Ambion. For the SEQC project, samples A and B were then combined with Ambion External RNA Control Consortium (ERCC) Spike-In control RNA mixes 1 and 2, respectively, to achieve a final concentration of 2% in samples A and B based on the total RNA concentration.

[0105]

[0145] Each ERCC RNA control spike-in mixture was 6 Bigger Each mixture contained the same controls spanning a wide dynamic range, but in different formulations. Within each formulation mixture there were four subgroups that represented known fold differences in abundance between mixtures 1 and 2: 0.5x, 0.67x, 1.0x and 4.0x differences. Samples A and B were then combined in mixtures in 3:1 and 1:3 ratios to create samples C and D, respectively. The gDNA "reference" material represents a sample in which the majority of endogenous targets are in a very close 1:1 ratio to each other. Meanwhile, samples A-D were used to characterize the analytical performance characteristics of the method over a 10-fold period. 6 Represents a complex mixture of synthetic (ERCC control) and endogenous RNA targets in known ratios that can be used as a ground truth benchmark for evaluation over a greater than 2-fold dynamic range of abundance.

[0106]

[0146] Reverse transcription of RNA reference material

[0147] Samples A through D contain 10 micrograms of reference RNA at a concentration of 1 μg / μL each. Material was obtained from the FDA-funded SEQC program (fda.gov / Scientific Research / Bioinformatics Tools / Microarray Quality Control Program). For each sample, a 2 μg aliquot of RNA was reverse transcribed. Each reverse transcription reaction was performed in a 90 μL volume using Superscript III reverse transcription (Life Technologies) and the manufacturer's protocol for oligo(dT) priming. After reverse transcription, the two 90 μL cDNA products for each sample were combined into a single 180 μL volume (reverse transcription 1; RT1). For sample A, an additional set of two 2 μg aliquots of RNA was reverse transcribed using a separate master mix (reverse transcription 2; RT2).

[0107]

[0148] Selection of STARSEQ assay targets

[0149] The Microarray Quality Control (MAQC) Consortium previously conducted multiplex qPCR A list of 1,297 genes was selected to evaluate the performance of the STARSEQ and microarray platforms. From this list, 150 endogenous targets were selected for developing STARSEQ assays. These 150 assays were selected in part because the gene targets they represent are 100%. 6 These were selected because they are expressed across a larger dynamic range. These reagents were used to measure the absolute and relative ratios of each gene target in gDNA and reverse transcribed reference RNA samples A-D. In addition, 28 of the 92 External RNA Control Consortium (ERCC) targets were also selected for developing the STARSEQ assay.

[0108]

[0150] Design and synthesis of STARSEQ primers

[0151] Forward and reverse PCR primers were aligned to 150 unique sequences in the human genome. Primers were designed for the corresponding 101 bp amplicon region for each of the 28 ERCC targets and the genes transcribed in the first place. Each forward and reverse primer set was designed to have a uniform melting temperature of 68°C using Primer3 software (Untergasser et al, NAR, 2012). To minimize off-target priming, primer pair specificity was verified using GenomeTester 1.3 to identify any additional amplicons less than 1000 bp in size. Each primer also contains a universal tail sequence not present in the human genome, which can be used for the addition of barcodes and platform-specific sequencing adapters in multi-template PCR. The forward universal tail is identical in sequence to the adapter (APEX-2) used for arrayed primer extension, while the reverse tail sequence is identical to the forward tail sequence except for the last four 3' bases, which allows directionality during sequencing. Target-specific primers with universal tails for 150 endogenous targets and 28 ERCC targets were synthesized by Integrated DNA Technologies (IDT) and Life technologies, respectively. Primer pools for endogenous or ERCC targets were generated by combining the synthesized primers in an equimolar ratio and diluting them in dilute Tris-EDTA buffer to a final working concentration of 50 nM for each primer.

[0109]

[0152] Design and synthesis of STARSEQ competitive internal standard mixtures

[0153] Each 101 bp competitive internal standard (IS) was They were designed to retain target-specific priming sites identical to their nucleic acid targets (Figures 18A-18B). Within these identical priming sites are six nucleotide substitutions so that the competitive ISs can be distinguished from their corresponding native targets during post-sequencing data analysis. 150 competitive ISs corresponding to endogenous targets were synthesized by Integrated DNA Technologies (IDT), and 28 competitive ISs corresponding to ERCC targets were synthesized by Life technologies.

[0110]

[0154] For 150 competing IS templates corresponding to endogenous targets, the concentration was The concentrations of the ISs were measured by optical density in the genomic DNA of the 1:1 stoichiometric molar ratio of each IS, which were then combined based on these measurements. The concentration of each IS was experimentally determined by cross-titration of the mixture compared to a constant gDNA input of 100,000 copies (ID723). In gDNA from phenotypically healthy individuals, where the majority of the loci are in a 1:1 ratio relative to each other, it is believed that this will provide a valid and cost-effective reference material for determining the actual concentrations for each competing IS template.

[0111]

[0155] For 28 competing IS templates corresponding to ERCC targets, such a standard Therefore, each standard was amplified separately with forward and reverse primers (without universal sequences) and column purified (QIAquick PCR purification kit), visualized and quantified for a single peak at 101 bases using a DNA chip with the reagents of the DNA 1000 kit on an Agilent 2100 Bioanalyzer according to the manufacturer's protocol (Agilent Technologies Deutschland GmbH, Waldbronn, Germany). The quantified standards were then combined in a 1:1 stoichiometric molar ratio to create a stock concentrated mixture of internal standards (IS). Both endogenous and ERCC target mixtures of competitive IS were then serially diluted to working concentrations and used as reference mixtures to quantify the absolute copy number of each transcript in samples A-D in all subsequent experiments (Figures 18A-18B).

[0112]

[0156] Multiplex competitive PCR using target-specific primers with universal tails

[0157] For each multiplex competitive polymerase chain reaction (PCR): A 10 μL reaction volume was prepared containing: 1 μL of natural template, 1 μL of competitive IS mix at various input concentrations, 1 μL of corresponding primer mix, 1 μL of 2 mM dNTPs, 1 μL of 10× Idaho Technology reaction buffer (containing 30 mM MgCl2), 0.1 μL of Promega GoTaq Hot Start Taq polymerase (5 u / μL) and 4.9 μL of RNAse-free water (FIG. 18A). Genomic DNA was spiked into 10 separate multiplex PCR reactions containing serially diluted mixtures of competitive IS mix representing 150 endogenous targets. These 10 dilutions were 2×10 6 ~10 3 The cDNA of samples A-D for RT1 was spiked into five separate multiplex PCR reactions containing serially diluted mixtures of competitive IS mixtures representing 28 ERCC targets. These five dilutions represent a dilution series of IS mixtures: 10 6 , 10 5 , 10 4 , 10 3and 300 copies loaded. Reverse transcribed RNA for samples A (RT1 and RT2), B, C, and D were spiked into 12 separate multiplex PCR reactions containing serially diluted mixtures of competitive IS mixtures representing 150 endogenous targets. These 12 dilutions were 6×10 7 ~3.4×10 2 This corresponds to a series of 3-fold dilutions of the IS mixture ranging in copy abundance loading. A total of 17 μL of each cDNA sample was consumed during the multiplex competitive PCR, corresponding to approximately 377 ng of RNA for each sample.

[0113]

[0158] Standardized RNA Sequencing (STARSEQ) Workflow and Data analysis

[0159] FIG. 18A NT = native target (e.g., cDNA, gDNA, etc.); IS = internal target An IS template is a ssDNA or dsDNA molecule that a) is homologous to a particular natural target in the primer sequence and thus competes for amplification with that natural target, but b) contains one or more base substitutions internal to the primer site and thus can be distinguished from the natural target. The IS template for each gene is in a fixed relationship to the IS for the other genes in the internal standard mixture.

[0114]

[0160] FIG. 18B shows that a) competition between each NT and its respective IS Fig. 18B-Top panel: Linearity of cross-titration of competitive internal standard mixture with a fixed amount of genomic DNA (gDNA) for 123 targets. Dotted lines represent 95% prediction interval for NT:IS ratio values. Fig. 18B-Middle panel: Linearity of cross-titration of competitive internal standard mixture with a fixed amount of 26 ERCC natural targets from samples A, B, C and D. Each ERCC target has a 10% increase in abundance. 6 Different over a larger dynamic range Figure 18B-lower panel shows the linearity of cross-titration of competitive internal standard mixtures with constant amounts of endogenous cDNA native targets (same targets as assessed in gDNA; upper panel) from samples A, B, C and D.

[0115]

[0161] Touchdown PCR by multiplex competitive PCR

[0162] Increasing the level of multiplex PCR can be achieved by varying the concentration of primers used. A commensurate reduction is required. Reducing primer concentration has two main effects in multiplex PCR: 1) it reduces the formation of primer-dimer products, and 2) it prevents plateau amplicon product formation before dNTPs become the limiting reagent (the less primers method). This latter effect is important because it allows all target templates to reach the plateau phase, greatly reducing oversampling / sequencing of high abundance targets without signal compression in the presence of a competing IS (Figures 19A-19C, Figures 20A-20B).

[0116]

[0163] STARSEQ reduces oversampling without signal compression

[0164] FIG. 19A shows two native targets (NT) in a hypothetical cDNA sample. 1 natural target is highly abundant, 10 8 copies (the “abundant” NT), while another natural target is low-abundance, 10 2 copies ("rare" NT), which represents a million-fold difference in abundance between targets. 5 It is combined with a mixture of internal standards (IS) that have a fixed concentration relationship in copies.

[0117]

[0165] FIG. 19B shows multiplex competitive PCR library preparation for FIG. 19A. The PCR amplification plots for both the "abundant" and "rare" NTs are separated for purposes of clarity, but occur in the same reaction. During multiplex competitive PCR, each natural target competes equally with its respective competitive internal standard for dNTPs, polymerase, and a limiting concentration of primers. Because the starting concentrations of primer pairs for each target are the same, each competitive reaction reaches approximately the same end concentration (approximately 10 9 Copy) will plateau.

[0118]

[0166] In FIG. 19C, equal competition between each NT and each IS. maintains the proportionality between the natural targets in the original sample, which allows for the measurement of the natural target abundance without signal compression. 6 10-fold coverage after multiplex competitive PCR library preparation 3 resulting in a 1,000-fold reduction in oversampling of high abundance targets.

[0119]

[0167] Mixing samples of the native target with the IS mixture in multiple ratios (Figure 18A) The result is a greater degree of uniformity in template concentration than can be obtained by spiking in a single internal standard (Figure 19A).

[0120]

[0168] STARSEQ reduces the required sequencing reads by up to 10,000-fold. R

[0169] FIG. 20A shows ERCC (n=104) and endogenous (n=400) cDNA Actual proportional sequencing data for targets are shown. The X-axis represents the lowest abundant target (10 0 The Y-axis represents the proportional abundance of each target in the library prep normalized to the target sequence (set to 1). The Y-axis is in units of the proportional sequencing reads (coverage) required to sequence the lowest abundant target at least once.

[0121]

[0170] FIG. 20B is a tabular summary of FIG. 20A, where the number of sequencing reads is The required number of traditional RNA sequencing reads is calculated based on an assumed 1:1 relationship between the copy number of targets present in the library and the required sequencing coverage. The fold reduction in required sequencing reads by STARSEQ is the quotient of traditional RNA sequencing and STARSEQ sequencing reads.

[0122]

[0171] However, the primers can be diluted and still successfully amplify the target of interest. There is a limit to the number of primers that can be bound to each other. This limit can be pushed down through several approaches: 1) increasing the melting temperature of the primers, and 2) increasing the time during which annealing occurs to allow for final primer binding. Both of these solutions can exacerbate off-target priming. Here we show that this apparent obstacle can be ameliorated by the use of a modified touchdown PCR protocol, in which a high annealing temperature is incorporated during the first cycle of PCR to increase the stringency of primer binding and reduce off-target priming. The annealing temperature is gradually lowered in subsequent cycles, resulting in increased yields once sufficient specific products have been formed during the earlier high stringency cycles. Using this framework, the following protocol was developed: each multiplex competitive reaction mixture was cycled in an air thermocycler (RapidCycler (Idaho Technology, Inc., Idaho Falls, ID)) under modified touchdown PCR conditions with low primer concentrations: 95°C / 3 min (Taq activation); 5 cycles of 94°C / 30 s (denaturation), 72°C / 4 min (annealing), and 72°C / 15 s (extension); 5 cycles of 1°C decrease to 71°C; 5 cycles of 1°C decrease until the annealing temperature was 64°C (45 cycles in total).

[0123]

[0172] In certain embodiments, only primer dimer products would otherwise be observed. Hot-start Taq polymerase is used because off-target priming and enzymatic activity are sufficiently high during the reaction preparation that will be used.

[0124]

[0173] Performance of STARSEQ with ERCC reference materials

[0174] FIG. 21A shows the abundance of ERCC targets in samples A, B, C, and D. The signals obtained by sequencing are shown. The dots represent the median ERCC measurements from those library preps with at least 15 sequencing reads for both NT and IS. The units on the x-axis are derived from Ambion product literature for known concentrations of ERCC spike-in controls, the SEQC project's material preparation protocol, and an assumed 100% reverse transcription yield for each target.

[0125]

[0175] FIG. 21B shows the data in FIG. 21A sorted numerically by ERCC ID. 21A and 21B show plots of the difference between the ERCC target and the ERCC-170 target. Each ERCC target shown was measured at least once in all four samples, A through D. For clarity, ERCC-170 is highlighted in orange in FIG. 21A and FIG. 21B.

[0126]

[0176] In FIG. 21C, samples C and D are the total RNAs from samples A and B. These ratios correspond to 3:1 and 1:3 mixtures, respectively. Using these ratios, the expected measurements for samples C and D from the measurements for A and B were calculated (x-axis) and plotted against the actual measurements for samples C and D (y-axis) (n=52).

[0127]

[0177] In FIG. 21D, the points are at least 15 alignments for both NT and IS. Figure 1 depicts the standard deviation in measurements of ERCC targets in SEQC A, B, C, and D for those assays with at least two IS dilution points with sequence-defined readings. The red line indicates the expected standard deviation based on a Poisson sampling distribution + baseline 0.08 technical replicate standard deviation.

[0128]

[0178] FIG. 21E shows the corresponding area under the curve (AUC) with 95% confidence intervals. ROC curves for detecting fold changes are shown. The ROC curves are derived from comparison of differential ratio subpools of ERCC targets in samples: A vs. B, A vs. C, A vs. D, B vs. C, B vs. D, and C vs. D. Results for 1.1 fold changes represent the following ranges of differential ratio subpools: [1.05-1.174] (control n=100, study n=96); 1.25 [1.175-1.374] (control n=163, study n=163); 1.5 [1.375-1.74] (control n=229, study n=227); 2.0 [1.75-2.49] (control n=229, study n=223); ≧4.0 [2.5-10.0] (control n=286, study n=290).

[0129]

[0179] Performance of STARSEQ with endogenous cDNA targets.

[0180] The absolute number of cDNA targets in sample A in units of copy number per library preparation Pairwise signal abundances were measured on separate days, at different sites (OU = Ohio University; UTMC = University of Toledo Medical Center), and between different reverse transcription preparations (RT1 and RT2). Points represent the median ERCC measurements from those library preparations with at least 15 sequencing reads for both NT and IS. Figure 22A shows the effect between days (n = 88). Figure 22B shows the effect between days and between sites (n = 81). Figure 22C shows the effect between days and between libraries (n = 92). Figure 22D shows the effect between days, between sites and between libraries (n = 80). Figures 22E-22F show that samples C and D represent a 3:1 and 1:3 mixture of total RNA from samples A and B, respectively. These ratios were used to calculate the expected measurements for samples C and D from the measurements for A and B (x-axis) and plotted against the actual measurements (y-axis) for samples C (n = 86) and D (n = 90).

[0130]

[0181] STARSEQ TaqMan qPCR and Illumina RNA Cross-platform comparison with sequencing.

[0182] STARSEQ and TaqMan qPCR (Figure 24 shows TaqMan and The average difference for the measurements of samples A and B between TaqMan qPCR or Illumina RNA sequencing (Figure 25 shows a plot of the difference between Illumina RNA sequencing and STARSEQ measurements) was determined for each endogenous target. This difference was subtracted from the measurements for samples C and D from TaqMan qPCR or Illumina RNA sequencing and plotted (x-axis) against the STARSEQ measurements of C and D (y-axis).

[0131]

[0183] STARSEQ measurements have at least 15 distributions for both NT and IS. Figures 26A and 26B show median values ​​of measurements from library preparations with sequence-determined reads. Figure 26A shows a comparison of TaqMan qPCR with STARSEQ (n=292). Figure 26B shows a comparison of Illumina RNA sequencing with STARSEQ (n=340).

[0132]

[0184] Assay performance

[0185] As shown in FIG. 26, the assay performance was compared between ERCC and endogenous The efficacy of the endogenous cDNA targets was evaluated in SEQC samples A, B, C, and D. The endogenous target was also evaluated against a gDNA control (see FIG. 18B).

[0133]

[0186] True negative results were sufficient across all spike-in concentrations of the internal standard. This occurs when a number of competing internal standards have been sequenced (at least 15 times) but insufficient natural template is observed. The upper limit of expression for these assays can be calculated using the lowest IS concentration still present as [1 / (IS sequencing counts)] × the concentration of IS loaded during the library preparation. These measurements represent true negative measurements, and an accurate lower limit of quantification can be determined from these data.

[0134]

[0187] The failed assays were due to “low sequencing depth” for both NT and IS. These represent true assay failures (neither the native nor the internal standard were sequenced at least 15 times). In this way, the competitive IS mixture can be controlled for false negative reports.

[0135]

[0188] Addition of barcode and sequencing adapters

[0189] A set of fusion primers are added at their 3' ends during multiplex competitive PCR. The fusion primers were designed to be complementary to the universal APEX-2 sequence tails that were designed to be sequenced using the fusion primers described above. These fusion primers have a 4 nucleotide index / barcode sequence and a tail of either a forward or reverse ion torrent amplicon sequencing adapter 5' to it (Figure 26). Both the forward and reverse sequencing primers are purposely barcoded to double index each sample and reduce the chance of misindexing the sequence reads; both barcodes must match. For each barcode addition reaction, a 10 μL reaction volume was prepared containing the following: 1 μL of multiplex competitive PCR product, 1 μL of 1 μM forward and reverse barcode addition primers, 1 μL of 2 mM dNTPs, 1 μL of 10× Idaho Technology reaction buffer (with 30 mM MgCl2), 0.1 μL of Promega GoTaq Hot Start Taq Polymerase (5u / μL) and 4.9 μL of RNAse-free water. Each barcoding reaction was cycled in an air thermocycler (RapidCycler (Idaho Technology, Inc., Idaho Falls, ID)) under the following conditions: 95°C / 3 min (Taq activation); 15 cycles of 94°C / 5 s (denaturation), 58°C / 10 s (annealing), and 72°C / 15 s (extension). The reaction vessel was immediately removed and kept at 4°C during all subsequent steps. The goal during this step is to prevent heterodimerization of the barcoded products. Depending on the type of heterodimerization, post-sequencing alignment errors can result from false sequencing base calls, resulting in reduced precision and accuracy of the measurement. The newly barcoded multiplex competitive PCR sequencing libraries are then quantified on an Agilent 2100 Bioanalyzer using a DNA chip with the reagents of the DNA 1000 kit according to the manufacturer's protocol (Agilent Technologies Deutschland GmbH, Waldbronn, Germany).The uniquely barcoded sequencing libraries are then mixed in known stoichiometric ratios to optimize the percentage of sequencing reads that each library will ultimately receive; in most cases 1:1 is used.

[0136]

[0190] STARSEQ "True Negatives" versus Taqman and RNA Sequencing

[0191] 26 STARSEQ results were less than measurable. The authors had enough data to report back a positive result (ND) for the 26 measurements. Of the 26 measurements, TaqMan reported not detected (ND) for 14 and RNA sequencing reported ND for 1 (see Figure 27). These represent false negative detections for TaqMan and RNA detection because STARSEQ was able to detect the IS but was unable to accurately quantify the NT present. The negative results were calculated as [1 / (IS sequencing counts)] x the concentration of IS loaded during the library preparation.

[0137]

[0192] Standard deviation of ERCC measurements.

[0193] FIG. 28 shows the SD of the differences calculated from the data presented in FIG. The within-assay intra-sample SD is calculated from the median of the within-assay SD within each sample A-D. The within-assay inter-sample SD is calculated from the median of the within-assay SD across samples A-D. The inter-assay inter-sample SD is calculated from the average of the residuals across samples A-D. The SD is calculated from the median of the interassay SD. 10 Since values ​​are reported, it is roughly equivalent to reporting the coefficient of variation (CV).

[0138]

[0194] Product purification and sequencing

[0195] In certain embodiments, purification of a barcoded sequencing library is It is necessary that the system does not use strong denaturing agents or chaotropic salts, such as guanidine hydrochloride or guanidine thiocyanate, during sequencing. These agents result in downstream template heterodimerization, false sequencing base calls, and post-sequencing alignment errors. For this reason, each barcoded sequencing library mixture was purified using a Life Technologies E-Gel SizeSelect 2% agarose gel, which does not report the use of denaturing agents or chaotropic salts and can be run in a cold room to prevent heat denaturation during electrophoretic separation. The purified sequencing libraries were then quantified using the KAPA Library Quantification Kit for Ion Torrent Sequencing Platforms (Kapa Biosystems). Based on this quantification, the libraries were appropriately diluted and prepared for Ion Torrent PGM sequencing services at the University of Toledo Medical Center (UTMC), Toledo, OH, and Ohio University (OU), Athens, OH, following the manufacturer's recommendations.

[0139]

[0196] FASTQ file processing

[0197] Raw sequencing data from NGS services is provided in FASTQ format. Extract the sequencing reads and parse each sequencing read into three separate FASTQ files: 1) forward (query-barcode.fastq) and 2) the reverse barcode (query-revbarcode.fastq) region, and 3) the central portion of the amplicon corresponding to the region internal to the target-specific priming site (query-subject.fastq). , where a 6 nucleotide substitution should exist between the NT and the corresponding competing IS.

[0140]

[0198] BFAST of sequences against target database

[0199] Each of the three FASTQ files was analyzed using a known reference F that corresponds to either the barcode (barcode.fa) or the amplicon region (subject.fa). Alignment to the ASTA database was performed using a BLAT-like fast and accurate search tool (BFAST, version 0.7.0a) and output files in sequence alignment / map (SAM) format. BFAST matches against the index database and output of SAM files were performed on trimmed FASTQ files containing 1) forward barcodes, 2) reverse barcodes, and 3) captured amplicon target sequences.

[0141]

[0200] Binning of array coefficients

[0201] The ID of the sequence read is then used as a key for matching: 1) forward and Each of the three SAM files from the forward, reverse, and amplicon regions were combined into a Practical Extraction and Reporting Language (PERL) hash table (http: / / www.perl.org / ). Based on the barcode and amplicon alignment, each sequencing read was binned into an array corresponding to the IS input concentration for a given sample preparation and whether it was determined to be NT or IS by the BFAST alignment. If the forward and reverse barcode alignment calls did not match, the sequence read was not binned. The resulting hash table of binned sequencing reads was output in a comma-separated format and processed as outlined in the Statistical Methods section.

[0142]

[0202] Relative abundance measurements

[0203] At least 14 sequencing reads are required for each of the NT and IS. The exact dilution factor was determined based on the change in the NT:IS ratio across multiple assay targets and across multiple spike-ins of serially diluted internal standards. The dilution of the internal standard was then multiplied by the NT:IS ratio. Each assay had multiple measurements per assay due to the multiple dilutions of the internal standard. If the STDEV of these measurements was less than 10-fold in variance, the median of these measurements was accepted. The exact measurement was based on the predetermined assay systematic bias of the internal standard concentration. The population of these measurements was normalized to the population median.

[0143]

[0204] Inclusion / Exclusion Criteria for STARSEQ Measurements

[0205] Each native target (gDNA or cDNA) was titrated using ISM crossover The ratios were measured relative to their respective internal standards within the concentrations set forth above (FIG. 18). An experimental threshold of at least 15 sequencing reads for each native target (NT) and each competing internal standard (IS) was the optimal inclusion / exclusion criterion for considering an NT:IS ratio valid (power >80%; type I error rate <0.05; detecting a 2-fold change in the NT:IS ratio) (FIG. 18). For those assays with more than one measurement that met the above criteria, a coefficient of variation (CV) between measurements greater than 1000% triggered exclusion for that assay measurement in that particular sample.

[0144]

[0206] Statistical methods: Estimation of natural target concentrations

[0207] The inputs of each gene target and each IS mixture are indicated by the subscript i. Estimate the concentration of the native target (NC) for technical replicates with the force concentration i ) to the natural target (NT i ) and internal standard (IS i ) as well as the known starting concentration (in units of template copy number per library preparation) of the internal standard (SC i ) was calculated based on

[0145] [ka]

[0146]

[0208] To estimate summarization quantities The optimal method and QC parameters determined by the experiments are: 1) the NC i Median of technical replicates scale (NC 中央値 ), 2) NT i and IS i and 3) a sequencing count of less than 1.00 on a base 10 logarithmic scale. i The coefficient of variation (CV) over the range was chosen to have sufficient sampling of a given target to allow detection of a two-fold change in abundance between targets with a type I error rate of less than 0.05 and a type II error rate of less than 0.20.

[0147]

[0209] Example 4

[0210] Non-limiting examples of application

[0211] In some embodiments, a method for obtaining a numerical index indicative of a biological state comprises: The method includes providing two samples corresponding to a first biological state and a second biological state, respectively; measuring and / or counting the amount of each of the two nucleic acids in each of the two samples; providing the amounts as a numerical value that can be directly compared between several samples; mathematically calculating the numerical value corresponding to each of the first and second biological states; and determining a mathematical calculation that distinguishes the two biological states. As used herein, a first and a second biological state correspond to two biological states to be compared, e.g., two phenotypic states to be distinguished. Non-limiting examples include, for example, non-diseased (normal) tissue versus diseased tissue; cultures that show a therapeutic drug response versus a culture that shows less than a therapeutic drug response. These include cultures that exhibit a therapeutic drug response; subjects that exhibit an adverse drug response versus subjects that exhibit a less adverse response; groups of treated subjects versus groups of untreated subjects; and the like.

[0148]

[0212] "Biological condition" as used herein refers to, for example, a clinically relevant It may refer to a phenotype or a phenotypic state related to the metabolic state of another subject. Biological states may include, for example, disease phenotypes, predisposition to disease or non-disease states; therapeutic drug response or predisposition to such response, adverse drug response (e.g., drug toxicity) or predisposition to such response, resistance to a drug or predisposition to show such resistance, and the like. In preferred embodiments, the obtained numerical index can serve as a biomarker, for example by correlating with the subject's phenotype. In some embodiments, the drug can be an anti-tumor drug. In certain embodiments, the use of the methods described herein can provide personalized medicine.

[0149]

[0213] In certain embodiments, the biological state corresponds to the normal expression level of the gene. If the biological condition does not correspond to normal levels, e.g., is outside a desired range, then an abnormal, e.g., disease state, may be indicated.

[0150]

[0214] A numerical index that identifies a particular biological state, e.g., a disease or metabolic state, is It can be used as a biomarker for a given condition(s) associated with it.For example, in some embodiments, the biological condition indicated can be at least one of the following conditions: angiogenesis-related condition, antioxidant-related condition, apoptosis-related condition, cardiovascular-related condition, cell cycle-related condition, cell structure-related condition, cytokine-related condition, defense response-related condition, development-related condition, diabetes-related condition, differentiation-related condition, DNA replication and / or repair-related condition, endothelial cell-related condition, hormone receptor-related condition, folate receptor-related condition, inflammation-related condition, intermediary metabolism-related condition, membrane transport-related condition, neurotransmission-related condition, cancer-related condition, oxidative metabolism-related condition, protein maturation-related condition, signal transduction-related condition, stress response-related condition, tissue structure-related condition, transcription factor-related condition, transport-related condition, and xenobiotic metabolism-related condition. In other specific embodiments, gene expression patterns related to antioxidant and xenobiotic metabolism enzyme genes; microvascular endothelial cell gene expression; membrane transporter gene expression; immune resistance; transcriptional regulation of hormone receptor expression; and drug resistance in carcinomas and tumors can be assessed in human cells.

[0151]

[0215] In some embodiments, one or more of the nucleic acids to be measured are One is associated to a greater degree than the other(s). For example, in some embodiments, one or more of the nucleic acids to be evaluated is associated with a first biological state and not with a second biological state.

[0152]

[0216] Nucleic acids are expressed as a function of their biological state, whether positively or negatively. A nucleic acid can be said to be "associated with" a particular biological state if it is present in a higher amount in the first biological state compared to a second biological state. For example, a nucleic acid can be said to be "positively associated" with a first biological state if the nucleic acid is present in a higher amount in the first biological state compared to a second biological state. By way of illustration, a gene that is highly expressed in cancer cells compared to non-cancerous cells can be said to be positively associated with cancer. On the other hand, a nucleic acid that is present in a lower amount in the first biological state compared to the second biological state can be said to be negatively associated with the first biological state.

[0153]

[0217] The nucleic acids to be measured and / or counted are those that are related to a particular phenotype. The nucleic acid sequence can correspond to the transcribed region of the gene, the expressed region, The gene may correspond to a region that regulates the transcription of a gene, and / or a regulatory region (e.g., a regulatory region of a transcription factor, e.g., a transcription factor for co-regulation).

[0218] In some embodiments, the expression levels of more than two genes are measured, In some embodiments, the expression pattern of a large number of genes can be used to characterize a given phenotypic state, such as a clinically relevant phenotype. In some embodiments, at least about 5 genes, at least about 10 genes, at least about 20 genes, at least about 50 genes, or at least about 70 genes can be measured to provide a numerical index that indicates a biological state. In some embodiments of the present invention, less than about 90 genes, less than about 100 genes, less than about 120 genes, less than about 150 genes, or less than about 200 genes can be measured to provide a numerical index that indicates a biological state.

[0154]

[0219] What mathematical calculations are used to provide a numerical index of a biological state? Determining can be accomplished by any method known in the art, for example, in the art of mathematics, statistics, and / or computation. In some embodiments, determining the mathematical calculation includes the use of software. For example, in some embodiments, machine learning software can be used.

[0155]

[0220] Calculating numbers mathematically is the process of creating any number of equations for interacting with each other. It can refer to using expressions, operations, formulas and / or formulas, such as sums, differences, products, quotients, logarithms, powers and / or other mathematical calculations. In some embodiments, the numerical index is calculated by dividing the numerator by the denominator, where the numerator corresponds to the amount of one nucleic acid and the denominator corresponds to the amount of another nucleic acid. In certain embodiments, the numerator corresponds to the genes positively associated with a given biological state and the denominator corresponds to the genes negatively associated with the biological state. In some embodiments, more than one gene positively associated with the biological state being evaluated and more than one gene negatively associated with the biological state being evaluated can be used. For example, in some embodiments, a numerical index can be obtained that includes a value for positively associated genes in the numerator and an equal number of negatively associated genes in the denominator. In such a balanced numerical index, the values ​​of the reference nucleic acids cancel out. In some embodiments, the balanced values ​​can neutralize the effect of variations in the expression of the gene(s) that provide the reference nucleic acid(s). In some embodiments, the numerical index is calculated by a series of one or more mathematical functions.

[0156]

[0221] In some embodiments, more than two biological states can be compared, e.g., differentiated. For example, in some embodiments, samples can be provided from a range of biological states, such as biological states corresponding to different stages of disease progression, such as different stages of cancer. Cells at different stages of cancer include, for example, non-cancerous cells versus non-metastatic cancerous cells versus metastatic cells from a given patient at various times throughout the disease process. Cancer cells from various types of cancer can be used, including, for example, bladder cancer, bone cancer, brain cancer, breast cancer, colon cancer, endocrine system cancer, gastrointestinal cancer, gynecological cancer, head and neck cancer, leukemia, lung cancer, lymphoma, metastasis, myeloma, neoplastic tissue, childhood cancer, penile cancer, prostate cancer, sarcoma, skin cancer, testicular cancer, thyroid cancer, and ureter cancer. In a preferred embodiment, biomarkers can be developed to predict which chemotherapeutic agent will work best for a given type of cancer, for example in a particular patient.

[0157]

[0222] Non-cancerous cells include those found in hematomas and / or scar tissue, as well as in non-cancer patients, e.g. Morphologically normal parenchymal cells may be included from non-cancer patients, related or unrelated to the cancer patient. Non-cancerous cells may be from a cancer patient, e.g., from a site close to the site of the cancer in the same tissue and / or organ; from a site further away from the site of the cancer, e.g., in a different tissue and / or organ in the same organ system; or even further away, e.g., from a different tissue and / or organ in the same organ system. Morphologically normal parenchyma from sites in the organ and / or different organ systems may also be included.

[0158]

[0223] The numerical index obtained can be provided as a database. The objective index and / or database may find use in diagnostics, for example in the development and application of clinical tests.

[0159]

[0224] Diagnostic Applications

[0225] In some embodiments, a method for identifying a biological state is provided. In one embodiment, the method includes the steps of: measuring and / or counting the amount of each of two types of nucleic acids in a sample and providing the amounts as a numerical value; and using the numerical value to provide a numerical index, whereby the numerical index is indicative of the biological state.

[0160]

[0226] The numerical index indicative of the biological state may be calculated as described above according to various embodiments. The amount of the nucleic acid(s) of the subject in the sample can then be determined. The sample can be obtained from a specimen, for example, a specimen taken from the subject to be treated. The subject can be in a clinical setting, including a hospital, a health care provider's office, a clinic, and / or other health care and / or research facility. The amount of the nucleic acid(s) of the subject in the sample can then be measured and / or counted.

[0161]

[0227] In certain embodiments where a given number of genes are to be evaluated, the given number of genes Expression data for a particular gene can be obtained simultaneously. By comparing the expression pattern of a particular gene with those in the database, it is possible to determine the chemotherapeutic agent to which a tumor having that gene expression pattern is most likely to respond.

[0162]

[0228] In some embodiments, the method is used to detect a mutated endogenous gene in the presence of The exogenous normal gene can be quantified. Using primers spanning the deleted region, expression from the transfected normal gene and / or the constitutive abnormal gene can be selectively amplified and quantified.

[0163]

[0229] In some embodiments, normal expression levels are determined using the methods described herein. For example, a numerical value corresponding to a normal gene transcript expression level can be provided. Such an embodiment can be used to indicate a normal biological state with respect to at least the expression of the assessed gene.

[0164]

[0230] Normal expression levels usually decline with disease, trauma, and / or other cellular insults. It can refer to the expression level of a transcript under unrelated conditions. In some embodiments, normal expression level can be provided as a number, or preferably as a range of values ​​that corresponds to the normal expression range of a particular gene, for example within a percentage range of + / - experimental error. Comparison of the obtained value for a given nucleic acid in a sample, for example the nucleic acid corresponding to a particular gene, can be compared to an established normal value, for example by comparison to data in a database provided herein. Since the value can indicate the number of molecules of that nucleic acid in the sample, this comparison can indicate whether the gene is expressed within a normal level range.

[0165]

[0231] In some embodiments, the method is used to identify a biological state. can be performed, which includes assessing the amount of nucleic acid in a first sample and providing said amount as a numerical value, where said numerical value is directly comparable between several other samples. In some embodiments, the numerical value may be directly comparable to an unlimited number of other samples. Samples can be assessed at different times, e.g., on different days; at the same or different times in the same laboratory; and / or in different experiments in different laboratories.

[0166]

[0232] Therapy

[0233] Some embodiments provide methods for improving drug development. For example, the detection of internal standards Standardized mixtures, databases of values, and / or databases of numerical indices can be used to improve drug development.

[0167]

[0234] In some embodiments, the regulation of gene expression is at one or more of these stages. The effect of the candidate drug is determined by measuring and / or counting. For example, the candidate drug (e.g., identified at a given stage) can be administered to a biological entity. The biological entity can be any entity that can have nucleic acid as described above, and can be appropriately selected based on the stage of drug development. For example, in the lead identification stage, the biological entity can be an in vitro culture. In the clinical trial stage, the biological entity can be a human patient.

[0168]

[0235] The effect of the candidate drug on gene expression can then be assessed, for example, using various aspects of the invention. For example, a nucleic acid sample can be taken from the biological entity and the amount of the nucleic acid of interest can be measured and / or counted. For example, the amount can be provided as a number and / or a numerical index. An amount can then be compared to other amounts of the nucleic acid at different stages of drug development; and / or to numbers and / or indices in a database. This comparison can provide information to alter the drug development process in one or more ways.

[0169]

[0236] Altering the drug development process preferably reduces the time and It can refer to making one or more changes in the process of developing a drug to reduce the number of patients and / or costs. For example, the changes can include stratifying clinical trials. Stratifying clinical trials can refer to, for example, dividing patient populations within a clinical trial and / or determining whether a particular individual can enroll in the clinical trial and / or continue to a subsequent phase of the clinical trial. For example, patients can be divided based on one or more features of their genetic makeup determined using various aspects of the present invention. For example, in the preclinical phase, consider a value obtained from an in vitro culture that is known to correspond to, for example, a lack of response to a candidate drug. In the clinical trial phase, subjects that show the same or similar value can be excused from participating in the trial. Thus, the drug development process has been changed, saving time and money.

[0170]

[0237] kit

[0238] The internal amplification control (IAC) / competitive internal standard (IS) described herein is The IAC may be combined and provided in the form of a kit. In some embodiments, the kit provides the IAC and the reagents required to perform PCR, including multiplex PCR and next-generation sequencing (NGS). The IAC may be provided in a single concentrated form with a known concentration, or may be serially diluted in solution to at least one of several known working concentrations.

[0171]

[0239] The kit contains 150 identified endogenous The IS may include an IS for a sexual target, or an IS for 28 ERCC targets as described herein, or both. The IS may be provided in a solution that allows the IS to remain stable for up to several years.

[0172]

[0240] This kit contains IS for 150 endogenous targets and ERC for 28 targets. The kit can also be provided with IS for the C targets and primers specifically designed to amplify their corresponding native targets. One or more containers filled with one or more necessary PCR reagents, including, but not limited to, IAC, enzyme, and RNase-free water, may also be provided. Optionally associated with such container(s) is a notice in a form prescribed by a governmental agency regulating the manufacture, use, or sale of the IAC and associated reagents, which notice reflects approval by the agency of the manufacture, use, or sale for research use.

[0173]

[0241] The kit includes multiplex PCR and NPCR using the IS included in the kit. Suitable instructions for preparing, performing, and analyzing PCR involving a GS may also be included, and may be in any suitable form, including, but not limited to, printed matter, videotape, computer readable disk, or optical disc.

[0174]

[0242] All publications, including patent and non-patent literature, referenced in this specification The materials are expressly incorporated herein by reference. Citation of any of the documents recited herein is not intended as an admission that any of the foregoing is pertinent prior art. All statements as to the date or representation as to the contents of these documents are based on the information available to the applicants and do not constitute any admission as to the correctness of the dates or contents of these documents.

[0175]

[0243] While the present invention has been described with reference to various preferred embodiments, it will be apparent to those skilled in the art that the present invention may be modified in various ways without departing from the spirit and scope of the present invention. It should be understood that various changes may be made and equivalents may be substituted for the elements without departing from the essential scope thereof. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof.

[0176]

[0244] It is intended, therefore, that the invention not be limited to the particular embodiments disclosed herein contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. In one aspect, the present invention may be as follows. [Aspect 1] The steps of: Including an internal amplification control (IAC) that shares an identical priming site with the native nucleic acid target template of interest in the NGS library preparation; mimicking the kinetics of a natural nucleic acid target in an amplification reaction; and Control for sample-, platform-, experimental-, operator- and / or target-specific variations in amplification efficiency; 1. A method for controlling for non-systematic errors in amplification-based next-generation sequencing (NGS) library preparation, comprising: [Aspect 2] The method of aspect 1, wherein the amplification-based NGS comprises PCR-based next-generation sequencing (NGS). [Aspect 3] The steps of: including one or more internal amplification controls (IACs) that share the same priming site as one or more natural nucleic acid templates of interest; causing convergence of abundance among the target analytes while preserving the original ratio of each native nucleic acid target template to its respective IAC; and retains quantitative information regarding the original abundance representation of each target analyte, allowing quantification with low numbers of sequencing reads; 4. A method for controlling the abundance of a target analyte while retaining quantitative information about the original abundance of the target analyte, comprising: [Aspect 4] The steps of: i) preparing a mixture containing a known number of internal amplification control (IAC) nucleic acid molecules corresponding to each native nucleic acid target; and ii) mixing the IAC mixture of step i) with a sample containing natural nucleic acid targets prior to preparation of a library for sequencing, or prior to sequencing if library preparation is not required; wherein each natural nucleic acid target is similar to its respective IAC except for one or more changes to the nucleic acid sequence that are identifiable by sequencing, and wherein such changes may include one or more of deletions, additions, or substitutions to the order or composition of nucleotides used; iii) assessing the ratio of sequencing events between the native nucleic acid target and its respective IAC with the known number of IAC nucleic acid molecules input into the sample prior to library preparation; and iv) quantifiably determining the original amount of each native nucleic acid target in the original sample prior to library preparation and sequencing; A method for reducing stochastic sampling errors associated with oversampling and deep sequencing of over-represented natural nucleic acid targets, comprising: [Embodiment 5] The method of embodiment 4, wherein the known number in step i) comprises one or more of abundance, concentration, and amount. [Embodiment 6] The method of embodiment 4, wherein the sequencing events in step iii) include one or more of observing, counting, and reading. [Aspect 7] The steps of: Introducing an internal amplification control (IAC) into the sample of nucleic acid targets prior to library preparation, so that non-systematic errors and / or sequencer instrument-specific biases are similarly experienced by both the native nucleic acid target and the internal amplification control (IAC); 1. A method for quantitatively determining the natural nucleic acid target copy number of a natural nucleic acid target in a sample, comprising: [Embodiment 8] The method of embodiment 7, wherein at the end of sequencing library preparation, the relative ratio between each unique natural target will converge towards equimolar abundance in the library. [Aspect 9] The method of aspect 8, wherein equimolar abundance comprises uniform abundance in the library. [Aspect 10] The method of any of the preceding aspects, wherein 10-fold fewer sequencing reads are required for every 10-fold reduction in abundance range among natural nucleic acid targets. [Aspect 11] Further steps of: Conducting interlaboratory comparisons of clinical molecular diagnostic results generated by nucleic acid quantification using sequencing with internal amplification controls (IACs);

[0026] The method of any of the preceding aspects, comprising: [Aspect 12] The steps of: i) assessing the ratio of sequencing events between at least one native nucleic acid target and its respective internal amplification control (IAC) standard; ii) assessing the original number of IAC molecules input into the sample prior to library preparation; and iii) determining the original number of molecules for each native nucleic acid target in the sample prior to library preparation and sequencing by multiplying the original IAC input number by the ratio times the native nucleic acid target / IAC; The method includes: [Embodiment 13] The method of embodiment 12, wherein the method includes using the same mixture of competing IAC molecules in multiple different tests. [Aspect 14] further comprising the steps of: Conducting highly multiplexed analysis of multiple natural nucleic acid targets across multiple samples, experiments and / or platforms;

[0026] The method of any of the preceding aspects, comprising: [Embodiment 15] The method of any of the preceding embodiments, wherein the abundance between the natural nucleic acid target templates differs by more than 1 million fold. [Embodiment 16] The method of any of the preceding embodiments, wherein the abundance between the natural nucleic acid target templates differs by more than 100,000 fold. [Embodiment 17] The method of any of the preceding embodiments, wherein the abundance between the natural nucleic acid target templates differs by more than 10,000 fold. [Embodiment 18] The method of any of the preceding embodiments, wherein the abundance between the native nucleic acid target templates differs by more than 1000-fold. [Embodiment 19] The method further comprises determining the amount of the first naturally occurring nucleic acid target in the first sample, the method comprising the steps of: i) providing a standardized mixture comprising a competitor template for said first nucleic acid and a competitor template for a second nucleic acid in said first sample, wherein said competitor templates are at known concentrations relative to each other; ii) combining said first sample with said standardized mixture; iii) co-amplifying said first nucleic acid and said competitor template for said first nucleic acid to generate a first amplification product thereof; iv) diluting the first amplification product; v) further co-amplifying the diluted first amplification products of the first nucleic acid and of the competitor template for the first nucleic acid to generate second amplification products thereof; and vi) co-amplifying said second nucleic acid and said competitor template for said second nucleic acid to generate a first amplification product thereof;

[0026] The method of any of the preceding aspects, comprising: [Aspect 20] A method for determining the amount of a first naturally occurring nucleic acid target, comprising the steps of: i) providing a series of serially diluted standardized mixtures comprising a competitive internal amplification control (IAC) for said first native nucleic acid template and a competitive IAC for a second native nucleic acid target present in a number of samples comprising said first native nucleic acid target, said competing IACs being at known concentrations relative to each other; ii) combining one of said samples containing said first native nucleic acid target with a first of said serially diluted standardized mixtures; iii) co-amplifying said first naturally occurring nucleic acid target and said competing IAC for said first naturally occurring nucleic acid target to generate an amplification product thereof; iv) obtaining a first relationship, said first relationship comparing said amplification product of said first native nucleic acid target to said amplification product of said competing IAC for said first native nucleic acid target; v) determining whether said first relationship is within the range of about 1:10 to about 10:1; if not, repeating said combining, co-amplifying, obtaining and determining steps with a second of said serially diluted standardized mixtures; vi) co-amplifying the second naturally occurring nucleic acid target and the competing IAC for the second naturally occurring nucleic acid target to generate an amplification product thereof; vii) obtaining a second relationship, said second relationship comparing said amplification product of said second naturally occurring nucleic acid target to said amplification product of said competing IAC for said second naturally occurring nucleic acid target; and viii) comparing said first and said second relationships; The method comprising: [Aspect 21] In comparing the amplification product of the first native nucleic acid target to the amplification product of the competitive IAC for the first native nucleic acid sequence, determining whether the first relationship is within the range of about 1:100 to about 100:1, or about 1:1000 to about 1000:1, or about 1:10,000 to about 10,000:1; If not, repeating said combining, co-amplifying, obtaining and determining steps with a second of said serially diluted standardized mixtures; coamplifying said second nucleic acid and said competitor template for said second nucleic acid to generate an amplification product thereof; obtaining a second relationship, said second relationship comparing said amplification product of said second nucleic acid to said amplification product of said competitor template for said second nucleic acid; and 22. The method of embodiment 21, wherein said first and said second relationships are compared. [Embodiment 22] The method of embodiment 21, wherein the products from the coamplification reaction are combined and amplified in a second round of amplification using primer pairs that recognize the respective natural nucleic acid target and competing IAC products from the first round of amplification and also have a gene-specific barcode primer and a universal primer at the 5' end to facilitate sequencing. [Embodiment 23] The method of any of the preceding embodiments, further comprising diluting the amplification products of the first native nucleic acid target and the competitive IAC for the first native nucleic acid target; and further co-amplifying the diluted amplification products to generate further amplification products thereof. [Embodiment 24] The method of any of the preceding embodiments, further comprising diluting the amplification products of the second naturally occurring nucleic acid target and the competitive IAC for the second naturally occurring nucleic acid target; and further co-amplifying the diluted amplification products to generate further amplification products thereof. [Embodiment 25] The method of any of the preceding embodiments, further comprising performing a series of serial dilutions of the mixture comprising the second natural nucleic acid target. [Embodiment 26] The method of any of the preceding embodiments, wherein one of the serial dilutions is selected to provide the second natural nucleic acid target that is approximately calibrated to the competitive IAC for the second natural nucleic acid target in the first of the serially diluted standardized mixtures. [Aspect 27] The method of any of the preceding aspects, wherein the second natural nucleic acid target serves as a first reference nucleic acid. [Embodiment 28] The method of any of the preceding embodiments, wherein the first reference nucleic acid is a control for loading. [Aspect 29] The method of any of the preceding aspects, wherein the first reference nucleic acid corresponds to at least one gene selected from GADP, ACTB, and β-actin. [Embodiment 30] The method of any of the preceding embodiments, wherein the first reference nucleic acid is present at two different concentrations in two of the serially diluted standardized mixtures. [Aspect 31] The series of serially diluted standardized mixtures further comprises at least 10 6 Pieces or about 10 6

[0023] The method of any of the preceding embodiments, comprising a sufficient amount of said competitive IAC to evaluate said first natural nucleic acid target in a sample. [Embodiment 32] The method of any of the preceding embodiments, wherein the series of serially diluted standardized mixtures further comprise several other competing IAC molecules for other naturally occurring nucleic acid target(s), wherein the competing IAC molecules are at known concentrations relative to each other, thereby enabling evaluation of the other naturally occurring nucleic acid target(s). [Aspect 33] The method of any of the preceding aspects, wherein at least one of the other naturally occurring nucleic acid targets serves as a second reference nucleic acid. [Embodiment 34] The method of any of the preceding embodiments, wherein the second reference nucleic acid corresponds to at least one gene selected from CADP, ACTB, and β-actin.

[35] The evaluation further comprises the steps of: i) co-amplifying said second reference nucleic acid and said competing IAC for said second reference nucleic acid to generate an amplification product thereof; ii) obtaining a third relationship, said third relationship comparing said amplification product of said second reference nucleic acid to said amplification product of said competitor template for said second reference nucleic acid; and iii) comparing said first and said third relationships;

[0026] The method of any of the preceding aspects, comprising:

[36] The series of serially diluted standardized mixtures is further 6 More than or about 10 6 The method of any of the preceding embodiments, comprising a sufficient amount of said number of other competing IAC(s) to evaluate said other naturally occurring nucleic acid target(s) in the sample. [Embodiment 37] The method of any of the preceding embodiments, wherein the amounts of the first naturally occurring nucleic acid target and the other naturally occurring nucleic acid target(s) differ by more than, or over a range of about, two orders of magnitude. [Embodiment 38] The method of any of the preceding embodiments, wherein the competing IAC for the first naturally occurring nucleic acid target is present at a range of concentrations compared to the competing IAC for the second naturally occurring nucleic acid target. [Embodiment 39] The method of any of the preceding embodiments, wherein obtaining the first or second relationship comprises use of a microfluidic device, capillary electrophoresis, an oligonucleotide array, mass spectrometry, or chromatography. [Embodiment 40] The method of any of the preceding embodiments, wherein the standardized mixture is controlled for at least two sources of variation selected from cDNA loading, intranucleic acid amplification efficiency, internucleic acid amplification efficiency, interspecimen amplification efficiency, intersample amplification efficiency, and intrasample amplification efficiency. [Embodiment 41] The method of any of the preceding embodiments, wherein at least one of the standardized mixtures of the series is capable of counting less than or about 1,000 molecules of the first native nucleic acid target in one of the samples. [Embodiment 42] The method of any of the preceding embodiments, wherein the method is computer-implemented. [Embodiment 43] The method of any of the preceding embodiments, wherein said computerized implementation includes instructing a robotic handler to select said first of said serially diluted standardized mixtures for combination. [Aspect 44] The method of any of the preceding aspects, wherein said computer implementation includes obtaining said first relationship. [Embodiment 45] The method of any of the preceding embodiments, wherein obtaining the first relationship comprises determining an area under the curve. [Embodiment 46] The method of any of the preceding embodiments, wherein the computer implementation includes instructing the robotic handler to select the second of the serially diluted standardized mixtures based on the first relationship. [Aspect 47] The method of any of the preceding aspects, wherein the first nucleic acid comprises an RNA molecule. [Aspect 48] The method of any of the preceding aspects, wherein the first natural nucleic acid target comprises a DNA molecule.

Claims

1. The following steps: Including an internal amplification control (IAC) that shares the same priming site as the natural nucleic acid target template of interest in the NGS library preparation; mimicking the kinetics of a natural nucleic acid target in an amplification reaction; and Control for sample-, platform-, experimental-, operator- and / or target-specific variations in amplification efficiency; 1. A method for controlling for non-systematic errors in amplification-based next generation sequencing (NGS) library preparation, comprising:

2. 2. The method of claim 1, wherein the amplification-based NGS comprises PCR-based next-generation sequencing (NGS).

3. The following steps: including one or more internal amplification controls (IACs) that share the same priming site as one or more natural nucleic acid templates of interest; causing abundance convergence among the target analytes while preserving the original ratio of each native nucleic acid target template to its respective IAC; and retains quantitative information regarding the original abundance representation of each target analyte, allowing quantification with low numbers of sequencing reads; 4. A method for controlling the abundance of a target analyte while retaining quantitative information about the original abundance of the target analyte, comprising:

4. The following steps: i) preparing a mixture containing a known number of internal amplification control (IAC) nucleic acid molecules corresponding to each naturally occurring nucleic acid target; and ii) mixing the IAC mixture of step i) with a sample containing natural nucleic acid targets prior to preparation of a library for sequencing, or prior to sequencing if library preparation is not required; wherein each natural nucleic acid target is similar to its respective IAC except for one or more changes to the nucleic acid sequence that are identifiable by sequencing, and wherein such changes may include one or more of deletions, additions, or substitutions to the order or composition of nucleotides used; iii) assessing the ratio of sequencing events between the native nucleic acid target and its respective IAC with the known number of IAC nucleic acid molecules input into the sample prior to library preparation; and iv) quantifiably determining the original amount of each native nucleic acid target in the original sample prior to library preparation and sequencing; A method for reducing stochastic sampling errors associated with oversampling and deep sequencing of over-represented natural nucleic acid targets, comprising:

5. 5. The method of claim 4, wherein the known number in step i) comprises one or more of abundance, concentration and amount.

6. The method of claim 4, wherein the sequencing events in step iii) include one or more of observing, counting and reading.

7. The following steps: The nucleic acid targets are sequenced prior to library preparation so that non-systematic errors and / or sequencer instrument-specific biases are similarly experienced by both the natural nucleic acid targets and the internal amplification controls (IACs). introducing an internal amplification control (IAC) into the sample; 1. A method for quantitatively determining the natural nucleic acid target copy number of a natural nucleic acid target in a sample, comprising:

8. 8. The method of claim 7, wherein at the end of sequencing library preparation, the relative ratio between each unique natural target will converge towards equimolar abundance in the library.

9. The method of claim 8 , wherein equimolar abundance comprises uniform abundance in the library.

10. 10. The method of any one of the preceding claims, wherein 10-fold fewer sequencing reads are required for every 10-fold reduction in abundance range among natural nucleic acid targets.

11. Further steps: Performing inter-laboratory comparisons of clinical molecular diagnostic results generated by nucleic acid quantification using sequencing with internal amplification controls (IAC); 2. The method of any one of the preceding claims, comprising:

12. The following steps: i) assessing the ratio of sequencing events between at least one native nucleic acid target and its respective internal amplification control (IAC) standard; ii) assessing the original number of IAC molecules input into the sample prior to library preparation; and iii) determining the original number of molecules for each native nucleic acid target in the sample prior to library preparation and sequencing by multiplying the original IAC input number by the ratio times the native nucleic acid target / IAC; The method includes:

13. The method of claim 12, wherein the method comprises using the same mixture of competing IAC molecules in multiple different tests.

14. Further steps: performing highly multiplexed analysis of multiple natural nucleic acid targets across multiple samples, experiments and / or platforms; 2. The method of any one of the preceding claims, comprising:

15. 2. The method of any one of the preceding claims, wherein the abundance between the natural nucleic acid target templates differs by more than 1 million fold.

16. 2. The method of any one of the preceding claims, wherein the abundance between the natural nucleic acid target templates differs by more than 100,000 fold.

17. 2. The method of any one of the preceding claims, wherein the abundance between the natural nucleic acid target templates differs by more than 10,000 fold.

18. 2. The method of any one of the preceding claims, wherein the abundance between the natural nucleic acid target templates differs by more than 1000-fold.

19. Further comprising determining the amount of the first naturally occurring nucleic acid target in the first sample, comprising the steps of: i) providing a standardized mixture comprising a competitor template for said first nucleic acid and a competitor template for a second nucleic acid in said first sample, wherein said competitor templates are compared to each other in known concentration; ii) combining said first sample with said standardized mixture; iii) co-amplifying said first nucleic acid and said competitor template for said first nucleic acid to generate a first amplification product thereof; iv) diluting the first amplification product; v) further co-amplifying the diluted first amplification products of the first nucleic acid and of the competitor template for the first nucleic acid to generate second amplification products thereof; and vi) co-amplifying said second nucleic acid and said competitor template for said second nucleic acid to generate a first amplification product thereof; 2. The method of any one of the preceding claims, comprising:

20. 1. A method for determining the amount of a first naturally occurring nucleic acid target, comprising the steps of: i) providing a series of serially diluted standardized mixtures comprising a competitive internal amplification control (IAC) for said first native nucleic acid template and a competitive IAC for a second native nucleic acid target present in a number of samples comprising said first native nucleic acid target, said competitive IACs being at known concentrations relative to each other; ii) combining one of said samples containing said first native nucleic acid target with a first of said serially diluted standardized mixtures; iii) co-amplifying said first native nucleic acid target and said competing IAC for said first native nucleic acid target to generate an amplification product thereof; iv) obtaining a first relationship, said first relationship comparing said amplification product of said first native nucleic acid target to said amplification product of said competing IAC for said first native nucleic acid target; v) determining whether said first relationship is within the range of about 1:10 to about 10:1; if not, repeating said combining, co-amplifying, obtaining and determining steps with a second of said serially diluted standardized mixtures; vi) co-amplifying said second naturally occurring nucleic acid target and said competing IAC for said second naturally occurring nucleic acid target to generate an amplification product thereof; vii) obtaining a second relationship, said second relationship comparing said amplification product of said second native nucleic acid target to said amplification product of said competing IAC for said second native nucleic acid target; and viii) comparing said first and said second relationships; The method comprising:

21. in comparing said amplification product of said first naturally occurring nucleic acid target to said amplification product of said competitive IAC for said first naturally occurring nucleic acid sequence, determining whether said first relationship is within the range of about 1:100 to about 100:1, or about 1:1000 to about 1000:1, or about 1:10,000 to about 10,000:1; If not, repeating said combining, co-amplifying, obtaining and determining steps with a second of said serially diluted standardized mixtures; coamplifying said second nucleic acid and said competitor template for said second nucleic acid to generate an amplification product thereof; obtaining a second relationship, said second relationship comparing said amplification product of said second nucleic acid to said amplification product of said competitor template for said second nucleic acid; and 22. The method of claim 21 further comprising comparing said first and said second relationships.

22. 22. The method of claim 21, wherein the products from the co-amplification reaction are combined and amplified in a second round of amplification using primer pairs that recognize each natural nucleic acid target and competing IAC product from the first round of amplification and also have a gene-specific barcode primer and a universal primer at their 5' ends to facilitate sequencing.

23. 2. The method of any one of the preceding claims, further comprising diluting the amplification products of the first native nucleic acid target and the competitive IAC for the first native nucleic acid target; and further co-amplifying the diluted amplification products to generate further amplification products thereof.

24. 2. The method of any one of the preceding claims, further comprising diluting the amplification products of the second native nucleic acid target and the competitive IAC for the second native nucleic acid target; and further co-amplifying the diluted amplification products to generate further amplification products thereof.

25. 2. The method of any one of the preceding claims, further comprising performing a series of serial dilutions of the mixture comprising said second natural nucleic acid target.

26. 2. The method of any one of the preceding claims, wherein one of the serial dilutions is selected to provide the second natural nucleic acid target approximately calibrated to the competitive IAC for the second natural nucleic acid target in the first of the serially diluted standardized mixtures.

27. 2. The method of any one of the preceding claims, wherein said second natural nucleic acid target serves as a first reference nucleic acid.

28. 2. The method of any one of the preceding claims, wherein said first reference nucleic acid is a control for loading.

29. 10. The method of any one of the preceding claims, wherein said first reference nucleic acid corresponds to at least one gene selected from GADP, ACTB and β-actin.

30. 2. The method of any one of the preceding claims, wherein said first reference nucleic acid is present in two of said serially diluted standardized mixtures at two different concentrations.

31. The series of serially diluted standardized mixtures are further diluted with at least 10 ６ Pieces or about 10 ６ 2. The method of any one of the preceding claims, comprising a sufficient amount of said competitive IAC to evaluate said first natural nucleic acid target in said sample.

32. The method of any one of the preceding claims, wherein the series of serially diluted standardized mixtures further contain several other competitive IAC molecules for other natural nucleic acid target(s), the competitive IAC molecules being at known concentrations relative to each other, thereby enabling evaluation of the other natural nucleic acid target(s).

33. 2. The method of any one of the preceding claims, wherein at least one of said other naturally occurring nucleic acid targets serves as a second reference nucleic acid.

34. 10. The method of any one of the preceding claims, wherein said second reference nucleic acid corresponds to at least one gene selected from CADP, ACTB and β-actin.

35. The evaluation further comprises the steps of: i) co-amplifying said second reference nucleic acid and said competitive IAC for said second reference nucleic acid to generate an amplification product thereof; ii) obtaining a third relationship, said third relationship being a function of said amplification product of said second reference nucleic acid; comparing to said amplification product of said competitor template for said second reference nucleic acid; and iii) comparing said first and said third relationships; 2. The method of any one of the preceding claims, comprising:

36. The series of serially diluted standardized mixtures were then diluted for a further 10 ６ More than or about 10 ６ 2. The method of any one of the preceding claims, comprising a sufficient amount of said number of other competing IAC(s) to evaluate said other naturally occurring nucleic acid target(s) in the sample.

37. 2. The method of any one of the preceding claims, wherein the amounts of said first naturally occurring nucleic acid target and said other naturally occurring nucleic acid target(s) differ by more than or over a range of about two orders of magnitude.

38. 2. The method of any one of the preceding claims, wherein said competing IAC for said first naturally occurring nucleic acid target is present at a range of concentrations compared to said competing IAC for said second naturally occurring nucleic acid target.

39. 2. The method of any one of the preceding claims, wherein obtaining said first or said second relationship comprises the use of a microfluidic device, capillary electrophoresis, an oligonucleotide array, mass spectrometry, or chromatography.

40. 10. The method of any one of the preceding claims, wherein the standardized mixture is controlled for at least two sources of variation selected from cDNA loading, intranucleic acid amplification efficiency, internucleic acid amplification efficiency, inter-specimen amplification efficiency, inter-sample amplification efficiency, and intra-sample amplification efficiency.

41. 2. The method of any one of the preceding claims, wherein at least one of said standardized mixtures of said series is capable of counting less than or about 1,000 molecules of said first native nucleic acid target in one of said samples.

42. 13. The method of any one of the preceding claims, wherein said method is computer-implemented.

43. 10. The method of any one of the preceding claims, wherein said computer implementation comprises instructing a robotic handler to select said first of said serially diluted standardized mixtures for combination.

44. 2. The method of any one of the preceding claims, wherein said computer implementation comprises obtaining said first relationship.

45. 2. The method of claim 1, wherein said obtaining said first relationship comprises determining an area under the curve.

46. 10. The method of any one of the preceding claims, wherein said computer implementation comprises instructing said robotic handler to select said second of said serially diluted standardized mixtures based on said first relationship.

47. 2. The method of any one of the preceding claims, wherein the first nucleic acid comprises an RNA molecule.

48. 2. The method of any one of the preceding claims, wherein the first natural nucleic acid target comprises a DNA molecule.

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