Artificial small RNA blend for process control and normalization

Abstract

The present invention relates to compositions useful as admixture mixtures. Furthermore, the invention relates to a kit comprising said composition. Furthermore, the present invention relates to a method for detecting a sample, in which said composition is used.

Description

Technical Field

[0001] The present invention relates to a composition that can be used as a spike-in cocktail. Furthermore, the present invention relates to a kit comprising said composition. Furthermore, the present invention relates to a method for detecting a sample, wherein said composition is used. Background Art

[0002] RNA (e.g., small RNA) in biological samples (e.g., biofluids) plays an important role as biomarkers for the prognosis and diagnosis of many human disease states. However, accurate analysis of these RNAs in biological samples (e.g., biofluids) is crucial for the significance of these data in the field of biomarkers.

[0003] Analysis of RNA expression profiles involves several complex steps. (1) RNA, including small RNAs, must be extracted from a biological source (e.g., blood or saliva) without distorting the original relative abundance of the RNA (linearity must be maintained). (2) Small RNA abundance must be reliably quantified. Because both steps can inconsistently introduce bias (e.g., through the presence of cDNA synthesis inhibitors), it is critical to standardize and monitor the efficiency of both RNA extraction and detection. Meeting these challenges can be achieved in a variety of ways.

[0004] First, RNA integrity analysis of endogenous RNA can be used for quality control by comparing the RNA electropherogram to the expected profile based on previously analyzed samples (e.g., small RNA bioanalyzer traces). However, this global analysis only provides a bird's-eye view of sample integrity and lacks granularity.

[0005] Secondly, it is possible to define reference values ​​for endogenous small RNAs (e.g. housekeeping genes belonging to nuclear or spliced ​​RNAs) that can serve as a yardstick for comparison of newly measured samples. Thus, samples with housekeeping genes outside the expected range may be disqualified from analysis as outliers. However, using endogenous characteristics of samples for quality control makes it impossible to distinguish samples that have undergone technically problematic experimental treatments from samples that represent true biological outliers.

[0006] To overcome this problem, academic and commercial laboratories have begun to adopt exogenous spike sequences that can mimic endogenous target RNA populations and thus allow monitoring of experimental inefficiencies. The most common spike for small RNA analysis is cel-miR-39-3p from C.elegans worm. However, the use of a single spike (e.g., cel-miR-39-3p) itself has several disadvantages. First, a single spike does not represent the significant primary sequence heterogeneity of the overall small RNA complement well. Since primary sequence-specific effects (e.g., RNA secondary structure), free energy, and GC content can have an important impact on extraction and detection efficiency, a single type of spike is not enough to faithfully reflect this. In addition, a single spike cannot assess the true preservation of the relative abundance of different RNA levels. Linearity, as the gold standard for nucleic acid separation and detection, cannot be assessed from a single data point. Instead, in order to robustly assess the linearity of an experiment, at least three different spikes given at different concentrations are required to calculate the Pearson coefficient.

[0007] In order to overcome the above-mentioned limitations of using a single dopant (e.g., cel-miR-39-3p), the inventors designed and optimized a universal dopant system for artificial small RNA molecules that broadly reflects the behavior of endogenous miRNAs during RNA extraction and detection. Then, at the beginning of the process, this mixture of artificial small RNA molecules is added to a biological sample (e.g., a clinical sample) and acts as an end-to-end quality control measure: only those biological samples (e.g., clinical samples) whose analysis meets the standards are used for downstream analysis, and the analysis produces artificial small RNA molecules recovered at their expected levels, sequence, and linearity. In addition, the evaluation of the mixture of artificial small RNA molecules can be used as a bioinformatics normalization tool to remove batch effects from different experiments.

[0008] In addition, the inventors have found that artificial small RNA molecules can be used to identify unwanted attachments to target RNA molecules. They have found that excluding target RNA molecules with such attachments improves RNA data quality and thus improves target RNA analysis.

[0009] Artificial small RNA molecules are also referred to as inclusions. Summary of the invention

[0010] In a first aspect, the present invention relates to a composition comprising at least 3 RNA molecules, wherein the at least 3 RNA molecules are selected from the group consisting of RNA molecules having a nucleotide sequence according to SEQ ID NO: 1 to SEQ ID NO: 4, fragments thereof, and sequences having at least 80%, preferably at least 85%, more preferably at least 90% and even more preferably at least 95% (e.g. 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%) sequence identity thereto.

[0011] In a second aspect, the present invention relates to a kit comprising the composition of the first aspect.

[0012] In a third aspect, the present invention relates to the composition of the first aspect or the kit of the second aspect (as a standard) for the following uses: process control, sample detection, normalization and / or data processing control. Preferably, the data processing control comprises / is a raw data processing control.

[0013] In a fourth aspect, the present invention relates to a method for detecting a sample, the method comprising the following steps:

[0014] The sample is assessed relative to at least 3 RNA molecules comprised in / derived from the composition of the first aspect.

[0015] In a fifth aspect, the present invention relates to a method for optimized processing of a biological sample, the method comprising the following steps:

[0016] Implement the method of the fourth aspect.

[0017] In a sixth aspect, the present invention relates to a method for optimized RNA preparation from a biological sample or a method for RNA analysis of a biological sample, the method comprising the following steps:

[0018] Implement the method of the fourth aspect.

[0019] In a seventh aspect, the invention relates to a method for improving the quality of a (RNA) dataset, the method comprising the following steps:

[0020] Implement the method of the fourth aspect.

[0021] In an eighth aspect, the invention relates to a method for improving the quality of a (RNA) dataset, the method comprising the following steps:

[0022] (i) determining the sequence of RNA molecules in a sample,

[0023] (ii) identifying additions to the 5' end and / or the 3' end of the RNA molecule, said additions being not part of the RNA molecule in its naturally occurring form, and

[0024] (iii) Excluding RNA molecules with 5' and / or 3' end additions from the (RNA) dataset analysis / Removing RNA molecules with 5' and / or 3' end additions from the (RNA) dataset.

[0025] Preferably, the (RNA) data set analysis is a raw (RNA) data set analysis or the (RNA) data set is a raw (RNA) data set.

[0026] This summary of the invention does not necessarily describe all features of the invention. Other embodiments will become apparent from a review of the detailed description that follows. DETAILED DESCRIPTION

[0027] definition

[0028] Before describing the present invention in detail below, it will be understood that the present invention is not limited to the specific methodology, scheme and reagent described herein, as they may vary. It will also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not intended to limit the scope of the present invention, which will only be limited by the appended claims. Unless otherwise defined, all technical terms and scientific terms used herein have the same meaning as those generally understood by those of ordinary skill in the art.

[0029] Preferably, the terms used herein are as defined in "A multilingual glossary of biotechnological terms: (IUPAC Recommendations)", Leuenberger, HGW, Nagel, B. and H. ed. (1995), Helvetica Chimica Acta, CH-4010 Basel, Switzerland.

[0030] A plurality of files are quoted in the full text of this application document.Whether it is above or below, each file (comprising all patents, patent applications, scientific publications, manufacturer's specifications, instructions, GenBank accession number sequence submissions etc.) quoted herein is all incorporated by reference in its entirety at this.Any content herein should not be construed as admitting that the present invention does not have the right to advance this type of disclosure by means of previous invention.When there is conflict between the definition of this type of reference incorporated into or the definition or the instruction described in this application document, the text of this application document has priority.

[0031] The term "comprising" or variations such as "including" or "containing" according to the present invention is intended to include the stated integer or group of integers, but not to exclude any other integers or groups of integers. The term "consisting essentially of" according to the present invention is intended to include the stated integer or group of integers, while excluding other integers or modifications that would substantially affect or change the stated integer. The term "consisting of" or variations such as "consisting of" according to the present invention is intended to include the stated integer or group of integers, and to exclude any other integers or groups of integers.

[0032] The terms "a and an" and "the" and similar referents used in the context of describing the invention (especially in the context of the claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0033] As used herein, the term "about" indicates a certain variation from the quantitative value preceding it. In particular, unless otherwise indicated or inferred, the term "about" allows a variation of ±5% from the quantitative value preceding it. Unless otherwise expressly stated, the use of the term "about" also includes the specific quantitative value itself. For example, the expression "about 80°C" allows a variation of ±4°C, thus referring to a range from 76°C to 84°C.

[0034] The similarity of nucleotide and amino acid sequences, ie the percentage of sequence identity, can be determined by sequence alignment. Such an alignment can be performed using a variety of algorithms known in the art, preferably the mathematical algorithm of Karlin and Altschul (Karlin & Altschul (1993) Proc. Natl. Acad. Sci. USA 90: 5873-5877), hmmalign (HMMER package) or the CLUSTAL algorithm (Thompson, JD, Higgins, DG & Gibson, TJ (1994) Nucleic Acids Res. 22, 4673-80) or the CLUSTALW2 algorithm (Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG. (2007). Clustal W and Clustal X version2.0.Bioinformatics,23,2947-2948).

[0035] The rank of sequence identity (sequence match) can be calculated using, for example, BLAST, BLAT or BlastZ (or BlastX). Similar algorithms are incorporated into the BLASTN and BLASTP programs of Altschul et al., (1990) J. Mol. Biol. 215:403-410. BLAST protein searches are performed using the BLASTP program, which is available, for example, at: http: / / blast.ncbi.nlm.nih.gov / Blast.cgi?PROGRAM=blastp&BLAST_PROGRAMS=blastp&PAGE_TYPE=BlastSearch&SHOW_DEFAULTS=on&LINK_LOC=blasthome

[0036] The preferred algorithm parameters used are the default parameters as they are set on the indicated website:

[0037] Expectation threshold = 10, wordlength = 3, maximum match in query range = 0, matrix = BLOSUM62, gap penalty = existence: 11 extension: 1, composition adjustment = conditional composition scoring matrix adjustment with non-redundant protein sequence database (nr).

[0038] To obtain gapped alignments for comparison purposes, Gapped BLAST was used as described in Altschul et al., (1997) Nucleic Acids Res. 25:3389-3402. When using BLAST and Gapped BLAST programs, the default parameters of the corresponding programs were used. Sequence matching analysis can be supplemented by established homology mapping techniques such as Shuffle-LAGAN (Brudno M., Bioinformatics 2003b, 19 Suppl 1:154-162) or Markov random fields.

[0039] As used herein, the term "nucleotide" refers to an organic molecule consisting of a nucleoside and a phosphate. In particular, a nucleotide consists of three subunit molecules: a nucleobase, a pentose (ribose or deoxyribose) and a phosphate group consisting of 1 to 3 phosphate groups. The four nucleobases in DNA are guanine, adenine, cytosine and thymine; in RNA, uracil is used to replace thymine. Nucleotides serve as monomeric units of nucleic acid polymers such as deoxyribonucleotides (DNA) or ribonucleotides (RNA). Therefore, nucleotides are molecular building blocks of DNA and RNA.

[0040] The terms "nucleotide sequence" or "polynucleotide" are used interchangeably herein and refer to single-stranded and double-stranded polymers of nucleotide monomers, including but not limited to 2'-deoxyribonucleotides (DNA) and ribonucleotides (RNA), or nucleotide analogs linked by internucleotide phosphodiester linkages, and associated counterions, such as H + NH4 + , trialkylammonium, Mg 2+ 、Na + Etc. A nucleotide sequence or polynucleotide may be composed entirely of deoxyribonucleotides, entirely of ribonucleotides, or of chimeric mixtures thereof, and may include nucleotide analogs. A nucleotide monomer unit may include any nucleotide described herein, including but not limited to nucleotides and / or nucleotide analogs.

[0041] As used herein, the term "nucleic acid molecule" refers to single-stranded and double-stranded polymers of nucleotide monomers, including but not limited to 2'-deoxyribonucleotides (DNA) and ribonucleotides (RNA), or nucleotide analogs linked by internucleotide phosphodiester bonds, and associated counterions, such as H + NH4 + , trialkylammonium, Mg2 + 、Na + etc. A nucleic acid molecule may consist entirely of deoxyribonucleotides, entirely of ribonucleotides, or of chimeric mixtures thereof, and may include nucleotide analogs.

[0042] As used herein, the term "RNA molecule" refers to a polymeric form of ribonucleotides of any length. Like DNA, RNA is assembled into chains of nucleotides, but unlike DNA, RNA is found in nature as a single strand folded onto itself, rather than a paired double strand.

[0043] Cellular organisms use messengers (mRNA) to transmit genetic information (using the nitrogenous bases guanine, uracil, adenine, and cytosine, represented by the letters G, U, A, and C) to direct the synthesis of specific proteins. Some RNA molecules play active roles within cells by catalyzing biological reactions, controlling gene expression, or sensing and communicating responses to cellular signals. One of these active processes is protein synthesis, a universal function in which RNA molecules direct the synthesis of proteins on ribosomes. This process uses transfer RNA (tRNA) molecules to deliver amino acids to the ribosome, where ribosomal RNA (rRNA) then links the amino acids together to form the encoded protein.

[0044] In the context of the present invention, an RNA molecule is an artificial RNA molecule. Therefore, it does not exist in nature. It is exogenous to the cell. It is artificially designed / produced. The RNA molecule is included in a composition that can be used as a universal admixture system or mixture.

[0045] The inventors designed and optimized RNA molecules that broadly reflect the behavior of endogenous miRNAs during RNA extraction and detection. Specifically, the inventors generated 20 random sequences of 21 nucleotides in length (reflecting the typical length of miRNAs). Next, they evaluated their molecular characteristics, such as melting temperature (Tm°C) and GC%. Subsequently, they selected a short list of 10 artificial sequences for wet lab validation based on several criteria. Specifically, they wanted to minimize primer dimers and select RNAs with relatively weak secondary structures. They selected RNA molecules as spikes, the GC content of which ranged from 38.1% to 61.9%, which covered the GC content of most endogenous miRNAs. Finally, the inventors selected four artificial RNA molecules with nucleotide sequences according to SEQ ID NO: 1 to SEQ ID NO: 4 as components of the composition, which can be used as a universal spike system or mixture. At least three of these four RNA molecules must be present in the composition in order to effectively monitor the inefficiency of the experiment. For example, to robustly assess the linearity of an experiment, at least three different RNA molecules (spikes) administered at different concentrations are required for statistical analysis (eg, for calculation of Pearson's coefficient).

[0046] The RNA molecule is characterized in that:

[0047] secondary structure that minimizes primer-dimer formation,

[0048] The G / C content is between 38.1% and 61.9%, and thus covers the G / C content of most endogenous small RNAs (e.g., miRNAs), and

[0049] The 5' phosphate group reflects endogenous mature small RNA (eg, miRNA).

[0050] As used herein, the term "small RNA molecule" refers to a polymeric RNA molecule of less than 200 ribonucleotides in length, preferably less than 50 ribonucleotides in length. Specifically, the length of a small RNA molecule is between 10 and <200 ribonucleotides. More specifically, the length of a small RNA molecule is between 10 and <50 ribonucleotides. Small RNA molecules are typically non-coding RNA molecules. RNA molecules having a nucleotide sequence according to SEQ ID NO: 1 to SEQ ID NO: 4 are small RNA molecules.

[0051] As used herein, the term "composition" refers to a composition comprising at least 3 RNA molecules, wherein the at least 3 RNA molecules are selected from the group consisting of RNA molecules having nucleotide sequences according to SEQ ID NO: 1 to SEQ ID NO: 4. The RNA molecules contained in the composition are artificial, exogenous small RNA molecules. They do not exist in nature. In addition, they widely reflect the behavior of endogenous miRNAs during RNA extraction and detection.

[0052] In one embodiment, the at least three RNA molecules contained in the composition have a characteristic distribution, in particular with respect to their amounts. In particular, the at least three RNA molecules are contained in the composition in different amounts. More particularly, the at least three RNA molecules are contained in the composition in a gradient of defined amounts. In particular, the at least three RNA molecules are titrated to a linear range of different amounts.

[0053] The composition can be used as a spike-in mixture or can be considered as a spike-in mixture. A spike-in mixture is a universal spike-in mixture that is independent of the downstream detection method.

[0054] As used herein, the term "inclusion" refers to the addition (inclusion) of a known amount / number of RNA molecules into a sample. The sample may comprise biological material. The sample may also be a processed sample based on / derived from biological material. The method of measuring the response (recovery) of the incorporated sample is then run. The method may be run at any processing step to determine the quantity or quality of sample processing and / or sample analysis. For example, the method may be run after biological material lysis, RNA extraction, RNA or DNA (DNA derived from RNA) amplification and / or DNA (DNA derived from RNA) sequencing.

[0055] As used herein, the term "inclusion mixture" refers to a composition comprising RNA molecules (also referred to as RNA inclusions) of known sequence and amount / quantity. The RNA molecules contained in the composition are artificial, exogenous small RNA molecules. They do not exist in nature. In addition, they widely reflect the behavior of endogenous miRNAs during RNA extraction and detection.

[0056] The spike-in mixture of the present invention is a composition comprising at least 3 RNA molecules, wherein the at least 3 RNA molecules are selected from the group consisting of RNA molecules having a nucleotide sequence according to SEQ ID NO: 1 to SEQ ID NO: 4.

[0057] The RNA molecules are added to a sample (eg, a biological sample or a processed biological sample) to obtain the performance of a molecular biology experiment such as a nucleic acid quantification experiment (eg, qPCR, next generation sequencing (NGS), and / or a microarray experiment).

[0058] As described above, at least three RNA molecules contained in the composition have a characteristic distribution, in particular with respect to their amounts. In particular, at least three RNA molecules are contained in the composition in different amounts. More particularly, at least three RNA molecules are contained in the composition in a gradient of defined amounts. Thus, any RNA molecule contained in the composition is present in a specific and known amount that is different from the amounts of other RNA molecules. Specifically, at least three RNA molecules are titrated to a linear range of different amounts.

[0059] In the method of detecting a sample, the sample is evaluated with respect to at least 3 RNA molecules contained in / from the composition described herein, wherein the at least 3 RNA molecules are selected from the group consisting of RNA molecules having a nucleotide sequence according to SEQ ID NO: 1 to SEQ ID NO: 4. The at least 3 RNA molecules are part of the sample. They have been added to the sample.

[0060] In this method, it is assessed whether at least three RNA molecules (during / after sample processing and analysis) still have this characteristic distribution. Specifically, it is assessed whether at least three RNA molecules are present at their expected levels, expected order and / or expected linearity. In this way, the quality or quantity of sample processing and / or sample analysis can be controlled.

[0061] In this regard, it will be understood that the term "RNA molecule" can refer to the RNA molecule itself as well as surrogates thereof, such as amplification products (eg, cDNA derived therefrom).

[0062] As used herein, the term "expected level" refers to the level of the amount of a specific RNA molecule that is expected to be added to a sample or included in a composition in subsequent sample processing and / or analysis. As described above, the RNA molecule is added to a sample or included in a composition in a specific amount. The amount of the RNA molecule can correspond / correlate with any expected level of the RNA molecule that can be measured during subsequent sample processing and / or analysis. Specifically, the amount of the RNA molecule can correspond / correlate with a specific read count (reads per million (RPM)) in the next generation sequencing assay, and / or the amount of the RNA molecule can correspond / correlate with a specific cycle threshold (Ct) in a real-time PCR experiment. In particular, the cycle threshold (Ct) is the number of cycles in real-time PCR required to exceed a previously defined threshold in a measurement signal (e.g., a fluorescent signal) of the amplified DNA. Before PCR, the more DNA (RNA) already present in the sample solution, the fewer amplification cycles required to reach the relevant threshold.

[0063] For example, an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1 added to a sample or included in a composition of the invention in an amount of about 3400 amol will produce a predicted Ct value of about 26 (with a measured Ct average of about 24.3); an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2 added to a sample or included in a composition of the invention in an amount of about 725 amol will produce a predicted Ct value of about 29.3 (with a measured Ct average of about 27.1); an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3 added to a sample or included in a composition of the invention in an amount of about 20 amol will produce a predicted Ct value of about 35.9 (with a measured Ct average of about 32); and / or an RNA molecule having a nucleotide sequence according to SEQ ID NO: 4 added to a sample or included in a composition of the invention in an amount of about 7 amol will produce a predicted Ct value of about 39.2 (with a measured Ct average of about 33.6).

[0064] For example, an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1 is added to a sample or included in a composition of the present invention in an amount of about 3400 amol, which will produce a log2 RPM of about 11.972 or a read count of about 30628; an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2 is added to a sample or included in a composition of the present invention in an amount of about 725 amol, which will produce a log2 RPM of about 10.926 or a read count of about 5554; an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3 is added to a sample or included in a composition of the present invention in an amount of about 20 amol, which will produce a log2 RPM of about 6.559 or a read count of about 491; and / or an RNA molecule having a nucleotide sequence according to SEQ ID NO: 4 is added to a sample or included in a composition of the present invention in an amount of about 7 amol, which will produce a log2 RPM of about 4.061 or a read count of about 94.

[0065] As used herein, the term "level" refers to the amount (e.g., measured in grams, moles, or ion counts) or concentration (e.g., absolute or relative concentration, e.g., reads per million (RPM) or NGS counts) of an RNA molecule contained in a composition or in a sample. As used herein, the term "level" also includes a converted, normalized, or converted and normalized amount or value (e.g., RPM). In particular, the level of an RNA molecule is determined by sequencing, preferably next generation sequencing (e.g., ABI SOLID, Illumina Genome Analyzer, Roche 454GS FL, BGISEQ), nucleic acid hybridization (e.g., microarray or beads), nucleic acid amplification (e.g., PCR, RT-PCR, qRT-PCR, or high throughput RT-PCR), polymerase extension, mass spectrometry, flow cytometry (e.g., LUMINEX), or any combination thereof. Specifically, the level of an RNA molecule is the expression level of the RNA molecule.

[0066] As used herein, the term "expected order" refers to the order / rank of specific RNA molecules that are expected to be added to a sample or included in a composition in subsequent sample processing and / or analysis. As described above, the RNA molecules added to a sample or included in a composition are each present in different amounts. Thus, where three RNA molecules are added to a sample or included in a composition, the first RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1) is present in the largest amount, the second RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2) is present in an amount less than the first RNA molecule, and the third RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3) is present in an amount less than the first and second RNA molecules. When four RNA molecules are added to a sample or included in a composition, the first RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1) is present in the largest amount, the second RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2) is present in an amount less than the first RNA molecule, and the third RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3) is present in an amount less than the first and second RNA molecules, and the fourth RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 4) is present in an amount less than the first, second, and third RNA molecules.

[0067] This expected order / rank representing the initial state / condition must be restored during sample processing and / or analysis. In this regard, it should be noted that the amount of RNA molecules contained in a sample or in a composition in a particular order / rank can correspond / correlate to any expected level of RNA molecules that can be measured during subsequent sample processing and / or analysis. For example, the amount of RNA molecules can correspond / correlate to a specific read count in a next generation sequencing assay, and / or the amount of RNA molecules can correspond / correlate to a specific cycle threshold (Ct) in a real-time PCR experiment.

[0068] As used herein, the term "expected linearity" refers to the linearity of a particular RNA molecule added to a sample or included in a composition that is expected to occur during subsequent sample processing and / or analysis.

[0069] This expected linearity, which represents the initial state / condition, must be restored during sample processing and / or analysis.

[0070] Only those samples whose analysis yielded RNA molecules or their surrogates recovered at their expected levels, sequence and linearity were used for downstream analysis and / or further processing.

[0071] In order to determine whether RNA molecules are present in their expected order and / or linearity, statistical analysis is performed using data associated with the RNA molecules (e.g., Ct values ​​or RPMs). The statistical analysis includes, but is not limited to, Spearman (rank) correlation analysis and / or Pearson correlation analysis.

[0072] For example, to determine whether an RNA molecule is present in its expected order, the Spearman's rank correlation coefficient (Spearman's p) is preferably determined.

[0073] Furthermore, in order to determine whether the RNA molecules are present with their expected linearity, the Pearson correlation coefficient (Pearson's r) is preferably determined.

[0074] As used herein, the term "Spearman's rank correlation coefficient or Spearman's p" is a nonparametric measure of rank correlation (statistical dependence between the ranks of two variables). It assesses the extent to which the relationship between two variables can be described using a monotonic function.

[0075] The Spearman correlation between two variables is equivalent to the Pearson correlation between the rank values ​​of the two variables; the Pearson correlation assesses a linear relationship, while the Spearman correlation assesses a monotonic relationship (either linear or not). If there are no repeated data values, a perfect Spearman correlation of +1 or -1 occurs when each variable is a perfect monotonic function of the other variable.

[0076] Intuitively, the Spearman correlation between two variables will be high when the observations have similar (or identical, correlation is 1) ranks (i.e., relative position labels of the observations within the variables: 1st, 2nd, 3rd, etc.), and will be low when the observations have different (or completely opposite, correlation is -1) ranks between the two variables.

[0077] In the context of the present invention, when the Spearman rank correlation coefficient (Spearman's p) is ≥ 0.95, the RNA molecules are present in their expected order. In this case, the sample is further processed / analyzed. Therefore, a Spearman rank correlation coefficient (Spearman's p) < 0.95 will result in the sample being discarded. In other words, such samples will not be further processed / analyzed.

[0078] As used herein, the term "Pearson correlation coefficient or Pearson's r" is a measure of the linear correlation between two sets of data. It is the ratio between the covariance of the two variables and the product of their standard deviations. Therefore, it is essentially a normalized measure of covariance so that the result is always between -1 and 1. Like the covariance itself, the measure can only reflect the linear correlation of the variables, ignoring many other types of relationships or correlations.

[0079] In the context of the present invention, when the Pearson correlation coefficient (Pearson's r) is ≥ 0.66, the RNA molecules are present with their expected linearity. In this case, the sample is further processed / analyzed. Therefore, a Pearson correlation coefficient (Pearson's r) < 0.66 will result in the sample being discarded. In other words, such samples will not be further processed / analyzed.

[0080] In addition, the evaluation of the mixture of artificial small RNA molecules can be used as a bioinformatics normalization tool to remove the batch effect of different experiments. As used herein, the term "normalization" refers to the technology required for comparing the RNA level between different samples. For example, it is necessary to normalize the high-throughput small RNA sequencing data to compare the small RNA levels in different samples. Due to the fluctuation of the small RNA population between tissues or body fluids, the commonly used relative normalization method may lead to erroneous conclusions. The inventors have developed a composition of RNA molecules (also referred to as RNA incorporation), which can make the small RNA data between independent experiments absolutely normalized. Usually the data from the small RNA sequencing experiment is normalized and reported in relative terms (e.g., the number of reads per million genome matching reads (RPMs)). If it is assumed that the small RNA subgroup has the same ratio between the different tissues or body fluid types introduced, the relative normalization effect is good. However, this assumption is usually invalid because the small RNA population is often dynamic in different tissues or body fluid types and in various mutation backgrounds. Therefore, the standard practice of comparing the relatively normalized small RNA sequencing values ​​may produce misleading results. In contrast, absolute normalization of small RNA sequencing data should enable accurate genome-wide comparison of small RNA levels in different cell types, mutant tissues or disease states. The inventors designed RNA molecules (also called RNA spike-ins) that can be used for robust absolute normalization (e.g., small RNA sequencing data between independent experiments).

[0081] The RNA molecule is characterized in that:

[0082] secondary structure that minimizes primer-dimer formation,

[0083] A G / C content between 38.1% and 61.9%, and thus covers the G / C content of most endogenous small RNAs (e.g., miRNAs), and

[0084] 5' phosphate group to mimic endogenous mature small RNA (eg, miRNA).

[0085] As used herein, the term "quality" with respect to a sample (comprising biological material) refers to the level of degradation of components in the sample relative to when the components are contained in a biological system (e.g., a cell). For example, assessing the quality of RNA and / or DNA can include assessing the level of partial degradation of RNA and / or DNA polymers. In some embodiments, assessing quality includes assessing partial degradation of RNA and / or DNA from an spike-in standard, for example, by assessing the presence of fragments (shorter molecules) of a spike-in standard relative to a full-length spike-in standard.

[0086] As used herein, the term "quantity" with respect to a sample (comprising biological material) refers to the level of a component present in the sample relative to the level of the component when contained in a biological system (e.g., a cell). In some embodiments, assessing the quantity of RNA and / or DNA may include assessing the level of degradation or loss of RNA and / or DNA polymers in the sample.

[0087] As used herein, the term "sample" refers to a mixture of a biological material and a composition of the present invention. Alternatively, a sample is based on / derived from a mixture of a biological material and a composition of the present invention. In this case, the sample is typically a processed sample. The processed sample is obtained by mixing the biological material with the composition of the present invention and further processing the resulting mixture. For example, the processed sample is a lysed sample, an extracted sample, an amplified sample, a sequenced sample, or a library prepared sample.

[0088] As used herein, the term "biological material" refers to any material having a biological origin. The biological material is preferably a tissue material or a body fluid material.

[0089] As used herein, the term "bodily fluid material" refers to any fluid material originating from the body of an individual.

[0090] The body fluid material can be urine, blood, sputum, breast milk, cerebrospinal fluid (CSF), cerumen (ear wax), gastric juice, mucus, lymph, endolymph, perilymph, peritoneal fluid, pleural fluid, saliva, sebum (skin oil), semen, sweat, tears, cheek swab, vaginal secretions, liquid biopsy or vomitus samples, including their components or fractions. The term "body fluid material" also covers body fluid components, for example, blood components, such as blood cells, serum or plasma, wherein blood cells represent the cellular components of blood, and serum and plasma represent the non-cellular components of blood.

[0091] As used herein, the term "blood material" encompasses whole blood or blood components. Preferably, the blood component is selected from the group consisting of a blood cell component, plasma and serum. For example, the blood cell component encompasses red blood cells, white blood cells and / or platelets. More preferably, the blood cell component is a white blood cell component or a mixture of red blood cells, white blood cells and platelets.

[0092] The blood material can be provided by taking blood from an individual, but can also be provided by using previously separated material.For example, blood material can be collected from an individual by conventional blood collection techniques.

[0093] Whole blood material can be collected in blood collection tubes, for example, in PAXgene Blood RNA tubes, TempusBlood RNA tubes, EDTA tubes, sodium citrate tubes, heparin tubes or ACD tubes (acid citrate dextrose).

[0094] As used herein, blood material (particularly whole blood material) can also be collected by blood spot technology, for example, using Mitra Microsampling Device. This technology requires a smaller sample volume, typically 45-60 μL, or less for humans. For example, whole blood can be extracted from an individual by pricking a finger with a needle or lancet. Therefore, the whole blood material can be in the form of a blood drop. The blood drop is then placed on an absorbent probe capable of absorbing whole blood, such as a hydrophilic polymer material, such as cellulose. Once sampling is complete, the blood spot is dried in the air and then transferred or mailed to a laboratory for processing. Because the blood is dry, it is not considered hazardous. Therefore, no special precautions are required during handling or transportation. Once the analysis site is reached, the desired components (e.g., RNAs) are extracted from the dried blood spot into the supernatant, which is then further analyzed. In this way, the level of RNA is determined.

[0095] As used herein, the term "tissue material" refers to any tissue material derived from the body of an individual. The material may be tumor / cancer tissue material or healthy tissue material from any organ of the individual. For example, the tissue material may be material from the lung, kidney, liver, brain, colon, breast, stomach, uterus, ovary, pancreas or prostate. It may be removed from the individual by conventional biopsy techniques.

[0096] As used herein, the term "target RNA" refers to an endogenous ribonucleotide sequence that is sought to be detected and contained in a biological material. The target RNA can be obtained from any source and can contain any number of different components. For example, the target RNA is isolated from an organism, a tissue, a cell or a body fluid (such as blood). Preferably, the target RNA encompasses non-coding RNA. In particular, the target RNA is a microRNA (miRNA) or an miRNA isomer (isomiR) and may include variants, analogs and mimics. In some cases, the target RNA is also referred to as one or more target RNA molecules.

[0097] In addition, it will be understood that the term "target RNA" can refer to the target molecule itself and its substitutes, such as amplification products (e.g., cDNA derived therefrom) and native sequences. In certain embodiments, the target RNA is a miRNA or miRNA isomer (isomiR) molecule. In certain embodiments, the target RNA is a mature small RNA molecule, particularly a non-coding small RNA molecule (i.e., length <200 ribonucleotides, such as a length between 10 and <200 ribonucleotides). The target RNA described herein can be derived from any number of sources, including but not limited to humans and animals. These sources may include but are not limited to whole blood, tissue biopsy, lymph, bone marrow, amniotic fluid, hair, skin, semen, biological warfare agents, anal secretions, vaginal secretions, sweat, saliva or oral swabs. However, various environmental samples (e.g., agriculture, water and soil), general research samples, general purified samples, cultured cells and lysed cells can also be used as samples. It will be understood that any of the various procedures known in the art can be used to separate target RNA from a sample, for example, Applied Biosystems ABI 6100 Nucleic Acid PrepStation (Life Technologies, Foster City, CA) and ABI 6700 automatic nucleic acid workstation (Life Technologies, Foster City, CA), mirVana TMRNA isolation kit (Life Technologies, Austin, TX), PAXgene Blood RNA Kit (Qiagen, Hilden, Germany), etc.

[0098] A target RNA molecule having an endogenous origin in a biological material is different from an exogenous artificial RNA molecule also described herein. When an exogenous artificial RNA molecule is added to a biological material as part of a composition (e.g., an admixture mixture), then the target RNA molecule is part of the biological material.

[0099] As used herein, the term "miRNA" (also referred to as "microRNA") refers to a single-stranded RNA molecule. The miRNA can be a molecule of 10 to 50 nucleotides in length, for example, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 or 50 nucleotides in length, excluding optional markers and / or extended sequences (e.g., biotin extensions).

[0100] miRNA regulates gene expression and is encoded by the gene that transcribes their DNA, but miRNA is not translated into protein (i.e., miRNA is a non-coding RNA). The gene encoding miRNA is longer than the processed mature miRNA molecule. miRNA is initially transcribed into a longer precursor molecule (>1000 nucleotides long) called a primary miRNA transcript (pri-miRNA). Pri-miRNA has a hairpin structure processed by the Drosha enzyme (as part of the microprocessor complex). After Drosha processing, pri-miRNA is only 60 to 100 nucleotides long and is called a precursor miRNA (pre-miRNA). At this point, pre-miRNA is exported to the cytoplasm, where it encounters the Dicer enzyme. Dicer cuts the miRNA into two copies, producing a duplex miRNA chain. Traditionally, only one of these miRNA arms is considered important in gene regulation: an arm destined to be loaded into the RNA-induced silencing complex (RISC) and appears in higher concentrations in cells. This is often referred to as the "guide" chain and is referred to as miR. The other arm is called the "minor miRNA" or "passenger miRNA" and is often referred to as miR *Passenger miRNAs were thought to be completely degraded, but deep sequencing studies have found that some minor miRNAs still exist and actually play functional roles in gene regulation. As a result of these developments, the naming convention has changed. The miR-5p / miR-3p nomenclature has been adopted to replace miR / miR * Nomenclature scheme. With the new system, the 5' arm of the miRNA is always referred to as miR-5p, and the 3' arm as miR-3p. The current nomenclature is as follows: the prefix "miR" is followed by a hyphen and a number, the latter of which generally indicates the order of nomenclature. For example, hsa-miR-16 was named, and it was probably discovered earlier than hsa-miR-342. Capitalized "miR-" refers to the mature form of the miRNA (e.g., hsa-miR-16-5p and hsa-miR-16-3p), while uncapitalized "mir-" refers to pre-miRNA and pri-miRNA (e.g., hsa-mir-16), and "MIR" refers to the genes encoding them. However, as this is a recent change, the literature will often refer to the original miR / miR* names. After processing, the duplex miRNA strands are loaded onto the Argonaute (AGO) protein to form the precursor to RISC. The complex causes the duplex to unwind, and the passenger RNA strand is discarded, leaving a mature RISC carrying a mature single-stranded miRNA. The miRNA is still part of the RISC because it silences the expression of its target gene. Although this is the classical pathway for miRNA biogenesis, many other pathways have been discovered. These pathways include Drosha-independent pathways (such as mirtron pathways, snoRNA-derived pathways, and shRNA-derived pathways) and Dicer-independent pathways (such as pathways that rely on AGO for cutting and another pathway that relies on tRNaseZ).

[0101] As used herein, the term "miRBase" refers to a well-established repository of validated miRNAs. miRBase (www.mirbase.org) is a searchable database of published miRNA sequences and annotations. Each entry in the miRBase sequence database represents a predicted hairpin portion of a miRNA transcript (referred to as mir in the database), with information about the position and sequence of a mature miRNA sequence (referred to as miR). Both hairpin sequences and mature sequences can be used for searching and browsing, and entries can also be grabbed by name, keyword, reference, and annotation. All sequence and annotation data are also available for download. In October 2018, miRbase version 22.1 was released. This is the current version [Please check].

[0102] The term "isomiR" (or "miRNA isomer") as used herein refers to a miRNA with slightly altered sequences, which is caused by changes in the cleavage site during miRNA biogenesis. In particular, imprecise cleavage by Drosha and Dicer or turnover of miRNAs can cause heterogeneity in length and / or sequence of miRNAs. IsomiRs (miRNA isomers) can be divided into three main categories: 3'isomiRs (one or more nucleotides are trimmed or added at the 3' position), 5'isomiRs (one or more nucleotides are trimmed or added at the 5' position), and polymorphic isomiRs (some nucleotides in the sequence are different from the wild-type mature miRNA sequence). It is foreseeable that increased expression of a single isomiR or miRNA variant causes loss or attenuation of the function of the corresponding wild-type mature miRNA, or causes regulation of different transcriptomes. Recent studies have shown that isomiRs may play an important role in a variety of cancers, tissues, and cell types. Therefore, the detection of miRNAs and isomiRs is absolutely necessary to accurately reflect the underlying biological conditions and make correct diagnostic and therapeutic decisions.

[0103] RNA can be isolated from a sample using any of a variety of procedures known in the art, for example, Applied Biosystems ABI 6100 Nucleic Acid PrepStation (Life Technologies, Foster City, CA) and ABI 6700 automatic nucleic acid workstation (Life Technologies, Foster City, CA), mirVana TM RNA isolation kit (Life Technologies, Austin, TX), PAXgene Blood RNA kit (Qiagen, Hilden, Germany), etc.

[0104] RNA molecule detection requires the presence of a higher amount of RNA molecules. Due to the reverse transcription process, cDNA molecules are generated from RNA molecules. In different embodiments, these cDNA molecules are amplified. RNA molecules can also be directly amplified. As used herein, the term "amplification" refers to any means of replicating at least a portion of a nucleic acid molecule described herein, usually in a template-dependent manner, including but not limited to a variety of techniques for amplifying nucleic acid sequences (linearly or exponentially). Any of several methods can be used to amplify nucleic acid molecules. Any in vitro means for multiplying copies of nucleic acid target sequences can be used. These means include linear, logarithmic or other amplification methods.

[0105] Examples of amplification techniques that can be used include, but are not limited to: PCR, quantitative PCR, quantitative fluorescence PCR (QF-PCR), multiplex fluorescence PCR (MF-PCR), real-time PCR (RT-PCR), single-cell PCR, restriction fragment length polymorphism PCR (PCR-RFLP), hot start PCR, nested PCR, in situ Polony PCR, in situ rolling circle amplification (RCA), bridge PCR, picotiter PCR and emulsion PCR. Other suitable amplification methods include ligase chain reaction (LCR), transcription amplification, self-sustained sequence amplification, selective amplification of target polynucleotide sequences, consensus sequence-primed polymerase chain reaction (CP-PCR), random primed polymerase chain reaction (AP-PCR), degenerate oligonucleotide-primed PCR (DOP-PCR) and nucleic acid-based sequence amplification (NABSA).

[0106] In different embodiments, the DNA molecule derived from the RNA molecule is sequenced. As used herein, the term "sequencing" includes any method for determining the sequence of a nucleic acid molecule. These methods include Maxam-Gilbert sequencing, chain termination method, shotgun method, PCR sequencing, bridge PCR, massively parallel signature sequencing (MPSS), Polony sequencing, pyrophosphate sequencing, Illumina (Solexa) sequencing, SOLiD sequencing, ion semiconductor sequencing, DNA nanoball sequencing, Heliscope single molecule sequencing, single molecule real-time (SMRT) sequencing, nanopore DNA sequencing, hybrid sequencing, mass spectrometry sequencing, microfluidics Sanger sequencing, microscope-based technology, RNAP sequencing, (in vitro virus) high throughput sequencing (HTS).

[0107] As used herein, the term "next generation sequencing (NGS)" refers to a new method for sequencing nucleotide sequences at high speed and low cost. Therefore, next generation sequencing (NGS) is a high-throughput method that enables rapid sequencing of base pairs in DNA or RNA samples. NGS supports a wide range of applications (including gene expression profiles, chromosome counting, detection and molecular analysis of epigenetic changes), and NGS is driving discovery and enabling the future of personalized medicine. NGS is also referred to as second-generation sequencing (SGS) or massively parallel sequencing (MPS).

[0108] In the context of the present invention, the term "kit of parts (in short: kit)" is understood to mean any combination of at least some of the components described herein, which are combined spatially coexistently into a functional unit and may contain further components.

[0109] As used herein, the term "data processing control" refers to an early step during data analysis where portions of the data are removed or downsized to improve aspects of subsequent analysis, such as improving data quality and improving the reliability of findings during analysis. Preferably, the data processing control is a raw data processing control.

[0110] As used herein, the term "raw data" refers to data collected during an experiment. These data have not been further processed. Raw data can be quantitative (digital) and / or qualitative (descriptive). The size (usually very large), file type and structure of raw data depend on the technology used to generate the data. Before obtaining the final analyzable data, the raw data needs to be processed using dedicated software, such as mapping to the measured biological condition / biology, or finding statistical differences between biological sample groups (e.g., patients with different medical conditions).

[0111] In the next generation sequencing process control, data quality control and preprocessing are very important for data analysis, because the raw data generated after sequencing must be processed so that the results will not have false positive and false negative results. Data preprocessing not only evaluates each analysis step, but also reduces the amount of low-quality sequence reads. Removing such low-quality reads reduces the time and cost of computational analysis, and also obtains reliable and high-quality results.

[0112] In any downstream analysis of NGS data, false positive and false negative results are generated due to the following reasons:

[0113] Experimental factors: such as sample contamination or PCR errors,

[0114] Sequencing factors: including sequencing quality and data contamination due to index hopping during data segmentation, and / or

[0115] Analysis software parameter factors: This includes the precise type of parameter adjustments made in the alignment software or in downstream personalized analyses.

[0116] The raw sequences generated after sequencing contain not only the sequence of interest (e.g., the sequence of the target RNA molecule), but they also have sequence biases (e.g., through systematic effects such as Poisson sampling) and complex artifacts generated due to sequencing and experimental steps. These sequence biases and artifacts affect and interfere with accurate read number alignment, which affects genotyping and variant calling. Therefore, in order to improve the reliability and quality of downstream analysis and reduce the amount of computing resources required, it is necessary to preprocess the raw sequence reads.

[0117] The inventors have found that the 5' and / or 3' end additions of the (target) RNA molecule (e.g., generated or occurred during a sequencing process, such as a next generation sequencing process) can mislead and falsify the original data results. Specifically, it is necessary to eliminate / eliminate the number of sequence reads contaminated by adapters. The inventors have found that by eliminating / eliminating the number of sequence reads contaminated by adapters, the quality of the original data can be improved.

[0118] As used herein, the term "5'-end additions (also referred to as prefix sequences)" refers to nucleotides attached to the 5' end of an RNA molecule or a molecule derived therefrom (e.g., a cDNA molecule). They are specially generated / are remnants of 5' sequencing adapters or other RNAs present in the same physical mixture, and they have been (completely or partially) fused during the technical process of sequencing. In the case of sequencing adapters, there may be no expected cleavage to remove the adapter after the expected fusion after sequencing is completed.

[0119] As used herein, the term "3'-end addition (also referred to as suffix sequence)" refers to nucleotides attached to the 3' end of an RNA molecule or a molecule derived therefrom (e.g., a cDNA molecule). They are specially produced / are remnants of other RNAs present in the same physical mixture, and they have been (completely or partially) fused during the technical process of sequencing. In this regard, it will be understood that the term "RNA molecule" can refer to the RNA molecule itself as well as its surrogates, such as amplification products (e.g., cDNAs derived therefrom).

[0120] Specifically, during next generation sequencing (NGS), parts of other RNA or adapter sequences are found to be attached to the (target) RNA molecule at one or both ends (5' and / or 3'). These parts are referred to herein as suffix sequences, which include both the prefix sequence (5') and the suffix sequence (3').

[0121] Preferably, the length of the 5' and / or 3' addition is between 5 and 30 nucleotides, more preferably 5 to 20 nucleotides, and even more preferably 7 to 15 nucleotides, such as 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 nucleotides.

[0122] Specifically, 5' and / or 3' additions occur during:

[0123] - ligating a 5' adaptor and / or a 3' adaptor to the (denatured) (target) RNA molecule, e.g. using a double-stranded RNA ligase (e.g. T4 RNA ligase 2 (Rnl2) or Kod1 ligase),

[0124] - reverse transcribing (the) (target) RNA molecule (to which a 5' adapter and / or a 3' adapter is attached, also referred to as a ligation product) into a cDNA molecule, for example, using a reverse transcriptase (RT, such as Maxima H-RT or Tth polymerase),

[0125] - amplification of the cDNA molecule, for example by polymerase chain reaction (PCR), and / or

[0126] - Sequencing of (the) cDNA molecules, such as next generation sequencing.

[0127] The PCR can be real-time PCR (quantitative PCR or qPCR, such as TaqMan qPCR), multiplex PCR, nested PCR, high-fidelity PCR, fast PCR, hot-start PCR, or high GC PCR.

[0128] More specifically, 5'-end and / or 3'-end additions occur / appear during the library preparation process for next generation sequencing or during the next generation sequencing process. Even more specifically,

[0129] The next generation sequencing process preferably covers:

[0130] - ligating a 5' adaptor and / or a 3' adaptor to the (denatured) (target) RNA molecule, e.g. using a double-stranded RNA ligase (e.g. T4 RNA ligase 2 (Rnl2) or Kod1 ligase),

[0131] - reverse transcribing (the) (target) RNA molecule (to which a 5' adapter and / or a 3' adapter is attached, also referred to as a ligation product) into a cDNA molecule, for example, using a reverse transcriptase (RT, such as Maxima H-RT or Tth polymerase),

[0132] - amplification of the cDNA molecule, for example by polymerase chain reaction (PCR), and / or

[0133] - Sequencing of (the) cDNA molecules.

[0134] The terms "5' and / or 3' additions" or "5' and / or 3' attachments" are used interchangeably herein.

[0135] The term "linker" as used herein refers to any non-biological RNA / DNA sequence that is intentionally added to the 5' or 3' end of a biological (target) RNA / cDNA molecule (derived from a sample to be sequenced) as part of the design of sequencing (e.g., an NGS method). This includes any free-floating linker molecules that may fuse with the biological RNA in an unexpected manner. The linker can also be a combination / fusion of multiple separate linkers and indexing RNAs. The indexing RNA is used as a UMI (unique molecular identifier) ​​to assign RNA molecules to the samples from which they originated during multiple sequencing (i.e., sequencing multiple samples simultaneously). The UMI sequence is a short non-specific (random) sequence of a predetermined length (e.g., 12 nucleotides), but the length can also vary and be incorporated between other linker sequences.

[0136] As used herein, the term "RNA molecules in naturally occurring form" refers to RNA molecules in a form in which they are present in nature or in a natural environment, e.g., in a body fluid (e.g., whole blood or tissue). However, these RNA molecules may be further processed (e.g., into cDNA molecules). In this case, they have a natural origin.

[0137] As used herein, the term "RNA molecule in a naturally occurring form" further refers to an RNA molecule that has an endogenous source in a biological material / sample. The RNA molecule is part of the biological material / sample and may be present in its natural functional form, or may be a fragment of a longer functional RNA sequence that has been degraded or processed as part of a specific biological process or over time by non-specific physical / chemical forces.

[0138] Embodiments of the present invention

[0139] The present invention will now be further described. In the following paragraphs, different aspects of the present invention are defined in more detail. Unless otherwise expressly indicated to the contrary, each aspect so defined may be combined with any other aspect. Specifically, unless otherwise expressly indicated to the contrary, any feature indicated as being preferred or advantageous may be combined with any other feature indicated as being preferred or advantageous.

[0140] Academic and commercial laboratories have begun to adopt exogenous spike-in sequences that mimic the endogenous target RNA population and thus allow monitoring of experimental inefficiencies. The most common spike-in for small RNA analysis is cel-miR-39-3p from Caenorhabditis elegans. However, the use of a single spike-in (e.g., cel-miR-39-3p) itself has several disadvantages. First, a single spike-in does not well represent the significant primary sequence heterogeneity of the entire small RNA complement. Since primary sequence-specific effects (e.g., RNA secondary structure), free energy, and GC content can have an important impact on extraction and detection efficiency, a single type of spike-in is not sufficient to faithfully reflect this. In addition, a single spike-in cannot assess the true preservation of the relative abundance of different RNA levels. Linearity, the gold standard for nucleic acid separation and detection, cannot be assessed from a single data point. Instead, in order to robustly assess the linearity of an experiment, at least three different spike-ins given at different concentrations need to be calculated to calculate the Pearson coefficient.

[0141] In order to overcome the above-mentioned limitations of using a single dopant (e.g., cel-miR-39-3p), the inventors designed and optimized a universal dopant system for artificial small RNA molecules that broadly reflects the behavior of endogenous miRNAs during RNA extraction and detection. Then, at the beginning of the process, this mixture of artificial small RNA molecules is added to a sample containing biological material (e.g., clinical material) and acts as an end-to-end quality control measure: only those biological samples whose analysis produces the following are used for downstream analysis, and the analysis allows the recovery of artificial small RNA molecules at their expected levels, sequence, and linearity. In addition, the evaluation of the mixture of artificial small RNA molecules can be used as a bioinformatics normalization tool to remove batch effects from different experiments.

[0142] In addition, the inventors have found that artificial small RNA molecules can be used to identify unwanted attachments to target RNA molecules. They have found that excluding target RNA molecules with such attachments improves RNA data quality and thus improves target RNA analysis.

[0143] Artificial small RNA molecules are also referred to as inclusions.

[0144] Thus, in a first aspect, the present invention relates to a composition comprising at least 3 RNA molecules (e.g. 3 or 4 RNA molecules), wherein the at least 3 RNA molecules (e.g. 3 or 4 RNA molecules) are selected from the group consisting of RNA molecules having a nucleotide sequence according to SEQ ID NO: 1 to SEQ ID NO: 4, fragments thereof, and sequences having at least 80%, preferably at least 85%, more preferably at least 90% and even more preferably at least 95% (e.g. 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%) sequence identity thereto.

[0145] Specifically, the RNA molecules contained in the composition

[0146] (i) having a nucleotide sequence according to SEQ ID NO: 1, 2, 3 or 4,

[0147] (ii) is a nucleotide sequence that is a fragment of the nucleotide sequence according to (i), preferably a nucleotide sequence that is 1 to 12, more preferably 1 to 8, and most preferably 1 to 5 or 1 to 3 (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12) nucleotides shorter than the nucleotide sequence according to (i), or

[0148] (iii) is a nucleotide sequence having at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99% (e.g., at least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%) sequence identity with the nucleotide sequence according to (i) or the nucleotide sequence fragment according to (ii).

[0149] The RNA molecule having SEQ ID NO: 1 has the following nucleotide sequence: GAUAGAUACGCCAGUACCGCC,

[0150] The RNA molecule having SEQ ID NO: 2 has the following nucleotide sequence: AACGAAGCUCCACGAUGUAGG,

[0151] The RNA molecule having SEQ ID NO: 3 has the following nucleotide sequence: UGUACGGAAAUAUUGGCUACC, and

[0152] The RNA molecule having SEQ ID NO: 4 has the following nucleotide sequence: UUCAUACGUUGCCCAAUCCAG.

[0153] The RNA molecules contained in the composition are artificial, exogenous small RNA molecules. They do not exist in nature. They are exogenous to the cell. In addition, they broadly reflect the behavior of endogenous miRNA during RNA extraction and detection. In addition, the RNA molecules contained in the composition have

[0154] secondary structure that minimizes primer-dimer formation,

[0155] A G / C content between 38.1% and 61.9%, and thus covers the G / C content of most endogenous small RNAs (e.g. miRNAs), and

[0156] 5' phosphate group to reflect endogenous mature small RNA (eg miRNA).

[0157] Preferably, the composition comprises 4 RNA molecules, wherein the 4 RNA molecules are selected from the group consisting of RNA molecules having nucleotide sequences according to SEQ ID NO: 1 to SEQ ID NO: 4, fragments thereof, and sequences having at least 80%, preferably at least 85%, more preferably at least 90% and even more preferably at least 95% (e.g. 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%) sequence identity thereto.

[0158] Thus, the composition may comprise an RNA molecule having a nucleotide sequence according to:

[0159] (i) SEQ ID NO: 1, SEQ ID NO: 2, and SEQ ID NO: 3,

[0160] (ii) SEQ ID NO: 2, SEQ ID NO: 3, and SEQ ID NO: 4,

[0161] (iii) SEQ ID NO: 1, SEQ ID NO: 3, and SEQ ID NO: 4,

[0162] (iv) SEQ ID NO: 1, SEQ ID NO: 2, and SEQ ID NO: 4, or

[0163] (v) SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, and SEQ ID NO: 4.

[0164] Fragments of the RNA molecules listed in (i) to (v), or sequences having at least 80%, preferably at least 85%, more preferably at least 90% and even more preferably at least 95% (e.g. 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%) sequence identity to the RNA molecules listed in (i) to (v) are also encompassed.

[0165] It should be noted that at least 3 RNA molecules (especially 4 RNA molecules) contained in the composition have a characteristic distribution. In particular, at least 3 RNA molecules (especially 4 RNA molecules) contained in the composition have a characteristic distribution in terms of their amount.

[0166] Specifically, at least three RNA molecules (particularly four RNA molecules) are included in the composition in different amounts. For example, any arbitrary pair of two RNA molecules included in the composition have different amounts.

[0167] Therefore, when three RNA molecules are contained in the composition, the first RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1) is present in the largest amount, the second RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2) is present in an amount less than the first RNA molecule, and the third RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3) is present in an amount less than the first RNA molecule and the second RNA molecule. In the case where four RNA molecules are contained in the composition, the first RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1) is present in the largest amount, the second RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2) is present in an amount less than the first RNA molecule, and the third RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3) is present in an amount less than the first and second RNA molecules, and the fourth RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 4) is present in an amount less than the first, second, and third RNA molecules.

[0168] More specifically, at least 3 RNA molecules (especially 4 RNA molecules) are included in the composition in a gradient of defined amounts. Specifically, at least 3 RNA molecules (especially 4 RNA molecules) are titrated to a linear range of different amounts.

[0169] In one embodiment, the at least 3 RNA molecules (in particular 4 RNA molecules) contained in the composition are present in an amount of between 0.001 amol and 6000 amol, preferably, between 0.01 amol and 5000 amol, more preferably, between 0.1 amol and 4000 amol, and even more preferably, between 1 amol and 3500 amol, for example 0.001 amol, 0.002 amol, 0.003 amol, 0.004 amol, 0.005 amol, 0.006 amol, 0.007 amol, 0.008 amol, 0.009 amol, 0.01 amol, 0.02 amol, 0.03 amol, 0.04 amol, 0.05 amol, 0.06 amol, 0.07 amol, 0.08 amol, 0.09 amol, 0.1 amol, 0.2amol, 0.3amol, 0.4amol, 0.5amol, 0.6amol, 0.7amol, 0.8amol, 0.9amol, 1amol, 2amol, 3amol, 4am ol, 5amol, 6amol, 7amol, 8amol, 9amol, 10amol, 20amol, 30amol, 40amol, 50amol, 60amol, 70amol, 80amol ol, 90amol, 100amol, 200amol, 300amol, 400amol, 500amol, 600amol, 700amol, 800amol, 900amol, 1000 amol, 1500amol, 2000amol, 3000amol, 3500amol, 4000amol, 4500amol, 5000amol, 5500amol or 6000amol.

[0170] In one embodiment, the composition comprises at least 3 RNA molecules, wherein

[0171] The first RNA molecule is included in an amount of about 3400 amol, the second RNA molecule is included in an amount of about 725 amol, and the third RNA molecule is included in an amount of about 20 amol, or

[0172] The first RNA molecule was included in an amount of about 1360 amol, the second RNA molecule was included in an amount of about 290 amol, and the third RNA molecule was included in an amount of about 80 amol.

[0173] In particular, the first RNA molecule has a nucleotide sequence according to SEQ ID NO:1, the second RNA molecule has a nucleotide sequence according to SEQ ID NO:2, and the third RNA molecule has a nucleotide sequence according to SEQ ID NO:3.

[0174] In a specific embodiment, the 4 RNA molecules comprised in the composition are present in an amount between 0.001 amol and 6000 amol, preferably, between 0.01 amol and 5000 amol, more preferably, between 0.1 amol and 4000 amol, and even more preferably, between 1 amol and 3500 amol, for example 0.001 amol, 0.002 amol, 0.003 amol, 0.004 amol, 0.005 amol, 0.006 amol, 0.007 amol, 0.008 amol, 0.009 amol, 0.01 amol, 0.02 amol, 0.03 amol, 0.04 amol, 0.05 amol, 0.06 amol, 0.07 amol, 0.08 amol, 0.09 amol, 0.1 amol, 0.2 amol. l, 0.3amol, 0.4amol, 0.5amol, 0.6amol, 0.7amol, 0.8amol, 0.9amol, 1amol, 2amol, 3amol, 4amol, 5a mol, 6amol, 7amol, 8amol, 9amol, 10amol, 20amol, 30amol, 40amol, 50amol, 60amol, 70amol, 80amol, 90amol, 100amol, 200amol, 300amol, 400amol, 500amol, 600amol, 700amol, 800amol, 900amol, 1000a mol, 1500amol, 2000amol, 3000amol, 3500amol, 4000amol, 4500amol, 5000amol, 5500amol or 6000amol.

[0175] In a specific embodiment, the composition comprises 4 RNA molecules, wherein

[0176] The first RNA molecule is included in an amount of about 3400 amol, the second RNA molecule is included in an amount of about 725 amol, the third RNA molecule is included in an amount of about 20 amol, and the fourth RNA molecule is included in an amount of about 7 amol, or

[0177] The first RNA molecule was included in an amount of about 1360 amol, the second RNA molecule was included in an amount of about 290 amol, the third RNA molecule was included in an amount of about 80 amol, and the fourth RNA molecule was included in an amount of about 27 amol.

[0178] Specifically, the first RNA molecule has a nucleotide sequence according to SEQ ID NO: 1, the second RNA molecule has a nucleotide sequence according to SEQ ID NO: 2, the third RNA molecule has a nucleotide sequence according to SEQ ID NO: 3, and the fourth RNA molecule has a nucleotide sequence according to SEQ ID NO: 4.

[0179] Preferably, the composition is a solution. More preferably, the solution is an aqueous solution, such as water (e.g. nuclease-free water). Even more preferably, no other components (other than the RNA molecules and water as solvent) are part of the composition.

[0180] As mentioned above, analysis of RNA expression profiles involves several complex steps. (1) RNA, including small RNAs, must be extracted from a biological source (e.g., blood or saliva) without distorting the original relative abundance of the RNA (linearity must be maintained). (2) Small RNA abundance must be reliably quantified. Since both steps can inconsistently introduce bias (e.g., through the presence of cDNA synthesis inhibitors), it is critical to standardize and monitor the efficiency of both RNA extraction and detection.

[0181] Compositions as described above are suitable as / are admixtures. Admixtures are general admixtures that are independent of downstream detection methods. In addition, compositions are suitable as / are process control and / or normalized standards. For this purpose, compositions as described above are added to samples comprising biological material or to processed samples based on / derived from biological material.

[0182] In particular, the compositions are generally applicable to any downstream processing of biological material, samples comprising biological material, samples comprising material having a biological origin, and / or samples based on / derived from biological material.

[0183] The biological material comprises a target RNA molecule whose detection is sought, for example for the purpose of diagnosing a disease such as cancer or a neurodegenerative disease such as Parkinson's disease (PD) or Alzheimer's disease (AD).

[0184] The target RNA molecule has its endogenous source in the biological material and is different from the exogenous artificial RNA molecule. When the exogenous artificial RNA molecule is added to the biological material as part of a composition (e.g., an admixture mixture), then the target RNA molecule is part of the biological material.

[0185] Process control specifically covers: monitoring / detecting experimental inefficiencies during target RNA processing and analysis, monitoring target RNA extraction and / or detection efficiency, standardization of target RNA extraction and detection, and monitoring of target RNA quantification and / or detection.

[0186] The composition can be used as an extraction control standard, a sequencing control standard, or a library preparation control standard.

[0187] In addition, the composition can be used as a normalization indicator (to compare with different samples having the composition). In particular, the composition can be used as a normalization indicator (to compare with different samples having the composition) so that the measured expression values ​​of the spike-in are used to control and correct inefficiencies in extraction, library preparation or sequencing.

[0188] In a second aspect, the present invention relates to a kit comprising the composition of the first aspect.

[0189] The composition may also be referred to as an admixture mixture.

[0190] Preferably, the kit comprises means for determining the levels of at least 3 RNA molecules, such as 3 or 4 RNA molecules, comprised in the composition.

[0191] More preferably, the tool is

[0192] Polynucleotides (probes) for detecting RNA molecules,

[0193] Primers / primer pairs for binding to RNA molecules,

[0194] an antibody capable of binding to a hybrid of the polynucleotide probe and the RNA molecule, and / or

[0195] Tools for performing next-generation sequencing (NGS).

[0196] The polynucleotides (probes) may be part of a microarray / biochip or may be attached to beads of a bead-based multiplex system. The primers / primer pairs may be part of a RT-PCR system, a PCR system or a next generation sequencing system.

[0197] The tools may further include a microarray, a RT-PCR system, a PCR system, a flow cytometer, a Luminex system, and / or a next generation sequencing system.

[0198] The kit may comprise instructions on how to carry out the methods of the invention (see fourth to sixth aspects).The kit is also useful for carrying out the methods of the invention (see fourth to sixth aspects).

[0199] In addition, the kit may include

[0200] container, and / or

[0201] Data carrier.

[0202] The data carrier may be a non-electronic data carrier, such as a graphic data carrier (e.g., an information brochure, an information sheet, a bar code, or an access code), or an electronic data carrier, such as a floppy disk, a compact disk (CD), a digital versatile disk (DVD), a microchip, or another semiconductor-based electronic data carrier. The access code may allow access to a database (e.g., an Internet database, a centralized database, or a decentralized database). The access code may also allow access to application software, which enables a computer to perform tasks for a computer user, or to a mobile application, which is software designed to run on smartphones and other mobile devices.

[0203] The data carrier may further comprise information regarding expected levels, expected order / rank, and / or expected linearity of RNA molecules contained in the composition in subsequent RNA processing / analysis experiments.

[0204] The data carrier may also comprise information or instructions on how to carry out the method of the invention (see the fourth to sixth aspects).

[0205] The kit is preferably used in vitro / ex vivo.

[0206] In a third aspect, the present invention relates to the (in vitro) use of the composition of the first aspect or the kit of the second aspect (as a standard) for process control, sample detection, sample analysis, normalization and / or data processing control.

[0207] The composition may also be referred to as an admixture mixture.

[0208] Preferably, the data processing controls comprise raw data processing controls.

[0209] As mentioned above, analysis of RNA expression profiles involves several complex steps. (1) RNA, including small RNAs, must be extracted from a biological source (e.g., blood or saliva) without distorting the original relative abundance of the RNA (linearity must be maintained). (2) Small RNA abundance must be reliably quantified. Because both steps can inconsistently introduce bias (e.g., through the presence of cDNA synthesis inhibitors), it is critical to standardize and monitor the efficiency of both RNA extraction and detection.

[0210] The composition of the first aspect (as a standard) is used for process control, sample detection, sample analysis, normalization and / or data processing control. Preferably, the data processing control comprises raw data processing control. For this purpose, the composition of the first aspect is added to a sample comprising a biological material or to a processed sample based on / derived from a biological material.

[0211] In particular, the compositions are generally applicable to any downstream processing of biological material, samples comprising biological material, samples comprising material having a biological origin, and / or samples based on / derived from biological material.

[0212] The biological material comprises a target RNA molecule whose detection is sought, for example for the purpose of diagnosing a disease such as cancer or a neurodegenerative disease such as Parkinson's disease (PD) or Alzheimer's disease (AD).

[0213] The target RNA molecule has an endogenous source in the biological material and is different from the exogenous artificial RNA molecule. When the exogenous artificial RNA molecule is added to the biological material as part of a composition (e.g., an admixture mixture), then the target RNA molecule is part of the biological material.

[0214] Process control specifically covers: monitoring / detecting experimental inefficiencies during target RNA processing and analysis, monitoring target RNA extraction and / or detection efficiency, standardization of target RNA extraction and detection, and monitoring of target RNA quantification and / or detection.

[0215] Preferably, process control comprises quality control, quantity control or end-to-end control. Specifically, end-to-end control measures refer to using only those samples whose analysis results in the recovery of artificial small RNA molecules according to their expected levels, sequence and linearity for downstream analysis.

[0216] In particular, the composition of the first aspect can be used as an extraction control standard, a sequencing control standard or a library preparation control standard.

[0217] In addition, in particular, the composition of the first aspect can be used as a normalized indicator (to compare different samples with said composition). This allows to remove batch effects in different experiments and / or control inefficiencies in extraction, library preparation and / or sequencing.

[0218] Data preprocessing, process control, and quality control are very important for data analysis because the data (especially raw data) generated after sequencing (such as next-generation sequencing) must be processed so that the results should not have false positive and false negative results. Data preprocessing not only evaluates each analysis step, but also reduces the amount of low-quality sequence reads. Removing such low-quality reads reduces the time and cost of computational analysis, and also obtains reliable and high-quality results.

[0219] In any downstream analysis of NGS data, false positive and false negative results are generated due to the following reasons:

[0220] Experimental factors: such as sample contamination or PCR errors,

[0221] Sequencing factors: including sequencing quality and data contamination due to index hopping during data segmentation, and / or

[0222] Analysis software parameter factors: This includes the precise type of parameter adjustments in the alignment software or downstream personalized analysis.

[0223] The raw sequences generated after sequencing contain not only the sequence of interest (e.g., the sequence of the target RNA molecule), but they also have sequence biases (e.g., through systematic effects such as Poisson sampling) and complex artifacts generated due to sequencing and experimental steps. These sequence biases and artifacts can affect and interfere with accurate read number alignment, which affects genotyping and variant calling. Therefore, in order to improve the reliability and quality of downstream analysis and reduce the amount of computing resources required, it is necessary to preprocess the raw sequence reads.

[0224] The inventors have found that the 5' end and / or 3' end additives of the target RNA molecule (e.g., generated or occurring during a sequencing process, such as a next-generation sequencing process) can mislead and falsify the original data results. Specifically, it is necessary to eliminate / eliminate the number of sequence reads of joint contamination. The inventors have found that the quality of the original data can be improved by eliminating / eliminating the number of sequence reads of joint contamination. Advantageously, at least 3 artificial small RNA molecules (admixtures) included in / from the composition of the first aspect of the present invention can be used to detect the 5' end and / or 3' end additives of the target RNA molecule. The presence of the 5' end and / or 3' end additives identified relative to at least 3 artificial small RNA molecules (admixtures) included in / from the composition of the first aspect of the present invention indicates the presence of the 5' end and / or 3' end additives in the target RNA molecule. In this way, (original) data processing can be controlled, specifically by excluding RNA of non-biological origin and should not be used for biological interpretation of data and is improved.

[0225] In a fourth aspect, the present invention relates to a method for detecting a sample, the method comprising the following steps:

[0226] The sample is assessed relative to at least 3 RNA molecules (eg 3 or 4 RNA molecules) comprised in / derived from the composition of the first aspect.

[0227] In one embodiment, the sample is a mixture of biological material and the composition of the first aspect.

[0228] Preferably, the biological material is a tissue or a body fluid. More preferably, the body fluid is blood. Even more preferably, the blood is whole blood or a blood component. In particular, the blood component is selected from the group consisting of a blood cell component and plasma or serum. The blood cell component represents the cellular part of (whole) blood. Plasma and serum represent the non-cellular part of (whole) blood. More specifically, the blood cell component comprises red blood cells, white blood cells or platelets, the blood cell component is a red blood cell, white blood cell or platelet component, or the blood cell component is a mixture of red blood cells, white blood cells and platelets.

[0229] In an alternative embodiment, the sample is based on / derived from a mixture of biological material and the composition of the first aspect.

[0230] In particular, the sample based on / derived from a mixture of biological material and the composition of the first aspect is a processed sample. In particular, the processed sample is a lysed sample, an extracted sample, an amplified sample, a sequenced sample or a library prepared sample.

[0231] The processed sample may be obtained by adding the composition of the first aspect to a biological material, mixing the composition of the first aspect with the biological material and further processing the sample.

[0232] Preferably, the processed sample is obtained by mixing the biological material with the composition of the first aspect and further processing the resulting mixture.

[0233] In order to assess a sample relative to the at least 3 RNA molecules (eg 3 or 4 RNA molecules) comprised in / from the composition of the first aspect, preferably the levels of said at least 3 RNA molecules (eg 3 or 4 RNA molecules) in the sample are determined.

[0234] In a preferred embodiment, the evaluation comprises

[0235] Determine whether at least 3 RNA molecules (eg, 3 or 4 RNA molecules) show a characteristic distribution, or determine whether the characteristic distribution of RNA molecules in the sample matches an expected characteristic distribution.

[0236] As described above, the at least three RNA molecules (e.g., three or four RNA molecules) contained in the composition of the first aspect have a characteristic distribution, in particular with respect to their amounts. In particular, the at least three RNA molecules (e.g., three or four RNA molecules) are contained in the composition of the first aspect in different amounts. More particularly, the at least three RNA molecules (e.g., three or four RNA molecules) are contained in the composition of the first aspect in a gradient of defined amounts.

[0237] In order to determine whether at least 3 RNA molecules (eg 3 or 4 RNA molecules) show a characteristic distribution, it is preferred to determine the levels of said at least 3 RNA molecules (eg 3 or 4 RNA molecules) in the sample.

[0238] If a characteristic profile is given, the sample is further processed and / or analyzed.

[0239] In particular, if the characteristic distribution is given by

[0240] At least 3 RNA molecules are present at their expected levels,

[0241] at least 3 RNA molecules are present in their expected order / rank (defined by the relationship of the levels (particularly the amounts) of the at least 3 RNA molecules), and / or

[0242] At least 3 RNA molecules were present in their expected linearity.

[0243] If no characteristic distribution is given, the sample is not further processed and / or analyzed. It is discarded.

[0244] In particular, if the characteristic distribution is not given as follows

[0245] At least three RNA molecules were not present at their expected levels,

[0246] At least 3 RNA molecules are not present in their expected order / rank (defined by the relationship of the levels (particularly the amounts) of at least 3 RNA molecules), and / or

[0247] At least three RNA molecules did not exist in their expected linearity.

[0248] Expected level refers to the level of the amount of a specific RNA molecule expected to be added to a sample in subsequent sample processing and / or analysis. As described above, RNA molecules are added to or included in a sample in a specific amount. The amount of RNA molecules can correspond / correlate with any expected level of RNA molecules that can be measured during subsequent sample processing and / or analysis. Specifically, the amount of RNA molecules can correspond / correlate with a specific read count (reads per million (RPM)) in the next generation sequencing assay, and / or the amount of RNA molecules can correspond / correlate with a specific cycle threshold (Ct) in a real-time PCR experiment. In particular, the cycle threshold (Ct) is the number of cycles in real-time PCR required for exceeding a previously defined threshold in a measurement signal (e.g., fluorescent signal) of amplified DNA. Before PCR, the more DNA (RNA) already present in the sample solution, the fewer amplification cycles required to reach the relevant threshold.

[0249] For example, an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1 added to a sample or comprised in the composition of the first aspect in an amount of about 3400 amol will result in a predicted Ct value of about 26 (average Ct of about 24.3), an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2 added to a sample or comprised in the composition of the first aspect in an amount of about 725 amol will result in a predicted Ct value of about 29.3 (average Ct of about 27.1), an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3 added to a sample or comprised in the composition of the first aspect in an amount of about 20 amol will result in a predicted Ct value of about 35.9 (average Ct of about 32), and / or an RNA molecule having a nucleotide sequence according to SEQ ID NO: 4 added to a sample or comprised in the composition of the first aspect in an amount of about 7 amol will result in a predicted Ct value of about 39.2 (average Ct of about 33.6).

[0250] For example, an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1 added to a sample or contained in a composition of the present invention in an amount of about 3400 amol will produce a log2 RPM of about 11.972 or a read count of about 30628, an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2 added to a sample or contained in the composition of the first aspect in an amount of about 725 amol will produce a log2 RPM of about 10.926 or a read count of about 5554, an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3 added to a sample or contained in the composition of the first aspect in an amount of about 20 amol will produce a log2 PRM of about 6.559 or a read count of about 491, and / or an RNA molecule having a nucleotide sequence according to SEQ ID NO: 4 added to a sample or contained in the composition of the first aspect in an amount of about 7 amol will produce a log2 PRM of about 4.061 or a read count of about 94.

[0251] The expected order / rank refers to the order / rank of specific RNA molecules expected to be added to the sample in subsequent sample processing and / or analysis. As described above, the RNA molecules added to the sample are different in amount. Therefore, in the case where three RNA molecules are added to the sample or included in the composition of the first aspect, the first RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1) is present in the largest amount, the second RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2) is present in an amount less than the first RNA molecule, and the third RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3) is present in an amount less than the first RNA molecule and the second RNA molecule. When four RNA molecules are added to a sample or included in the composition of the first aspect, the first RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 1) is present in the largest amount, the second RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 2) is present in an amount less than the first RNA molecule, and the third RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 3) is present in an amount less than the first and second RNA molecules, and the fourth RNA molecule (e.g., an RNA molecule having a nucleotide sequence according to SEQ ID NO: 4) is present in an amount less than the first, second, and third RNA molecules.

[0252] This expected order / rank representing the initial state / condition must be restored during sample processing and / or analysis. In this regard, it should be noted that the amount of RNA molecules contained in a sample or in a composition in a particular order / rank can correspond / correlate to any expected level of RNA molecules that can be measured during subsequent sample processing and / or analysis. For example, the amount of RNA molecules can correspond / correlate to a specific read count in a next generation sequencing assay, and / or the amount of RNA molecules can correspond / correlate to a specific cycle threshold (Ct) in a real-time PCR experiment.

[0253] Expected linearity refers to the linearity of a particular RNA molecule that is expected to be added to a sample during subsequent sample processing and / or analysis.

[0254] This expected linearity, which represents the initial state / condition, must be restored during sample processing and / or analysis.

[0255] Thus, only those samples whose analysis yielded RNA molecules or their surrogates recovered at their expected levels, sequence and linearity are used for downstream analysis and / or further processing.

[0256] In order to determine whether RNA molecules are present in their expected order and / or linearity, statistical analysis is performed using data associated with the RNA molecules (e.g., Ct values ​​or RPMs). The statistical analysis includes, but is not limited to, Spearman (rank) correlation analysis and / or Pearson correlation analysis.

[0257] For example, to determine whether an RNA molecule is present in its expected order, the Spearman's rank correlation coefficient (Spearman's p) is preferably determined.

[0258] Furthermore, in order to determine whether the RNA molecules are present with their expected linearity, the Pearson correlation coefficient (Pearson's r) is preferably determined.

[0259] Preferably, when the Spearman rank correlation coefficient (Spearman's p) is ≥ 0.95, the RNA molecules are present in their expected order. In this case, the sample is further processed / analyzed. Therefore, a Spearman rank correlation coefficient (Spearman's p) < 0.95 will cause the sample to be discarded. In other words, such samples will not be further processed / analyzed.

[0260] Preferably, when the Pearson correlation coefficient (Pearson's r) is ≥ 0.66, the RNA molecules are present in their expected linearity. In this case, the sample is further processed / analyzed. Therefore, a Pearson correlation coefficient (Pearson's r) < 0.66 will cause the sample to be discarded. In other words, such samples will not be further processed / analyzed.

[0261] Further processing may include lysing the sample, extracting the sample, amplifying the sample, sequencing the sample and / or preparing a library from the sample. Specifically, further processing includes lysing the cells to release the nucleotide sequence contained in the sample, extracting the nucleotide sequence contained in the sample, amplifying the nucleotide sequence contained in the sample, sequencing the nucleotide sequence contained in the sample and / or preparing a library from the nucleotide sequence contained in the sample. In particular, the nucleotide sequence is a ribonucleotide sequence. Preferably, the ribonucleotide sequence belongs to a target RNA molecule. More preferably, the target RNA molecule is a small RNA molecule. Even more preferably, the small RNA molecule is a non-coding small RNA molecule. Most preferably, the non-coding small RNA molecule is a miRNA and / or isomiR molecule.

[0262] In this regard, it should be noted that artificial exogenous RNA molecules added to the biological material are processed like endogenous target RNA molecules contained in the biological material.

[0263] RNA (e.g., small RNA) in biological samples (e.g., biofluids) plays an important role as biomarkers for the prognosis and diagnosis of many human disease states. However, accurate analysis of these RNAs in biological samples (e.g., biofluids) is crucial for the significance of these data in the field of biomarkers.

[0264] In the method of detecting a sample, the sample is evaluated with respect to at least 3 RNA molecules contained in / from the composition described herein, wherein the at least 3 RNA molecules are selected from the group consisting of RNA molecules having a nucleotide sequence according to SEQ ID NO: 1 to SEQ ID NO: 4. The at least 3 RNA molecules are part of the sample. They have been added to the sample (as spikes).

[0265] In this method, alternatively or additionally, at least three RNA molecules having a nucleotide sequence according to SEQ ID NO: 1 to SEQ ID NO: 4 are verified / analyzed whether they comprise 5'-end and / or 3'-end additions.

[0266] In this regard, it will be understood that the term "RNA molecule" can refer to the RNA molecule itself as well as surrogates thereof, such as amplification products (eg, cDNA derived therefrom).

[0267] Thus, in a preferred embodiment, the assessment comprises identifying 5' and / or 3' additions of at least 3 RNA molecules (eg 3 or 4 RNA molecules) comprised in / from the composition of the first aspect.

[0268] Preferably, the 5' end and / or 3' end additive has a length of at least 5 nucleotides. More preferably, the 5' end and / or 3' end additive has a length between 5 and 30 nucleotides, even more preferably, 5 to 20 nucleotides, and even more preferably, 7 to 15 nucleotides, such as 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 nucleotides. The 5' end additive may also be referred to as a prefix sequence, the 3' end additive may also be referred to as a suffix sequence, and the 5' end additive and the 3' end additive may also be referred to as an affixed sequence.

[0269] In particular, at least 5 nucleotides extend beyond the original length of at least 3 RNA molecules.

[0270] Prefix sequences, suffix sequences and suffix sequences can also extend the original length of the RNA molecule by 1, 2, 3 or 4 nucleotides. However, to reduce the risk of erroneously identifying an RNA molecule as having 5' and / or 3' additions relevant to the context of this article, a minimum length of 5 nucleotides is required / recommended herein.

[0271] The 5' end and / or 3' end additives of at least 3 RNA molecules contained in / from the composition of the first aspect can be easily determined using sequencing and / or sequence alignment methods. Since the sequence of the artificial small RNA molecule (incorporation) is known, the identification / analysis of the added nucleotides is not a practical problem and is a standard procedure for the technician. As mentioned above, in order to reduce the risk of mistakenly identifying RNA molecules as having 5' end and / or 3' end additives relevant to the context of this article, it is necessary to refer to the minimum length of 5 nucleotides as 5' end additives and / or 3' end additives in this article.

[0272] The 5' and / or 3' end additions can be the result of RNA molecule / joint fusion, RNA molecule / RNA molecule fusion or joint / joint fusion. The joint can be any non-biological RNA / DNA sequence intentionally added to the 5' or 3' end of the RNA / cDNA molecule, for example as part of sequencing (e.g., NGS method). This includes any free-floating joint molecules that may fuse with RNA in an unexpected manner. The joint can also be a combination / fusion of multiple separate joints and index RNAs. The index RNA is used as a UMI (unique molecular identifier) ​​to assign RNA molecules to samples from which they are derived during multiple sequencing (i.e., sequencing multiple samples at the same time). The UMI sequence is a short non-specific (random) sequence with a predetermined length (e.g., 12 nucleotides), but the length can also vary.

[0273] The sample evaluated in the above-mentioned embodiments preferably contains the target RNA molecule, for example, is contained in a biological material (eg, whole blood) or is derived from a biological material (eg, whole blood).

[0274] Therefore, in the next step, it is preferably determined whether the 5'-end and / or 3'-end additives identified with respect to the at least 3 RNA molecules (artificial small RNA molecules / inclusions) contained in / from the composition of the first aspect are also found in the target RNA molecule or its surrogate (e.g., cDNA molecule) as part of / contained in the sample. In other words, the presence of the 5'-end and / or 3'-end additives identified with respect to the at least 3 RNA molecules (artificial small RNA molecules / inclusions) contained in / from the composition of the first aspect preferably indicates the presence of the 5'-end and / or 3'-end additives in the target RNA molecule or its surrogate (e.g., cDNA molecule).

[0275] Preferably, the 5' and / or 3' additions occur during:

[0276] - ligating a 5' adaptor and / or a 3' adaptor to the (denatured) (target) RNA molecule, e.g. using a double-stranded RNA ligase (e.g. T4 RNA ligase 2 (Rnl2) or Kod1 ligase),

[0277] - reverse transcribing the (target) RNA molecule (to which a 5' adapter and / or a 3' adapter is attached, also referred to as a ligation product) into a cDNA molecule, for example, using a reverse transcriptase (RT, such as Maxima H-RT or Tth polymerase),

[0278] - amplification of the cDNA molecule, for example by polymerase chain reaction (PCR), and / or

[0279] - Sequencing of (the) cDNA molecules, such as next generation sequencing.

[0280] More preferably, the 5' end and / or 3' end additions occur / appear during the process of library preparation for next generation sequencing or during the next generation sequencing process.

[0281] Even more preferably, the next generation sequencing process preferably encompasses:

[0282] - ligating a 5' adaptor and / or a 3' adaptor to the (denatured) (target) RNA molecule, e.g. using a double-stranded RNA ligase (e.g. T4 RNA ligase 2 (Rnl2) or Kod1 ligase),

[0283] - reverse transcribing the (target) RNA molecule (to which a 5' adapter and / or a 3' adapter is attached, also referred to as a ligation product) into a cDNA molecule, for example, using a reverse transcriptase (RT, such as Maxima H-RT or Tth polymerase),

[0284] - amplification of the cDNA molecule, for example by polymerase chain reaction (PCR), and / or

[0285] - Sequencing of (the) cDNA molecules.

[0286] Still even more preferably, the 5'-end and / or 3'-end addition is selected from the group consisting of an addition having a nucleotide sequence according to CGATC (SEQ ID NO: 10), GGGGC (SEQ ID NO: 11), ACGATC (SEQ ID NO: 12), GGGCGT (SEQ ID NO: 13), CGGCGG (SEQ ID NO: 14), GGGGCG (SEQ ID NO: 15), GACGATC (SEQ ID NO: 16), GGGGCGT (SEQ ID NO: 17), GGGCGTG (SEQ ID NO: 18), GGGGGCG (SEQ ID NO: 19), GGGGGTG (SEQ ID NO: 20), GGGGCGTG (SEQ ID NO: 21), CGGGGCGG (SEQ ID NO: 22), GGGAGGCC (SEQ ID NO: 23), GGAGGCGT (SEQ ID NO: 24). NO: 24), GGGCGTGG (SEQ ID NO: 25), TGGAGGCG (SEQ ID NO: 26), CGACGATC (SEQ ID NO: 27), GGGGCGTT (SEQ ID NO: 28), GGGCGTGT (SEQ ID NO: 29), GGGGGCGT (SEQ ID NO: 30), GGGAGCCA (SEQ ID NO: 31), GGGGGTGT (SEQ ID NO: 29) NO: 32), GGAGGCCC (SEQ ID NO: 33), CCGACGATC (SEQ ID NO: 34), GGGGGCGTG (SEQ ID NO: 35), TACCTGGTT (SEQ ID NO: 36), TGGAGGCGT (SEQ ID NO: 37), GGGCGTGGG (SEQ ID NO: 38), CGGCGGCGG (SEQ ID NO: 39), GGGGGTGTA (SEQ ID NO: 40), GGGGGCGTT (SEQ ID NO: 41), GGCTGGGCG (SEQ ID NO: 42), TCGGGGCGG (SEQ ID NO: 43), GGGGCGTGG (SEQ ID NO: 44), GGGGAGCCA (SEQ ID NO: 45), GGGAGGCCC (SEQ ID NO: 46), CGGAGGGCGG (SEQ ID NO: 47), GTCCGCGATC (SEQ ID NO: 48), GTCGACGATC (SEQ ID NO: 49), CGGGCGGATC (SEQ ID NO: 50),TGGAGGCGTG (SEQ ID NO: 51), TCCGACGATC (SEQ ID NO: 52), GGGGC GTGGG (SEQ ID NO: 53), AAGCGGGGCT (SEQ ID NO: 54), CGGGGAGCCA (SEQ ID NO: 55), GTCCGACGATC (SEQ ID NO: 56), TCGGAGGGCGG (SEQ ID NO: 57), AGTCCGACGATC (SEQ ID NO: 58), AAGCGGGGCTGG (SEQ ID NO: 59), GTCCGACGGATC (SEQ ID NO: 60), TCGGGCTGGGGC (SEQ ID NO: 61), TACCTGGTTGAT (SEQ ID NO: 62), TCGGGGGCGGCGG (SEQ ID NO: 63), CAGTCCGACGATC (SEQ ID NO: 64), TACCTGGTTGATC (SEQ ID NO: 65), TCGGGCTGGGGCG (SEQ ID NO: 66), TGGAGGCGTGGGT (SEQ ID NO: 67), ACAGTCCGACGATC (SEQ ID NO: 68), GGTCGGGCTGGGGC (SEQ ID NO: 69), CGGAAGCGTGCTGGG (SEQ ID NO: 70), GGTCGGGCTGGGGCG (SEQ ID NO: 71), TACAGTCCGACGATC (SEQ ID NO: 72), CGGAAGCGTGCTGGGC (SEQ ID NO: 73), CTACAGTCCGACGATC (SEQ ID NO: 74), TCTACAGTCCGACGATC (SEQ ID NO: 75), CGGAAGCGTGCTGGGCCC (SEQ ID NO: 76), TCGGGGGCGGCGGCGGCGG (SEQ ID NO: 77), TTCTACAGTCCGACGATC (SEQ ID NO: 78), TAGCAGCACATCATGGTT (SEQ ID NO: 79), GGATCATTA (SEQ ID NO: 80), GGGGC GTGGG (SEQ ID NO: 81), and TGGAGGCGTGGGT (SEQ ID NO: 82).

[0287] In particular, determining the 5' and / or 3' additions of a (target) RNA molecule encompasses analyzing whether the (target) RNA molecule is at least partially identical in sequence to an adapter sequence used in a sequencing (preferably next generation sequencing) process.

[0288] More specifically, the adapter sequence used in the next generation sequencing process is selected from the group consisting of TGGAATTCTCGGGTGCCAAGG (SEQ ID NO: 83), GTTCAG AGTTCTACAGTCCGACGATC (SEQ ID NO: 84), TGGAATTCTCGG GTGCCAAGG (SEQ ID NO: 85), GAATTCCACCACGTTCCCGTGG (SEQ ID NO: 86), AATGATACGGCGACCACCGAGATCTACACGT TCAGAGTTCTACAGTCCGA (SEQ ID NO: 87), CAAGCAGAAGAC GGCATACGAGAT (SEQ ID NO: 88), GTGACTGGAGTTCCTTGGC ACCCGAGAATTCCA (SEQ ID NO: 89), CAAGCAGAAGACGGCA TACGA (SEQ ID NO: 90), GTTCAGAGTTCTACAGTCCGACGATC (SEQ ID NO: 91), TCGTATGCCGTCTTCTGCTTGT (SEQ ID NO: 92). NO: 92), ATCTCGTATGCCGTCTTCTGCTTG (SEQ ID NO: 93), CAAG CAGAAGACGGCATACGA (SEQ ID NO: 94), AATGATACGGCGAC CACCGACAGGTTCAGAGTTCTACAGTCCGA (SEQ ID NO: 95), CGACAGGTTCAGAGTTCTACAGTCCGACGATC (SEQ ID NO: 95) NO: 96), AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA (SEQ ID NO: 97), AGATCGGAAGAGCACACGTCTGAACT CCAGTCA (SEQ ID NO: 98), AGATCGGAAGAGCGTCGTGTAGGG AAAGAGTGT (SEQ ID NO: 99), GATCGGAAGAGCACACGTCTGAACTCCAGTCAC (SEQ ID NO: 96) NO: 100), ATCTCGTATGCCGTCTTCT GCTTG (SEQ ID NO: 101), AATGATACGGCGACCACCGAGATCTA CAC (SEQ ID NO: 102), and ACACTCTTTCCCTACACGACGCTCTTCCGATCT (SEQ ID NO: 103).

[0289] Data preprocessing, process control, and quality control are very important for data analysis because the raw data generated after sequencing must be processed so that the results should not have false positive and false negative results. Data preprocessing not only evaluates each analysis step but also reduces the amount of low-quality sequence reads. Removing such low-quality reads reduces the time and cost of computational analysis and also yields reliable and high-quality results.

[0290] In any downstream analysis of NGS data, false positive and false negative results are generated due to the following reasons:

[0291] Experimental factors: such as sample contamination or PCR errors,

[0292] Sequencing factors: including sequencing quality and data contamination due to index hopping during data segmentation, and / or

[0293] Analysis software parameter factors: This includes the precise type of parameter adjustments in the alignment software or downstream personalized analysis.

[0294] The raw sequences generated after sequencing contain not only the sequence of interest (e.g., the sequence of the target RNA molecule), but they also have sequence biases (e.g., through systematic effects such as Poisson sampling) and complex artifacts generated due to sequencing and experimental steps. These sequence biases and artifacts can affect and interfere with accurate read number alignment, which affects genotyping and variant calling. Therefore, in order to improve the reliability and quality of downstream analysis and reduce the amount of computing resources required, it is necessary to preprocess the raw sequence reads.

[0295] The inventors have found that the 5' and / or 3' end additives of the target RNA molecule (e.g., generated or occurred during a sequencing process, such as a next-generation sequencing process) can mislead and falsify the original data results. Specifically, it is necessary to eliminate / eliminate the number of sequence reads contaminated by adapters. The inventors have found that by eliminating / eliminating the number of sequence reads contaminated by adapters, the quality of the original data can be improved.

[0296] Therefore, the target RNA molecules with 5'-end and / or 3'-end additives contained in the sample are excluded from further analysis / no further use. Alternatively or additionally, data from the target RNA molecules with 5'-end and / or 3'-end additives contained in the sample are excluded from further analysis / will not be further used or will not form part of the data set (preferably the original data set). For example, data related to these target RNA molecules are easily excluded from the (original) data set.

[0297] Preferably, the target RNA molecule is a small RNA molecule. More preferably, the small RNA molecule is a non-coding small RNA molecule. Even more preferably, the non-coding small RNA molecule is a miRNA and / or isomiR molecule.

[0298] The sample used in the above-mentioned embodiment is preferably a cracked sample, an extracted sample, an amplified sample and / or a sequenced sample. Sample cracking encompasses the cracking of cells contained in the sample and requires it to release the (target) RNA molecules contained therein. Sample extraction is required to extract the (target) RNA molecules contained therein. Sample amplification is required to amplify the (target) RNA molecules contained therein. Sample sequencing is required to sequence the (target) RNA molecules contained therein.

[0299] For example, a biological sample (e.g., a whole blood sample) is provided as a sample. In a first step, the sample (particularly the cells contained in the sample) is lysed to release the (target) RNA molecules contained therein. Then, cDNA is produced from the (target) RNA molecules using a linker connected to the denatured (target) RNA molecules by reverse transcription. The resulting cDNA molecules are subsequently amplified to ultimately allow sequencing, e.g., next generation sequencing of the cDNA molecules.

[0300] In any of the above processes, 5' and / or 3' end additions can be added to the (target) RNA molecules that need to be identified, and the (target) RNA molecules with these 5' and / or 3' end additions need to be excluded from further analysis or raw data evaluation. This improves data quality.

[0301] Analysis of RNA expression profiles involves several complex steps. (1) RNA, including small RNAs, must be extracted from a biological source (e.g., blood or saliva) without distorting the original relative abundance of the RNA (linearity must be maintained). (2) Small RNA abundance must be reliably quantified. Because both steps can inconsistently introduce bias (e.g., through the presence of cDNA synthesis inhibitors), it is critical to standardize and monitor the efficiency of both RNA extraction and detection. Meeting these challenges can be achieved in a variety of ways.

[0302] The inventors have designed and optimized a universal spike-in system for artificial small RNA molecules that broadly reflects the behavior of endogenous miRNAs during RNA extraction and detection. This mixture of artificial small RNA molecules is then added to a biological sample (e.g., a clinical sample) at the beginning of processing and serves as an end-to-end quality control measure: only those samples whose analysis meets the standards are used for downstream analysis, which produces artificial small RNA molecules recovered at their expected levels, sequence, and linearity.

[0303] Therefore, in a fifth aspect, the present invention relates to a method for optimized processing of a biological sample, said method comprising the following steps:

[0304] Implement the method of the fourth aspect.

[0305] In a sixth aspect, the present invention relates to a method for optimized RNA preparation from a biological sample or a method for RNA analysis of a biological sample, the method comprising the following steps:

[0306] Implement the method of the fourth aspect.

[0307] In a seventh aspect, the invention relates to a method for improving the quality of a (RNA) dataset, the method comprising the following steps:

[0308] Implement the method of the fourth aspect.

[0309] Data preprocessing, process control, and quality control are very important for data analysis because the data generated after sequencing (e.g., raw data) must be processed so that the results should not have false positive and false negative results. Data preprocessing not only evaluates each analysis step, but also reduces the amount of low-quality sequence reads. Removing such low-quality reads reduces the time and cost of computational analysis, and also results in reliable and high-quality results.

[0310] In any downstream analysis of NGS data, false positive and false negative results are generated due to the following reasons:

[0311] Experimental factors: such as sample contamination or PCR errors,

[0312] Sequencing factors: These include sequencing quality and data contamination due to index hopping during data segmentation, and / or

[0313] Analysis software parameter factors: This includes the precise type of parameter adjustments in the alignment software or downstream personalized analysis.

[0314] The raw sequences generated after sequencing contain not only the sequence of interest (e.g., the sequence of the target RNA molecule), but they also have sequence biases (e.g., through systematic effects such as Poisson sampling) and complex artifacts generated due to sequencing and experimental steps. These sequence biases and artifacts affect and interfere with accurate read number alignment, which affects genotyping and variant calling. Therefore, in order to improve the reliability and quality of downstream analysis and reduce the amount of computing resources required, it is necessary to preprocess the raw sequence reads.

[0315] The inventors have found that the 5' and / or 3' end additives of the (target) RNA molecule (e.g., generated or occurred during a sequencing process, such as a next generation sequencing process) can mislead and falsify the original data results. Specifically, it is necessary to eliminate / eliminate the number of sequence reads contaminated by adapters. The inventors have found that by eliminating / eliminating the number of sequence reads contaminated by adapters, the quality of data (e.g., original data) can be improved.

[0316] In an eighth aspect, the invention relates to a method for improving the quality of a (RNA) dataset, the method comprising the following steps:

[0317] (i) determining the sequence of a (target) RNA molecule (particularly a surrogate, such as a cDNA molecule derived therefrom) in a sample,

[0318] (ii) determining 5'- and / or 3'-end additions to the (target) RNA molecule, said additions being not part of the (target) RNA molecule in its naturally occurring form, and

[0319] (iii) Excluding (target) RNA molecules with 5' and / or 3' end additions from the (RNA) dataset analysis / removing (target) RNA molecules with 5' and / or 3' end additions from the (RNA) dataset.

[0320] In the step (i) of the above method, the sequence of the RNA molecule (particularly a substitute, such as a cDNA molecule derived therefrom) in the sample is determined. The sequence of the RNA molecule (particularly a substitute, such as a cDNA molecule derived therefrom) in the sample can be determined by any method known to the technician. Known sequencing methods include but are not limited to Sanger sequencing, capillary electrophoresis and fragment analysis or next generation sequencing (NGS). Preferably, the sequence of the RNA molecule (particularly a substitute, such as a cDNA molecule derived therefrom) in the sample is determined by NGS.

[0321] In particular, determining the sequence of an RNA molecule in a sample (particularly by next generation sequencing) encompasses:

[0322] - denaturation of the (target) RNA molecule and ligation of the denatured (target) RNA molecule with a 5' adapter and / or a 3' adapter, for example, using a double-stranded RNA ligase (such as T4 RNA ligase 2 (Rn12) or Kod1 ligase),

[0323] - reverse transcribing the RNA molecule (to which a 5' adapter and / or a 3' adapter are attached, also referred to as a ligation product) into a cDNA molecule, for example, using a reverse transcriptase RT, such as Maxima H-RT or Tth polymerase),

[0324] - amplification of the cDNA molecule, for example by polymerase chain reaction (PCR), and / or

[0325] - Sequencing of (the) cDNA molecules, in particular, next generation sequencing.

[0326] Preferably, PCR is selected from the group consisting of real-time PCR (quantitative PCR or qPCR, preferably TaqMan qPCR), multiplex PCR, nested PCR, high-fidelity PCR, fast PCR, hot-start PCR and high GC PCR.

[0327] In step (ii) of the above method, 5'- and / or 3'-end additions are determined for the (target) RNA molecule, said additions being not part of the (target) RNA molecule in its naturally occurring form.

[0328] Naturally occurring RNA molecules are in the form in which they occur in nature or in a natural environment, such as a body fluid (e.g., whole blood or tissue). However, these RNA molecules can be further processed, such as into substitutes thereof (e.g., cDNA molecules). In this case, they have a natural origin. Naturally occurring RNA molecules have an endogenous source in a biological material / sample. The RNA molecule was originally part of the biological material / sample.

[0329] Preferably, the 5' end and / or 3' end additive has a length of at least 5 nucleotides. More preferably, the length of the 5' end and / or 3' end additive is between 5 and 30 nucleotides, even more preferably, 5 to 20 nucleotides, and even more preferably, 7 to 15 nucleotides, such as 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 nucleotides. The 5' end additive may also be referred to as a prefix sequence, the 3' end additive may also be referred to as a suffix sequence, and the additive at the 5' end and the additive at the 3' end may also be referred to as an affixed sequence. Specifically, at least 5 nucleotides extend beyond the length of the RNA molecule in the naturally occurring form.

[0330] The length of the prefix sequence, suffix sequence and suffix sequence can also be 1, 2, 3 or 4 nucleotides. However, to reduce the risk of erroneously identifying (target) RNA molecules as having 5' and / or 3' end additions, a minimum length of 5 nucleotides is required / recommended herein.

[0331] The 5' and / or 3' end additions of the (target) RNA molecules contained in the sample can be determined using sequencing and comparing the 5' / 3' end of the sequence with the sequence of the additions.

[0332] 5' and / or 3' additions are the result of RNA molecule / adapter fusions, RNA molecule / RNA molecule fusions or incomplete adaptor processing / removal.

[0333] The adapter can be any non-biological RNA / DNA sequence that is intentionally added to the 5' or 3' end of a biological (target) RNA / cDNA molecule (derived from the sample to be sequenced) as part of the design of sequencing (e.g., NGS method). This includes any free-floating adapter molecules that may fuse to the biological RNA in an unexpected manner. The adapter can also be a combination / fusion of multiple individual adapters and index RNAs. The index RNA is used as a UMI (unique molecular identifier) ​​to assign RNA molecules to the sample from which they originated during multiple sequencing (i.e., sequencing multiple samples simultaneously). The UMI sequence is a short non-specific (random) sequence with a predetermined length (e.g., 12 nucleotides), but the length can also vary.

[0334] The 5' and / or 3' end additives are preferably selected from the group consisting of additives having nucleotide sequences according to the following: CGATC (SEQ ID NO: 10), GGGGC (SEQ ID NO: 11), ACGATC (SEQ ID NO: 12), GGGCGT (SEQ ID NO: 13), CGGCGG (SEQ ID NO: 14), GGGGCG (SEQ ID NO: 15), GACGATC (SEQ ID NO: 16), GGGGCGT (SEQ ID NO: 17), GGG CGTG (SEQ ID NO: 18), GGGGGCG (SEQ ID NO: 19), GGGGG TG (SEQ ID NO: 20), GGGGCGTG (SEQ ID NO: 21), CGGGGC GG (SEQ ID NO: 22), GGGAGGCC (SEQ ID NO: 23), GGAGGC GT (SEQ ID NO: 24). NO: 24), GGGCGTGG (SEQ ID NO: 25), TGGAGG CG (SEQ ID NO: 26), CGACGATC (SEQ ID NO: 27), GGGGCG TT (SEQ ID NO: 28), GGGCGTGT (SEQ ID NO: 29), GGGGGC GT (SEQ ID NO: 30), GGGAGCCA (SEQ ID NO: 31), GGGGGT GT (SEQ ID NO: 29) NO: 32), GGAGGCCC (SEQ ID NO: 33), CCGACG ATC (SEQ ID NO: 34), GGGGGCGTG (SEQ ID NO: 35), TACCTGGTT (SEQ ID NO: 36), TGGAGGCGT (SEQ ID NO: 37), GG GCGTGGG (SEQ ID NO: 38), CGGCGGCGG (SEQ ID NO: 39), GGGGGTGTA (SEQ ID NO: 40), GGGGGCGTT (SEQ ID NO: 41), GGCTGGGCG (SEQ ID NO: 42), TCGGGGCGG (SEQ ID NO: 43), GGGGCGTGG (SEQ ID NO: 44), GGGGAGCCA (SEQ ID NO: 45), GGGAGGCCC (SEQ ID NO: 46), CGGAGGGCGG (SEQ ID NO: 47), GTCCGCGATC (SEQ ID NO: 48), GTCGACGATC (SEQ ID NO: 49), CGGGCGGATC (SEQ ID NO: 50),TGGAGGCGTG (SEQ ID NO: 51), TCCGACGATC (SEQ ID NO: 52), GGGGC GTGGG (SEQ ID NO: 53), AAGCGGGGCT (SEQ ID NO: 54), CGGGGAGCCA (SEQ ID NO: 55), GTCCGACGATC (SEQ ID NO: 56), TCGGAGGGCGG (SEQ ID NO: 57), AGTCCGACGATC (SEQ ID NO: 58), AAGCGGGGCTGG (SEQ ID NO: 59), GTCCGACGGATC (SEQ ID NO: 60), TCGGGCTGGGGC (SEQ ID NO: 61), TACCTGGTTGAT (SEQ ID NO: 62), TCGGGGCGGCGG (SEQ ID NO: 63), CAGTCCGACGATC (SEQ ID NO: 64), TACCTGGTTGATC (SEQ ID NO: 65), TCGGGCTGGGGCG (SEQ ID NO: 66), TGGAGGCGTGGGT (SEQ ID NO: 67), ACAGTCCGACGATC (SEQ ID NO: 68), GGTCGGGCTGGGGC (SEQ ID NO: 69), CGGAAGCGTGCTGGG (SEQ ID NO: 70), GGTCGGGCTGGGGCG (SEQ ID NO: 71), TACAGTCCGACGATC (SEQ ID NO: 72), CGGAAGCGTGCTGGGC (SEQ ID NO: 73), CTACAGTCCGACGATC (SEQ ID NO: 74), TCTACAGTCCGACGATC (SEQ ID NO: 75), CGGAAGCGTGCTGGGCCC (SEQ ID NO: 76), TCGGGGGCGGCGGCGGCGG (SEQ ID NO: 77), TTCTACAGTCCGACGATC (SEQ ID NO: 78), TAGCAGCACATCATGGTT (SEQ ID NO: 79), GGATCATTA (SEQ ID NO: 80), GGGGC GTGGG (SEQ ID NO: 81), and TGGAGGCGTGGGT (SEQ ID NO: 82).

[0335] In case the above-mentioned 5'-end and / or 3'-end additions are detected or identified in a (target) RNA molecule or its surrogate, the (target) RNA molecule or its surrogate is excluded from the (RNA) dataset analysis in step (iii) of the above-mentioned method, or the (target) RNA molecule or its surrogate is removed from the (RNA) dataset in step (iii) of the above-mentioned method.

[0336] In particular, determining the 5' and / or 3' additions of a (target) RNA molecule encompasses analyzing whether the (target) RNA molecule is at least partially identical in sequence to an adapter sequence used in sequencing, preferably next generation sequencing.

[0337] More specifically, the adapter sequence used in the next generation sequencing process is selected from the group consisting of TGGAATTCTCGGGTGCCAAGG (SEQ ID NO: 83), GTTCAG AGTTCTACAGTCCGACGATC (SEQ ID NO: 84), TGGAATTCTCGG GTGCCAAGG (SEQ ID NO: 85), GAATTCCACCACGTTCCCGTGG (SEQ ID NO: 86), AATGATACGGCGACCACCGAGATCTACACGT TCAGAGTTCTACAGTCCGA (SEQ ID NO: 87), CAAGCAGAAGAC GGCATACGAGAT (SEQ ID NO: 88), GTGACTGGAGTTCCTTGGC ACCCGAGAATTCCA (SEQ ID NO: 89), CAAGCAGAAGACGGCA TACGA (SEQ ID NO: 90), GTTCAGAGTTCTACAGTCCGACGATC (SEQ ID NO: 91), TCGTATGCCGTCTTCTGCTTGT (SEQ ID NO: 92). NO: 92), ATCTCGTATGCCGTCTTCTGCTTG (SEQ ID NO: 93), CAAG CAGAAGACGGCATACGA (SEQ ID NO: 94), AATGATACGGCGAC CACCGACAGGTTCAGAGTTCTACAGTCCGA (SEQ ID NO: 95), CGACAGGTTCAGAGTTCTACAGTCCGACGATC (SEQ ID NO: 95) NO: 96), AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA (SEQ ID NO: 97), AGATCGGAAGAGCACACGTCTGAACT CCAGTCA (SEQ ID NO: 98), AGATCGGAAGAGCGTCGTGTAGGG AAAGAGTGT (SEQ ID NO: 99), GATCGGAAGAGCACACGTCTGAACTCCAGTCAC (SEQ ID NO: 96) NO: 100), ATCTCGTATGCCGTCTTCT GCTTG (SEQ ID NO: 101), AATGATACGGCGACCACCGAGATCTA CAC (SEQ ID NO: 102), and ACACTCTTTCCCTACACGACGCTCTTCCGATCT (SEQ ID NO: 103).

[0338] In case the above-mentioned adapter sequence or part thereof is detected or identified in a (target) RNA molecule or its surrogate, the (target) RNA molecule or its surrogate is excluded from the (RNA) dataset analysis in step (iii) of the above-mentioned method, or the (target) RNA molecule or its surrogate is removed from the (RNA) dataset in step (iii) of the above-mentioned method.

[0339] Specifically, the RNA dataset analysis is the RNA raw dataset analysis / the RNA dataset is the raw RNA dataset.

[0340] Preferably, the (target) RNA molecule is a small RNA molecule. More preferably, the small RNA molecule is a non-coding small RNA molecule. Even more preferably, the non-coding small RNA molecule is a miRNA and / or isomiR molecule.

[0341] The sample used in the above method can be a treated sample. Preferably, the sample used in the above method is a cracked sample, an extracted sample, an amplified sample and / or a sample sequenced. Sample cracking encompasses the cracking of the cells contained in the sample and requires it to release the (target) RNA molecules contained therein. Sample extraction is required to extract the (target) RNA molecules contained therein. Sample amplification is required to amplify the (target) RNA molecules contained therein. Sample sequencing is required to sequence the (target) RNA molecules contained therein.

[0342] For example, a biological sample such as a whole blood sample is provided as a sample. In a first step, the sample (particularly the cells contained in the sample) is lysed to release the RNA molecules contained therein. Then, cDNA is produced from the (target) RNA molecules using a linker connected to the denatured (target) RNA molecules by reverse transcription. The cDNA molecules are subsequently amplified to ultimately allow sequencing, such as next generation sequencing of the cDNA molecules.

[0343] In any of the above processes, 5' and / or 3' additions may be added to the (target) RNA molecules that need to be identified, and (target) RNA molecules with these 5' and / or 3' additions must be excluded from the (RNA) dataset analysis or removed from the (RNA) dataset.

[0344] The sample may comprise or be derived from a biological material. Preferably, the biological material is a tissue or a body fluid. More preferably, the body fluid is blood. Even more preferably, the blood is whole blood or a blood component. In particular, the blood component is selected from the group consisting of a blood cell component and plasma or serum. The blood cell component represents the cellular part of (whole) blood. Plasma and serum represent the non-cellular part of (whole) blood. More specifically, the blood cell component comprises red blood cells, white blood cells or platelets, the blood cell component is a red blood cell, a white blood cell or a platelet component, or the blood cell component is a mixture of red blood cells, white blood cells and platelets.

[0345] Various modifications and variations of the present invention will be apparent to those skilled in the art without departing from the scope of the present invention. Although the present invention has been described in conjunction with specific preferred embodiments, it should be understood that the claimed invention should not be unduly limited to such specific embodiments. Indeed, the present invention is intended to encompass various modifications of the described modes for carrying out the present invention that would be apparent to those skilled in the relevant art. BRIEF DESCRIPTION OF THE DRAWINGS

[0346] The following drawings are merely illustrative of the present invention and are not to be construed in any way to limit the scope of the present invention as indicated by the appended claims.

[0347] Figure 1 : Shown are Ct values ​​measured by qRT-PCR, plotted relative to the dilution factor of a mixture containing each spike-in individually.

[0348] Figure 2 : Shown are Ct values ​​measured by qRT-PCR, plotted against the concentration of a mixture containing each spike-in alone added to PAXgene RNA.

[0349] Figure 3 : Shown are Ct values ​​measured by qPCR for cDNA samples prepared from a single RNA sample containing a mixture of all four spike-ins, the measured Ct values ​​are plotted relative to the amount of the individual spike-ins added to the PAXgene samples prior to RNA extraction.

[0350] Figure 4 : Shows Figure 3 The NGS library was prepared using the sample as described. The raw read counts of the individual spike-ins were plotted against the amount.

[0351] Figure 5: A schematic diagram of a strategy for identifying non-biological, technological artifacts containing artificial suffix sequences is shown. In a first step, a sequencing data set containing an adduct RNA is analyzed and their suffix sequences are recorded. In a second step, these suffix sequences can be compared to the 5' and 3' ends of non-adduct sequences from the same data set or from different data sets (which may or may not contain the same adduct). Sequences with suffix sequences are removed, and therefore the sequences are excluded from further analysis.

[0352] Figure 6 : The distribution of artificial spike RNAs with nucleotide sequences according to SEQ ID NO: 1 to SEQ ID NO: 4 and the length of the suffix sequences is shown. Higher spike expression leads to the discovery of more suffix sequences. In addition, the total count of unique suffix sequences and the length-specific counts of lengths from 1 to 18 are given.

[0353] Figure 7 : Shown are the average expression of artificial spike-in RNAs (length: 21 nucleotides) and their longer artifacts (spikes plus suffix sequences of increasing length).

[0354] Figure 8 : shows the number of removed sequences based on the minimum suffix sequence length applied for filtering. The expected error percentage gives the theoretical probability of finding a suffix sequence of a given length among all possible RNA sequences with the same length (100(L / 4L), L=RNA length). At a minimum length of 5, this probability is less than 0.5%. Increasing the minimum length reduces the number of removed sequences while reducing the probability of erroneously removing true RNA sequences that match the suffix sequence by coincidence. Dataset 1 consists of clinical blood samples containing adulterations, and Dataset 2 refers to small RNA sequencing data provided by The Cancer Genome Atlas (TCGA) and does not contain any adulterations.

[0355] Example

[0356] The following examples are given for illustrative purposes only and are not intended to limit the above invention in any way.

[0357] Example 1

[0358] To overcome the above limitations of using a single spike-in (eg, cel-miR-39-3p), a universal spike-in system was designed and optimized that broadly reflects the behavior of endogenous miRNAs during RNA extraction and detection.

[0359] Specifically, 20 random sequences with a length of 21 nucleotides (reflecting the typical length of microRNA) were designed (not shown). Next, their molecular properties (such as melting temperature (Tm, ° C) and GC%) were evaluated. Subsequently, a short list of 10 artificial sequences was selected for wet laboratory verification according to several criteria. The purpose is to minimize the formation of primer dimers and select RNA molecules with relatively weak secondary structures. In addition, RNA molecules should widely reflect the endogenous GC content range. Therefore, an incorporation with a GC content ranging from 38.1% to 61.9% was selected, which covers the GC content of most endogenous microRNAs.

[0360] The artificial sequence listed briefly was used to program RNA synthesis including a 5' phosphate group (IDT, Norcross, VA, USA). The miRcury LNA assay (QIAgen, Venlo, The Netherlands) was programmed and incorporation was quantified using semiquantitative reverse transcription PCR (qRT-PCR).

[0361] First, ten-fold serial dilutions of each spike-in were prepared for each molecule separately and used for miRucry assay and measured on Quantstudio Flex 6 (ThermoFisher Scientific, Waltham, USA). The cycle threshold (Ct) was plotted against the dilution factor of the 6-fold dilution used. Figure 1 Data for four sequences (SEQ ID NO: 1 to SEQ ID NO: 4) are shown.

[0362] The spike-in having SEQ ID NO: 1 has the following nucleotide sequence: GAUAGAUACGCCAGUACCGCC,

[0363] The spike-in having SEQ ID NO: 2 has the following nucleotide sequence: AACGAAGCUCCACGAUGUAGG,

[0364] The spike-in having SEQ ID NO: 3 has the following nucleotide sequence: UGUACGGAAAUAUUGGCUACC, and

[0365] The spike-in having SEQ ID NO: 4 has the following nucleotide sequence: UUCAUACGUUGCCCAAUCCAG.

[0366] Data for other sequences are not shown.

[0367] Based on Figure 1 The linear regression fit of the curve shown in , calculate the slope, R square and primer efficiency. These are summarized in the following Table 1:

[0368]

[0369] Table 1: Data analysis Figure 1 as shown in .

[0370] All four spike-in sequences tested showed the expected slope (close to 3.33), high R-squared (above 0.99) and very good primer efficiency (between 90%-100%). Each specific miRcury LNA assay was used in qPCR tests against other non-homologous spike-in RNAs as well as biological PAXgene RNA samples. No amplification was observed (data not shown), indicating that the miRcury LNA assay is specific only for the homologous RNA.

[0371] Next, each spike-in was prepared as a 2-fold dilution, added to PAXgene RNA samples and subjected to miRcury qPCR assay. Figure 2 The data from this experiment are summarized.

[0372] Calculation from Figure 2 The R-squared values ​​for each spike-in RNA are shown in

[0373] Table 2. Overall, for all spike-in RNAs, the R-squared was 0.99 or close to it.

[0374]

[0375] Table 2: Data analysis Figure 2 as shown in .

[0376] Next, a test mixture of 4 spike-ins was prepared in nuclease-free water with the following concentrations of RNA: SEQ ID NO: 1-340 pM, SEQ ID NO: 2-72.5 pM, SEQ ID NO: 3-2 pM, SEQ ID NO: 4-0.7 pM. 10 μL of this test mixture was added to the PAXgene lysate shortly before extraction. PAXgene was extracted using the QIAsymphony PAXgene extraction kit (QIAgen, Venlo, the Netherlands) and eluted in 200 μL elution buffer. This RNA was then used for expression measurement using qPCR. The results are summarized in Table 3, where the predicted and measured Ct values ​​are compared. Overall, the measured Ct values ​​were lower than the predicted values, indicating that the extraction efficiency was good.

[0377]

[0378] Table 3: Summary of results from test extractions when spikes were provided to PAXgene samples in the given amounts. After RNA extraction, qRT-PCR data were obtained and compared to predicted data.

[0379] In the next step, the PAXgene RNA samples described in Table 3 were used to prepare next generation sequencing libraries using the miRNA QIAseq NGS library kit (QIAgen, Venlo, The Netherlands) according to the manufacturer's recommendations. Immediately after the cDNA purification step, the cDNA was diluted 1:10 and used for qPCR reactions using spike-in specific forward primers and universal reverse primers. Specifically, the following primers were used:

[0380] SpikeIn_1_Forward: GATAGATACGCCAGTACCGCC (SEQ ID NO: 5),

[0381] SpikeIn_2_Forward: AACGAAGCTCCACGATGTAGTAGG (SEQ ID NO: 6),

[0382] SpikeIn_3_Forward: TGTACGGAAATATTGGCTACC (SEQ ID NO: 7),

[0383] SpikeIn_4_Forward: TTCATACGTTGCCCAATCCAG (SEQ ID NO: 8), and

[0384] SpikeIn_Reverse: ATGCATGCATGCATTGATGGTGCCTACAGTT (SEQ ID NO: 9).

[0385] The results are listed in Table 4, while the R-squared of the spike-in expression measured by qPCR against the NGS library is shown in Figure 3 Shown in.

[0386]

[0387] Table 4: Data analysis Figure 3 as shown in .

[0388] Overall, acceptable linearity (>0.95) was obtained for cDNA measured from the library preparation process.

[0389] Finally, cDNA samples from previous experiments were used for library PCR and NGS. After data preprocessing, the raw read counts were assigned to the various spike-ins (Table 5). It was observed that the spike-ins were also expressed in the expected linear order for the level of NGS read counts ( Figure 4).

[0390]

[0391] Table 5: Data analysis Figure 4 as shown in .

[0392] Example 2

[0393] The above-mentioned artificial spike-in RNA (having a nucleotide sequence according to SEQ ID NO: 1 to SEQ ID NO: 4) is further used to identify artificial target RNAs produced by next-generation sequencing (NGS) technology. Specifically, target RNAs with 5' and / or 3' end attachments are identified. During NGS, portions of other RNAs are found to be attached to the spike-in RNA at both ends (5' and 3'). These portions are referred to herein as suffix sequences, and the suffix sequences include a prefix sequence (5') and a suffix sequence (3'). The adapter sequence of the adapter used in the NGS process is also found to be attached to the spike-in RNA.

[0394] The inventors' internal (HBDX) lung disease dataset (blood samples) and external TCGA dataset (tissue samples from cancer patients) were used. The HBDX dataset contains spike-ins and was used to identify suffixed sequences. The same suffixed sequences were also found in TCGA (with a lower probability) despite the addition of spike-ins. In part, the suffixed sequences can be linked to the 5'-linker sequence GTTCAGAGTTCTACAGTCCGACGATC (SEQ ID NO: 84) commonly used during small RNA NGS. In this regard, we would like to refer to Figure 5 .

[0395] Using the identified suffix sequences attached to the spike-in RNA, it is also possible to identify target RNAs that may be artificial / modified, i.e., they are generated by the same "suffix sequence attachment" mechanism as the spike itself. Since these target RNAs may be misleading during data analysis, they should be removed to improve the quality of the dataset and subsequent analysis.

[0396] To prevent erroneous removal of target RNAs, only target RNAs with a minimum suffix sequence length of more than 5 nucleotides were removed from subsequent analyses / datasets.

[0397] Figure 6 : shows the distribution of spike-in RNAs having nucleotide sequences according to SEQ ID NO: 1 to SEQ ID NO: 4 and the length of the suffixed sequences. Higher spike-in expression leads to the discovery of more suffixed sequences.

[0398] Figure 7Shown are the average expression of spike-in RNAs (length: 21 nucleotides) as well as their longer artifacts (spiking-in plus suffixed sequences of increasing length).

[0399] Figure 8 Shown is the number of sequences that would be removed in two data sets (HBDX, TCGA) if only the suffixed sequences of the minimum length (or longer) are considered. Theoretical guidance is given as the percentage of coincidental matches in all possible nucleotide sequences of the same length as the suffixed sequence. The lower this theoretical error percentage is, the longer the minimum length that must be selected is. However, when the minimum length is 5, the error percentage is reduced to less than 0.5%, which may be acceptable in most backgrounds.

Claims

1. A composition comprising at least 3 RNA molecules, wherein: The at least three RNA molecules are selected from the group consisting of RNA molecules having nucleotide sequences according to SEQ ID NO: 1 to SEQ ID NO: 4, fragments thereof, and sequences having at least 80% sequence identity therewith.

2. The composition according to claim 1, wherein The composition comprises four RNA molecules, wherein the four RNA molecules are selected from the group consisting of RNA molecules having nucleotide sequences according to SEQ ID NO: 1 to SEQ ID NO: 4, fragments thereof, and sequences having at least 80% sequence identity therewith.

3. The composition according to claim 1 or 2, wherein The composition comprises an RNA molecule having a nucleotide sequence according to: (i) SEQ ID NO: 1, SEQ ID NO: 2, and SEQ ID NO: 3, (ii) SEQ ID NO: 2, SEQ ID NO: 3, and SEQ ID NO: 4, (iii) SEQ ID NO: 1, SEQ ID NO: 3, and SEQ ID NO: 4, (iv) SEQ ID NO: 1, SEQ ID NO: 2, and SEQ ID NO: 4, or (v) SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, and SEQ ID NO:

4.

4. The composition according to any one of claims 1 to 3, wherein The at least three RNA molecules contained in the composition have a characteristic distribution.

5. The composition according to claim 4, wherein The at least 3 RNA molecules contained in the composition have the characteristic distribution in terms of their amounts.

6. The composition according to claim 5, wherein The at least three RNA molecules are included in the composition in different amounts.

7. The composition according to claim 6, wherein Any arbitrary pair of two RNA molecules contained in the composition has different amounts.

8. The composition according to any one of claims 6 or 7, wherein The at least three RNA molecules are contained in the composition in a gradient of defined amounts.

9. The composition according to any one of claims 1 to 8, wherein The at least 3 RNA molecules contained in the composition are present in an amount between 0.001 amol and 6000 amol, preferably, in an amount between 0.01 amol and 5000 amol, more preferably, in an amount between 0.1 amol and 4000 amol, and even more preferably, in an amount between 1 amol and 3500 amol.

10. The composition according to claim 9, wherein The 4 RNA molecules contained in the composition are present in an amount between 0.001 amol and 6000 amol, preferably, in an amount between 0.01 amol and 5000 amol, more preferably, in an amount between 0.1 amol and 4000 amol, and even more preferably, in an amount between 1 amol and 3500 amol.

11. The composition according to any one of claims 1 to 10, wherein The composition comprises at least 3 RNA molecules, and wherein The first RNA molecule is included in an amount of about 3400 amol, the second RNA molecule is included in an amount of about 725 amol, and the third RNA molecule is included in an amount of about 20 amol, or The first RNA molecule is included in an amount of about 1360 amol, the second RNA molecule is included in an amount of about 290 amol, and the third RNA molecule is included in an amount of about 80 amol.

12. The composition according to claim 11, wherein The composition comprises 4 RNA molecules, and wherein The first RNA molecule is included in an amount of about 3400 amol, the second RNA molecule is included in an amount of about 725 amol, the third RNA molecule is included in an amount of about 20 amol, and the fourth RNA molecule is included in an amount of about 7 amol, or The first RNA molecule is included in an amount of about 1360 amol, the second RNA molecule is included in an amount of about 290 amol, the third RNA molecule is included in an amount of about 80 amol, and the fourth RNA molecule is included in an amount of about 27 amol.

13. The composition according to any one of claims 1 to 12, wherein The RNA molecule is an artificial RNA molecule, which does not exist in nature.

14. The composition according to any one of claims 1 to 13, wherein The composition is suitable as / is an admixture mixture.

15. The composition according to any one of claims 1 to 14, wherein The composition is suitable as / is a standard for process control and / or normalization.

16. The composition according to any one of claims 1 to 15, wherein The composition is a solution.

17. The composition according to claim 16, wherein The solution is an aqueous solution.

18. The composition according to claim 17, wherein The aqueous solution is water.

19. The composition according to claim 18, wherein The water is nuclease-free water.

20. A kit comprising the composition according to any one of claims 1-19.

21. The kit according to claim 20, wherein The kit further comprises means for determining the levels of the at least three RNA molecules contained in the composition.

22. Use of the composition according to any one of claims 1 to 19 or the kit according to claim 20 or 21 as a standard for process control, sample detection, normalization and / or data processing control.

23. The use according to claim 22, wherein The process control includes quality control or end-to-end control.

24. The use according to claim 22 or 23, wherein The data processing controls include raw data processing controls.

25. A method for detecting a sample, the method comprising the following steps: The sample is assessed with respect to at least 3 RNA molecules contained in / from the composition according to any one of claims 1-19.

26. The method according to claim 25, wherein: The sample is a mixture of biological material and the composition according to any one of claims 1-19.

27. The method according to claim 26, wherein: The biological material is blood.

28. The method according to claim 27, wherein: The blood is whole blood or a blood fraction.

29. The method according to claim 28, wherein: The blood component is selected from the group consisting of blood cell components and plasma or serum.

30. The method according to any one of claims 25 to 29, wherein: The sample is based on / derived from a mixture of biological material and a composition according to any one of claims 1-19.

31. The method according to any one of claims 25 to 30, wherein: The samples are processed samples.

32. The method according to claim 31, wherein: The processed sample is a lysed sample, an extracted sample, an amplified sample, a sequenced sample or a library prepared sample.

33. The method according to claim 31 or 32, wherein: The processed sample is obtained by mixing a biological material with a composition according to any one of claims 1 to 19 and further processing the mixture thus obtained.

34. The method according to any one of claims 25 to 33, wherein: The evaluating comprises determining whether the at least three RNA molecules show a characteristic distribution.

35. The method of claim 34, wherein: If the characteristic profile is given, the sample is further processed and / or analyzed.

36. The method of claim 35, wherein: The characteristic distribution is given if: The at least three RNA molecules are present at their expected levels, The at least 3 RNA molecules are present in their expected order / rank, which order / rank is defined by the relationship of the levels, in particular the amounts, of the at least 3 RNA molecules, and / or The at least three RNA molecules are present in their expected linearity.

37. The method of claim 36, wherein: The expected order is such that the Spearman rank correlation coefficient (Spearman's p) is ≥ 0.95, and / or the expected linearity is such that the Pearson correlation coefficient (Pearson's r) is ≥ 0.

66.

38. The method of claim 34, wherein: If the characteristic profile was not given, the sample was not processed further and was discarded.

39. The method of claim 38, wherein: The characteristic distribution is not given if: The at least three RNA molecules are not present at their expected levels, The at least 3 RNA molecules are not present in their expected order / rank, which is defined by the relationship of the levels, in particular the amounts, of the at least 3 RNA molecules, and / or The at least three RNA molecules are not present in their expected linearity.

40. The method of claim 39, wherein: The expected order is such that the Spearman rank correlation coefficient (Spearman's p) is ≥ 0.95, and / or the expected linearity is such that the Pearson correlation coefficient (Pearson's r) is ≥ 0.

66.

41. The method according to any one of claims 33 to 40, wherein: The further processing encompasses lysing the sample, extracting the sample, amplifying the sample, sequencing the sample and / or preparing a library from the sample.

42. The method according to claim 41, wherein: The further processing encompasses lysing the cells to release the nucleotide sequences contained in the sample, extracting the nucleotide sequences contained in the sample, amplifying the nucleotide sequences contained in the sample, sequencing the nucleotide sequences contained in the sample and / or preparing a library from the nucleotide sequences contained in the sample.

43. The method of claim 42, wherein: The nucleotide sequence is a ribonucleotide sequence.

44. The method of claim 43, wherein: The ribonucleotide sequence belongs to a target RNA molecule.

45. The method of claim 44, wherein: The target RNA molecule is a small RNA molecule.

46. ​​The method of claim 45, wherein: The small RNA molecule is a non-coding small RNA molecule, preferably a miRNA molecule.

47. The method according to any one of claims 25 to 46, wherein: The assessment comprises identifying 5' and / or 3' additions to the at least 3 RNA molecules contained in / from the composition according to any one of claims 1-19.

48. The method of claim 47, wherein: The 5' and / or 3' additions have a length of at least 5 nucleotides.

49. The method of claim 48, wherein: The at least 5 nucleotides extend beyond the original length of the at least 3 RNA molecules.

50. The method according to any one of claims 47 to 49, wherein: The 5'-end and / or 3'-end additions are the result of RNA molecule / linker fusion, RNA molecule / RNA molecule fusion, or linker / linker fusion.

51. The method according to any one of claims 25 to 50, wherein: The sample contains target RNA molecules.

52. The method of claim 51, wherein: The presence of 5' and / or 3' terminal additions identified relative to the at least 3 RNA molecules contained in / from the composition according to any one of claims 1-19 indicates the presence of 5' and / or 3' terminal additions in the target RNA molecule.

53. The method according to claim 51 or 52, wherein: Target RNA molecules with 5'-end and / or 3'-end additions contained in the sample are excluded from further analysis / are not used further.

54. The method according to any one of claims 51 to 53, wherein: Data from target RNA molecules with 5'-end and / or 3'-end additions contained in said sample are excluded from further analysis / are not used further or do not form part of the dataset, preferably the original dataset.

55. The method according to any one of claims 51 to 54, wherein: The target RNA molecule is a small RNA molecule.

56. The method of claim 55, wherein: The small RNA molecule is a non-coding small RNA molecule, preferably a miRNA molecule.

57. A method for optimizing the processing of a biological sample, the method comprising the following steps: Implement the method according to any one of claims 25-56.

58. A method for optimized RNA preparation from a biological sample or a method for RNA analysis of said biological sample, said method comprising the steps of: Implement the method according to any one of claims 25-56.

59. A method for improving the quality of an RNA dataset, the method comprising the following steps: Implement the method according to any one of claims 25-56.

60. A method for improving the quality of an RNA dataset, the method comprising the following steps: (i) determining the sequence of RNA molecules in a sample, (ii) identifying 5' and / or 3' additions to the RNA molecule, said additions not being part of the RNA molecule in its naturally occurring form, and (iii) excluding RNA molecules having 5'-end and / or 3'-end additions from the RNA dataset analysis / removing RNA molecules having 5'-end and / or 3'-end additions from the RNA dataset.

61. The method of claim 60, wherein: Determining the sequence of the RNA molecule in the sample encompasses: Denaturation of RNA molecules and attachment of 5' adapters and / or 3' adapters to the denatured RNA molecules, The RNA molecule connected with the 5' linker and / or the 3' linker is reverse transcribed into a cDNA molecule, amplification of (the) cDNA molecules, and / or Sequencing of (the) cDNA molecules, preferably next generation sequencing.

62. The method of claim 60 or 61, wherein: The 5' and / or 3' additions have a length of at least 5 nucleotides.

63. The method of claim 62, wherein: The at least 5 nucleotides extend beyond the length of the naturally occurring form of the RNA molecule.

64. The method according to any one of claims 60 to 63, wherein: The 5'-end and / or 3'-end additions are the result of RNA molecule / linker fusion, RNA molecule / RNA molecule fusion, or linker / linker fusion.

65. The method according to any one of claims 60 to 64, wherein: The 5' and / or 3' end additions are selected from the group consisting of additions having nucleotide sequences according to the following: CGATC (SEQ ID NO: 10), GGGGC (SEQ ID NO: 11), ACGATC (SEQ ID NO: 12), GGGCGT (SEQ ID NO: 13), CGGCGG (SEQ ID NO: 14), GGGGCG (SEQ ID NO: 15), GACGATC (SEQ ID NO: 16), GGGGCGT (SEQ ID NO: 17), GGGCGTG (SEQ ID NO: 18), GGGGGCG (SEQ ID NO: 19), GGGGGTG (SEQ ID NO: 20), GGGGCGTG (SEQ ID NO: 21), CGGGGCGG (SEQ ID NO: 22), GGGAGGCC (SEQ ID NO: 23), GGAGGCGT (SEQ ID NO: 24), GGGCGTGG (SEQ ID NO: 25). NO: 25), TGGAGGCG (SEQ ID NO: 26), CGACGATC (SEQ ID NO: 27), GGGGCGTT (SEQ ID NO: 28), GGGCGTGT (SEQ ID NO: 29), GGGGGCGT (SEQ ID NO: 30), GGGAGCCA (SEQ ID NO: 31), GGGGGTGT (SEQ ID NO: 32), GGAGGCCC (SEQ ID NO: 32) NO: 33), CCGACGATC (SEQ ID NO: 34), GGGGGCGTG (SEQ ID NO: 35), TACCTGGTT (SEQ ID NO: 36), TGGAGGCGT (SEQ ID NO: 37), GGGCGTGGG (SEQ ID NO: 38), CGGCGGCGG (SEQ ID NO: 39), GGGGGTGTA (SEQ ID NO: 40), GGGGGCGTT (SEQ ID NO: 41), GGCTGGGCG (SEQ ID NO: 42), TCGGGGCGG (SEQ ID NO: 43), GGGGCGTGG (SEQ ID NO: 44), GGGGAGCCA (SEQ ID NO: 45), GGGAGGCCC (SEQ ID NO: 46), CGGAGGGCGG (SEQ ID NO: 47), GTCCGCGATC (SEQ ID NO: 48), GTCGACGATC (SEQ ID NO: 49), CGGGCGGATC (SEQ ID NO: 50),TGGAGGCGTG(SEQ ID NO:51),TCCGACGATC(SEQ ID NO:52),GGGGCGTGGG(SEQ ID NO:53),AAGCGGGGCT(SEQ ID NO:54),CGGGGAGCCA(SEQ ID NO:55),GTCCGACGATC(SEQ ID NO:56),TCGGAGGGCGG(SEQ ID NO:57),AGTCCGACGATC(SEQ ID NO:58),AAGCGGGGCTGG(SEQ ID NO:59),GTCCGACGGATC(SEQ ID NO:60),TCGGGCTGGGGC(SEQ IDNO:61),TACCTGGTTGAT(SEQ ID NO:62),TCGGGGCGGCGG(SEQ ID NO:63),CAGTCCGACGATC(SEQ ID NO:64),TACCTGGTTGATC(SEQ ID NO:65),TCGGGCTGGGGCG(SEQ ID NO:66),TGGAGGCGTGGGT(SEQ ID NO:67),ACAGTCCGACGATC(SEQ ID NO:68),GGTCGGGCTGGGGC(SEQID NO:69),CGGAAGCGTGCTGGG(SEQ ID NO:70),GGTCGGGCTGGGGCG(SEQ ID NO:71),TACAGTCCGACGATC(SEQ ID NO:72),CGGAAGCGTGCTGGGC(SEQ ID NO:73),CTACAGTCCGACGATC(SEQ ID NO:74),TCTACAGTCCGACGATC(SEQ ID NO:75),CGGAAGCGTGCTGGGCCC(SEQ ID NO:76),TCGGGGCGGCGGCGGCGG(SEQ ID NO:77),TTCTACAGTCCGACGATC(SEQ ID NO:78),TAGCAGCACATCATGGTT(SEQ ID NO:79),GGATCATTA(SEQ ID NO:80),GGGGCGTGGG(SEQ IDNO:81),TGGAGGCGTGGGT(SEQ ID NO:82)。, 66. The method according to any one of claims 60 to 65, wherein: Determining the 5' and / or 3' additions of the RNA molecule encompasses analyzing whether the RNA molecule is at least partially identical in sequence to an adapter sequence used in a sequencing, preferably next generation sequencing, process.

67. The method of claim 66, wherein: Add the following identification number: TGGAAT GGTTCAGTCAGTCQCAQGGTCQ ID NO:83),GTTCAGAGTTCTACAGTCCGACGA TC(SEQ ID NO:84),TGGAATTCTCGGGTGCCAAGG(SEQ ID NO:85),GAATTCCACCACGTTCCCGTGG(SEQ ID NO:86),AAT GATACGCCGACCGATTCAGTCAGTCAGTCATTGATTGATTGATT GA(SEQ ID NO:87),CAAGCAGAAGACGGCATACGAGAT(SEQ ID NO:88),GTGACTGGAGTTCCTTGGCACCCGAGAATTCCA(SEQ ID NO:89),CAAGCAGAAGACGGCATACGA(SEQ ID NO:90)? NO:91),TC GTATGCCGTCTTCTGCTTGT(SEQ IDNO:92),ATCTCGTATGCC GTCTTCTGCTTG(SEQ ID NO:93),CAAGCAGAAGACGGCATAC GA(SEQ IDNO:94),AATGATACGACGACCAGGCGAGTCGAGTTCGAGTCAGAGTCGA(SEQ IDNO:92),ATCTCGTATGCC(SEQ IDNO:93). NO:95),CGACAGGTTCAGAGTT CTACAGTCCGACGATC(SEQ ID NO:96),AAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(SEQ ID NO:97),AGATCGGAAGAGCACACGTCTGAACTCCAGTCA(SEQ IDNO:98),AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT(SEQ ID NO:99),GATCGGAAGAGCACACGTGAACTCCAGTCAC(SEQ ID NO:100),ATCTCGTATGCCGTCTTCTGCTTG(SEQ ID NO:101),AATGATACGGCGACCACCGAGATCTACAC(SEQ ID NO:102),ACACTCTTTCCCTACACGACGCTCTTCCGATCT(SEQ ID NO:103)。 68. The method according to any one of claims 60-67, wherein the RNA dataset analysis is an RNA raw dataset analysis / wherein the RNA dataset is a raw RNA dataset.

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