Melt shape genotyping

Abstract

The present disclosure provides a method for differentiating genotypes of nucleic acid in a multiplex amplification reaction. The method includes generating one or more melt curve profiles for each target region of nucleic acid, classifying the melt curve profiles into genotype cluster(s), wherein classifying is performed by normalizing and calculating a derivative for each melt curve profile to form a processed melt curve profile, creating a density map using data from each processed melt curve profile as coordinates for the density map, wherein each genotype forms a genotype cluster in the density map and the proximity of each genotype cluster to another genotype cluster is determined by the similarity of the genotype's processed melt curve profile, and differentiating and optionally identifying the genotypes of nucleic acid based on classification of the melt curve profiles.

Description

MELT SHAPE GENOTYPINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is being filed on February 14, 2025, as a PCT International application and claims the benefit of and priority to U.S. Provisional Application No. 63 / 553,532, filed February 14, 2024; the disclosure of which is hereby incorporated by reference in its entirety.FIELD

[0002] The present disclosure relates generally to methods for detection and differentiation of target nucleic acids by melt profile analysis in multiplex nucleic acid amplification-based assays.BACKGROUND

[0003] Genotyping methods commonly involve the production of melting or annealing curves for the duplexes formed when a nucleic acid sample binds to a nucleic acid probe. Curves of the primary or derivative melting / annealing data are then analyzed in order to classify the tested nucleic acid sample into a genotype category. For example, studies show that nine (9) specific melting temperature (Tm) window patterns generated due to fluorescent probe-target hybrid melting in the Xpert MTB / RIF Ultra assay from four (4) rpoB melt probes can be utilized to specifically identify mutations in Rifampicin Resistance Determining Region (RRDR). Cao et al., 2019, J Clin Microbiol. These 4 probes are overlapping probes covering the 80 bp RRDR region and cover the entire region from codons 507 to 533. Cao et al. uses an algorithm to identify forty -one (41) different Tm window patterns to identify different rpoB RRDR sequence types and hence differentiate RIF-susceptible (RIF-S) and RIF- resistant (RIF-R) strains, as well as identify individual mutations in this 80 bp RRDR sequence using post PCR melt curves. Genotyping based on melting curve analysis has limitations. For example, such methods are of limited effectiveness when dealing with a diversity of genotypes, including assays with a large number of genotypes and / or instances where the quality of the melting curves is inconsistent. More specifically, Cao’s approach is limited to looking only at identifiable Tm values falling within specific defined Tm windows, which is a single-metric identification approach.Accordingly, there is a need for an automated method for genotyping melting curves in assays with large numbers of genotypes and / or where the quality of the melting curvesis inconsistent, and / or where Tm value of a genotype may fall outside a specific defined Tm window.SUMMARY

[0004] Various embodiments contemplated herein may include, but need not be limited to, one or more of the following:

[0005] Methods for detecting and differentiating genotypes of nucleic acid in a multiplex amplification reaction comprising contacting the nucleic acid with a set of primers for amplifying target regions of the nucleic acid and a set of fluorescent probes for detecting amplicon(s) formed therefrom; subjecting the nucleic acid, primers, and fluorescent probes to amplification conditions to amplify the target regions of the nucleic acid; generating one or more melt curve profiles for each target region of nucleic acid amplified; classifying the melt curve profiles into genotype cluster(s), wherein classifying is performed by normalizing and calculating a derivative for each melt curve profile to form a processed melt curve profile, creating a density map using data from each processed melt curve profile as coordinates for the density map, wherein each genotype cluster represents a genotype in the density map and the proximity of each genotype cluster to another genotype cluster is determined by a similarity in the value of the coordinates; and differentiating and optionally identifying the genoty pes of nucleic acid based on classification of the melt curve profiles. The higher similarity in the value of the coordinates represents more closely related genotypes. In some instances, differentiating and / or identifying the genotypes of nucleic acid is based on a machine learning model trained with historical densify maps generated from processed melt curve profiles of the genotypes to be differentiated and / or identified.

[0006] Automated methods for detecting and differentiating genotypes of nucleic acid in a multiplex amplification reaction are also provided herein. The automated method comprises a) receiving a biological sample comprising a cellular material into a cartridge comprising a plurality' of chambers therein, yvherein the plurality of chambers includes i) a sample chamber for receiving a biological sample comprising cellular material; ii) a lysis chamber in fluidic communication with the sample chamber and having a means for releasing nucleic acid from the cellular material; iii) a reaction vessel fluidically coupled to the plurality of chambers and configured for amplification of nucleic acids and detection of one or a plurality of amplification products; and iv) a filter disposed in a fluidic path between the lysis chamber and reaction vessel, for capturing nucleic acid released from the cellularmaterial; b) releasing nucleic acid from the biological sample inside the cartridge; c) contacting the nucleic acid with a set of primers for amplifying target regions of the nucleic acid and a set of fluorescent probes for detecting any amplicon formed; d) subj ecting the nucleic acid, primers, and fluorescent probes to amplification conditions to amplify' the target regions of the nucleic acid; e) generating one or more melt curve profiles for each target region of nucleic acid amplified; f) classifying the melt curve profiles into genotype cluster(s). wherein classifying is performed by normalizing and calculating a derivative for each melt curve profile to form a processed melt curve profile, creating a densify map using data from each processed melt curve profile as coordinates for the densify map, wherein each genotype forms a genotype cluster in the densify map and the proximity of each genotype cluster to another genotype cluster is determined by the similarity of the genotype’s processed melt curve profile; and g) differentiating and optionally identifying the genotypes of nucleic acid based on classification of the melt curve profiles.

[0007] In the methods provided herein, the means for releasing nucleic acid from the cellular material can comprise mechanical lysis, chemical lysis, or a combination thereof. In some embodiments, the reaction vessel of the cartridge comprises a single reaction chamber, wherein detection of all the genotypes of nucleic acid present in the multiplex amplification reaction is within the single reaction chamber. In other embodiments, the reaction vessel of the cartridge comprises a plurality of reaction chambers (such as 2, 3, or 4 reaction chambers), wherein detection of the genoty pes of nucleic acid is within the plurality' of reaction chambers.

[0008] The nucleic acid can be present in a sputum sample, a nasal aspirate sample, a nasal wash sample, a nasal swab sample, a nasopharyngeal swab sample, a saliva sample, an oropharyngeal swab sample, a throat swab sample, a bronchoalveolar lavage sample, a bronchial aspirate sample, a bronchial wash sample, an endotracheal aspirate sample, an endotracheal wash sample, a tracheal aspirate sample, a nasal secretion sample, a mucus sample, a pleural effusion sample, a cerebrospinal fluid sample, a stool sample, a tissue biopsy' sample, a breath sample, or a combination thereof. In some embodiments, the method is a point-of-care method. In some embodiments, the method comprises detecting, differentiating, and optionally identify ing the genotypes of nucleic acid within 150 minutes, within 140 minutes, within 130 minutes, or within 120 minutes of collecting the nucleic acid from a subject.

[0009] As described herein, the methods are for detecting and differentiating genotypes of nucleic acid in a multiplex amplification reaction. In some embodiments, the methods comprise detecting, differentiating, and optionally identifying 10 or more genotypes, 15 or more genotypes, 20 or more genotypes, 25 or more genotypes, 30 or more genotypes, 35 or more genoty pes, 40 or more genotypes in the multiplex amplification reaction. The genotypes can include genes from bacterial pathogens, viral pathogens, fungal pathogens, or a combination thereof. In certain embodiments, the genotypes are selected from one or more genes of Mycobacterium tuberculosis (such as Mycobacterium tuberculosis genes related to antimicrobial resistance, Mycobacterium tuberculosis genes related to antimicrobial susceptibility (e.g., RIF susceptibility), or wild-type Mycobacterium tuberculosis genes), one or more nontuberculous mycobacteria, or a combination thereof. For example, the genotypes can comprise Mycobacterium tuberculosis genes related to antimicrobial resistance and can be selected from Rifampicin (RIF) resistance, isoniazid (INH) resistance, fluoroquinolone (FLQ) resistance including Levofloxacin (LFX) and Moxifloxacin (MFX), ethionamide (ETH) resistance, rifampicin resistance, amikacin (AMK) resistance, capreomycin (CAP) resistance, kanamycin (KAN) resistance, bedaquiline (BDQ) resistance, clofazimine (CFZ) resistance, delamanid (DLM) resistance, ethambutol resistance (E), linezolid resistance (LZD), pyrazinamide (Z) resistance, streptomycin (S) resistance. Ethionamide (ETO) or Prothionamide (PTO) resistance, Pretomanid (Pa) resistance or a combination thereof.

[0010] The set of fluorescent probes used for detection of the genotypes can comprise one or more sloppy molecular beacon (SMB) probes. In some embodiments, the set of fluorescent probes consists of sloppy molecular beacon probes. In other embodiments, the set of fluorescent probes comprise molecular beacon probes, sloppy molecular beacon probes, TaqMan probes, or a combination thereof. Each probe in the set, optionally each of the SMB probe, can have a polynucleotide sequence from 20 to 80 nucleotides long, such as from 20 to 60 nucleotides long, or from 30 to 50 nucleotides long. In some examples, detecting, differentiating, and optionally identifying a genotype utilize one melt curve profile, optionally a melt curve profile derived from a SMB probe. In other examples, detecting, differentiating, and optionally identify ing a genotype utilize a plurality of melt curve profiles, optionally melt curve profiles derived from a plurality of SMB probes. At least one of the primers and / or probe(s) comprises a detectable label. In some examples, at least one probe, optionallyeach probe in the multiplex amplification reaction, comprises a fluorescent dye and a quencher molecule.

[0011] In some examples, the methods described herein comprise detecting, differentiating, and optionally identifying INH resistance genotype, and wherein the method utilizes melt curve profile(s) for each of the inhA promoter region, the katG gene, the fabGl gene, and the oxyR-ahpC (ahpC) intergenic region. In some examples, the methods comprise detecting, differentiating, and optionally identifying ETH resistance genotype, and wherein the method utilizes melt curve profile(s) for the inhA promoter region. In some examples, the methods comprise detecting, differentiating, and optionally identifying FLQ resistance genotype, and wherein the method utilizes melt curve profile(s) for each of the gyrA gene and the gyrB gene. In some examples, the methods comprise detecting, differentiating, and optionally identifying AMK, KAN, and CAP resistance genotypes, and wherein the method utilizes melt curve profile(s) for each of the rrs gene and the eis promoter region. In some examples, the methods comprise detecting, differentiating, and optionally identifying rifampicin resistance genotype, and wherein the method utilizes melt curve profile(s) for the rpoB gene.

[0012] The methods can further comprise generating real time-PCR profile for one or more of the target regions of nucleic acid. Optionally, the methods comprise generating real time-PCR profile for the target regions for detecting the IS6110 gene and IS 1081 gene of Mycobacterium tuberculosis. The IS61 10 gene and IS 1081 gene are highly conserved in Mycobacterium tuberculosis and can be used for detecting and / or identifying the presence of Mycobacterium tuberculosis, preferably by rt-PCR. In some embodiments, the methods can further comprise determining a melt temperature for each amplified nucleic acid. The melting temperature of the amplicons forming a probe-target hybrid can be utilized in detecting, differentiating, and optionally identifying the genotypes. In some embodiments, the methods disclosed herein do not utilize melt temperature in detecting, differentiating, and optionally identifying the genotypes. In some embodiments, the methods disclosed herein do not utilize high-resolution melt (HRM).

[0013] The methods described herein can differentiate and optionally identify two or more genoty pes having melt temperatures with less than 4°C, or less than 2°C, or less than 1°C temperature separation. In certain embodiments, the methods differentiate and optionally identify two or more genotypes having identical melttemperatures, but different melt patterns. In further embodiments, the methods differentiate and optionally identify two melt profiles due to SNP within a genotype.

[0014] As described herein, the method can be an automated method for detecting and differentiating genotypes of nucleic acid in a multiplex amplification reaction. Automated methods include cartridge-based methods, wherein the method comprises placing the nucleic acid in a sample chamber of a cartridge; and if the nucleic acid comprises cells, lysing the cells with one or more lysis reagents present within at least one of the plurality of chambers or capturing the cells in a filter within the cartridge and lysing the cells by means of ultrasonication to release the nucleic acid; or capturing the nucleic acids in a nucleic acid capture chamber and eluting the captured nucleic acid after washing to remove impurities.

[0015] Biological analysis systems for performing the methods herein are also disclosed. The system can comprise one or more modules, each of the one or more modules configured to process a biological sample via a cartridge and detect one or more properties of the biological sample; at least one processor; and a memory operatively coupled to the processor and having instructions stored thereon to cause the processor to execute, for each module from the one or more modules, and perform the method for detecting and differentiating genoty pes of nucleic acid; and upon detecting and differentiating genotypes of nucleic acid, present a message to at least one of a user, operator, and manager of the system.

[0016] Overall, methods and systems utilizing melt curve information beyond the melt temperature (Tm) values to detect and discriminate genoty pes are described herein. In some of the methods described, the spatial orientations of a first derivative melt curve contours are analyzed, optionally in addition to Tm values to perform genotypic discrimination of, for example, rpoB RRDR. Analysis of the melt curves demonstrates that: (i) melt curve signatures allowed discrimination of wild ty pe and individual non-wild type (NWT i.e. mutant) sequence variations, (ii) familial clusters involving different genotypes can be identified, (iii) discrimination of different genotypes even with same Tm values can be performed, (iv) Tm is but just a single point in the entire first derivative melt curve, which is limited in its capacity to discriminate sequences, and (v) using the melt pattern i.e. the dynamic change in the shape of the melt curve as the fluorescence of the melt probe changes while disassociating from the target result in much better genotype discrimination.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG. 1 illustrates a topographic density map based on Dynamic Quantum Clustering data (DQC).

[0018] FIG. 2 shows Wild Type (WT) samples form a homogenous cluster and is an output from DQC.

[0019] FIG. 3 shows RIF -INDETERMINATE result is caused by fluctuations in the early portion of the curve. DQC still correctly clusters these curves with other Wild Type results.

[0020] FIG. 4 illustrates non-tuberculous Mycobacteria (NTM) samples formed 4 distinct clusters, separate from all MTB genotypes.

[0021] FIG. 5 illustrates genotypes Wild Type and R529K have the same Tm’s in probe 2, but the curves are different between 58°C and 70°C. These differences allow DQC to separate the genotypes using melt curve shapes.

[0022] FIG. 6 shows 54 genotypes separated into 32 single-variant, “Pure” clusters and 9 “Mixed” clusters containing the remaining 22 genotypes. FIG. 6 is a scheme showing the hierarchical relationships between MTB wild type and variants clusters.

[0023] FIG. 7A shows a melt curve for Cluster 1.3. 1. 1. 1 from Fig. 6, and the melt curves for FIG. 7A-D do not vary significantly.

[0024] FIG. 7B shows a melt curve for Cluster 1.3. 1. 1. 1 from Fig. 6, and the melt curves for FIG. 7A-D do not vary significantly.

[0025] FIG. 7C shows a melt curve for Cluster 1.3. 1. 1. 1 from Fig. 6, and the melt curves for FIG. 7A-D do not vary significantly.

[0026] FIG. 7D shows a melt curve for Cluster 1.3. 1. 1. 1 from Fig. 6, and the melt curv es for FIG. 7A-D do not vary significantly.

[0027] FIG. 8A shows a normalized curve for the genotypes D516V and H526L. The normalized curv e shows consistent differences in two regions: from 55- 63°C and from 70-75°C.

[0028] FIG. 8B shows a normalized curve for the genotypes D516V and H526L. The normalized curve shows consistent differences in two regions: from 55- 63°C and from 70-75°C.

[0029] FIG. 9A shows a normalized curve illustrating how the normalization process reduces artifactual variation within a genotype.

[0030] FIG. 9B shows a normalized curve illustrating how the normalization process reduces artifactual variation within a genotype.DETAILED DESCRIPTIONDefinitions

[0031] The term “nucleic acid” refers to a nucleotide polymer, and unless otherwise limited, includes analogs of natural nucleotides that can function in a similar manner (e.g.. hybridize) to naturally occurring nucleotides.

[0032] The term nucleic acid includes any form of DNA or RNA, including, for example, genomic DNA; complementary DNA (cDNA), which is a DNA representation of mRNA, usually obtained by reverse transcription of messenger RNA (mRNA) or by amplification; DNA molecules produced synthetically or by amplification; mRNA; and non-coding RNA.

[0033] The term nucleic acid encompasses double- or triple-stranded nucleic acid complexes, as w ell as single-stranded molecules. In double- or triple-stranded nucleic acid complexes, the nucleic acid strands need not be coextensive (i . e. , a doublestranded nucleic acid need not be double-stranded along the entire length of both strands).

[0034] The term nucleic acid also encompasses any modifications thereof, such as by methylation and / or by capping. Nucleic acid modifications can include addition of chemical groups that incorporate additional charge, polarizability, hydrogen bonding, electrostatic interaction, and functionality to the individual nucleic acid bases or to the nucleic acid as a whole. Such modifications may include base modifications such as 2’-position sugar modifications, 5-position pyrimidine modifications, 8-position purine modifications, modifications at cytosine exocyclic amines, substitutions of 5- bromo-uracil, sugar-phosphate backbone modifications, unusual base pairing combinations such as the isobases isocytidine and isoguanidine, and the like.

[0035] More particularly , in some embodiments, nucleic acids, can include poly deoxyribonucleotides (containing 2-deoxy-D-ribose), polyribonucleotides (containing D-ribose). and any other type of nucleic acid that is an N- or C-gly coside of a purine or pyrimidine base, as well as other polymers containing nonnucleotidic backbones, for example, polyamide (e.g., peptide nucleic acids (PNAs)) and polymorpholino polymers (see, e.g., Summerton and Weller (1997) “Morpholino Antisense Oligomers: Design, Preparation, and Properties,” Antisense & Nucleic Acid Drug Dev. 7: 1817-195; Okamoto et al. (2002) “Development of electrochemicallygene-analyzing method using DNA-modified electrodes,” Nucleic Acids Res. Supplement No. 2: 171-172). and other synthetic sequence-specific nucleic acid polymers providing that the polymers contain nucleobases in a configuration which allows for base pairing and base stacking, such as is found in DNA and RNA. The term nucleic acid also encompasses locked nucleic acids (LNAs), which are described in U.S. Patent Nos. 6,794,499. 6,670,461, 6,262,490, and 6,770,748, which are incorporated herein by reference in their entirety for their disclosure of LNAs.

[0036] The nucleic acid(s) can be derived from a completely chemical synthesis process, such as a solid phase-mediated chemical synthesis, from a biological source, such as through isolation from any species that produces nucleic acid, or from processes that involve the manipulation of nucleic acids by molecular biology tools, such as DNA replication, PCR amplification, reverse transcription, or from a combination of those processes.

[0037] The term “sequence identity ,” in the context of two or more amino acid or nucleotide sequences, refers to two or more sequences that are the same or have a specified percentage of amino acid residues or nucleotides that are the same, when compared and aligned for maximum correspondence, as measured using a sequence comparison algorithm or by visual inspection.

[0038] For sequence comparison to determine percent nucleotide or amino acid sequence identity, typically one sequence acts as a “reference sequence,” to which a “test” sequence is compared. When using a sequence comparison algorithm, test and reference sequences are input into a computer, subsequence coordinates are designated, if necessary', and sequence algorithm program parameters are designated. The sequence comparison algorithm then calculates the percent sequence identity for the test sequence relative to the reference sequence, based on the designated program parameters. Alignment of sequences for comparison can be conducted using BLAST set to default parameters.

[0039] As used herein, the term “gene” encompasses coding sequences, introns, and any associated control sequences that participate in the expression of the coding sequences.

[0040] As used herein, the term '■complementary" refers to the capacity' for precise pairing between tw o nucleotides; i.e., if a nucleotide at a given position of a nucleic acid is capable of hydrogen bonding with a nucleotide of another nucleic acid to form a canonical base pair, then the two nucleic acids are considered to becomplementary to one another at that position. Complementarity between two singlestranded nucleic acid molecules may be “partial,” in which only some of the nucleotides bind, or it may be complete when total complementarity exists between the single-stranded molecules. The degree of complementarity between nucleic acid strands has significant effects on the efficiency and strength of hybridization between nucleic acid strands.

[0041] “Selective hybridization” or “selective annealing” refers to the binding of a nucleic acid to a target nucleic acid in the absence of substantial binding to other nucleic acids present in the hybridization mixture under defined stringency conditions. Those of skill in the art recognize that relaxing the stringency of the hybridization conditions allows sequence mismatches to be tolerated.

[0042] In some embodiments, hybridizations are carried out under stringent hybridization conditions. The phrase “stringent hybridization conditions” generally refers to a temperature in a range from about 5°C to about 20°C or 25°C below the melting temperature (Tm) for a specific sequence at a defined ionic strength and pH. As used herein, the Tm is the temperature at which a population of double-stranded nucleic acid molecules becomes half-dissociated into single strands. Methods for calculating the Tm of nucleic acids are well known in the art (see, e.g., Berger and Kimmel (1987) Methods in Enzymology, Vol. 152: Guide to Molecular Cloning Techniques, San Diego: Academic Press, Inc. and Sambrook et al. (1989) Molecular Cloning: A Laboratory Manual, 2ndEd., Vols. 1 -3, Cold Spring Harbor Laboratory), both incorporated herein by reference for their descriptions of stringent hybridization conditions). As indicated by standard references, a simple estimate of the Tmvalue may be calculated by the equation: Tm =81.5+0.41 (% G+C), when a nucleic acid is in aqueous solution at 1 M NaCl (see, e.g., Anderson and Young, Quantitative Filter Hybridization in Nucleic Acid Hybridization (1985)). The melting temperature of a hybrid (and thus the conditions for stringent hybridization) is affected by various factors such as the length and nature (DNA, RNA, base composition) of the primer or probe and nature of the target nucleic acid (DNA. RNA, base composition, present in solution or immobilized, and the like), as well as the concentration of salts and other components (e.g., the presence or absence of formamide, dextran sulfate, polyethylene glycol). The effects of these factors are well known and are discussed in standard references in the art. Illustrative stringent conditions suitable for achieving specifichybridization of most sequences include a temperature of at least about 60°C and a salt concentration of about 0.2 molar at pH 7. Tmcalculation for oligonucleotide sequences based on nearest-neighbors thermodynamics can carried out as described in "A unified view of polymer, dumbbell, and oligonucleotide DNA nearest- neighbor thermodynamics'’ John Santa Lucia, Jr., PNAS February' 17, 1998 vol. 95 no. 4 1460-1465 (which is incorporated by reference herein for this description).

[0043] The term ’‘oligonucleotide” is used to refer to a nucleic acid that is relatively short, generally shorter than 200 nucleotides, more particularly, shorter than 100 nucleotides, most particularly, shorter than 50 nucleotides. Typically, oligonucleotides are single-stranded DNA molecules.

[0044] The term “primer” refers to an oligonucleotide that is capable of hybridizing (also termed “annealing”) with a nucleic acid and serving as an initiation site for nucleotide (RNA or DNA) polymerization under appropriate conditions (i.e., in the presence of four different nucleoside triphosphates and an agent for polymerization, such as DNA or RNA polymerase or reverse transcriptase) in an appropriate buffer and at a suitable temperature. The appropriate length of a primer depends on the intended use of the primer, but primers are typically at least 7 nucleotides long and, in some embodiments, range from 10 to 30 nucleotides, or, in some embodiments, from 10 to 60 nucleotides, in length. In some embodiments, primers can be, e.g., 15 to 50 nucleotides long. Short primer molecules generally require cooler temperatures to form sufficiently stable hybrid complexes with the template. A primer need not reflect the exact sequence of the template but must be sufficiently complementary to hybridize with a template.

[0045] A primer is said to “anneal to” or “hybridize to” another nucleic acid if the primer, or a portion thereof, hybridizes to a nucleotide sequence within the nucleic acid. The statement that a primer hybridizes to a particular nucleotide sequence is not intended to imply that the primer hybridizes either completely or exclusively to that nucleotide sequence. For example, in some embodiments, amplification primers used herein are said to “anneal to” or be “specific for” a nucleotide sequence. This description encompasses primers that anneal wholly to the nucleotide sequence, as well as primers that anneal partially to the nucleotide sequence.

[0046] The term “primer pair” refers to a set of primers including a 5’ “upstream primer” or “forward primer” that hybridizes with the complement of the 5’end of the DNA sequence to be amplified and a 3’ "dow nstream primer” or “reverse primer” that hybridizes with the 3’ end of the sequence to be amplified. As will be recognized by those of skill in the art, the terms “upstream” and “downstream” or “forward” and “reverse” are not intended to be limiting, but rather provide illustrative orientations in some embodiments.

[0047] As used herein, the term “preamplification primer pair” refers to a pair of primers that is employed in an initial amplification, which is followed by at least one further amplification designed to amplify at least one of the same target nucleic acids as in the initial amplification.

[0048] As used herein, a “nested primer” selectively hybridizes within an amplicon produced from a previous amplification, such as an initial or “preamplification. ”

[0049] As used herein, “symmetric primers” produce double-stranded amplicons.

[0050] As used herein, “asymmetric primers” produce a nucleic acid strand complementary to that of the nucleic acid strand to which they anneal, i.e.. asymmetric primers produce single-stranded amplicons.

[0051] A “probe” is a nucleic acid capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, generally through complementary base pairing, usually through hydrogen bond formation, thus forming a duplex structure. The probe can be labeled with a detectable moiety to permit facile detection of the probe, particularly once the probe has hybridized to its complementary target. Alternatively, however, the probe may be unlabeled, but may be detectable by specific binding with a ligand that is labeled, either directly or indirectly. Probes can vary significantly in size.

[0052] As used herein w ith reference to a portion of a primer or a nucleotide sequence within the primer, the term “specific for” a nucleic acid, refers to a primer or nucleotide sequence that can specifically anneal to the target nucleic acid under suitable annealing conditions.

[0053] The term “target” is used herein with reference to “target nucleic acids,” as well as “target organisms.” The former refers to nucleic acids to be detected, and the latter refers to organisms to be detected. The term, “target nucleic acid” is generally used herein to refer to a segment of nucleic acid that is defined by a primer pair and that gives rise to an amplicon produced in an amplification reaction; the term “amplificationtarget” is also used herein to refer to this type of target nucleic acid. Primers and probes are also said to “target” nucleic acid sequences, and so these sequences can also be understood as “target nucleic acids.” Additionally, primers and probes are said to “target” or “be specific for” genes. In this usage, the primers and probes can be used to detect the presence of a particular gene by specifically hybridizing to a portion of the gene that indicates its presence. The meaning of “target” and “target nucleic acids” will be clear to one of skill in the art from the context in which the term is employed. In some embodiments, multiple target nucleic acids can be detected to detect a single target organism. In some embodiments, a single target nucleic acid can be detected to detect a single target organism. In some embodiments, an assay can employ multiple target nucleic acids for one or more target organisms and single target nucleic acids for one or more different target organisms.

[0054] Amplification according to the present teachings encompasses any means by which at least a part of at least one target nucleic acid is reproduced, typically in a template-dependent manner, including without limitation, a broad range of techniques for amplifying nucleic acid sequences, either linearly or exponentially. Illustrative means for performing an amplifying step include PCR, nucleic acid strandbased amplification (NASBA), two-step multiplexed amplifications, rolling circle amplification (RCA), and the like, including multiplex versions and combinations thereof, for example but not limited to, OLA / PCR, PCR / OLA, LDR / PCR, PCR / PCR / LDR, PCR / LDR, LCR / PCR, PCR / LCR (also known as combined chain reaction — CCR), helicase-dependent amplification (I), and the like. Descriptions of such techniques can be found in, among other sources, Ausubel et al.; PCR Primer: A Laboratory Manual, Diffenbach. Ed., Cold Spring Harbor Press (1995); The Electronic Protocol Book, Chang Bioscience (2002); Msuih et al., J. Clin. Micro. 34:501-07 (1996); The Nucleic Acid Protocols Handbook, R. Rapley, ed., Humana Press, Totowa, N.J. (2002); Abramson et al., Curr Opin Biotechnol. 1993 Feb.;4(l):41-7, U.S. Pat. No. 6,027,998; U.S. Pat. No. 6,605,451, Barany et al.. PCT Publication No. WO 97 / 31256; Wenz et al., PCT Publication No. WO 01 / 92579; Day et al., Genomics. 29(1): 152-162 (1995), Ehrlich et al.. Science 252: 1643-50 (1991); Innis et al., PCR Protocols: A Guide to Methods and Applications, Academic Press (1990); Favis et al., Nature Biotechnology 18:561-64 (2000); and Rabenau et al., Infection 28:97-102 (2000); Belgrader, Barany. and Lubin. Development of a Multiplex Ligation DetectionReaction DNA Typing Assay, Sixth International Symposium on Human Identification,1995; LCR Kit Instruction Manual, Cat. #200520, Rev. #050002, Stratagene, 2002; Barany, Proc. Natl. Acad. Sci. USA 88: 188-93 (1991); Bi and Sambrook, Nucl. Acids Res. 25:2924-2951 (1997); Zirvi et al., Nucl. Acid Res. 27:e40i-viii (1999); Dean et al.. Proc Natl Acad Sci USA 99:5261-66 (2002); Barany and Gelfand, Gene 109: 1-11 (1991); Walker et al., Nucl. Acid Res. 20:1691-96 (1992); Polstra et al., BMC Inf. Dis. 2: 18- (2002); Lage et al., Genome Res. 2003 Feb.;13(2):294-307. and Landegren et al., Science 241: 1077-80 (1988), Demidov. V., Expert Rev Mol Diagn. 2002 Nov. ;2(6): 542-8., Cook et al., J Microbiol Methods. 2003 May ;53(2): 165-74, Schweitzer et al., Curr Opin Biotechnol. 2001 Feb.;12(l):21-7, U.S. Pat. No. 5,830,711, U.S. Pat. No. 6,027,889, U.S. Pat. No. 5,686,243, PCT Publication No. WO0056927A3. and PCT Publication No. WO9803673A1.

[0055] In some embodiments, amplification comprises at least one cycle of the sequential procedures of: annealing at least one primer with complementary or substantially complementary sequences in at least one target nucleic acid; synthesizing at least one strand of nucleotides in a template-dependent manner using a polymerase; and denaturing the newly-formed nucleic acid duplex to separate the strands. The cycle may or may not be repeated. Amplification can comprise thermocycling or can be performed isothermally.

[0056] As used herein, the term “amplification conditions” refers to conditions that promote amplification of a target nucleic acid in the presence of suitable primers.

[0057] As used herein, “in solution” means not immobilized on a substrate of any kind, for example, a bead or a surface in a cassette, such as a chamber wall.

[0058] A “multiplex amplification reaction” is one in which two or more nucleic acids distinguishable by sequence are amplified simultaneously.

[0059] The term “qPCR” is used herein to refer to quantitative real-time polymerase chain reaction (PCR), which is also known as “real-time PCR” or “kinetic polymerase chain reaction;” all terms refer to PCR with real-time signal detection.

[0060] The terms “melt pattern,” “melt curve pattern,” “melt shape,” or “melt curve shape” are used interchangeably. The terms as used herein to refer to the morphological characteristics of the melt curve, particularly, a geometry (e.g., angular melt rate) throughout the curve that describes the dissociation characteristics of a segment of double-stranded nucleic during heating.

[0061] The term “melt curve analysis” refers to the use of the dissociation characteristics of a segment of double-stranded DNA during heating. Originally, stranddissociation was observed using UV absorbance measurements, but techniques based on fluorescence measurements are now the most common approach. The temperaturedependent dissociation between two DNA-strands can be measured in a ‘'melt assay,’’ for example, using a DNA-intercalating fluorophore, such as SYBR green or EvaGreen, or fluorophore-labelled DNA probes. In the case of SYBR green (which fluoresces 1000-fold more intensely while intercalated in the minor groove of two strands of DNA). the dissociation of the DNA during heating is measurable by the large reduction in fluorescence that results. Alternatively, juxtapositioned probes (one featuring a fluorophore and the other, a suitable quencher) can be used to determine the complementarity of the probe to the target nucleic acid sequence.

[0062] A "reagent” refers broadly to any agent used in a reaction, other than the analyte (e.g., nucleic acid being analyzed). Illustrative reagents for a nucleic acid amplification reaction include, but are not limited to, buffer, metal ions, polymerase, reverse transcriptase, primers, template nucleic acid, nucleotides, labels, dyes, nucleases, dNTPs, and the like. Reagents for enzyme reactions include, for example, substrates, cofactors, buffer, metal ions, inhibitors, and activators.

[0063] The term “label,” as used herein, refers to any atom or molecule that can be used to provide a detectable and / or quantifiable signal. In particular, the label can be attached, directly or indirectly, to a nucleic acid or protein. Suitable labels that can be attached to probes include, but are not limited to, radioisotopes, fluorophores. chromophores, mass labels, electron dense particles, magnetic particles, spin labels, molecules that emit chemiluminescence, electrochemically active molecules, enzymes, cofactors, and enzyme substrates.

[0064] The term “dye,” as used herein, generally refers to any organic or inorganic molecule that absorbs electromagnetic radiation and produces a detectable signal (e.g., a fluorescent signal).

[0065] The term “quencher,” as used herein generally refers to any organic or inorganic molecule that reduces the level of a detectable signal.

[0066] As used herein, the term “detecting” refers to “determining the presence of’ an item, such as a nucleic acid sequence.

[0067] As used herein, the term “treatment regimen” refers to any medical intervention intended to mitigate the symptoms and / or the pathology of a disorder. The treatment regimen can include one or more actions (e.g., bed rest, increasing fluidintake), non-prescription or prescription medications, supplements, foods, drinks, or the use of medical devices (e.g.. a respirator).

[0068] As used herein, “Clinical Laboratory Improvement Amendments (CLIA)” refers to The Clinical Laboratory Improvement Amendments of 1988 (CLIA) regulations in effect as of the original filing date of the present application. The CLIA regulations include federal standards applicable to all U.S. facilities or sites that test human specimens for health assessment or to diagnose, prevent, or treat disease. A “CLIA-complianf ’ test is one that complies with these regulations. “CLIA-waived” tests include tests that do not comply with all of these regulations. For example, CLIA- waived tests include test systems cleared by the U.S. Food and Drug Administration for home use and those tests approved for waiver under the CLIA criteria.

[0069] An “endogenous control,” as used herein refers to a moiety that is naturally present in the sample to be used for detection. In some embodiments, an endogenous control is a “sample adequacy control” (SAC), which may be used to determine whether there was sufficient sample used in the assay, or whether the sample comprised sufficient biological material, such as cells. In some embodiments, an endogenous control is an RNA (such as an rnRNA, tRNA, ribosomal RNA, etc.), such as a human RNA for a human sample. Nonlimiting exemplary7endogenous controls include ABL mRNA, GUSB mRNA, GAPDH mRNA, TUBB mRNA, and UPKla rnRNA. In some embodiments, an endogenous control, such as an SAC. is selected that can be detected in the same manner as the target nucleic acid (e.g., RNA) is detected and, in some embodiments, simultaneously with the target nucleic acid (e.g., RNA).

[0070] An “exogenous control,” as used herein, refers to a moiety that is added to a sample or to an assay, such as a “sample processing control” (SPC). In some embodiments, an exogenous control is included with the assay reagents. An exogenous control is typically selected that is not expected to be present in the sample to be used for detection, or is present at very7low levels in the sample such that the amount of the moiety naturally present in the sample is either undetectable or is detectable at a much lower level than the amount added to the sample as an exogenous control. In some embodiments, an exogenous control comprises a nucleotide sequence that is not expected to be present in the sample t pe used for detection of the target nucleic acid (e.g., RNA). In some embodiments, an exogenous control comprises a nucleotide sequence that is not known to be present in the species from whom the sample is taken.In some embodiments, an exogenous control comprises a nucleotide sequence from adifferent species than the subject from whom the sample was taken. In some embodiments, an exogenous control comprises a nucleotide sequence that is not known to be present in any species. In some embodiments, an exogenous control is selected that can be detected in the same manner as the target nucleic acid (e.g., RNA) is detected and, in some embodiments, simultaneously with the target nucleic acid (e.g., RNA). In some embodiments, the exogenous control is an RNA. In some such embodiments, the exogenous control is an Armored RNA®, which comprises RNA packaged in a bacteriophage protective coat. See, e.g., WalkerPeach et al, Clin. Chem. 45: 12: 2079-2085 (1999).Detecting and Differentiating Genotypes of Nucleic Acid in a Multiplex Amplification Reaction

[0071] The present disclosure describes methods, devices, and systems that rely on target-specific melt probe detection and melt profile analysis which facilitate multiplex target nucleic acid amplification (e.g., polymerase chain reaction), detection, and discrimination of genotypes. More specifically, the methods, devices, and systems use oligonucleotide probe-nucleic acid amplicon hybrid melt curve patterns to detect and identify a specific genotype and its associated phenotype, as opposed to melt curve temperatures (Tm) or high-resolution melt (HRM) analysis in a single assay that can be performed in an assay cartridge. The methods can be readily automated in devices and systems and can be employed in point-of-care devices.

[0072] The methods described herein, instead of using only Tm values, are more versatile and error free in identifying diverse genotypes accurately and unequivocally. The Tm signature approach looks only at a single point in the temperature axis in a first or second derivative melt curve to identify and discriminate sequences (i.e., the Tm). The melt pattern detection approach on the other hand, looks at the entire melt curve trajectory over a relatively large temperature range (for example, 50°°C to 85°°C) and is applicable to raw melt, first derivative or second derivative melt curves. Identifiable melt pattern differences generated by one or more probes binding to one or more sequences can be detected to specifically identify nucleic acid sequence variations as well as microorganisms.

[0073] Genetic variation analysis by high resolution melt (HRM) has been described. In HRM, double stranded DNA binding dye is necessary as opposed to DNA probes and very high precision melt curve analysis is required. More specifically, HRM requires highly sophisticated temperature and signal measurements to generate highresolution data and not all mutation types can be identified with equal efficiency. Furthermore, in HRM. there is limited multiplexing capability since DNA binding dye is used, limited capacity to accurately differentiate a wide range of mutations in the target sequence, and limited applicability in clinical matrix since HRM data may be confounded due to matrix interference. HRM can be useful in finding point mutations and SNP differences between two sequences, however, it is inefficient in finding entire sequence information. Additionally, it is not generally feasible to combine targetspecific melt and HRM using intercalating dye in a multiplexed PCR system in a single tube, especially when simultaneous amplification of multiple targets is carried out. Intercalating dyes are non-specific and can bind to any amplicon and hence has not previously been used to determine variants or mutations in genes in a single cartridge. In a multiplexed PCR system, which amplifies several targets at the same time, HRM with an intercalating dye will lead to a very complex and uninterpretable melt profile due to non-specific intercalation of the dye in all the amplicons being amplified in the tube.

[0074] The present disclosure pertains to identifying mutations in nucleic acid sequences by detecting differences in the patterns of post PCR melt curves (raw melt, first derivative, and / or second derivative) which are generated by the differential melting kinetics of oligonucleotide probes to target nucleic acid amplicons generated after PCR. The differences in melt curve patterns from a single probe binding to multiple different targets are expected to be specifically related to the type, position, and the number of mutations present in a target sequence to which the assay probes bind. Melt curve variations are also expected with pure and mixed nucleic acid sequences, wherein identity of the nucleic acid mixture is expected to be accurately- determined by the methods disclosed. The methods disclosed in the present disclosure allow: i) differentiation of wild type and mutant sequences by detecting specific probetarget hybrid melt curve patterns; ii) identification of DNA sequences from differential melt patterns generated by multiple overlapping probes to a target sequence; iii) identification and differentiation of specific phenotypes associated with these sequence types e.g. resistance or susceptibility- to one or more drugs; iv) identification of microorganisms from melt pattern “signatures” generated by multiple probes targeted against a variable region in the microbial genome, like 16S rRNA gene; v) robust identification of “rare variants7’ by detecting minor secondary melt peaks from mutantwild type mixtures which generates a clearly identifiable pattern of double-peaks; vi)differentiation between familial genoty pes, for example, differentiation between M. tuberculosis and non-tuberculous mycobacteria (NTM) and speciation ofNTMs; vii) make melt assay design easier since it has been shown to identify sequence differences even when the Tm values generated by a probe from two different sequences are the same or in the same Tm window; viii) higher levels of multiplexing can be performed, potentially accommodating several melt probes with the same dye in a single optical channel, since multiple merged melt peaks can be identified and differentiated from single melt peaks by the virtue of identifying robust difference in the patterns of merged peaks and individual peaks; and ix) the method is amenable to working with pattern recognition algorithm that can recognize these sequence specific melt pattern differences.Illustrative Embodiments of Methods, Devices, and Systems for Detecting and Differentiating Genoty pes

[0075] As described herein, the present disclosure provides methods for detecting and differentiating genotypes of nucleic acid in a multiplex amplification reaction that employ target-specific melt probes. An illustrative method that is particularly useful in automated, single-cartridge assays, such as those performed by Cepheid’s GeneXpert® system, are described below. The methods for detecting and differentiating genotypes of nucleic acid in a multiplex amplification reaction can comprise the following steps.

[0076] Nucleic acid from a sample is contacted with a set of primers for amplifying target region(s) of the nucleic acid and a set of probes (optionally sloppy molecular beacon (SMB) probes) for detecting any amplicon formed. In some embodiments, the methods described can detect and differentiate five or more genotypes in a single multiplex amplification reaction. In some instances, the methods can detect and differentiate seven or more genotypes, ten or more genotypes, fifteen or more genotypes, twenty7or more genotypes, twenty -five or more genoty pes, thirty7or more genotypes, or up to fifteen genotypes, up to twenty genotypes, up to thirty genotypes, up to forty genotypes, or up to fifty genotypes, optionally in a single multiplex amplification reaction. In one example, the method can be employed to detect and differentiate genes of My cobacterium tuberculosis. Particularly, the methods can detect and differentiate Mycobacterium tuberculosis genes related to antimicrobial resistance, Mycobacterium tuberculosis genes related to antimicrobial susceptibility, wild-type Mycobacterium tuberculosis genes, one or more nontuberculousmycobacteria, and combinations of these genes. In a specific example, the methods can be employed to detect and differentiate Mycobacterium tuberculosis genes related to antimicrobial resistance, optionally selected from Rifampicin (RIF) resistance, isoniazid (INH) resistance, fluoroquinolone (FLQ) resistance including Levofloxacin (LFX) and Moxifloxacin (MFX), ethionamide (ETH) resistance, rifampicin resistance, amikacin (AMK) resistance, capreomycin (CAP) resistance, kanamycin (KAN) resistance, bedaquiline (BDQ) resistance, clofazimine (CFZ) resistance, delamanid (DLM) resistance, ethambutol resistance (E), linezolid resistance (LZD), pyrazinamide (Z) resistance, streptomycin (S) resistance, Ethionamide (ETO) or Prothionamide (PTO) resistance, Pretomanid (Pa) resistance, or a combination thereof.

[0077] The Mycobacterium tuberculosis gene target used in the disclosed methods for assessing antimicrobial resistance, antimicrobial susceptibility, wild-type Mycobacterium tuberculosis, or nontuberculous mycobacteria can include the Mycobacterium tuberculosis gene encoding the beta subunit of RNA polymerase (rpoB gene including RRDR and / or non-RRDR codons 170 and 491). fabGl gene, inhA promoter, katG gene, gyrA gene, gyrB gene, pncA gene, rplC gene, rrl gene, ns gene, atpE gene, ahpC gene, eis promoter, oxyR-ahpC (ahpC) intergenic region, embB gene, inhA gene, ethA gene, ddn gene, fbiA, B and C genes or Rv0678 gene of Mycobacterium tuberculosis. In a particular example, the target region of Mycobacterium tuberculosis can be selected from one or more of the inhA promoter, katG gene, fabGl gene, gyrA gene, gyrB gene, rrs gene, ahpC gene, oxyR-ahpC (ahpC) intergenic region, and eis promoter. As described herein, all the different gene targets can be co-amplified in a single multiplex amplification reaction such as a single cartridge.

[0078] Illustrative primers, probes, and amplicons that can be used in the methods for multiplex detection of resistance to tuberculosis drugs are provided in U.S. Patent Publication No.: U.S. 2022 / 0064715; PMID: 28851844; PMID: 22535987; PMID: 27807153; and PMID: 21191047, disclosure of which are incorporated herein by reference in their entirety. The considerations for primers and probes for practicing the methods described herein are described in more detail below.

[0079] The methods for detecting and differentiating genot pes of nucleic acid in a multiplex amplification reaction can comprise subjecting the nucleic acid, primers, and the SMB probes to amplification conditions to amplify the target region of the nucleic acid. RT-PCR can be used to amplify the presence of the target nucleic acids.In some embodiments, the reverse transcription uses MMLV and / or CAT-A RT enzyme and an incubation of 5 to 20 minutes at 40°C to 50°C. In some embodiments, the PCR uses Taq polymerase with hot-start function, such as AptaTaq (Roche). In some embodiments, the initial denaturation is at 90°C to 100°C for 20 seconds to 5 minutes; the cycling denaturation temperature is 90°C to 100°C for 1 to 10 seconds; the cycling anneal and amplification temperature is 60°C to 75°C for 10 to 40 seconds; and up to 50 cycles are performed.

[0080] A double-denature method can be used to amplify low-copy number target nucleic acids. A double-denature method comprises, in some embodiments, a first denaturation step followed by addition of primers and / or probes for detecting target nucleic acids. All or a substantial portion of the nucleic acid-containing sample (such as a DNA eluate) is then denatured a second time before, in some instances, a portion of the sample is aliquoted for cycling and detection of the target nucleic acids. While not intending to be bound by any particular theory, the double-denature protocol may increase the chances that a low-copy number target nucleic acid (or its complement) will be present in the aliquot selected for cycling and detection because the second denaturation effectively doubles the number of target nucleic acids (i.e., it separates the target nucleic acid and its complement into two separate templates) before an aliquot is selected for cycling. In some embodiments, the first denaturation step comprises heating to a temperature of 90°C to 100°C for a total time of 30 seconds to 5 minutes. In some embodiments, the second denaturation step comprises heating to a temperature of 90°C to 100°C for a total time of 5 seconds to 3 minutes. In some embodiments, the first denaturation step and / or the second denaturation step is carried out by heating aliquots of the sample separately. In some embodiments, each aliquot may be heated for the times listed above. As a non-limiting example, a first denaturation step for an NA-containing sample (such as a DNA eluate) may comprise heating at least one, at least two, at least three, or at least four aliquots of the sample separately (either sequentially or simultaneously) to a temperature of 90°C to 100°C for 60 seconds each. As a non-limiting example, a second denaturation step for a NA- containing sample (such as a DNA eluate) containing enzyme, primers, and probes may comprise heating at least one, at least two, at least three, or at least four aliquots of the eluate separately (either sequentially or simultaneously) to a temperature of 90°C to 100°C for 5 seconds each. In some embodiments, an aliquot is the entire NA- containing sample (such as a DNA eluate). In some embodiments, an aliquot is lessthan the entire NA-containing sample (such as a DNA eluate). An off-line centrifugation can be used, for example, with samples with low cellular content. The sample, with or without a buffer added, is centrifuged and the supernatant removed. The pellet is then resuspended in a smaller volume of either supernatant or the buffer. The resuspended pellet is then analyzed as described herein.

[0081] The methods for detecting and differentiating genoty pes of nucleic acid can comprise generating one or more melt profiles for each target region of nucleic acid amplified. In general, different probes are labeled with dyes that have unique emission spectra. Spectral data are collected with discrete optics or dispersed onto an array for detection. Since fluorescence is temperature dependent and depends on the dye, spectral overlap and color compensation constants are also temperature dependent. The fluorescence of labeled probes depends on hybridization of the probe to the target, allowing study of the melting characteristics of the probe. Melting curves can be obtained during PCR, but data are usually acquired at near-equilibrium rates of 0.05- 0.2°C / s after PCR is complete. Using rapid-cycle PCR, amplification requires about 20 mm followed by a 10-min melting curve, greatly reducing result turnaround time.

[0082] Following PCR amplification and generating the one or more melt profiles for each target region of nucleic acid amplified, melt analysis can be performed. In some embodiments. Melt Curve analysis can be evaluated by GENEXPERT® Software to determine the presence of PCR product. As described herein, the method for detecting and differentiating genotypes of nucleic acid can include classifying each of the one or more melt profiles into genotype cluster(s). Clustering can be obtained by performing at least one of hierarchical clustering, fuzzy C means clustering, mean shift clustering, density-based spatial clustering, or Gaussian mixture models.

[0083] In some embodiments, the clustering can be obtained by dynamic quantum clustering (DQC). Dynamic Quantum Clustering (DQC) can be employ ed in the methods and system and method to address issues related to dimensionality. Numerical datasets are typically structured or conceptualized in a table. Each row of the table represents a single piece of numerical data (i.e., temperature data). The columns of the table provide the domain of definition of the features measured for each datum- which can comprise, for example, fluorescence. DQC treats each row' of the table as a point in an n-dimensional space, where n is the number of columns in the table. The Euclidean distance between the points is taken to be the measure of how alike tw o row sare. Hence, points that are near one another are more alike, and points that are far from one another are more different. For example, in the case of temperature data the question is whether spectroscopic (fluorescence) readings at times when amplicons belong to the same or familial genotype are more alike relative to each other, and less alike readings when amplicons belong to a different / non-familial genoty pe. With this definition of “alike”, it is intuitive to consider a sub-region with many nearby points that are more like one another (i.e., a dense region) separated by less dense regions as clusters of points. Stated this way, the problem of finding rows of the spreadsheet that are more like one another amounts to finding all the regions with higher density. That is the problem DQC solves, no matter how many data points we have, and no matter how many features (columns) are in the spreadsheet. In this sense DQC can be thought of as a density-based clustering algorithm. DQC is unique in that it identifies regions of higher density using methods borrowed from quantum physics, instead of familiar mathematical approaches. The unique properties of how quantum particles move in high dimension are what makes the density approach work in an efficient manner. DQC works by dividing the analysis into three steps: (a) use the data to create a function in the feature space that is an accurate proxy for density. By construction, this function goes down as the local density increases. For data with isolated clusters, dense regions correspond to local minima in the density function. However, for more complex data, where the density of the data is constant along some one-dimensional shapes and falls off as one moves away from these shapes, then the density function will have multidimensional troughs, (b) DQC identifies clusters of data by identifying points that he in the region of a local minimum, or trough. This is accomplished by moving the original data points in the direction of the nearest minimum, (c) DQC operates to avoid the disadvantages caused by gradient descent by treating each data point as a localized quantum particle moving in a quantum potential defined to be the density7function. Data points are then moved according to the rules of quantum evolution. DQC thereby successfully operates where other approaches fail because it can effectively leverage non-locality and quantum tunneling to erase the effects of small, local minima. Any extended structure in a DQC map can be thought of as a regression, in that it reveals a parameterized relationship between variables. An extended structure can be of any shape (since it’s defined by the data) and is not constrained by linearity or other limiting assumptions. Also, an extended structure may contain only a subset of the data,where other regression algorithms will always fit their models to all of the data. Thus, DQC has both flexibility and specificity as a regression tool.

[0084] In the methods described herein, melting temperature (Tm) and melt peak height of the curve is calculated automatically by the analysis software. More importantly, since the detectable labels (e.g., fluorescent probes) bound to double strand DNA lose fluorescence as the temperature increases depending on the characteristics thereof, different genotypes of a given assay will exhibit different fluorescent signal profiles, since it is dependent on the melt thermodynamics of the probe-target hybrid denaturation process, which determined by the specific target genotype. In order to use melting curves to determine genotypes, typically, the rawmelting curve, i.e.. the measured fluorescence over the range of temperatures during the melting step, is processed by taking the negative first derivative thereof. This results in curves with a series of peaks. The peaks are usually Gaussian shaped peaks, but can be peaks of other forms, such as Lorentzian, Voigt, Pearson, Compton Edge, or any other types of peaks. Curves which have peaks that resemble each other in shape and frequency, and curves with similar melting temperatures can be grouped together in a single genotype.

[0085] Current algorithms identity Tm values if they fall w ithin pre-defined ‘Tm windows', which are small temperature ranges where the peak change in fluorescence of the melt curve must occur for each probe. Mutations which create unexpected deviations of Tm away from the WT Tm can have two undesired results: the Tm may fall outside of any pre-defined Tm w indow or there may be no detected Tm at all (if the mutation prevents the probe from binding to the target). In the majority of such cases the result is an INDETERMINATE, which prevents end users from identifying the condition of a patient. Unexpected noise at lower temperatures (50- 60°C) can also result in INDETERMINATE results, for example, due to interference w ith the GeneXpert Tm calling software, which prevents it from determining a valid Tm peak. While existing methods may be able to identify limited number of genotypes, there is still a desire for an automated method for determining a wider range of genotypes when there is an unknown and / or relatively large number of genotypes for a given assay.

[0086] As described herein, the method for detecting and differentiating genotypes of nucleic acid includes classifying each of the one or more melt profiles into genotype cluster(s). Clustering of the genotypes described herein are optionally entirelyindependent of Tm windows and can use full-melt-curve patterns to correctly identify, for example, the RIF-susceptibility of RIF-INDETERMINATE results, irrespective of the actual position of the ‘peak’ along the temperature axis. The Tm system can also be confounded by low melt-peak heights (caused by significant probe destabilization due to large deletions or multiple successive mutations in the probe binding region); again, the disclosed methods can use melt-curve patterns to distinguish these cases from RIF- susceptible genotypes. Melt curves with missing portions, for example, because the Tm is very low-, <60°C, or noisy due to optical aberrations at the pre-defmed baseline start temperature, can also prevent a Tm call even when a valid Tm curve is present. The clustering method, combined with the normalization process, are able to correctly classify the examples.

[0087] In some embodiments, prior to clustering the melting curves, the method can include processing the raw? melting curves data. Processing the raw- melting curves data can include normalization for vertical position and scale to generate a processed melting curve. Such vertical normalization may be essential in some cases, for example, when melt peak height (MPH) variation due to the disparity in optical signal quality in different reaction wells / modules, is erroneously identified as a pattern variation. In other embodiments, the derivatives (first or second derivative) of each of a plurality of raw melting curves may be calculated to generate a processed melting curve. In some examples, the raw melt data can be normalized for vertical position and scale and the derivatives calculated to generate a processed melting curve. The raw melting curves can be processed in any suitable way for normalizing and / or calculating a derivative. While described herein as calculating a first derivative, it will be understood that the raw melting curves can be processed in any suitable way that assists in ascertaining features of the curv es (such as melting temperatures) that aid in genotyping the melting curv es.

[0088] DQC’s analytical process uses the entire pattern (shape) of the melt curves - the closer two curves are to each other (across all temperatures), the closer the two samples are to each other in the data space. The present disclosure described differentiating between genotypes, by successfully negating artifactual variations in signals between different samples due to optical or thermal aberrations. Normalizing the “noise” allow-s identification of melt signal variations between different samples as a definitive indication of genotypic variations with a high degree of confidence. In some embodiments, the normalization process, which can be applied separately foreach sample and each probe, includes subtraction of the mean value of the curve from the entire curve (this removes differences in vertical position), and dividing the entire curve by the L2 norm of the curve (this removes differences in scale). The L2 norm of a curve is the square root of the sum of the squares of all values along the curve. What’s left are the details of the pattern of each curve.

[0089] In the exemplified MTB Ultra data, the greatest differences between the curves are driven by different melt-peak locations on the temperature axis (as represented by Melt Peak Temperature, or Tm). However, normalization also reveals more subtle differences, which are also used by DQC to distinguish between genotypes. FIG.s 8 and 9 shows examples of this phenomenon. In FIG. 8 A and FIG. 8B. for the genotypes D516V and H526L, the normalized curves show consistent differences in two regions: from 55-63°C and from 70-75°C. FIG. 9A and FIG. 9B illustrate how the normalization process not only reduces variation within a specific genoty pe, but helps in distinguishing two different genotypes with the same Tm for a particular probe. In this case, normalization of first derivative melt accentuates the reproducible difference in the melt curve patterns at 60°C-69°C between the WT and R529K genotypes for probe 2, even though they have the same Tm values. This normalization process essentially eliminates differences in melt-peak heights, as seen in the WT / R529K plot, and at the same time enables an end-user to identify the genotype related signal differences which may not always be reflected as Tm differences.

[0090] In some examples, DQC is a two-step process. Step 1 can include building a density map. DQC treats each observation as a high-dimensional quantum object that can be plotted in space using its values from each dimension as coordinates. Observations with similar values are plotted near each other, observations with dissimilar values are separated. Each quantum object is represented by a multidimensional Gaussian curve. In the case of the exemplified MTB data, each observation was represented by a 496-dimensional quantum object. Based on the data plot, DQC constructs a high-dimensional topographical density map or ‘landscape’ (as illustrated in FIG. 1), where higher densities of points create depressions in the map, which can be thought of as ‘lakes’ or ‘rivers’. The density’ map also represents outliers as independent points. The DQC algorithm then moves points downhill’ in the map, in an iterative process. The process reveals the topography of the map, exposing clusters in the data. This process does not use outcome information and is not subject to overfitting.

[0091] In some examples, step 2 of the DQC process can include predicting future outcomes. After clustering, the data can be colored according to target outcome(s) of interest. In one embodiment, the "outcome’ of interest is the genotype of each sample. The first use case used melt curves to distinguish between MTB WT (Wild Type) and NWT (non-Wild Type) samples. Coloring the samples as WT or NWT (as shown in FIG. 2) showed that melt curves were effective in separating 100% of WT samples from NWT samples. This helps to identify the genotype of the rpoB gene RRDR region which is targeted by the 4 assay probes. After this confirmation, the density map can be used to classify7future observations as WT or NWT by iterating new samples through the original density map. New samples that congregate in the WT cluster would be classified as WT. NWT samples are predicted to either form an independent cluster or merge with one of the existing non-WT clusters depending on their individual melt patterns and genotype.

[0092] DQC provides insights, including number of clusters and relative position and structure of each cluster. DQC is not constrained by limiting assumptions (e.g.. the number of clusters expected to be found in the data). The visual nature of a DQC evolution provides rich information, including the number of clusters, the relationship between clusters (e.g., 2 clusters very' close together can be seen as a ‘supercluster’) and the structure of clusters (some clusters form extended structures, and some do not). Thus, DQC reveals more about the structure of a data set, more quickly, without biases or limiting assumptions.

[0093] DQC works in high dimension with no dimensionality' reduction. Algorithm that fits a model typically limit the number of variables it uses, to avoid overfitting. DQC reveals the inherent structure of the data, as opposed to fitting a model, and so is not subject to this problem. Embedding visualizations (e.g., t-SNE) use hard dimensionality reduction to create their visual results. In DQC, the dynamic evolution is visualized 3 dimensions at a time, but the evolution is driven by information from all dimensions - so, no dimensionality reduction is required.

[0094] DQC also reveals hierarchical relationships. DQC explores relationships between data at different degrees of similarity, in order to understand the hierarchy of similarity' within a data set. In a very' simple example, a data set may have 4 distinct clusters in 2 closely grouped pairs. This data set can then be thought of as either 4 clusters or 2 ‘superclusters'. DQC is well suited to discovering and understanding this kind of structure. In the MTB Ultra data set, the hierarchy of relationships betweenclusters can be visualized as a tree (see FIG. 6). Clusters closer to each other in the tree are more similar. For example, genotypes WT and Q513L can be distinguished from each other, but they are more similar to each other than to any other genotype (Cluster1.3.3). L533R is in a different nearby cluster (1.3.4), and thus is more similar to both WT and Q513L than it is to Q510V+D516Y (cluster 7). It is necessary to note however, that this hierarchy is based on the specific nature of the probe binding thermodynamics in the MTB Ultra assay, and not the exact sequence of a particular genotype. The MTB RIF Ultra assay probes were designed primarily to distinguish between WT and non- WT genoty pes and not for precise genotype identification. Thus, this dendrogram represents an overview of how the MTB Ultra assay melt signals may be used to classify different sequence types in a hierarchical manner and does not provide an indication of precise genomic sequence level relatedness, comparable to ribotyping techniques for microbial species identification. This may be achieved by specific probe design which is aimed at distinguishing genotypes; for example, probes targeted against the hypervariable regions of 16S rRNA gene (PMID: 19923485).

[0095] The DQC algorithm has a tunable parameter, called 'sigma’, which can be thought of as a ‘similarity’’ parameter and can be used for exploring the hierarchy of structure. For small sigma, only data points very near each other will draw7together in the evolution (e.g.. WT and Q513L, in Cluster 1.3.3). For some larger sigma, a larger group of points will draw together (e.g.. WT, Q513L. U533R. and others, in Cluster1 .3). Thus, building multiple DQC maps with different values of sigma reveals the hierarchical structure of the data. As an example, the tree structure for the MTB Ultra data set can be discovered as follows. First, a DQC map is built w ith a large value of sigma, identifying the 7 top-level clusters. A new map is then built only for Cluster 1 (the only main cluster with multiple genotypes), using a smaller value of sigma, to identify the main subclusters in Cluster 1. This process is repeated recursively wi thin each subcluster until all distinguishable genotypes are identified. The overall structure for the MTB Ultra data set is shown in FIG. 6.

[0096] In another aspect, a system for automated genotyping of melting curves is provided. The system may include at least one data processor. The system may further include at least one memory story instructions, which when executed by the at least one data processor, cause operations including generating a plurality of processed melting curves by normalizing and calculating a derivative of each of the plurality of melting curves over the range of temperatures, building a high-dimensionaltopographical density map or ‘landscape’ of the data points, and using the density map to classify future observations by iterating new samples through the original density map. New samples that congregate in the WT cluster would be classified as WT. NWT samples are predicted to group along with existing NWT clusters or form new independent NWT clusters.

[0097] In some embodiments, the system may output graphical representations showing genotypes of each of the curves in a given assay under consideration. To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, such as for example visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including acoustic, speech, or tactile input. Other possible input devices include touch screens or other touch-sensitive devices such as single or multi-point resistive or capacitive track pads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like.

[0098] The method for detecting and differentiating genotypes of nucleic acid can comprise differentiating and optionally identifying the genotypes of nucleic acid based on classification of the one or more melt profiles. As described herein, conventional differentiation and identification of genotypes by PCR generally require determining a melt temperature for each amplified nucleic acid, if present, and utilizing the melt temperature for classifying, differentiating, and / or identifying the genotypes. In some embodiments of the present disclosure, the method does not utilize melt temperature for classifying, differentiating, and / or identifying the genotypes. Differentiating each genotype can be based on the individual genotype clusters that are exposed in the topographical density' map discussed above.

[0099] As discussed above, DQC provides relationship between clusters (e.g., 2 clusters very close together can be seen as a ‘supercluster’) and the structure of clusters (some clusters form extended structures, and some do not). Differentiating and identifying genotypes of nucleic acid can be based in the relationships and structuresthat DQC provides. In some embodiments, DQC groups closely related clusters into familial clusters. For example. DQC can differentiate NTM from MTB genotypes in a data set. NTM genotypes will be grouped into familial clusters while MTB genotypes will be grouped into a different set of familial clusters. Each genotype cluster within a familial cluster and each familial cluster are separated by a distance. The proximity between genotype clusters within a familial cluster as well as the proximity between two familial clusters is determined by the similarity of the genotypes' melt curve derivative. Genotypes within the same family (e.g. NTM) are grouped close together, while genotypes in separate families (e.g., NTM family vs MTB family) are grouped further apart.

[0100] As discussed above, DQC also provides hierarchy of relationships between clusters, and thus can identify genotypes within a family. Clusters closer to each other are more similar. For example, genotypes WT and Q513L can be distinguished from each other, but they are more similar to each other than to any other genotype. L533R is in a different nearby cluster, and thus is more similar to both WT and Q513L than it is to Q510V+D516Y. Exploring the "similarity’ parameter in DQC maps can distinguish and identify all known genotypes within a sample.

[0101] In some embodiments, the methods provided herein include determining a melt temperature for each amplified nucleic acid, if present, and utilizing the melt temperature for further classifying, differentiating, and / or identifying the genotypes.

[0102] Classifying, differentiating, and identifying each of the melt profiles into genotype cluster(s) can be performed by a machine learning model trained with historical genotype cluster information. Preferably, the method is capable of classifying, differentiating, and identifying two or more genotypes having melt temperatures with less than 4°C, or less than 2°C, or less than I °C temperature separation. In some embodiments, the method is capable of classifying, differentiating, and identifying two or more genotypes having identical melt temperatures, but different melt patterns. In some embodiments, the method is capable of classifying, differentiating, and identifying wherein the method is capable of classifying and differentiating single nucleotide polymorphisms (SNPs). In certain embodiments, the method is capable of classifying, differentiating, and identifying genotypes based on data obtained from one or more probes, for example, from a single SMB probe, two SMB probes, three SMB probes, four SMB probes, five SMB probes, or six SMB probes.

[0103] Higher levels of multiplexing are possible using combinations of the rt- PCR and DQC methods disclosed herein. Particularly, a higher number of genotypes can be differentiated and identified, which can be achieved, for example, in an automated, single-cartridge assay, such as that performed by Cepheid’s GeneXpert® system. This degree of multiplexing, especially in an automated system, represents a significant advance in view of various issues with currently available multiplexed PCR methods. In some embodiments, the methods described herein can classify, differentiate, and optionally identify 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, 25 or more. 26 or more, 27 or more, 28 or more, 29 or more, 30 or more, 31 or more, 32 or more, 33 or more, 34 or more, 35 or more, 36 or more, 37 or more, 38 or more, 39 or more, or 40 or more target genotypes of nucleic acids in a multiplex amplification reaction mixture. This degree of multiplexing can be achieved even using primers and probes that cross-react or bind off-target. Conventionally, because a large number of oligonucleotides combined in one reaction mixture generally leads to unwanted interactions between them, modified nucleotides are used to reduce primer-primer interactions. The current methods eliminate or reduce the need for modified nucleotides.Controls

[0104] In some embodiments, an assay described herein can include detecting at least one endogenous control in addition to the selected target nucleic acids. In some embodiments, the endogenous control is a Sample Adequacy Control (SAC). The SAC ensures that the sample contains human cells or human DNA. This assay includes primers and probes for the detection of a single-copy human gene. The SAC signal is only to be considered when the sample is negative for all other targets. A negative SAC indicates that no human cells are present in the sample due to insufficient mixing of the sample or because of an inadequately collected sample. In some such embodiments, if no target nucleic acid is detected in a sample, and the SAC is also not detected in the sample, the assay result is considered ‘'invalid” because the sample may have been insufficient. While not intending to be bound by any particular theory', an insufficient sample may be too dilute, contain too little cellular material, or contain an assay inhibitor, etc. In some embodiments, the failure to detect an SAC may indicate that the assay reaction failed. In some embodiments, an endogenous control is an RNA(such as an mRNA, tRNA. ribosomal RNA, etc ). Nonlimiting exemplary endogenous controls include ABL mRNA, GUSB mRNA. GAPDH mRNA, TUBB mRNA, and UPKla mRNA.

[0105] In some embodiments, an assay described herein can include detecting the at least one exogenous control in addition to the selected target nucleic acids. In some embodiments, the exogenous control is a Sample Processing Control (SPC). The SPC verifies that sample processing is adequate. Additionally, this control detects sample-associated inhibition of the real-time PCR assay, ensures that the PCR reaction conditions (temperature and time) are appropriate for the amplification reaction, and that the PCR reagents are functional. The SPC should be positive in a negative sample and can be negative or positive in a positive sample. The SPC passes if it meets the validated acceptance criteria. In some such embodiments, if no target nucleic acid is detected in a sample, and the SPC is also not detected in the sample, the assay result is considered “invalid’' because there may have been an error in sample processing, including but not limited to, failure of the assay. Nonlimiting exemplary errors in sample processing include, inadequate sample processing, the presence of an assay inhibitor, the presence of a nuclease (such as an RNase), or compromised reagents, etc. In some embodiments, an exogenous control (such as an SPC) is added to a sample. In some embodiments, an exogenous control (such as an SPC) is added during performance of an assay, such as with one or more buffers or reagents. In some embodiments, when a GENEXPERT® system is to be used, the SPC can be included in the GENEXPERT® cartridge. In some embodiments, an exogenous control (such as an SPC) is an Armored RNA®. which is protected by a bacteriophage coat.

[0106] In some embodiments, an endogenous control and / or an exogenous control is / are detected contemporaneously, such as in the same assay, as detection of the selected target nucleic acids. In some embodiments, an assay comprises reagents for detecting the target nucleic acids, and a SAC and / or an exogenous control, simultaneously in the same assay reaction mixture. In some such embodiments, for example, an assay reaction mixture comprises primer sets for amplifying the target nucleic acids, a primer set for amplifying a SAC and / or a primer set for amplifying an exogenous control, as well as optional labeled probes for detecting the amplification products (such as, for example, melt or TaqMan® probes).

[0107] In some embodiments, the assay includes a probe check control (PCC). In some such embodiments, before the start of the PCR reaction, the system (e.g.,GENEXPERT System) measures the fluorescence signal from the probes to monitor bead rehydration, reaction tube filling, probe integrity, and dye stability-. The PCC passes if it meets the validated acceptance criteria.Polynucleotides

[0108] In some embodiments, polynucleotides are provided for detecting the biomarkers described above. In some embodiments, synthetic polynucleotides are provided. Synthetic polynucleotides, as used herein, refer to polynucleotides that have been synthesized in vitro either chemically or enzymatically. Chemical synthesis of polynucleotides includes, but is not limited to, synthesis using polynucleotide synthesizers, such as OligoPilot™ (GE Healthcare), ABI 3900 DNA Synthesizer (Applied Biosystems), and the like. Enzymatic synthesis includes, but is not limited, to producing polynucleotides by enzymatic amplification, e.g., PCR. A polynucleotide may comprise one or more analog of the canonical nucleotides (e.g., modified nucleotides).

[0109] In some embodiments, a polynucleotide is provided that comprises a region that is at least 85%. at least 90%. at least 91%. at least 92%. at least 93%. at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% identical to, or at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% complementary to. at least 6, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, or at least 30 contiguous nucleotides of the selected target nucleic acids, and / or exemplary controls discussed above.

[0110] In various embodiments, an exemplary polynucleotide comprises at least: 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. In various embodiments, a polynucleotide comprises fewer than: 200, 150, 100, 50, 40, 30, or 20 nucleotides. In various embodiments, an exemplary polynucleotide is between 6 and 200, between 8 and 200, between 8 and 150, between 8 and 100, between 8 and 75, between 8 and 50, between 8 and 40, between 8 and 30, between 15 and 100, between 15 and 75, between 15 and 50, between 15 and 40, or between 15 and 30 nucleotides long.

[0111] In some embodiments, detection of each target nucleic acid can be carried out using a single labeled primer or probe, specific for each target nucleic acid.Different primers and / or probes can have the same label. By using primers or probes labeled with different detectable moieties (e.g., different fluorescent reporter dyes), numerous target nucleic acids can be detected simultaneously in a single reaction mixture. In some embodiments, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 or more different labels can be used in a single reaction mixture or a plurality of reaction mixtures. Each target nucleic acid can be independently monitored using such multiplexing technology. In some embodiments, detection of a plurality of target nucleic acids can be carried out using a single labeled primer or probe. A melt curve may be generated in order to distinguish two or more target nucleic acids that each use the same label, but such analysis may not be necessarily required.Polynucleotide Modifications

[0112] In some embodiments, the methods of detecting at least one target nucleic acid described herein employ one or more polynucleotides that have been modified, such as polynucleotides comprising one or more affinity-enhancing nucleotide analogs. Modified polynucleotides useful in the methods described herein include primers for reverse transcription, PCR amplification primers, and probes. In some embodiments, the incorporation of affinity-enhancing nucleotides increases the binding affinity and specificity7of a polynucleotide for its target nucleic acid as compared to polynucleotides that contain only the canonical deoxyribonucleotides, which allows for the use of shorter polynucleotides or for shorter regions of complementarity7between the polynucleotide and the target nucleic acid.

[0113] In some embodiments, affinity -enhancing nucleotide analogs include nucleotides comprising one or more base modifications, sugar modifications, and / or backbone modifications. In some embodiments, modified bases for use in affinityenhancing nucleotide analogs include 5 -methylcytosine, isocytosine, pseudoisocytosine, 5-bromouracil, 5-propynyluracil, 6-aminopurine, 2-aminopurine, inosine, diaminopurine, 2-chloro-6-aminopurine, xanthine and hy poxanthine. In some embodiments, affinity-enhancing nucleotide analogs include nucleotides having modified sugars such as 2'-substituted sugars, such as 2'-O-alkyl-ribose sugars. 2'- amino-deoxyribose sugars, 2'-fluoro-deoxyribose sugars, 2'-fluoro-arabinose sugars, and 2'-O-methoxyethyl-ribose (2'MOE) sugars. In some embodiments, modified sugars are arabinose sugars, or d-arabino-hexitol sugars.

[0114] In some embodiments, affinity-enhancing nucleotide analogs include backbone modifications such as the use of peptide nucleic acids (PNA; e.g., anoligomer including nucleobases linked together by an amino acid backbone). Other backbone modifications include phosphorothioate linkages, phosphodiester-modified nucleic acids, combinations of phosphodiester and phosphorothioate nucleic acid, methyl phosphonate, alkylphosphonates, phosphate esters, alkylphosphonothioates, phosphoramidates, carbamates, carbonates, phosphate triesters, acetamidates, carboxymethyl esters, methylphosphorothioate, phosphorodithioate, p-ethoxy modifications, and combinations thereof.

[0115] In some embodiments, a polynucleotide includes at least one affinityenhancing nucleotide analog that has a modified base, at least nucleotide (which may be the same nucleotide) that has a modified sugar, and / or at least one intemucleotide linkage that is non-naturally occurring.

[0116] In some embodiments, an affinity -enhancing nucleotide analog contains a locked nucleic acid (“LNA”) sugar, which is a bicyclic sugar. In some embodiments, a polynucleotide for use in the methods described herein comprises one or more nucleotides having an LNA sugar. In some embodiments, a polynucleotide contains one or more regions consisting of nucleotides with LNA sugars. In other embodiments, a polynucleotide contains nucleotides with LNA sugars interspersed with deoxyribonucleotides. See, e.g., Frieden, M. et al. (2008) Curr. Pharm. Des.14(11): 1138-1142.Primers

[0117] In some embodiments, the polynucleotide is a primer. Primers useful in the methods described herein are generally capable of selectively hybridizing to: genomic DNA, a target RNA (genomic or transcript), a cDNA reverse transcribed from the target RNA, and / or an amplicon that has been amplified from genomic DNA, a target RNA, or a cDNA (collectively referred to as ‘'template’’), and, in the presence of the template, a polymerase and suitable buffers and reagents, can be extended to form a primer extension product. Primers are generally of a sufficient length to ensure selective hybridization to their target nucleic acids. Generally, primers of at least 15 nucleotides in length hybridize specifically in most contexts, and this length can be reduced, e.g., by including of affinity-enhancing modifications, such as those discussed above. Primers can but need not be exactly complementary' to their target nucleic acids. Primers can have any degree of complementarity described above for exemplary polynucleotides. In illustrative embodiments, primers can be 8 to 40 nucleotides in length and at least 90% complementary to their target nucleic acids; 8 to 40 nucleotidesin length and at least 95% complementary to their target nucleic acids; 8 to 40 nucleotides in length and at least 99% complementary’ to their target nucleic acids; 8 to 30 nucleotides in length and at least 90% complementary to their target nucleic acids; 8 to 30 nucleotides in length and at least 95% complementary to their target nucleic acids; 8 to 30 nucleotides in length and at least 99% complementary to their target nucleic acids. In embodiments wherein a primer is less than 100% complementary to it target nucleic acid, having the 3‘ nucleotide in the primer be complementary to its target nucleic acid facilitates the production of an extension product.

[0118] In some embodiments, a primer that selectively hybridizes to its target nucleic acid hybridizes to its target nucleic acid with at least 5-fold greater affinity than to non-target nucleic acid under the same assay conditions. In some embodiments, a primer that selectively hybridizes to its target nucleic acid hybridizes to its target nucleic acid with at least 10-fold greater affinity’ than to non-target nucleic acid under the same assay conditions.

[0119] In some embodiments, a primer pair is designed to produce an amplicon that is 50 to 1500 nucleotides long, 50 to 1000 nucleotides long, 50 to 750 nucleotides long, 50 to 500 nucleotides long, 50 to 400 nucleotides long, 50 to 300 nucleotides long, 50 to 200 nucleotides long, 50 to 150 nucleotides long, 100 to 300 nucleotides long, 100 to 200 nucleotides long, or 100 to 150 nucleotides long.

[0120] In some embodiments, the primer is labeled with a detectable moiety. In some embodiments, a primer is not labeled.Probes

[0121] In some embodiments, the polynucleotide is a probe. Probes useful in the methods described herein are generally capable of selectively hybridizing to: genomic DNA, a target RNA (genomic or transcript), a cDNA reverse transcribed from the target RNA, and / or an amplicon that has been amplified from genomic DNA, a target RNA, or a cDNA (collectively referred to as “template”). Generally, probes of at least 15 nucleotides in length hybridize specifically in most contexts, and this length can be reduced, e.g.. by including of affinity -enhancing modifications, such as those discussed above. Probes can but need not be exactly complementary to their target nucleic acids.

[0122] In some embodiments, molecular beacon probes having long probe sequences are used in the methods disclosed herein. In certain embodiments, sloppy molecular beacon probes having long probe sequences are used in the methodsdisclosed herein. Unlike sequence-specific molecular beacons, which possess short probe sequences (18 to 26 nucleotides long) that form probe-target hybrids only with perfectly complementary, or nearly perfectly complementary, target sequences, these “sloppy” molecular beacon probes are designed to form probe-target hybrids with the amplicons generated from all of the genotypes that needs to be differentiated and identified. In some embodiments, in order to enable this unusual permissive property, the probe sequences in the molecular beacons can be about 40 nucleotides long. Consequently, they form probe-target hybrids even if the duplexes possess a substantial number of mismatched base pairs.

[0123] The melt temperature (Tm) as well as melt profile of the probe-target hybrid reflects the degree to which the probe sequence is complementary to the target sequence in the amplicon. Conventionally, the Tm value of a hybrid formed by a sloppy molecular beacon probe does not provide sufficient information to identity7the genotype from which the amplicon was generated. Thus, the simultaneous use of a set of sloppy molecular beacons, each possessing a different probe sequence and each labeled with a differently colored fluorophore would be used to provide a set of Tm values that serves as a unique, species-specific signature. In the present disclosure, the melt profile of a hybrid formed by a sloppy molecular beacon probe can provide sufficient information to differentiate and identify the genotype from which the amplicon is generated, via DQC. In some instances, the methods provided herein utilizes data from the melt profile of a single probe-target hybrid (i.e., using one sloppy molecular beacon probe) to differentiate and identity7a genotype. In some instances, the methods provided herein utilizes data from the melt profile of two probe-target hybrids (i.e., using two sloppy molecular beacon probes) to differentiate and identify a genotype. In some instances, the methods provided herein utilizes data from the melt profile of three probe-target hybrids (i.e., using three sloppy molecular beacon probes) to differentiate and identify7a genotype. In some instances, the methods provided herein utilizes data from the melt profile of four single probe-target hybrids (i.e., using four sloppy molecular beacon probes) to differentiate and identify a genotype.

[0124] The probes used in the methods disclosed herein are not limited to molecular beacons or sloppy molecular beacon probes. In some embodiments, the probes used in the methods disclosed can include a TaqMan® probe, a molecular beacon probe, a sloppy molecular beacon probe, a Scorpion probe, or a combination thereof. In some examples, a probe that selectively hybridizes to its target nucleic acidwith at least 5 -fold greater affinity than to non-target nucleic acid under the same assay conditions can be used. In some embodiments, a probe that selectively hybridizes to its target nucleic acid hybridizes with at least 10-fold greater affinity than to non-target nucleic acid under the same assay conditions.

[0125] Probes can have any degree of complementarity described above for exemplary polynucleotides. In illustrative embodiments, probes can be 20 to 60 nucleotides in length and at least 50% complementary to their target nucleic acids; 8 20 to 60 nucleotides in length and at least 55% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 60% complementary' to their target nucleic acids; 20 to 60 nucleotides in length and at least 65% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 70% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 75% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 80% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 85% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 90% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 95% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 99% complementary' to their target nucleic acids; 20 to 60 nucleotides in length and at least 90% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 95% complementary to their target nucleic acids; 20 to 60 nucleotides in length and at least 99% complementary to their target nucleic acids. In embodiments wherein a primer is less than 100% complementary' to a target nucleic acid, any points or regions of non-complementarity are typically located so as not to disrupt the ability of the probe to selectively hybridize to its target nucleic acid.Polynucleotide Labels

[0126] In some embodiments, the primer or probe is labeled with a detectable moiety. Detectable moieties include directly detectable moi eties, such as fluorescent dyes, and indirectly detectable moieties. such as members of binding pairs. When the detectable moiety is a member of a binding pair, in some embodiments, the probe can be detectable by incubating the probe with a detectable label bound to the second member of the binding pair. In some embodiments, a primer or probe is not labeled, such as when a primer or probe is immobilized, e.g., on a microarray or bead. A labeled primer is extendable, e.g., by a polymerase. In some embodiments, a probe isextendable. In other embodiments, a probe is not extendable. The following discussion centers on probes, as these are more typically employed for detecting in the methods described here, but those of skill in the art appreciate that the polynucleotide labeling strategies described below apply equally to the labeling of primers.

[0127] In some embodiments, the probe is a FRET probe that, in some embodiments, is labeled at the 5'-end with a fluorescent dye (donor) and at the 3 '-end with a quencher (acceptor), a chemical group that absorbs (i.e.. suppresses) fluorescence emission from the dye when the groups are in close proximity (e g., attached to the same probe). Thus, in some embodiments, the emission spectrum of the dye should overlap considerably with the absorption spectrum of the quencher. In other embodiments, the dye and quencher are not at the ends of the FRET probe.

[0128] Illustrative FRET probes, which include, but are not limited to, a TaqMan® probe, a Molecular beacon probe and a Scorpion probe. A TaqMan® probe is a linear probe that ty pically has a fluorescent dye covalently bound at one end of the DNA and a quencher molecule covalently bound elsewhere, such as at the other end of the DNA. The FRET probe comprises a sequence that is complementary to a region of the cDNA or amplicon such that, when the FRET probe is hybridized to the cDNA or amplicon, the dye fluorescence is quenched, and when the probe is digested during amplification of the cDNA or amplicon, the dye is released from the probe and produces a fluorescence signal. In some embodiments, the amount of target nucleic in the sample is proportional to the amount of fluorescence measured during amplification.

[0129] Like TaqMan® probes, Molecular Beacons use FRET to detect a PCR product via a probe having a fluorescent dye and a quencher attached at the ends of the probe. Unlike TaqMan® probes, Molecular Beacons remain intact during the PCR cycles. Molecular Beacon probes form a stem-loop structure when free in solution, thereby allowing the dye and quencher to be in close enough proximity to cause fluorescence quenching. When the Molecular Beacon hybridizes to a target nucleic acid, the stem-loop structure is abolished so that the dye and the quencher become separated in space and the dye fluoresces. Molecular Beacons are available, e.g., from Gene Link™ (see www.genelink.com / newsite / products / mbintro.asp).

[0130] In some embodiments, Scorpion probes can be used as sequence-specific primers and for PCR product detection. Like Molecular Beacons, Scorpion probes form a stem-loop structure when not hybridized to a target nucleic acid. However,unlike Molecular Beacons, a Scorpion probe achieves both sequence-specific priming and PCR product detection. A fluorescent dye molecule is attached to the 5 ’-end of the Scorpion probe, and a quencher is attached elsewhere, such as to the 3 ’-end. The 3’ portion of the probe is complementary to the extension product of the PCR primer, and this complementary portion is linked to the 5 ’-end of the probe by a non-amplifiable moiety. After the Scorpion primer is extended, the target-specific sequence of the probe binds to its complement within the extended amplicon, thus opening up the stemloop structure and allowing the dye on the 5 ’-end to fluoresce and generate a signal. Scorpion probes are available from, e.g., Premier Biosoft International (see www.premierbiosoft.com / tech_notes / Scorpion.html).

[0131] In some embodiments, labels that can be used on the FRET probes include colorimetric and fluorescent dyes, such as Alexa Fluor dyes; BODIPY dyes, such as BODIPY FL, Cascade Blue, and Cascade Yellow; coumarin and its derivatives, such as 7-amino-4-methylcoumarin, aminocoumarin and hydroxycoumarin; cy anine dyes, such as Cy3 and Cy5; eosins and erythrosins; fluorescein and its derivatives, such as fluorescein isothiocyanate; macrocyclic chelates of lanthanide ions, such as Quantum Dye™; Marina Blue; Oregon Green; rhodamine dyes, such as rhodamine red, tetramethylrhodamine and rhodamine 6G; Texas Red; fluorescent energy' transfer dyes, such as thiazole orange-ethidium heterodimer; and TOT AB.

[0132] Specific examples of dyes include, but are not limited to, those identified above and the following: Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500. Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700. and, AlexaFluor 750; amine-reactive BODIPY dyes, such as BODIPY 493 / 503, BODIPY 530 / 550, BODIPY 558 / 568, BODIPY 564 / 570, BODIPY 576 / 589, BODIPY 581 / 591, BODIPY 630 / 650, BODIPY 650 / 655, BODIPY FL, BODIPY R6G, BODIPY TMR, and, BODIPY-TR; Cy3, Cy5, 6-FAM, Fluorescein Isothiocyanate, HEX. 6-JOE, Oregon Green 488, Oregon Green 500, Oregon Green 514, Pacific Blue. REG. Rhodamine Green, Rhodamine Red, Renographin, ROX, SYPRO, TAMRA, 2’, 4’,5’,7’-Tetrabromosulfonefluorescein, and TET.

[0133] Examples of dye / quencher pairs (i.e., donor / acceptor pairs) include, but are not limited to, fluorescein / tetramethylrhodamine; lAEDANS / fluoresceimfluorescein / QSY 7 or QSY 9 dyes. When the donor and acceptor are the same, FRET may be detected, in some embodiments, by fluorescence depolarization. Certain specific examples of dye / quencher pairs (i.e. , donor / acceptor pairs) include, but are not limited to, Alexa Fluor 350 / Alexa Fluor488; Alexa Fluor 488 / Alexa Fluor 546; Alexa Fluor 488 / Alexa Fluor 555; Alexa Fluor 488 / Alexa Fluor 568; Alexa Fluor 488 / Alexa Fluor 594; Alexa Fluor 488 / Alexa Fluor 647; Alexa Fluor 546 / Alexa Fluor 568; Alexa Fluor 546 / Alexa Fluor 594; Alexa Fluor 546 / Alexa Fluor 647; Alexa Fluor 555 / Alexa Fluor 594; Alexa Fluor 555 / Alexa Fluor 647; Alexa Fluor 568 / Alexa Fluor 647; Alexa Fluor 594 / Alexa Fluor 647; Alexa Fluor 350 / QSY35; Alexa Fluor 350 / dabcyl; Alexa Fluor 488 / QSY 35; Alexa Fluor 488 / dabcyl; Alexa Fluor 488 / QSY 7 or QSY 9; Alexa Fluor 555 / QSY 7 or QSY9; Alexa Fluor 568 / QSY 7 or QSY 9; Alexa Fluor 568 / QSY 21; Alexa Fluor 594 / QSY 21; and Alexa Fluor 647 / QSY 21. In some instances, the same quencher may be used for multiple dyes, for example, a broad spectrum quencher, such as an Iowa Black® quencher (Integrated DNA Technologies, Coralville, IA) or a Black Hole Quencher™ (BHQ™; Sigma-Aldrich, St. Louis, MO).

[0134] Specific examples of fluorescently labeled ribonucleotides useful in the preparation of probes for use in some embodiments of the methods described herein are available from Molecular Probes (Invitrogen), and these include, Alexa Fluor 488-5- UTP. Fluorescein- 12-UTP, BODIPY FL-14-UTP, BODIPY TMR-14-UTP, Tetramethylrhodamine-6-UTP, Alexa Fluor 546-14-UTP. Texas Red-5-UTP, and BODIPY TR-14-UTP. Other fluorescent ribonucleotides are available from Amersham Biosciences (GE Healthcare), such as Cy3-UTP and Cy5-UTP.

[0135] Specific examples of fluorescently labeled deoxyribonucleotides useful in the preparation of probes for use in the methods described herein include Dinitrophenyl (DNP)-l’-dUTP, Cascade Blue-7-dUTP, Alexa Fluor 488-5-dUTP, Fluorescein- 12-dUTP, Oregon Green 488-5-dUTP, BODIPY FL-14-dUTP, Rhodamine Green-5-dUTP, Alexa Fluor 532-5-dUTP, BODIPY TMR-14-dUTP, Tetramethylrhodamine-6-dUTP. Alexa Fluor 546-14-dUTP, Alexa Fluor 568-5-dUTP. Texas Red-12-dUTP, Texas Red-5-dUTP. BODIPY TR-14-dUTP, Alexa Fluor 594-5- dUTP, BODIPY 630 / 650- 14-dUTP, BODIPY 650 / 665- 14-dUTP; Alexa Fluor 488-7- OBEA-dCTP, Alexa Fluor 546-16-OBEA-dCTP, Alexa Fluor 594-7-OBEA-dCTP, and Alexa Fluor 647-12-OBEA-dCTP. Fluorescently labeled nucleotides are commercially available and can be purchased from, e.g.. Invitrogen.

[0136] As noted above, exemplary detectable moieties also include members of binding pairs. Exemplary binding pairs include, but are not limited to, biotin and streptavidin, antibodies and antigens, etc.Sample

[0137] The sample to be tested can be any sample suspected of containing the target nucleic acids. In some embodiments, the sample is a biological sample collected from a subject. In other embodiments, the sample is a sample that is not collected directly from a subject, such as, e.g., a wastewater sample or a sample from an air filter in a building.

[0138] Illustrative biological samples include the following: a sputum sample, a nasal aspirate sample, a nasal wash sample, a nasal swab sample, a nasopharyngeal swab sample, a saliva sample, an oropharyngeal swab sample, a throat swab sample, a bronchoalveolar lavage sample, a bronchial aspirate sample, a bronchial wash sample, an endotracheal aspirate sample, an endotracheal wash sample, a tracheal aspirate sample, a nasal secretion sample, a mucus sample, a pleural effusion sample, a cerebrospinal fluid sample, a stool sample, a tissue biopsy sample, a breath sample, or a combination thereof.

[0139] The sample to be tested is, in some embodiments, fresh (i.e., never frozen). In other embodiments, the sample is a frozen specimen. In some embodiments, the sample is a tissue sample, such as a formalin-fixed paraffin embedded sample. In some embodiments, the sample is a liquid cytology sample. In some embodiments, e.g., assays for resistance to tuberculosis drugs, the sample can be a sputum sample that is unprocessed or decontaminated, digested, and concentrated.

[0140] In some embodiments, a sample to be tested is contacted with a buffer after collection. For example, in the case of a sputum sample, a buffer (including, e.g., a preservative) can be added to the sample. In embodiments where the sample is a swab sample, the swab can simply be placed in a buffer. In some embodiments, that sample is contacted with the buffer immediately; in the case of a swab, the swab is immediately placed in the buffer. In some embodiments, the sample (e.g., including the swab) is contacted with buffer within 5 minutes, within 10 minutes, within 30 minutes, within 1 hour, or within 2 hours of sample collection.

[0141] In some embodiments, less than 5 ml, less than 4 ml, less than 3 ml. less than 2 ml, less than 1 ml, or less than 0.75 ml of sample or buffered sample are used inthe present methods. In some embodiments, 0. 1 ml to 1 ml of sample or buffered sample is used in the present methods.Subjects

[0142] A biological sample useful in the methods described herein can be collected from any subject that may have one or more of the target nucleic acids. In various embodiments, the subject can include non-human animals, e.g., canines, felines, equines, primates, and other non-human mammals, as well as humans.

[0143] In some embodiments, the sample to be tested is obtained from an individual who has one or more symptoms of tuberculosis or who has been exposed to such an individual. The primary symptoms of tuberculosis include persistent cough, cough with blood in the sputum, fever, and chest pain.

[0144] In some embodiments, methods described herein can be used for routine screening of apparently healthy individuals with no risk factors. In some embodiments, methods described herein are used to screen asymptomatic individuals, for example, during routine or preventative care. In some embodiments, methods described herein are used to screen women who are pregnant or who are attempting to become pregnant.

[0145] In some embodiments, the methods described herein can be used to assess the effectiveness of a treatment in an individual undergoing treatment e.g., for tuberculosis.Assay Methods

[0146] Any analytical procedure capable of permitting specific detection of a target nucleic acid by melt can be used in the methods herein presented. In some embodiments, DNA targets can be detected by amplification of the DNA template and detection of the amplicon by melt. In some embodiments, RNA targets can be detected by direct hybridization or, more easily, by reverse transcribing a target RNA to produce a cDNA that is complementary to the target RNA. This cDNA can be directly detected by direct hybridization or by amplification of the cDNA template.

[0147] Nucleic acid amplification provides rapid, sensitive, and specific detection of nucleic acid targets, and has been employed in a wide variety of assay formats to detect nucleic acid targets. Those of skill in the art can, following the guidance herein, carry out the methods described herein in any number of different nucleic acid amplification-based assays, using, for example, any of the nucleic acid amplification methods discussed above. Such methods can entail thermocycling, but need not do so, as in the case of isothermal amplification. Exemplary methods include,but are not limited to, isothermal amplification, real-time RT-PCR, endpoint RT-PCR, and amplification using T7 polymerase from a T7 promoter annealed to a DNA, such as provided by the SenseAmp Plus™ Kit available at Implen, Germany. Amplification and detection can be carried out in solution or can make use of a solid support (e.g., a biochip). Nucleic acid amplification-based assays can employ a single reaction chamber or multiple reaction chambers. Amplification can be nested or non-nested.

[0148] In some embodiments, target nucleic acids, and / or optional controls, can be detected by (a) contacting nucleic acid from the sample with a set of primers and optional probes for detecting the presence of the desired target nucleic acids, (b) subjecting the nucleic acid, primers, and optional probes to amplification conditions; (c) detecting the presence of any amplification product(s), optionally via real-time PCR, melt curve analysis, or a combination thereof, and (d) differentially identifying the presence of a viral pathogen in the sample, or determining that no viral pathogen detectable using the set of primers is present, based on detection of the amplification product(s) or lack thereof, respectively. In this context, “differentially identifying"’ refers to the ability to determine that a particular target organism is present and that one or more other target organisms of the assay are not. In some embodiments, the assay is able to determine the presence of any target organism this present in the sample, while ruling out the presence of the other target organisms (above the detection limit of the assay).

[0149] In some embodiments of amplification by polymerase chain reaction (PCR), an exemplary' cycle comprises an initial denaturation at 90°C to 100°C for 20 seconds to 5 minutes, followed by cycling that comprises denaturation at 90°C to 100°C for 1 to 10 seconds, followed by annealing and amplification at 60°C to 75°C for 10 to 40 seconds. A further exemplary cycle comprises 20 seconds at 94°C, followed by up to 3 cycles of 1 second at 95°C, 35 seconds at 62°C, 20 cycles of 1 second at 95°C, 20 seconds at 62°C, and 14 cycles of 1 second at 95°C, 35 seconds at 62°C. In some embodiments, for the first cycle following the initial denaturation step, the cycle denaturation step is omitted. In some embodiments, Taq polymerase is used for amplification. In some embodiments, the cycle is carried out at least 10 times, at least 15 times, at least 20 times, at least 25 times, at least 30 times, at least 35 times, at least 40 times, or at least 45 times. In some embodiments, Taq is used with a hot-start function. In some embodiments, detection of the target nucleic acids occurs in less than 3 hours, less than 2.5 hours, less than 2 hours, less than 1 hour, or less than 30 minutesfrom initial denaturation through the last extension. In some embodiments, target nucleic acids are detected by a method that includes real-time quantitative PCR, e.g., using FRET probes, such as those described above.

[0150] In some embodiments, quantitation of the results of real-time PCR assays is done by constructing a standard curve from a nucleic acid of known concentration and then extrapolating quantitative information for target nucleic acids of unknown concentration. In some embodiments, the nucleic acid used for generating a standard curve is a DNA (for example, an endogenous control, or an exogenous control). In some embodiments, the nucleic acid used for generating a standard curve is a purified double-stranded plasmid DNA or a single-stranded DNA generated in vitro.

[0151] In some embodiments, in order for an assay to indicate that a given target nucleic acid is not present in a sample, the Ct values for an endogenous control (such as an SAC) and / or an exogenous control (such as an SPC) must be within previously-determined valid ranges. For example, in some embodiments, the absence of a particular target nucleic acid cannot be confirmed unless the controls are detected, indicating that the assay was successful.

[0152] In some embodiments, a threshold Ct (or a “cutoff Ct”) value for a target nucleic acid (including an endogenous control and / or exogenous control), below which the gene is considered to be detected, has previously been determined. In some embodiments, a threshold Ct is determined using substantially the same assay conditions and system (such as a GENEXPERT®) on which the samples wall be tested.

[0153] Real-time PCR is performed using any PCR instrumentation available in the art. Typically, instrumentation used in real-time PCR data collection and analysis comprises a thermal cycler, optics for fluorescence excitation and emission collection, and optionally a computer and data acquisition and analysis software.

[0154] In some embodiments, the PCR amplification can be follow ed by a melt analysis for detection by melt analysis. Another approach to detect target nucleic acids can include high-resolution melt alone. Endpoint melting curve data to detect target nucleic acids and analyses can be performed to generate a result for each analyte.

[0155] Target nucleic acids can also be detected by real-time PCR but in more than one reaction chambers. Another approach to detect a target nucleic acid can include digital microfluidics or electrowetting and electrochemical detection. For example, digital microfluidics or electrowetting, responsible for the movement and transfer of samples and reagents inside a cartridge can be conducted. Systems for suchcan include a microarray for detection, consisting of target-specific capture probes attached to gold electrodes (solid-support), which generates a voltage signal if a "‘target DNA / signal probe" hybridizes with the capture probes. Target nucleic acids can also be detected using a chip that includes an integrated sensor array.

[0156] Examples of other approaches that can be employed in the methods describe herein include bead-based flow cytometric assay. See Lu J. et al. (2005) Nature 435:834-838. which is incorporated herein by reference for this description. An example of a bead-based flow cytometric assay is the xMAP® technology of Luminex, Inc. See www.luminexcorp.com / technology / index.html. Another approach uses microfluidic devices and single-molecule detection. See U.S. Patent Nos. 7,402,422 and 7,351,538 to Fuchs et al, U.S. Genomics, Inc., each of which is incorporated herein by reference in its entirety. Yet another approach is simple gel electrophoresis and detection with labeled probes (e.g., probes labeled with a radioactive or chemiluminescent label), such as by northern blotting.

[0157] In some embodiments, the approach for detecting a target nucleic acid does not include bead-based flow cytometric assay, microfluidic devices and singlemolecule detection, simple gel electrophoresis, use of a capture probe attached to a solid-support, separation of reaction mixture into multiple reaction chambers, arraybased detection, nested amplification, electrochemical detection, high resolution melt only, or a combination thereof.Automated Assay Methods

[0158] Readily automated approaches are of great interest. The methods described herein can be carried out in a substantially automated manner using a commercially available nucleic acid amplification system. Exemplary nonlimiting nucleic acid amplification systems that can be used to carry out the methods of the invention include the GENEXPERT® system, a GENEXPERT® Infinity system, and GENEXPERT® Xpress System (Cepheid, Sunnyvale, Calif.). In some embodiments, the amplification system may be available at the same location as the individual to be tested, such as a health care provider’s office, a clinic, or a community hospital, so processing is not delayed by transporting the sample to another facility. Assays according to the method described herein can be completed in under 3 hours, in some embodiments, under 2 hours, in some embodiments, under 1 hour, in some embodiments, under 45 minutes, in some embodiments, under 35 minutes, and in some embodiments, under 30 minutes, using an automated system, for example, theGENEXPERT® system. The GENEXPERT® utilizes a self-contained, single-use cartridge. Sample extraction, amplification, and detection may all carried out within this self-contained sample cartridge as described herein.

[0159] In some embodiments, after the sample is added to the cartridge, the sample is contacted with lysis buffer and released nucleic acid (NA) is bound to a NA- binding substrate, such as a silica or glass substrate. The sample supernatant is then removed and the NA eluted in an elution buffer, such as a Tris / EDTA buffer. The eluate may then be processed in the cartridge to detect target nucleic acids as described herein. In some embodiments, the eluate is used to reconstitute at least some of the PCR reagents, which are present in the cartridge as lyophilized particles.

[0160] A cartridge having a plurality of chambers can have the set of primers and optional probes described herein, or a subset thereof, disposed in a chamber. In some embodiments, the set of primers and optional probes described herein, or a subset thereof, are disposed in more than one of the plurality of chambers.

[0161] In certain embodiments, the cartridge is configured for insertion into a reaction module. The module is configured to receive the cartridge therein. In certain embodiments the reaction module provides heating plates to heat the temperature controlled chamber or channel. The module can optionally additionally include a fan to provide cooling where the temperature controlled channel or chamber is a thermocycling channel or chamber. Electronic circuitry can be provided to pass information (e.g., optical information) top a computer for analysis. In certain embodiments the module can contain optical blocks 306 to provide excitation and / or detection of one or more (e.g., 1, 2, 3, 4. 5, 6, 7, 8, 9, 10, or more) optical signals representing, e.g., signal DNAs amplified for various PCR targets. In various embodiments an electrical connector can be provided for interfacing the module with a system (e.g. system controller or with a discrete analysis / controller unit). The sample can be introduced into the cartridge using a pipette. In certain embodiments, the module also contains a controller that operates a plunger in the syringe barrel and the rotation of the valve body.

[0162] While the methods described herein are described primarily with reference to the GENEXPERT® cartridge by Cepheid Inc. (Sunnyvale, Calif.), it will be recognized, that in view of the teachings provided herein the methods can be implemented on other cartridge / microfluidic systems, including alternative cartridge designs having valve assemblies that involve multiple interfacing components, as wellas cartridge body defined by multiple interfacing components to form the multiple chambers of the cartridges, for example, those described in Korean Application No. 102293717B1 and KR102362853B1, cartridges that utilizes ultrasonic waves to lyse cells in a biological sample, for example, those described in International Application No. WO2021 / 245390A1, cartridges and systems that utilizes an electrowetting grid for microdroplet manipulation and electrosensor arrays configured to detect analytes of interest, for example, those described in International Application No.WO2016 / 077341 A2, cartridges that facilitate movement of nucleic acid from one chamber to the next chamber by opening a vent pocket, for example, those described in International Application No. WO2012 / 145730A2, multiplexed assay systems comprising a plurality of thermocycling units such that individual chambers can be heated, cooled, and / or compressed to mix fluid within the chamber or to propel fluid in the chamber into another chamber, for example, those described in International Application No. WO2015 / 138343A1, and as well as systems for rapid amplification of nucleic acids facilitated by flexible portions of the sample cartridge aligned to accomplish temperature cycling for nucleic acid amplification, for example, those described in International Application No.WO2017 / 147085 Al. Such cartridge / microfluidic systems can include, for example microfluidic systems implemented using soft lithography, micro / nano-fabricated microfluidic systems implemented using hard lithography, and the like.

[0163] In an exemplary embodiment, the cartridge can include a plurality of reaction chambers, particularly, the reaction vessel can include a plurality of reaction chambers. In these embodiments, different types of lyophilized primers and probes can be provided in each reaction chamber. For example, primers and probes for viral- associated nucleic acids can be provided in one reaction chamber, and primers and probes for viral-associated nucleic acids can be provided in a second chamber for amplification and detection, and such the like. Of course, it is possible to perform various amplification and detection processes at the same time in a single reaction chamber. Accordingly, amplification of each target nucleic acid described herein may be performed individually in separate reaction chambers or wells or carried out in a multiplex reaction in a single reaction chamber or well.

[0164] Additionally, it is appreciated that the panel assay methods described herein (i.e., identification of multiple conditions based on comparative levels of multiple-target nucleic acids obtained from a single sample) can further be realized inentirely different systems, including: gradient PCR, isothermal nucleic acid amplification systems, digital RT-PCR, electrochemical PCR, lateral flow testing cartridges, electrochemical sensors, nucleic acid sequencing, CRISPR / Cas based technologies, chemiluminescence, and nanoparticle-based colorimetric detection.

[0165] In various embodiments, the signal DNA(s) from PCR (nucleic acid amplification) reactions are amplified for detection and / or quantification. In certain embodiments, the amplification comprise any of a number of methods including, but not limited to polymerase chain reaction (PCR), ligase chain reaction (LCR), ligase detection reaction (LDR), multiplex ligation-dependent probe amplification (MLP A), ligation followed by Q-replicase amplification, primer extension, strand displacement amplification (SDA), hyperbranched strand displacement amplification, multiple displacement amplification (MDA), nucleic acid strand-based amplification (NASBA), rolling circle amplification (RCA), and the like.Reagents

[0166] An exemplary sample cartridge than can be used as part of the GENEXPERT® system is provided herein. The exemplary cartridge can include: lyophilized reagents in the form of one or more beads as described herein; an optional lysis reagent; alkaline agent, optional binding reagent, filtering reagent, washing reagent, and eluting reagent. The last four reagents are named according to the functions they perform with respect to nucleic acid. Thus, for example the binding reagent facilitates the binding of nucleic acid to a substrate, the filtering reagent facilitates filtration of the nucleic acid.

[0167] In some embodiments, the lysis reagent can include a chaotropic agent, a chelating agent, a buffer, an alkaline agent, or a detergent. The chaotropic agent can be selected from a guanidinium compound such as guanidinium thiocyanate or guanidinium hydrochloride, an alkali perchlorate such as lithium perchlorate, an alkali iodide, magnesium chloride, urea, thiourea, a formamide, or a combination thereof. The concentration of the chaotropic agent can range from about 1 M to about 10 M, such as from about 2.5 M to about 7.5 M, or less than 4.5 M, less than 2 M, or less than 1 M. The chelating agent can be selected from N-acetyl-L-cysteine, ethylenediaminetetraacetic acid (EDTA), diethylene triamine pentaacetic acid (DTP A), ethylenediamine-N,N'-disuccinic acid (EDDS), l,2-bis(o-aminophenoxy)ethane- N,N,N',N'-tetraacetic acid (BAPTA), and a phosphonate chelating agent. The concentration of the chelating agent can range from about 10 mM to about 100 mMand / or comprises about 0.5% to about 5% of the lysis reagent. The buffer can be selected from the group consisting of Tris, phosphate buffer, PBS, citrate buffer, TAPS, Bicine, Tncine, TAPSO, HEPES, TES, MOPS, PIPES, Cacodylate, SSC, and MES. The concentration of the buffer can range from about 5 mM to about 100 mM, such as from about 5 mM to about 50 mM. The detergent can be selected from an ionic detergent or a non-ionic detergent. In some examples, the detergent comprises a detergent selected from the group consisting of N-lauroylsarcosine. sodium dodecyl sulfate (SDS), cetyl methyl ammonium bromide (CTAB), TRITON®-X-100, n-octyl-0- D-glucopyranoside, CHAPS, n-octanoylsucrose, n-octyl-P-D-maltopyranoside, n-octyl- P-D-thioglucopyranoside, PLURONIC® F-127. TWEEN® 20, and n-heptyl-|3-D- glucopyranoside. The detergent can comprise about 0. 1% to about 2% of the lysis reagent, and / or ranges from about 10 mM up to about 100 mM. The lysis reagent can have a pH ranging from about pH 3.0 to about pH 5.5.

[0168] In some embodiments, the assays disclosed herein do not utilize a chaotropic agent or a lysis buffer. When a chaotropic agent or lysis buffer is not used, the sample can be contacted with a buffer (or filtering reagent) including, for example, saline (including one or more inorganic salts, such as CaCh, MgSCh, KC1, NaHCCh, NaCl, etc.), phosphate buffer, Tris buffer, 2-amino-2-hydroxymethyl-l,3-propanediol, HEPES, PBS, citrate buffer, TES, MOPS, PIPES, Cacodylate, SSC, MES, saccharide or disaccharide, or combinations thereof. For example, the buffer can be a commercially available buffer such as Hanks’ Balanced Salt Solution available from Sigma Aldrich or TE Buffer available from Fisher BioReagents.

[0169] In some embodiments, the alkaline agent can be selected from an alkali metal hydroxide, such as sodium hydroxide or potassium hydroxide. The concentration of the alkaline agent can be about 0.5 N to 5 N.

[0170] The binding reagent can promote binding of nucleic acids to the filter, facilitating the removal of non-target material. In some embodiments, the binding reagent can include a binding polymer such as polyacrylic acid (PAA), polyacrylamide (PAM), polyethylene glycol (PEG), poly(sulfobetaine). or a salt, or combinations thereof. In some embodiments, the filtering reagent and / or the washing reagent can include the binding reagent. For example, the binding reagent, the filtering reagent, and / or the washing reagent can include a binding polymer (e.g., PEG 200), buffer, inorganic salt, antioxidant and / or chelating agent, antifoam SEI 5, sodium azide, disaccharide or disaccharide derivative, carrier protein, detergent, or DMSO. Thebinding polymer can be present in an amount of at least 10% v / v, at least 20% v / v, at least 30% v / v, and / or less than 60% v / v. less than 40% v / v, less than 30% v / v, less than 20% v / v, or less than 10% v / v or can fall within any range bounded by any of these values, e.g., from 10% to 60% v / v, of the binding reagent, fdtering reagent, and / or the washing reagent. The buffer can be selected from the group consisting of Tris, 2- amino-2-hydroxymethyl-l,3-propanediol, HEPES, phosphate buffer, PBS, citrate buffer. TAPS, Bicine, Tncine. TAPSO. HEPES, TES, MOPS, PIPES, Cacodylate. SSC, and MES. The concentration of the buffer can range from about 5 mM to about 100 mM, such as from about 5 mM to about 50 mM. The salt, such as NaCl, KC1, or MgCb. can be present at a concentration from about 0.05 M to about 1 M, such as from about 0. 1 M to about 0.5 M. The antioxidant and / or chelating agent comprises an agent selected from the group consisting of N-acetyl-L-cysteine, ethylenediaminetetraacetic acid (EDTA), diethylene triamine pentaacetic acid (DTP A), ethylenedi amine-N,N'- disuccinic acid (EDDS), l,2-bis(o-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid (BAPTA), and a phosphonate chelating agent. In some embodiments the antioxidant and / or chelating agent comprises EDTA. In certain embodiments the antioxidant and / or chelating agent comprise 0.2% to about 5%, about 0.2% to about 3%, or about 0.5% to about 2%, or about 0.5% of the binding reagent, filtering reagent, and / or the washing reagent. In some embodiments the concentration of the antioxidant and / or chelating agent in the binding reagent, filtering reagent, or the washing reagent ranges from about 2 mM to about 50 mM or about 5 mM to about 20 mM. In some embodiments, the detergent is an ionic detergent or a non-ionic detergent. The detergent can be selected from an ionic detergent or a non-ionic detergent. In some examples, the detergent comprises a detergent selected from the group consisting of N-lauroylsarcosine, sodium dodecyl sulfate (SDS), cetyl methyl ammonium bromide (CTAB), TRITON®-X-100, n-octyl-P-D-glucopyranoside, CHAPS, n-octanoylsucrose, n-octyl-P-D- maltopyranoside, n-octyl-P-D-thioglucopyranoside, PLURONIC® F-127, TWEEN® 20, Brij-35, and n-heptyl-P-D-glucopyranoside. The detergent can comprise about 0.1% to about 2% of the binding reagent, filtering reagent, and / or the washing reagent, and / or ranges from about 10 mM up to about 100 mM. The binding reagent, filtering reagent and / or the washing reagent can have a pH ranging from about pH 6.0 to about pH 8.0 (such as from about 6.5 to about 7.5).

[0171] In some embodiments, the eluting reagent can have a pH greater than about 9, greater than about 10, greater than about 11, or greater than about 12. The useof high pH to elute nucleic acid such as DNA is unique especially to the cartridges described herein and provides improved speed and performance of the disclosed methods. Speed is provided by the rapid neutralization of acidic ammonium ions by the high concentration of hydroxide ions. Alkylamines have a pKa -10-11 and are immediately deprotonated at pH 12.7, to form the neutral free base on the solid surface, and release the cationic DNA. A further advantage of the high pH is the denaturing effect of KOH on captured DNA or RNA. Acidic functional groups in the heterocyclic bases of DNA or RNA are immediately deprotonated and cannot form Watson-Crick bonds. Double-stranded structures and other secondary structures are disrupted, but can re-nature when neutralized for example, with Tris HC1. This chemical denaturing of captured genomic DNA can be an advantage for isothermal assays that do not undergo the usual heat denaturing of PCR. The cartridges provided herein allow for rapid neutralization of eluted DNA or RNA in KOH followed by reaction with Tris to produce a final pH of about 8.5 for downstream PCR or other nucleic acid assays. In some embodiments, the eluting reagent can have a pH less than about 9, less than about 8.5, or less than about 8. This lower-pH elution of bound DNA or RNA can be an advantage, especially for devices that don’t facilitate rapid neutralization of the KOH solution. It is known that RNA is hydrolyzed by high pH, but short exposure times to KOH can provide for good quality RNA. In some examples, the eluting reagent comprises a poly anion, a poly cation, ammonia or an alkali metal hydroxide. For example, the eluting reagent may comprise a polyanion such as a carrageenan, a carrier nucleic acid, or a combination thereof.

[0172] In some instances, to reduce bubble formation in one or more of the chambers, the detergent Brij may be added to one or more of the reagents described herein.

[0173] It is understood that various other reagents and initial volumes can be used for performing an automated PCR panel assay on a sample inserted into the cartridge.Exemplary Detection Methods, Results, and Handling of Results

[0174] In some embodiments, a computer-based analysis program is used to translate the raw data generated by the detection assay into data of predictive value for a clinician.

[0175] Melt Curve analysis can be evaluated by GENEXPERT® Software to determine the presence of PCR product. Melting temperature (Tm) and Melt peak height(MPH) of the curve is calculated automatically by the analysis software. The melt curve is detected as positive if Tm falls inside the valid Tm range specified for each target nucleic acid. The melt curve is called as negative if melt curve is not in the appropriate Tmrange. The software automatically calculates the cycle threshold (Ct), Endpoint and Probe check values. Illustrative limit-of-detection concentrations, Ct cut-offs, and methods for determining the same are provided in the examples below.

[0176] Before the start of the PCR reaction, the GENEXPERT® System measures the fluorescence signal from the probes to monitor bead rehydration, reaction tube filling, probe integrity, and dye stability. This Probe Check Control (PCC) passes if it meets validated acceptance criteria.

[0177] In some embodiments, a computer-based analysis program is used to translate the raw data generated by the detection assay into data of predictive value for a clinician. The clinician can access the predictive data using any suitable means. Thus, in some embodiments, the present invention provides the further benefit that the clinician, who is not likely to be trained in genetics or molecular biology, need not understand the raw data. The data is presented directly to the clinician in its most useful form. The clinician is then able to immediately utilize the information in order to optimize the care of the subject.

[0178] When the GENEXPERT® System is used, the results are interpreted automatically and are shown in a “View Results” window. Positive targets are highlighted in red color, negative targets are highlighted in green color, and indeterminate targets are highlighted in light gray color. Samples with coinfection may appear with positives results for multiple targets. Invalid, Error or No result are highlighted in light gray color.

[0179] Exemplary detection methods, results, and handling of results for host biomarker targets are described in US Patent Publication No. 2022 / 0298572, which is incorporated by reference for this description.

[0180] The present disclosure contemplates any method capable of receiving, processing, and transmitting the information to and from laboratories conducting the assays, information provides, medical personal, and subjects. For example, in some embodiments of the present invention, a sample is obtained from a subject and submitted to a testing service (e.g., clinical lab at a medical facility, genomic profiling business, etc.), located in any part of the world (e.g., in a country different than the country where the subject resides or where the information is ultimately used) togenerate raw data. Where the sample comprises a tissue or other biological sample, the subject may visit a medical center to have the sample collected and sent to the testing service, or subjects may collect the sample themselves and directly send it to a testing service. Where the sample includes previously determined biological information, the information may be directly sent to the testing service by the subject (e.g., an information card containing the information may be scanned by a computer and the data transmitted to a computer of the profiling center using an electronic communication systems). Once received by the testing service, the sample is processed and a set of test results is produced, specific for the diagnostic or prognostic information desired for the subject.

[0181] The test results can be prepared in a format suitable for interpretation by a treating clinician. For example, rather than providing raw expression or melt curve data, the prepared format may represent a diagnosis or risk assessment for the subject, with or without recommendations for particular treatment options. The test results maybe displayed to the clinician by any suitable method. For example, in some embodiments, the testing service generates a report that can be printed for the clinician (e g., at the point of care) or displayed to the clinician on a computer monitor.

[0182] In some embodiments, the information is first analyzed at the point of care or at a regional facility. The raw data is then sent to a central processing facility for further analysis and / or to convert the raw data to information useful for a clinician or patient. The central processing facility- provides the advantage of privacy (all data is stored in a central facility7with uniform security- protocols), speed, and uniformity7of data analysis. The central processing facility- can then control the fate of the data following treatment of the subject. For example, using an electronic communication system, the central facility can provide data to the clinician, the subject, or researchers.

[0183] In some embodiments, the subject is able to directly7access the data using the electronic communication system. The subject may choose further interv ention or counseling based on the results. In some embodiments, the data is used for research use. For example, the data may be used to further optimize the inclusion or elimination of markers as useful indicators of a particular condition or stage of disease or as a companion diagnostic to determine a treatment course of action.Kits0184] Also contemplated is a kit for carrying out the methods described herein.Such kits include one or more reagents useful for practicing any of these methods. Akit generally includes a package with one or more containers holding the reagents, as one or more separate compositions or. optionally, as an admixture where the compatibility of the reagents will allow. The kit can also include other material(s) that may be desirable from a user standpoint, such as a buffer(s), a diluent(s), a standard(s), and / or any other material useful in sample processing, washing, or conducting any other step of the assay.

[0185] Kits preferably include instructions for carrying out one or more of the screening methods described herein. Instructions included in kits can be affixed to packaging material or can be included as a package insert. While the instructions are typically written or printed materials, they are not limited to such. Any medium capable of storing such instructions and communicating them to an end user can be employed. Such media include, but are not limited to, electronic storage media (e.g., magnetic discs, tapes, cartridges, chips), optical media (e.g., CD ROM), and the like. As used herein, the term “instructions” can include the address of an internet site that provides the instructions.

[0186] In some embodiments, a kit includes primer pairs for amplifying and / or detecting the selected nucleic acid targets, optionally with probes specific for these targets. Such kits can additionally include primers pairs and optional probes for detecting one or more of the above-described host biomarker targets. In some embodiments, these kits can include primers pairs and optional probes for detecting one or more of the above-described controls.

[0187] In some embodiments, the kit can include any the reagents described above provided with or in one or more GENEXPERT® cartridge(s). See e.g., US Patents 5,958,349, 6,403,037, 6.440,725, 6,783.736, 6,818,185; each of which is herein incorporated by reference for this description. Any of the kits described here can include, in some embodiments, a receptacle for a sample and / or a swab for collecting a sample.EXAMPLESExample 1: Dynamic Quantum Clustering for Melt Curve Analysis

[0188] In this example, a series of analyses were run, using data from the Xpert MTB Ultra Assay, to evaluate the effectiveness of a new process called Dynamic Quantum Clustering (DQC) using information derived from the entire melt peak and not just the Tm. The MTB Ultra Assay produces melt curves from 4 PCR probes. Tm’s produce 4 dimensions of data (one Tm per probe). DQC uses info from the full extentof melt curves within a temperature range of 55 to 80°C to produce 496 dimensions of data (4 x 124 measurements per probe). Whether DQC can extract more useful information to better identify different genotypes from the full melt curves than the Tm approach can from its 4 peak values (Tms) was investigated. The results show that using analytical technology to analyze melt curve pattern differences generated by the probe-target hybrids provides better differentiation and more accurate prediction of MTB genotypes than is possible using Tm’s alone.

[0189] Analysis of melt curves leads to better results than melting temperatures from the Xpert MTB / RIF Ultra assay. The example provided herein is the result of a series of analyses conducted on the melt curves generated in the Xpert MTB / RIF Ultra assay. The production algorithm for the MTB Ultra assay converts post-PCR melt results into Melt Peak Temperatures (Tm), which captures the location of the peak of each of four melt curves. In comparison, complete melt curve analysis and an algorithm specially designed to manage highdimensional data, DQC, was used for comparative analyses. The analyses showed that there is useful information in the melt curves outside of the specific Tm windows which can be used to increase predictive accuracy.

[0190] A melt curve for a single probe includes 124 observed values for the 1stderivative of fluorescence as temperature rises from 55°C to 80°C and the probe gradually dissociates from the amplified PCR target. The MTB Ultra assay has 4 melt curves corresponding to the 4 probes used in the assay. The Melt Peak Temperature (Tm) approach reduces each assay to a data object with 4 values, one for the peak from each probe. In comparison, using full melt curves allows for more robust data objects consisting of 496 values (4 probes x 124 values per probe).

[0191] The Tm approach has the following drawbacks: Common causes ofTm errors in the GeneXpert Tm detection software. The main causes of Tm errors are peaks occurring outside of pre-defined temperature windows due to unexpected Tm shifts caused by new / unknown mutations in the target, high variability in fluorescence at lower temperatures, which interferes with accurate Tm determination even if they are minor variations, and shallow peaks below the pre-defined melt peak height thresholds. Design limitations. Tm-based probe design requires repetitive iteration to create probes with precise Tm values and a high signal to noise ratio. Tm windows defined by the GeneXpert Tm detection software must be ~4 degrees wide, limiting the number of Tm windows per optical channel over a temperature range of 55 to 80°C and limiting thelength of sequences that can be queried in a single channel using a limited number of probes. Probe length and number are important for covering targets with mutations spread across a larger stretch of sequence. Multiplexity is limited. It is difficult to accommodate multiple probes in a single optical channel, since the Tm-detection software cannot efficiently deconvolute and call out Tm’s for merged peaks with less than 4 degrees of temperature separation.

[0192] Using the data contained in the entire melt curve eliminates the common causes of errors and improves multiplexing by eliminating the need for Tm windows spaced 4 degrees apart in a single optical channel, while also accelerating assay design. However, the computational difficulty of 496-dimensional data requires an advanced algorithm.

[0193] The DQC algorithm improves on traditional statistical and machinelearning methods with a multi-step approach, using principles from quantum mechanics, that enables analysis of high-dimensional data with little dimensional reduction and minimal risk of overfitting. DQC is a two-step process.

[0194] Step 1: Building a Density Map: DQC treats each observation as a highdimensional quantum object that can be plotted in space using its values from each dimension as coordinates. Observations with similar values are plotted near each other; observations with dissimilar values are separated. Each quantum object is represented by a multi-dimensional Gaussian curve. In the case of MTB Ultra, each observation was represented by a 496-dimensional quantum object. Based on the data plot, DQC constructs a high-dimensional topographical density map or ‘landscape’ (FIG. 1), where higher densities of points create depressions in the map, which can be thought of as ‘lakes’ or ‘rivers’. The density map also represents outliers as independent points. The DQC algonthm then moves points ‘downhill’ in the map, in an iterative process that is displayed in a resulting animation (not shown). The process reveals the topography of the map, exposing clusters in the data. This process does not use outcome information and is not subject to overfitting. DQC methods are described in US Patent Nos. 10,169,445B2 and 9,646,074B2.

[0195] Step 2: Predicting Future Outcomes. After clustering, the data is colored according to target outcome(s) of interest. For this example, the ‘outcome’ of interest was the genotype of each sample. The first use case used melt curves to distinguish between MTB WT (Wild Type) and NWT (non-Wild Type) samples with known genotypes and build a reference DQC cluster map. Distinguishing the samples as WTor NWT (FIG. 2) showed that melt curves were effective in separating 100% of WT samples from NWT samples. This helps to identify the genotype of the rpoB gene RRDR region which is targeted by the 4 assay probes. After this confirmation, the reference density map can be used to classify future observations as WT or NWT by iterating new samples through the original density map. New samples that come to rest in the WT cluster would be classified as WT. NWT samples are predicted to either form an independent cluster or merge with one of the existing NWT clusters depending on their individual melt patterns and genotype.

[0196] Advantages of Melt Curves. There are issues that affect Tm interpretation that don’t affect melt-curve interpretation. The current Tm algorithm can only identify Tm values if they fall within pre-defined 'Tm windows’, which are small temperature ranges where the peak change in fluorescence of the melt curve must occur for each probe. Mutations which create unexpected deviations of Tm away from the WT Tm can have two undesired results: the Tm may fall outside of any pre-defined Tm window, or there may be no detected Tm at all (if the mutation prevents the probe from binding to the target). In the majority of such cases the result is RIF- INDETERMINATE, due to a missing Tm, which prevents end users from identifying the RIF-susceptibility of the patient. Even minor variations in fluorescence due to unexpected noise at lower temperatures (50-60°C) can interfere with the Tm calling software algorithm and also result in RIF-INDETERMINATE results due to missing Tm’s. The melt pattern determination algorithm is entirely independent of Tm windows and can use full-melt-curve patterns to correctly identity7the RIF-susceptibility7of such RIF-INDETERMINATE results, irrespective of the actual position of the ‘peak’ along the temperature axis. This approach is also more accommodating of minor fluorescence variations at lower temperatures, since these do not lead to any significant differences of melt patterns in the DQC algorithm.

[0197] When applied on the data set of 35 RIF-INDETERMINATE results from the Xpert MTB / RIF Ultra runs performed on clinical samples, DQC correctly resolved all 35 RIF-INDETERMINATE samples in the data set into RIF-Resistant (NWT) or RIF-Susceptible (WT) clusters. DQC was also able to predict the genoty pe of 31 of the 35 Indeterminates, when compared to a reference database containing melt pattern clustering data obtained from known NWT genoty pes. The remaining 4 Indeterminates were significantly different from all genotypes available forcomparison; these 4 samples are thus expected to be new or unknown genotypes not included in the reference data set.

[0198] Melt curves detect different bacterial species. The MTB Ultra Assay was not developed to identify or differentiate nontuberculous mycobacteria (NTM) from MTB, but melt curves are still generated for NTM samples, due to cross-reactivity of some rpoB probes. Melt curves from several NTM samples were presented to DQC as a test of its discriminatory ability, to see whether the NTM samples would be confused with any MTB genotypes and how efficiently the DQC system can distinguish between actual melt signals and “noise” due to cross-reactivity.

[0199] DQC was able to clearly differentiate NTM from all MTB genotypes in the data set, and it further clustered the NTM samples into 4 familial clusters (FIG. 4). The rpoB probes in the MTB Ultra Assay were designed to have minimal crossreactivity with NTMs. However, the NTM melt curves still provide enough information for DQC to distinguish them from all MTB genotypes, and even to make some distinctions between different species of NTM. The key observation here was that the DQC system did not overlap the NTM data with any of the MTB non-WT clusters, clearly demonstrating its ability to differentiate between specific and non-specific signals.

[0200] Melt curves differ between MTB genotypes even when Tm ’s are identical. FIG. 5 shows the tuberculosis Wild Type and R529K variant have identical Tm’s in probe 2. However, the normalized melt curves show consistent differences between the genotypes - even though the melt peaks are identical. DQC is sensitive to differences anywhere along the curves; the differences shown here would be enough by themselves for DQC to differentiate these genotypes. Importantly, these differences are entirely independent of Tm value. In general, any significant and reproducible difference between melt curve patterns for different genotypes, in any temperature range, even in a single probe, may be enough for DQC to reliably separate the genotypes from each other. The implications of this finding are important in applications which aim at differentiating two closely related genotypes differing by a single SNP in the same codon, even though their Tm profiles may be identical. These differences allow DQC to separate the genotypes using melt curves.

[0201] A hierarchical analysis of 54 MTB genotypes using melt curves. The last analysis demonstrates DQC’s ability to extract sophisticated information from melt curves, offering examples of DQC’s usefulness for multiplexing and identifying targetsequences. The data was a pool of MTB Ultra Assay results representing MTB Wild Type (WT) and 53 other mutant (NWT) variants in the rpoB gene. DQC was able to separate the data into: 1) 32 pure, single-genotype clusters; 2) 9 mixed clusters, representing the remaining 22 genotypes. In these mixed clusters, the genotypes were indistinguishable from each other but clearly different from genotypes in other clusters; 3) additionally, DQC was able to show that some genotypes, as measured by the MTB Ultra Assay, were related hierarchically. The relationships are shown in FIG. 6 (54 genotypes separated into 32 single-variant, ‘'Pure” clusters and 9 '‘Mixed” clusters containing the remaining 22 genotypes).

[0202] The observed clustering reflects the interaction between the genotype of the tuberculosis bacteria (location of the mutation in the queried target sequence) and the probe design. For example. Cluster 1.3. 1.1.1 at the bottom-left ofthe figure is a cluster of H526 variants, consisting of H526H, H526L, H526N, H526Y, and one unsequenced, "Unknown" sample (that is likely to be an H526 variant). Given the design of the 4 probes in the MTB Ultra Assay, these genotypes are indistinguishable from each other (FIG. 7A. FIG. 7B. FIG. 7C. and FIG. 7D). A different probe design is needed to resolve them individually.

[0203] Cao et al. (2019) used Tm windows to define signatures for 34 unique MTB genotypes, and an additional 8 signatures that defined heterogeneous groups of genotypes. The exact data from Cao’s paper was not available, but the set gathered for this analysis had a high degree of overlap. (Both sets contained genotypes that were not found in the other set, but most genotypes were common to both sets). Comparing the results from DQC with the results from the paper indicated that Cao’s Tm-based approach may have some data overfit. For example. DQC clusters H526Y with H526N, H526L, and H526H, as previously described (Cluster 1.3. 1.1.1). Cao et al. listed H526Y as a distinctly identifiable genotype, separate from the H526N and H526L “grouped genotypes” (multiple genotypes defined by the same Tm signature). Given the curves shown in FIG. 7A, FIG. 7B, FIG. 7C, and FIG. 7D, all three genotypes have similar Tm’s and melt curves and cannot be separated using these readings. Cao also reports the same Tm signatures for both clusters.Table 1: Genotype signatures from Cao et al. (2019). All probes show Tm differences of less than 0.5 degrees, which is below the 2-degree separation required for a meaningful difference.

[0204] There are 12 other genotypes that DQC classifies as members of mixed clusters based on their melt curves but Cao classified as unique based on Tm’s. Cao et al. list the following genotypes as distinct: L51 IP, D516V, D516Y. H526Stop. H526Y, H526S, S531Q, S531L, L533P, L511P+D516G, D516G+L533P, H526N or H56L, D516D del or D516V+N518D. DQC classifies them in 10 mixed clusters based on the similarity of their melt curves.

[0205] Cao also lists several genotypes in multiple alternative versions of Tm signature. The versions of a genotype differ by which probe Tm’s are missing from the Tm signature for a given sample, forcing the Tm-window algorithm to treat each version differently. Certain Tm values are presumably missing because they were not recorded when they fell outside the defined Tm windows. The DQC approach does not suffer from this type of arbitrary “edge-case” effect; each of these genotypes appears as a single cluster in the DQC results.

[0206] The results of this example illustrate how DQC brings considerable advantage by providing a more comprehensive understanding of melt data and by revealing more / novel information, in the form of more specific or accurate results and informing assay improvement and algorithms. In addition to the advantages discussed above, the melt curve methodology described herein can also provide identification of multiple viral, bacterial, and fungal species in a single cartridge.

[0207] In another particular example, the data above was combined with an additional 205 wild type RRDR samples, for a total of 431 Mtb samples to further detect and discriminate genoty pes in sequence specific probe-target hybrid melt curves. An additional sample set including Tm data from 26 non-tuberculous mycobacteria (NTM) was also included to demonstrate how efficiently Mtb and NTM can be differentiated. These two organisms belong to the same Mycobacterial genus, but the NTM contains a wide range of different species. In addition to the first derivative melt, raw melt curve and second derivative melt curve data were analyzed as well. The melt curve ty pes can be either analyzed individually or in combination to show combined information from raw and first derivative melt curve can be synergized to augment the pattern recognition further. The melt curve analyses described herein demonstrate i)clustering ofWT from non-WT sequence with >99% accuracy in the 436 sample set challenged with increased number of WT sequences ii) identity of three different RIF- susceptible sequence variants with 100% accuracy iii) NTM signals clustered into a separate group and did not affect the RIF-S or RIF-R clusters. The accuracy of the genotyping methods disclosed herein is 100%. The minimum and / maximum number of different mutant rpoB genoty pes included in an assay can be individually identified by recognizing the differences in the melt curve patterns of the 4 rpoB probes.

[0208] Summary. These analyses show that there is more information in melt curves than is captured by Tm’s. Using melt curves in assay design can reduce the design time required, increases the measurements and information available per probe, and reduces the frequency of indeterminate results. One of DQC’s strength is its ability to use complex information to find subtle patterns. DQC is Tm agnostic and looks at fluorescence change pattern of dual labeled probes (SMBs) as they melt off from their targets. No Tm window or Tm detection necessary (alternatively: additional analysis after Tm detection). DQC has greater accuracy since subtle differences in probe melt patterns due to changes in target genotype can be identified; large delta Tms (>2°C) are not necessary; and Tm + Melt pattern information can be combined for better genotype discrimination (sequential analysis). DQC resolves “Indeterminates” and can identify “orphan melt peaks” in “null Tm window zones”. New variant ID is expected to be easier. DQC is also more conducive to using multiple probes in a single channel since merged peaks are expected to be easily differentiated from individual peaks, thus facilitating higher multiplexing. DQC is expected to make melt probe design process easier since precise Tm values are not necessary for this algorithm. As long as a reproducible melt pattern can be generated from a specific probe, such melt pattern recognition algorithm is expected to work irrespective of the presence or absence of a pronounced or well-defined Tm peak. Fluorescent probe and amplicon target hybrid melts can result in reproducibly identifiable melt pattern differences corresponding to single SNP variants, even at a low resolution of optical data acquisition per degree increase in temperature from the melt stage. Thus, this approach does not require high resolution melt and hetero-dimer melting for detection of SNP variants unlike intercalating dyes. This is an important aspect of the present disclosure since the methods and systems provided herein make assay design and development easier and faster. Assay developers no longer need to design probes which will yield “perfect” melt profiles and precise Tm values, such as in SYBR green melt assays.

Claims

CLAIMS1. A method for detecting and differentiating genotypes of nucleic acid in a multiplex amplification reaction, the method comprising: contacting the nucleic acid with a set of primers for amplifying target regions of the nucleic acid and a set of fluorescent probes for detecting any amplicon formed; subj ecting the nucleic acid, primers, and fluorescent probes to amplification conditions to amplify the target regions of the nucleic acid; generating one or more melt curve profiles for each target region of nucleic acid amplified; classifying the one or more melt curve profiles into one or more genotype cluster(s), wherein classifying is performed by: normalizing and calculating a derivative for each melt curve profile to form a processed melt curve profile, and creating a densify map using data from each processed melt curve profile as coordinates for the densify map, wherein the proximity of each genotype cluster to another genotype cluster in the density map is determined by a similarity in the value of the coordinates; and differentiating and optionally identify ing the genoty pes of nucleic acid based on classification of the one or more melt curve profiles.

2. An automated method for detecting and differentiating genotypes of nucleic acid in a multiplex amplification reaction, the method comprising: a) receiving a biological sample comprising a cellular material into a cartridge comprising a plurality of chambers therein, wherein the plurality of chambers includes i) a sample chamber for receiving a biological sample comprising cellular material; ii) a lysis chamber in fluidic communication with the sample chamber and having a means for releasing nucleic acid from the cellular material; iii) a reaction vessel fluidically coupled to the plurality’ of chambers and configured for amplification of nucleic acids and detection of one or a plurality of amplification products; and iv) a filter disposed in a fluidic path between the lysis chamber and reaction vessel, for capturing nucleic acid released from the cellular material; b) releasing nucleic acid from the biological sample inside the cartridge;c) contacting the nucleic acid with a set of primers for amplify ing target regions of the nucleic acid and a set of fluorescent probes for detecting any amplicon formed; d) subjecting the nucleic acid, primers, and fluorescent probes to amplification conditions to amplify the target regions of the nucleic acid; e) generating one or more melt curve profiles for each target region of nucleic acid amplified; f) classifying the one or more melt curve profiles into one or more genotype cluster(s). wherein classifying is performed by normalizing and calculating a derivative for each melt curve profile to form a processed melt curve profile and creating a densify’ map using data from each processed melt curve profile as coordinates for the density map, wherein the proximity of each genotype cluster to another genotype cluster in the density map is determined by a similarity in the value of the coordinates; and g) differentiating and optionally identify ing the genotypes of nucleic acid based on classification of the one or more melt curve profiles.

3. The method of claim 2, wherein said means for releasing nucleic acid from the cellular material is by mechanical lysis, chemical lysis, or a combination thereof.

4. The method of any one of claims 2-3, wherein the reaction vessel comprises a reaction chamber, and detection of the genotypes of nucleic acid in the multiplex amplification reaction is within the reaction chamber.

5. The method of any one of claims 2-3, wherein the reaction vessel comprises up to 4 reaction chambers, and detection of the genotypes of nucleic acid in the multiplex amplification reaction is within the up to 4 reaction chambers.

6. The method of any one of claims 1-4, wherein differentiating and optionally identifying the genotypes of nucleic acid is based on a machine learning model trained with historical density maps generated from processed melt curve profiles of the genotypes.

7. The method of any one of claims 1-6, wherein the method comprises detecting. differentiating, and optionally identifying 10 or more genotypes, 15 or more genotypes,20 or more genotypes, 25 or more genotypes, 30 or more genotypes, 35 or more genotypes, or 40 or more genotypes.

8. The method of any one of claims 1-7, wherein the genotypes are selected from bacterial pathogen, viral pathogen, fungal pathogen, or a combination thereof.

9. The method of any one of claims 1-8, wherein the genotypes are selected from one or more genes of Mycobacterium tuberculosis (such as Mycobacterium tuberculosis genes related to antimicrobial resistance, Mycobacterium tuberculosis genes related to antimicrobial susceptibility (e.g., RIF susceptibility), or wild-type Mycobacterium tuberculosis genes), one or more nontuberculous mycobacteria, or a combination thereof.

10. The method of claim 9, wherein the genotypes comprise Mycobacterium tuberculosis genes related to antimicrobial resistance and are selected from Rifampicin (RIF) resistance, isoniazid (INH) resistance, fluoroquinolone (FLQ) resistance including Levofloxacin (LFX) and Moxifloxacin (MFX), ethionamide (ETH) resistance, rifampicin resistance, amikacin (AMK) resistance, capreomycin (CAP) resistance, kanamycin (KAN) resistance, bedaquiline (BDQ) resistance, clofazimine (CFZ) resistance, delamanid (DLM) resistance, ethambutol resistance (E), linezolid resistance (LZD), pyrazinamide (Z) resistance, streptomycin (S) resistance. Ethionamide (ETO) or Prothionamide (PTO) resistance, Pretomanid (Pa) resistance or a combination thereof.1 1. The method of any one of claims 1-10, wherein the set of fluorescent probes comprise one or more sloppy molecular beacon (SMB) probes.

12. The method of any one of claims 1-10, wherein the set of fluorescent probes consists of sloppy molecular beacon (SMB) probes.

13. The method of any one of claims 1-12, wherein each fluorescent probe in the set. and optionally each of the SMB probes, has a polynucleotide sequence from 20 to 60 nucleotides long.

14. The method of any one of claims 1-13, wherein detecting, differentiating, and optionally identifying a genotype utilizes one melt curve profile, optionally a melt curve profile derived from a SMB probe.

15. The method of any one of claims 1-14, wherein detecting, differentiating, and optionally identifying a genot pe utilizes a plurality of melt curve profiles, optionally melt curve profiles derived from a plurality of SMB probes.

16. The method of any one of claims 1-15, wherein the method comprises detecting, differentiating, and optionally identifying INH resistance genotype, and wherein the method utilizes melt curve profile(s) from one or more of the inhA promoter region, the katG gene, the fabGl gene, and the oxyR-ahpC (ahpC) intergenic region.

17. The method of any one of claims 1-16, wherein the method comprises detecting, differentiating, and optionally identifying ETH resistance genotype, and wherein the method utilizes melt curve profile(s) for the inhA promoter region.

18. The method of any one of claims 1-17, wherein the method comprises detecting, differentiating, and optionally identifying FLQ resistance genotype, and wherein the method utilizes melt curve profile(s) from one or more of the gyrA gene and the gyrB gene.

19. The method of any one of claims 1-18, wherein the method comprises detecting, differentiating, and optionally identifying AMK, KAN, and CAP resistance genotypes, and wherein the method utilizes melt curve profile(s) from one or more of the rrs gene and the eis promoter region.

20. The method of any one of claims 1-19, wherein the method comprises detecting, differentiating, and optionally identifying rifampicin resistance genotype, and wherein the method utilizes melt curve profile(s) for the rpoB gene.

21. The method of any of the claims 1-20, wherein the method comprises detecting, differentiating, and optionally identifying Bedaquiline resistance genotype, and wherein the method utilizes the melt curve profile (s) for the Rv0678 and the atpE genes and the respective promoter regions.

22. The method of any of the claims 1-21, wherein the method comprises detecting, differentiating, and optionally identifying Linezolid resistance genotype, and wherein the method utilizes the melt curve profile (s) for the rplc and rrl genes.

23. The method of any of the claims 1-22, wherein the method comprises detecting, differentiating, and optionally identifying Pyrazinamide resistance genotype, and wherein the method utilizes the melt curve profile (s) for the pncA gena and its promoter region.

24. The method of any one of claims 1-23, wherein at least one of the primers and / or fluorescent probe(s) comprises a fluorescent dye and a quencher molecule.

25. The method of any one of claims 1-24, wherein each fluorescent probe comprises a fluorescent dye and a quencher molecule.

26. The method of any one of claims 1-25, wherein higher similarity in the value of the coordinates represents more closely related genotypes.

27. The method of any one of claims 1-26, further comprising generating a real time-PCR profile for one or more of the target regions of nucleic acid, optionally wherein the target regions comprise IS6110 gene and IS 1081 gene of Mycobacterium tuberculosis.

28. The method of any one of claims 1-27, further comprising determining a melt temperature for each amplified nucleic acid, and utilizing the melt temperature in detecting, differentiating, and optionally identifying the genotypes.

29. The method of any one of claims 1-27, where the method does not utilize melt temperature in detecting, differentiating, and optionally identifying the genotypes.

30. The method of any one of claims 1-27, wherein the method differentiates and optionally identifies two or more genotypes having melt temperatures with less than 4°C, or less than 2°C, or less than 1°C temperature separation.

31. The method of any one of claims 1-30, wherein the method differentiates and optionally identifies two or more genoty pes having identical melt temperatures, but different melt patterns.

32. The method of any one of claims 1-31, wherein the method differentiates and optionally identifies two melt profiles due to SNP within a genotype.

33. The method of any one of claims 1-32, wherein the nucleic acid is present in a sputum sample, a nasal aspirate sample, a nasal wash sample, a nasal swab sample, a nasopharyngeal swab sample, a saliva sample, an oropharyngeal swab sample, a throat swab sample, a bronchoalveolar lavage sample, a bronchial aspirate sample, a bronchial wash sample, an endotracheal aspirate sample, an endotracheal wash sample, a tracheal aspirate sample, a nasal secretion sample, a mucus sample, a pleural effusion sample, a cerebrospinal fluid sample, a stool sample, a tissue biopsy sample, a breath sample, or a combination thereof.

34. The method of any one of claims 1-33, wherein the method is a point-of-care method.

35. The method of any one of claims 1-34, wherein the method comprises detecting, differentiating, and optionally identifying the genotypes of nucleic acid within 150 minutes, within 140 minutes, within 130 minutes, or within 120 minutes of collecting the nucleic acid from a subject.

36. The method of any one of claims 1-35, wherein the method is a cartridge-based method and further comprises:placing the nucleic acid in a sample chamber of a cartridge; and if the nucleic acid comprises cells, lysing the cells with one or more lysis reagents present within at least one of the plurality of chambers or capturing the cells in a filter within the cartridge and lysing the cells by means of ultrasonication to release the nucleic acid; or capturing the nucleic acids in a nucleic acid capture chamber and eluting the captured nucleic acid after washing to remove impurities.

37. The method of any one of claims 1-36, wherein the method does not utilize high-resolution melt (HRM).

38. A biological analysis system comprising one or more modules, each of the one or more modules configured to process a biological sample via a cartridge and detect one or more properties of the biological sample; at least one processor; and a memory operatively coupled to the processor and having instructions stored thereon to cause the processor to: execute, for each module from the one or more modules, and perform the method of any one of claims 1-34 for detecting and differentiating genotypes of nucleic acid; and upon detecting and differentiating genotypes of nucleic acid, present a message to at least one of a user, operator, and manager of the system.

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