EP4695825A1 - Method for categorizing molecular diagnostic signals in real time - Google Patents

Method for categorizing molecular diagnostic signals in real time

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Publication number
EP4695825A1
EP4695825A1 EP24723392.7A EP24723392A EP4695825A1 EP 4695825 A1 EP4695825 A1 EP 4695825A1 EP 24723392 A EP24723392 A EP 24723392A EP 4695825 A1 EP4695825 A1 EP 4695825A1
Authority
EP
European Patent Office
Prior art keywords
sample
amplification
nucleic acid
signal
optionally
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
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EP24723392.7A
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German (de)
French (fr)
Inventor
Daniel Jacob KOSLOVER
Eugene Davis
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Becton Dickinson and Co
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Becton Dickinson and Co
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Publication date
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Publication of EP4695825A1 publication Critical patent/EP4695825A1/en
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Definitions

  • end-point processing inherently prevents a system from making a call until all data are collected, extending the time required to report a result.
  • end- point processing can put products at a serious disadvantage.
  • Real-time methods that make use of a single metric, such as the Amplitude or Gradient of a fluorescence data point often fail to capture the essential signal characteristics that define amplified vs non-amplified behaviors. Even when multiple metrics are employed in a real-time context, they are typically evaluated in isolation with independent thresholds for each, limiting the potential sensitivity and specificity of a test.
  • the method comprises: receiving time series data from an instrument in real time, wherein the time series data comprises a plurality of data points forming a sample curve.
  • the method can comprise: calculating two or more quantitative metrics for each data point in real time.
  • the method can comprise: calculating a likelihood ratio (LR) at each data point in real time.
  • Said calculating step can comprise employing defined classifiers for each of the two or more quantitative metrics derived from (i) a reference population of positive curves and (ii) a reference population of negative curves, optionally derived via Quadratic Discriminant Analysis (QDA).
  • the method can comprise: calling the sample curve in real time.
  • a sample curve is called positive in real time once the LR calculated for a data point exceeds a defined LR threshold.
  • a sample curve is called negative in real time if the LR calculated for all data points of the sample curve falls at or below the defined LR threshold.
  • the quantitative metrics comprise amplitude (A), gradient (G), and/or span (S), optionally S is the change in A over a defined window.
  • the time series data are derived from instrument analysis of a sample, optionally a sample suspected of comprising a target analyte.
  • the instrument is configured to produce time series data by analyzing the sample, optionally the instrument is configured to perform a molecular diagnostic assay.
  • instrument analysis of the sample comprises subjecting said sample to one or more reaction(s), optionally said reaction(s) are configured to detect the presence and/or amount of a target analyte in the sample.
  • the instrument comprises one or more sensor(s) configured to detect signals derived from the sample, and wherein the time series data comprises time series signal data, optionally said signals are generated from said one or more reaction(s).
  • the signal is a calorimetric signal, a potentiometric signal, an amperometric signal, an optical signal (e.g., a fluorescent signal and/or colorimetric signal), a piezo-electric signal, or any combination thereof.
  • signals are generated in the presence of the target analyte. In some embodiments, signals are generated in the absence of the target analyte.
  • instrument analysis comprises one or more of spectrometry, Raman spectroscopy, FFT (Fast-Fourier Transform) spectroscopy, Fourier- Transform Infrared Spectroscopy (FTIR), infrared spectrometry, Nuclear Magnetic Resonance (NMR) spectrometry, electrochemical detection, polynucleotide detection, volatile organic compound methods, fluorescence anisotropy, fluorescence resonance energy transfer, electron transfer, enzyme assay, magnetism, electrical conductivity, electrochemical detection, isoelectric focusing, lateral flow assay (LFA), microfluidics, amino acid sequencing, nucleic acid sequencing, flow cytometry, chromatography, immunoprecipitation, immunoseparation, aptamer binding, filtration, electrophoresis, use of a CCD camera, immunoassay, enzyme-linked immunosorbent assay (ELISA), Gram staining, immunostaining, microscopy, immunofluorescence, size/weight
  • the instrument comprises a thermocycler, such as, for example, a thermocycler configured for real-time PCR amplification and fluorescence monitoring.
  • a sample curve being called positive indicates the presence of a target analyte in the sample.
  • a sample curve being called negative indicates the absence of the target analyte in the sample.
  • the target analyte is a target nucleic acid sequence.
  • the one or more reaction(s) comprise nucleic acid detection reaction(s).
  • the sample curve comprises a nucleic acid amplification curve, and wherein the signals comprise fluorescence signals indicative of amplification of the target nucleic acid sequence.
  • a sample curve being called positive indicates the presence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the presence and/or amount of a target nucleic acid sequence in the sample.
  • a sample curve being called negative indicates the absence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the absence of a target nucleic acid sequence in the sample.
  • the step of calculating two or more quantitative metrics comprises applying a median filter (e.g., a 3-point median filter) to the time series data to generate median-filtered data.
  • said 3-point median filter comprises: ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ , ⁇ ⁇ 1, ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ 1 ⁇ , ⁇ ⁇ ⁇ ⁇ , ⁇ ⁇ ⁇ ⁇ 1 ⁇ , ⁇ ⁇ 2, ... , ⁇ ⁇ 1 [0009]
  • the median filter provides smoothing, removal of single-point spikes, and/or removal of system noises.
  • the step of calculating two or more quantitative metrics comprises calculating the span (S) value by: employing the median-filtered signal values ( ⁇ ⁇ ) in the formula: ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ [0015] wherein a boundary point of a defined span window is either k + a or k – a. [0016]
  • the method comprises providing defined classifiers for each of the two or more quantitative metrics.
  • the LR is calculated at point y test provided the following conditions are satisfied: y test occurs at or after a defined Minimum Call Cycle; Atest, Gtest, and Stest are all > 0; and no step dislocations occur within 3 points of y test .
  • the calling step comprises calling the sample curve as invalid if: there are two or more contiguous missing points within the time series data prior to the sample curve being called positive; and/or there are more than two missing points within the time series data prior to the sample curve being called positive, optionally said missing points are either contiguous or non-contiguous.
  • one or more of the defined classifiers, the defined LR threshold, t1, t2, Signalmin, Signalmax, the defined step dislocation threshold, the defined Minimum Call Cycle, and the defined span half window are provided via an assay definition file (ADF).
  • ADF assay definition file
  • the method comprises multiplexed calling comprising: receiving two or more sets of time series data from the instrument in real time, wherein each set of time series data comprises a plurality of data points forming a sample curve; and calling each of the two or more sample curves in real time, optionally said each of the sample curves are nucleic acid amplification curves related to different target nucleic acid sequences.
  • the method is capable of calling a curve as positive at least about 1 minute, about 2 minutes, about 5 minutes, about 10 minutes, about 15 minutes, about 20 minutes, about 25 minutes, about 30 minutes, about 35 minutes, about 40 minutes, about 45 minutes, about 50 minutes, about 55 minutes, or about 60 minutes, earlier than a method employing end-state processing of the time series data.
  • the step- detection and correction does not make any assumptions regarding the correct and/or absolute signal baseline.
  • the instrument is capable of: amplifying a target nucleic acid sequence in an amplification reaction mixture, thereby generating a nucleic acid amplification product, optionally the nucleic acid amplification product is generated at detectable levels within about 20 minutes, about 15 minutes, or about 10 minutes; and detecting the nucleic acid amplification product with a signal-generating oligonucleotide, wherein the signal-generating oligonucleotide is capable of hybridizing to the nucleic acid amplification product, optionally the signal-generating oligonucleotide is a TaqMan detection probe oligonucleotide, a molecular beacon detection probe oligonucleotide, or a molecular torch detection probe oligonucleotide.
  • the signal-generating oligonucleotide comprises a label
  • the label comprises a quenchable label
  • the quenchable label is a fluorophore
  • the signal-generating oligonucleotide comprises a quencher capable of quenching a signal generated by the label when the quencher and the label are in close proximity.
  • the label is capable of generating a detectable signal upon: (i) the signal-generating oligonucleotide hybridizing the nucleic acid amplification product; and/or (ii) the nucleic acid amplification product being extended to generate an extended nucleic acid amplification product hybridized to the signal-generating oligonucleotide, optionally the signal is fluorescence.
  • amplifying the target nucleic acid sequence comprises generating the nucleic acid amplification product at detectable levels within about 20 minutes, about 15 minutes, or about 10 minutes.
  • the method can comprise: contacting a sample comprising biological entities with a lysis buffer to generate a treated sample, wherein the lysis buffer comprises one or more lytic agents capable of lysing biological entities to release sample nucleic acids comprised therein, and wherein the sample nucleic acids are suspected of comprising the target nucleic acid sequence; and contacting a reagent composition with the treated sample to generate the amplification reaction mixture, wherein the reagent composition comprises one or more amplification reagents.
  • the method is performed in a single reaction vessel; does not comprise using any enzymes other than the reverse transcriptase and the enzyme having a hyperthermophile polymerase activity; does not comprise using any enzyme other than the enzyme having a hyperthermophile polymerase activity; does not comprise heat denaturing and/or enzymatic denaturing the nucleic acid during the amplification step; and/or does not comprise contacting the nucleic acid with a single-stranded DNA binding protein.
  • the amplifying is performed: for a period of about 5 minutes to about 60 minutes, optionally the amplifying is performed for a period of about 15 minutes; and/or in helicase-free, single-stranded binding protein-free, cleavage agent-free, and recombinase-free, isothermal amplification conditions.
  • the amplifying is carried out using a method selected from the group consisting of polymerase chain reaction (PCR), ligase chain reaction (LCR), loop-mediated isothermal amplification (LAMP), strand displacement amplification (SDA), replicase-mediated amplification, Immuno-amplification, nucleic acid sequence based amplification (NASBA), self-sustained sequence replication (3SR), rolling circle amplification, and transcription-mediated amplification (TMA), optionally the PCR is real-time PCR and/or quantitative real-time PCR (QRT-PCR).
  • PCR polymerase chain reaction
  • LCR loop-mediated isothermal amplification
  • SDA strand displacement amplification
  • Immuno-amplification nucleic acid sequence based amplification
  • NASBA nucleic acid sequence based amplification
  • SR self-sustained sequence replication
  • TMA transcription-mediated amplification
  • the PCR is real-time PCR and/or quantitative real-time PCR (QRT-PCR).
  • the biological entities comprise one or more of prokaryotic cells, eukaryotic cells, viral particles, exosomes, protoplasts, and microvesicles.
  • the biological entities comprise a virus, a bacteria, a fungi, a protozoa, portions thereof, or any combination thereof.
  • the target nucleic acid sequence is a nucleic acid sequence of a virus, bacteria, fungi, or protozoa, optionally the sample nucleic acids are derived from a virus, bacteria, fungi, or protozoa.
  • the virus is SARS-CoV-2, Human Immunodeficiency Virus Type 1 (HIV-1), Human T-Cell Lymphotrophic Virus Type 1 (HTLV-1), Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Herpes Simplex, Herpesvirus 6, Herpesvirus 7, Epstein-Barr Virus, Respiratory Syncytial Virus (RSV), Cytomegalo-virus, Varicella-Zoster Virus, JC Virus, Parvovirus B19, Influenza A, Influenza B, Influenza C, Rotavirus, Human Adenovirus, Rubella Virus, Human Enteroviruses, Genital Human Papillomavirus (HPV), or Hantavirus.
  • HSV-1 Human Immunodeficiency Virus Type 1
  • HBV Hepatitis B Virus
  • HCV Hepatitis C Virus
  • RSV Respiratory Syncytial Virus
  • Cytomegalo-virus Varicella-Zoster
  • the bacteria comprises one or more of Mycobacteria tuberculosis, Rickettsia rickettsii, Ehrlichia chaffeensis, Borrelia burgdorferi, Yersinia pestis, Treponema pallidum, Chlamydia trachomatis, Chlamydia pneumoniae, Mycoplasma pneumoniae, Mycoplasma sp., Legionella pneumophila, Legionella dumoffii, Mycoplasma fermentans, Ehrlichia sp., Haemophilus influenzae, Neisseria meningitidis, Neisseria gonorrhoeae, Streptococcus pneumonia, S.
  • the fungi comprises one or more of Cryptococcus neoformans, Pneumocystis carinii, Histoplasma capsulatum, Blastomyces dermatitidis, Coccidioides immitis, and Trichophyton rubrum.
  • the protozoa comprises one or more of Trypanosoma cruzi, Leishmania sp., Plasmodium, Entamoeba histolytica, Babesia microti, Giardia lamblia, Cyclospora sp., and Eimeria sp.
  • the sample is a biological sample or an environmental sample.
  • the environmental sample is, or is obtained from, a food sample, a beverage sample, a paper surface, a fabric surface, a metal surface, a wood surface, a plastic surface, a soil sample, a fresh water sample, a waste water sample, a saline water sample, exposure to atmospheric air or other gas sample, cultures thereof, or any combination thereof.
  • the biological sample is, or is obtained from, a tissue sample, saliva, blood, plasma, sera, stool, urine, sputum, mucous, lymph, synovial fluid, cerebrospinal fluid, ascites, pleural effusion, seroma, pus, swab of skin or a mucosal membrane surface, cultures thereof, or any combination thereof.
  • the amplifying does not comprise one or more of the following: Archaeal Polymerase Amplification (APA), loop-mediated isothermal Amplification (LAMP), helicase-dependent Amplification (HDA), recombinase polymerase amplification (RPA), strand displacement amplification (SDA), nucleic acid sequence-based amplification (NASBA), transcription mediated amplification (TMA), nicking enzyme amplification reaction (NEAR), rolling circle amplification (RCA), multiple displacement amplification (MDA), Ramification (RAM), circular helicase-dependent amplification (cHDA), single primer isothermal amplification (SPIA), signal mediated amplification of RNA technology (SMART), self-sustained sequence replication (3SR), genome exponential amplification reaction (GEAR) and isothermal multiple displacement amplification (IMDA), optionally the amplifying does not comprise LAMP.
  • APA Archaeal Polymerase Amplification
  • LAMP loop-mediated isothermal Amplification
  • HDA helicase-dependent
  • the amplifying comprises one or more of the following: APA, LAMP, HDA, RPA, SDA, NASBA, TMA, NEAR, RCA, MDA, RAM, cHDA, SPIA, SMART, 3SR, GEAR and IMDA, optionally the amplifying does not comprise LAMP.
  • the method comprises and/or does not comprise one or more of the following: (i) dilution of the treated sample; (ii) dilution of the amplification reaction mixture; (iii) heat denaturation of the treated sample; (iv) sonication of the treated sample; (v) sonication of the amplification reaction mixture; (vi) the addition of ribonuclease inhibitors to the treated sample; (vii) the addition of ribonuclease inhibitors to the amplification reaction mixture; (viii) purification of the sample; (ix) purification of the sample nucleic acids; (x) purification of the nucleic acid amplification product; (xi) removal of the one or more lytic agents from the treated sample or the amplification reaction mixture; (xii) heat denaturing and/or enzymatic denaturing of the sample nucleic acids prior to and/or during amplification; and (xiii) the addition of ribonuclease H
  • systems for real-time calling can comprise: an instrument configured to produce time series data by analyzing a sample.
  • the system can comprise: a processor comprising memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to perform a method provided herein.
  • computer systems for real-time calling can comprise: a hardware processor; and non-transitory memory having instructions stored thereon, which when executed by the hardware processor causes the processor to perform a method provided herein.
  • computer readable medium comprising code for performing a method provided herein.
  • an assay definition file comprising one or more of defined classifiers, a defined LR threshold, t 1 , t 2 , Signal min , Signal max , a defined step dislocation threshold, a defined Minimum Call Cycle, and a defined span half window, for use in a method provided herein.
  • ADF assay definition file
  • FIG. 3A-3B depict non-limiting exemplary schematics related to the Calling Algorithm provided herein: Initial Average Test (is the curve valid; FIG. 3A), and Assertion Test (is the curve amplifying; FIG.3B)).
  • FIG. 4 depicts a non-limiting exemplary Initial Average and Assertion Test flow chart. Nodes correspond to relevant calling algorithm data and/or metrics. Annotations in black are functions used to calculate metrics from data or other metrics. Annotations in blue correspond to ADF inputs used within these functions/calculations.
  • FIGS. 5A-5B depict a non-limiting exemplary application of median filter to raw fluorescence data. Raw fluorescence data (FIG. 5A) and median-filtered data (FIG.
  • FIG. 6 depicts a non-limiting exemplary distribution of Initial Average Values. Dataset is comprised of 100 asserted and 41 non-asserted curves. Initial Average measured between 75 and 120 seconds.
  • FIG.7 depicts a non-limiting exemplary Step Dislocation Curve.
  • FIGS. 8A-8F depict non-limiting exemplary Step Detection Functions. Pairwise difference calculations (FIG. 8C, FIG. 8D; Blue Curves), median-filtering of differences (FIG. 8C, FIG.
  • FIG. 9 depicts non-limiting exemplary Asserted versus Non-Asserted Curves. LR peaks in Reference Data are plotted in 3 Dimensions, with the Amplitude, Gradient, and Span values of reference positive (blue) and reference negative (red) curves at their Likelihood Ratio peaks in a three-dimensional plot. The hyperplane separating these populations (green) corresponds to a defined Likelihood Ratio threshold within this space. [0045] FIGS.
  • FIGS. 10A-10B depict non-limiting exemplary higher decision logic for FluA/B Test. Higher decision logic for FluA (FIG. 10A) and FluB (FIG. 10B) are shown. FluA and Internal Control test are duplexed.
  • FIG. 11 depicts a non-limiting exemplary plot of Initial Likelihood Ratio Peaks. Amplitude, Gradient, and Span values for the y PLR points in the curve population are plotted, one point per curve. Peaks for amplified curves are in blue and peaks for non-amplified curves in red.
  • FIGS. 12A-12B depict non-limiting exemplary plots of Refined Likelihood Ratio Peaks.
  • the method comprises: receiving time series data from an instrument in real time, wherein the time series data comprises a plurality of data points forming a sample curve.
  • the method can comprise: calculating two or more quantitative metrics for each data point in real time.
  • the method can comprise: calculating a likelihood ratio (LR) at each data point in real time.
  • Said calculating step can comprise employing defined classifiers for each of the two or more quantitative metrics derived from (i) a reference population of positive curves and (ii) a reference population of negative curves, optionally derived via Quadratic Discriminant Analysis (QDA).
  • QDA Quadratic Discriminant Analysis
  • the method can comprise: calling the sample curve in real time.
  • a sample curve is called positive in real time once the LR calculated for a data point exceeds a defined LR threshold.
  • a sample curve is called negative in real time if the LR calculated for all data points of the sample curve falls at or below defined LR threshold.
  • systems for real-time calling can comprise: an instrument configured to produce time series data by analyzing a sample.
  • the system can comprise: a processor comprising memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to perform a method provided herein.
  • computer systems for real-time calling can comprise: a hardware processor; and non-transitory memory having instructions stored thereon, which when executed by the hardware processor causes the processor to perform a method provided herein.
  • computer readable medium comprising code for performing a method provided herein.
  • an assay definition file comprising one or more of defined classifiers, a defined LR threshold, t 1 , t 2 , Signal min , Signal max , a defined step dislocation threshold, a defined Minimum Call Cycle, and a defined span half window, for use in a method provided herein.
  • ADF assay definition file
  • Methods for Categorizing Molecular Diagnostic Signals in Real Time There are provided, in some embodiments, methods, systems, compositions, algorithms, and kits for categorizing molecular diagnostic signals in real time via an iteratively refined Quadratic Discriminant Analysis–based algorithm employing localized step detection and correction. Provided herein include robust methods for evaluating fluorescence signals generated by molecular diagnostic tests.
  • Methods provided herein can detect amplification of target DNA in real time and can reliably differentiate true amplification from background noise.
  • Methods, systems, compositions, algorithms, and kits provided herein can integrate multiple quantitative metrics to provide a comprehensive analysis of a signal (e.g., a fluorescence signal).
  • a signal e.g., a fluorescence signal.
  • Three metrics in particular – the Amplitude, Gradient, and Span (the change in Amplitude over a defined window) – can be calculated at each point in real time and compared to values derived from reference data sets of known Positives and Negatives as part of a Likelihood Ratio test (also known as a Wilks Test).
  • Likelihood Ratios can be calculated at each point according to classifiers derived from reference populations of known Positives and Negatives. These classifiers can be obtained via Quadratic Discriminant Analysis (QDA), a statistical method for binning multi- dimensional data into two or more groups. To classify entire curves comprised of multiple data points, QDA can be applied to metrics at characteristic peaks that optimally discriminate between amplified and non-amplified curves.
  • QDA Quadratic Discriminant Analysis
  • QDA coefficients for the non-amplified reference population can be initially defined according to the distribution of metrics observed over the set of all negative points, rather than at any specific peak.
  • Corresponding inputs for the amplified population can be initially calculated based on the point in each curve that is most statistically distinct (has the largest Mahalanobis Distance (a multi-dimensional generalization of a standard deviation)) from the population of non-amplified points.
  • initial Likelihood Ratios can be calculated for all points in the non-amplified and amplified training sets.
  • the points corresponding to the Peak Likelihood Ratio in each curve can then be used to define revised QDA coefficients for the amplified and (optionally) non-amplified populations, which can in turn be used to generate revised Likelihood Ratios for each point.
  • Multiple iterations of this process – calculation of Peak Likelihood Ratios to generate revised sets of QDA coefficients – can be performed until the coefficients converge to their final values.
  • This iterative process can yield a set of model parameters optimized for discriminating between amplified and non- amplified curves according to the Likelihood Ratio metric.
  • Step dislocations can represent macroscopic system noises, such as bubbles obstructing the optical path or condensed drops of fluid falling into the reaction, that create a risk of false-positive calls if left uncorrected.
  • step dislocations can be detected by measuring the pairwise differences between adjacent median-filtered points.
  • Likelihood Ratio Tests can be applied in a real-time context.
  • the characteristic peaks used to generate the QDA coefficients can be obtained through an iterative process that has never previously been considered or employed for discriminating populations. The approach can enable optimization of QDA coefficients based on the specific data points within reference signals – the Likelihood Ratio Peaks – that are most relevant for classifying test signals as Positive or Negative.
  • the real-time method for step correction represents an improvement over prior art in that it makes no assumptions regarding the correct (absolute) fluorescence baseline.
  • the method comprises: receiving time series data from an instrument in real time, wherein the time series data comprises a plurality of data points forming a sample curve.
  • the method can comprise: calculating two or more quantitative metrics for each data point in real time.
  • the method can comprise: calculating a likelihood ratio (LR) at each data point in real time.
  • Said calculating step can comprise employing defined classifiers for each of the two or more quantitative metrics derived from (i) a reference population of positive curves and (ii) a reference population of negative curves, optionally derived via Quadratic Discriminant Analysis (QDA).
  • QDA Quadratic Discriminant Analysis
  • the method can comprise: calling the sample curve in real time.
  • a sample curve is called positive in real time once the LR calculated for a data point exceeds a defined LR threshold. In some embodiments, a sample curve is called negative in real time if the LR calculated for all data points of the sample curve falls at or below defined LR threshold.
  • the methods e.g., Calling Algorithm
  • the instrument e.g., clinical instrument
  • it applies both an Initial Average Test and an Assertion Test to each fluorescence curve (FIGS. 3A-3B).
  • measured parameters for each metric derived from reference populations of known positives and negatives via Quadratic Discriminant Analysis can be supplied via the ADF to calculate a Likelihood Ratio at each point.
  • the Likelihood Ratio represents the relative likelihood of a point occurring in amplified vs non-amplified populations. If a valid curve contains at least one point with a Likelihood Ratio above a defined threshold (specified in the ADF), that curve can be called as asserted. But if all points within a valid curve fall below the threshold, then the curve can be called as not asserted.
  • FIG. 4 depicts a flow chart outlining the Initial Average and Assertion Test calculations used to make real-time assertions.
  • a basic principle behind the SG filter is that a sliding window is moved through the data and a 2nd order linear regression applied to each window: ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ [0071]
  • the x values in each window are coded as ⁇ -3,...3 ⁇ .
  • the reference curves can be collected using representative target concentrations in appropriate sample types.
  • the Amplitude, Gradient, and Span can be identified at a characteristic peak yPLR, which can be identified through an iterative process (See Section Derivation of Calling Algorithm Input Parameters) for maximally separating known positives and negatives within the A,G,S, space.
  • the means and covariance matrices for these metrics can be then calculated for the amplified and non-amplified populations: ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ , ⁇ , ⁇ ⁇ , ⁇ , ⁇ ⁇ , ⁇ ⁇ ⁇ can be used to distinguish between amplified and non-amplified curves via a Likelihood Ratio Test.
  • These coefficients, which together comprise a total of 18 inputs – three for ⁇ ⁇ , three for ⁇ ⁇ , and six for each (symmetric) covariance matrix ⁇ – can be assay specific and defined for each test and internal control curve within an Assay Definition File (ADF).
  • ADF Assay Definition File
  • the calling algorithm can calculate the Mahalanobis Distance separating each point in a test curve from the distribution of known amplified and non-amplified curves. This is a multi-dimensional generalization of the number of standard deviations separating a point from the mean of a distribution. Here, the calculation can be performed over three dimensions, corresponding to the Amplitude, Gradient, and Span. The method assumes these metrics are normally distributed across the amplified and non-amplified populations, but is relatively robust to deviations from normality.
  • Step Detection Curves collected on the system may occasionally exhibit step dislocations, sudden shifts in baseline signal unrelated to true amplification (FIG. 7). These can represent macroscopic system noises, such as bubbles in a detection window or condensed drops of fluid falling into a reaction. To prevent step dislocations from triggering false assertion calls, there are provided, in some embodiments, methods to identify and correct for these signal noises. [0086] Step dislocations can be detected by measuring the pairwise differences between adjacent median-filtered points.
  • step dislocations can be characterized by a sharp, single-point peak above baseline (FIG.8B and FIG.8D) due to the (nearly) instantaneous shift in signal.
  • a 3-point median filter can be applied to the pairwise-difference curves (FIG. 8C and FIG. 8D - red).
  • the smoothed differences Y s can be then subtracted from the unsmoothed differences such that only the “sharp” peaks remain (FIG. 8E and FIG. 8F). Any points in Y – Y s with a value above the corresponding threshold defined in the ADF can be identified as step dislocations and the magnitude J recorded. Detection of a step dislocation alters the Span and Likelihood Ratio calculations as discussed above in Sections The Span Metric and Calculating the Likelihood Ratio.
  • Curves may also be called as Invalid in other rare circumstance such as: (a) there are ⁇ 2 contiguous missing points within Fraw prior to an Asserted call; and/or (b) there are >2 missing points (either contiguous or non-contiguous) within F raw prior to an Asserted call.
  • the curve passes the Initial Average Test (i.e., the curve is valid)
  • the curve can be Asserted as soon as a point is determined to have a Likelihood Ratio above the threshold defined in the ADF.
  • a single point with an LR above the threshold can be sufficient to trigger the Asserted call. If the run completes without the LR of any point exceeding the defined threshold, then the curve can be called as Not Asserted.
  • the term “real time” shall be given its ordinary meaning, and shall also refer to processing and providing information within a time interval brief enough to not be discernable by a user. In some embodiments, real time means “near real time.”
  • PLR Peak Likelihood Ratio
  • PLR Time The time at which the point with the Peak Likelihood Ratio is measured.
  • PLR Amplitude The value of SG Amp at the point where the Peak Likelihood Ratio is measured.
  • PLR Gradient The value of SG Grad at the point where the Peak Likelihood Ratio is measured.
  • PLR Span The Span value at the point where the Peak Likelihood Ratio is measured.
  • Step Dislocations The (integer) total number of Step Dislocations above the ADF-defined cutoff that are detected within a curve.
  • Test Decision Logic FIGS. 10A-10B depict non-limiting exemplary higher decision logic for FluA/B Test. Higher decision logic for FluA (FIG. 10A) and FluB (FIG. 10B) are shown. FluA and Internal Control tests are duplexed in this example. Derivation of Calling Algorithm Input Parameters [0108] The assertion test inputs that comprise ⁇ ⁇ ⁇ , ⁇ ⁇ , ⁇ ⁇ , ⁇ ⁇ ⁇ can be obtained from reference populations of known amplified and non-amplified curves.
  • Step 1 Estimation of initial coefficients ⁇ ⁇ , ⁇ and ⁇ ⁇ , ⁇ from the distributions of Amplitude, Gradient, and Span values measured at all points among the non- amplified population of curves.
  • Step 2 Estimation of initial coefficients ⁇ ⁇ , ⁇ and ⁇ ⁇ , ⁇ from the distributions of Amplitude, Gradient, and Span values measured at the point yMpeak in each amplified curve, where y Mpeak is the point having the largest Mahalanobis distance relative to ⁇ ⁇ , ⁇ and ⁇ ⁇ , ⁇ , subject to the requirement that the Amplitude, Gradient, and Span at yMpeak must all be greater than 0.
  • Step 3 Calculation of preliminary Likelihood Ratios for all points in the non- amplified reference curves using input coefficients ⁇ ⁇ ⁇ , ⁇ , ⁇ ⁇ , ⁇ , ⁇ ⁇ , ⁇ .
  • Step 4 Calculation of ⁇ ⁇ and ⁇ ⁇ from the distributions of Amplitude, Gradient, and Span values measured at the point y PLR in each non-amplified curve, where y PLR is the point where the Likelihood Ratio is maximized.
  • Step 5 Calculation of preliminary Likelihood Ratios for all points in the amplified reference curves using input coefficients ⁇ ⁇ ⁇ , ⁇ ⁇ , ⁇ , ⁇ ⁇ , ⁇ ⁇ .
  • Step 6 Calculation of ⁇ ⁇ and ⁇ ⁇ from the distributions of Amplitude, Gradient, and Span values measured at the point yPLR in each amplified curve, where yPLR is the point where the Likelihood Ratio is maximized.
  • Step 7 Additional iterations (as needed) of steps #3-#6, with the output coefficients ⁇ ⁇ ⁇ , ⁇ ⁇ , ⁇ ⁇ , ⁇ ⁇ ⁇ from one iteration used as input coefficients ⁇ ⁇ ⁇ , ⁇ , ⁇ ⁇ , ⁇ , ⁇ ⁇ , ⁇ in the next.
  • Non-amplified curves are, by definition, curves having an absence of signal. In some embodiments, and without being bound by any particular theory, the expected value of each point within these curves should be the same, with variability in Amplitude, Gradient, and Span directly attributable to system noise.
  • the initial coefficients ⁇ ⁇ , ⁇ and ⁇ ⁇ , ⁇ can be obtained by calculating the mean, variance, and covariance values for the Amplitude, Gradient, and Span at all points in the set of non-amplified curves.
  • Amplified curves contain a specific signal relative to background, that a subject-matter expert has determined to be distinct from the non-amplified curves and of a type that should be asserted by the calling algorithm.
  • the initial coefficients ⁇ ⁇ , ⁇ and ⁇ ⁇ , ⁇ can be calculated by first identifying the point in each curve that is most statistically distinct from the population of non-amplified curves.
  • y Mpeak is the point in each curve with the largest Mahalanobis Distance (d) relative to the population of non-amplified curves, subject to the requirement that the Amplitude, Gradient, and Span must all be positive at y Mpeak : ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ , ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ , ⁇ ⁇ ⁇ ⁇ ⁇ , ⁇ ⁇ [0119] The be determined from the means, variances, and Span values observed at yMpeak over the set of amplified curves.
  • FIGS. 12A-12B show the Amplitude, Gradient, and Span values of the y PLR points identified in the same data set used to generate FIG. 11, but after three rounds of refinement. In the refined model, the y PLR points have collapsed to an almost-linear distribution within the 3D space.
  • FIG.12B shows the hyperplane (green) defined by these populations for an assertion algorithm having an LR cutoff of 8.
  • systems for real-time calling include an instrument (e.g., an apparatus) configured to produce data, and a processor configured to analyze the data.
  • an instrument e.g., an apparatus
  • a processor configured to analyze the data.
  • the systems disclosed herein can beneficially provide rapid target detection. In the clinical setting, the methods provided herein can further help to avoid the delays of conventional nucleic acid testing thereby enabling clinicians to determine diagnoses within the typical timeframe of a patient's office visit.
  • the disclosed systems enable clinicians to develop treatment plans for patients during their initial office visit, rather than requiring the clinician to wait for hours or even days to receive test results back from a laboratory. For example, when a patient visits a clinic a nurse or other healthcare practitioner can collect a sample from the patient and begin testing using the described system. The system can provide the test result by the time the patient consults with their doctor or clinician to determine a treatment plan. Particularly when used to diagnose pathologies that progress quickly, the disclosed systems can avoid the delays associated with laboratory testing that can negatively impact the treatment and outcome of the patient.
  • disclosed systems can be used outside of the clinical setting (e.g., in the field, in rural settings without easy access to an established healthcare clinic) to detect health conditions such as contagious diseases (e.g., Ebola), thus enabling the appropriate personnel to take immediate action to prevent or mitigate the spread of a contagious disease.
  • the disclosed systems can be used in the field or at the site of a suspected hazardous contaminant (e.g., anthrax) to quickly determine whether a sample contains the hazardous contaminant, thus enabling the appropriate personnel to take immediate action to prevent or mitigate human exposure to the contaminant.
  • the disclosed systems can be used to detect contaminants in the blood or plasma supply or in the food industry.
  • Instruments configured to produce time series data by analyzing a sample.
  • Time series data can be derived from instrument analysis of a sample, such as, for example, a sample suspected of comprising a target analyte.
  • the instrument can be configured to produce time series data by analyzing the sample.
  • the instrument can be configured to perform a molecular diagnostic assay.
  • the real-time calling methods, compositions, systems, and kits provided herein can advantageously detect a target analyte with a low threshold of detection.
  • threshold of detection is used herein to describe the minimal amount of target analyte (e.g., nucleic acid comprising a target nucleic acid sequence) that must be present in a sample in order for detection to occur. For example, when a threshold of detection is 10 nM, then a signal can be detected when a target nucleic acid is present in the sample at a concentration of 10 nM or more.
  • the threshold of detection is less than or equal to 5 nM, 1 nM, 0.5 nM, 0.1 nM, 0.05 nM, 0.01 nM, 0.005 nM, 0.001 nM, 0.0005 nM, 0.0001 nM, 0.00005 nM, 0.00001 nM, 10 pM, 1 pM, 500 fM, 25004, 10004, 50 fM, 10 fM, 5 fM, 104, 500 attomole (aM), 100 aM, 50 aM, 10 aM, or 1 aM.
  • the threshold of detection is in a range of from 1 aM to 1 nM, 1 aM to 500 pM, 1 aM to 200 pM, 1 aM to 100 pM, 1 aM to 10 pM, 1 aM to 1 pM, 1 aM to 50004, 1 aM to 100 fM, 1 aM to 1 fM, 1 aM to 500 aM, 1 aM to 100 aM, 1 aM to 50 aM, 1 aM to 10 aM, 10 aM to 1 nM, 10 aM to 500 pM, 10 aM to 200 pM, 10 aM to 100 pM, 10 aM to 10 pM, 10 aM to 1 pM, 10 aM to 500 fM, 10 aM to 100 fM, 10 aM to 1 fM, 10 aM to 500 aM, 10 aM to 100 aM, 10 aM to
  • the threshold of detection in a range of from 800 fM to 100 pM, 1 pM to 10 pM, 10 fM to 500 fM, 10 fM to 50 fM, 50 fM to 100 fM, 10004 to 250 fM, or 25004 to 500 fM.
  • the instrument can comprise a spectrometer, an electrochemical detection device, a polynucleotide detection device, a fluorescence anisotropy device, a fluorescence resonance energy transfer device, an electron transfer device, an enzyme assay, a lateral flow assay, a magnetism device, an electrical conductivity device, an isoelectric focusing device, a chromatograph, an immunoprecipitation device, an immunoseparation device, an aptamer binding device, a filtration device, electrophoresis device, a CCD camera, an immunoassay, an ELISA, a Gram staining device, an immunostaining device, a flow cytometer, a microscope, an immunofluorescence device, a western blot device, a polymerase chain reaction (PCR) device, RT-PCR device, an isothermal amplification device, a fluorescence in situ hybridization device, a sequencing device, a next gen sequencing device, a mass spectrometer, an
  • the target analyte can be a cell, a cancer cell, a virus, a bacterium, a fungus, a protein, a nucleic acid, a DNA molecule, an RNA molecule, an miRNA molecule, an mRNA molecule, a peptide, a polypeptide, an antibody, a tissue, a nanoparticle, a drug metabolite, a lipid, a carbohydrate, a hormone, a vitamin, a fragment thereof, or any combination thereof.
  • a sample curve being called negative can indicate the absence of the target analyte in the sample.
  • the instrument can comprise a thermocycler, such as, for example, a thermocycler configured for real-time PCR amplification and fluorescence monitoring.
  • the target analyte is a target nucleic acid sequence.
  • the one or more reaction(s) can comprise nucleic acid detection reaction(s).
  • the sample curve can comprise a nucleic acid amplification curve, and the signals can comprise fluorescence signals indicative of amplification of the target nucleic acid sequence.
  • a sample curve being called positive indicates the presence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the presence and/or amount of a target nucleic acid sequence in the sample.
  • a sample curve being called negative indicates the absence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the absence of a target nucleic acid sequence in the sample.
  • the one or more reaction(s) can comprise an isothermal amplification reaction.
  • the term “isothermal amplification reaction” shall be given its ordinary meaning and shall also include reactions wherein the temperature does not significantly change during the reaction.
  • the temperature of the isothermal amplification reaction does not deviate by more than 10°C, for example by not more than 5°C or by not more than 2°C during the main enzymatic reaction step where amplification takes place.
  • different enzymes can be used for amplification. Isothermal amplification compositions and methods are described in WO2017176404, the content of which is incorporated herein by reference in its entirety.
  • the instrument can be capable of amplifying a target nucleic acid sequence in an amplification reaction mixture, thereby generating a nucleic acid amplification product, optionally the nucleic acid amplification product is generated at detectable levels within about 20 minutes, about 15 minutes, or about 10 minutes.
  • the instrument can be capable of detecting the nucleic acid amplification product with a signal-generating oligonucleotide (e.g., a TaqMan detection probe oligonucleotide, a molecular beacon detection probe oligonucleotide, or a molecular torch detection probe oligonucleotide), wherein the signal-generating oligonucleotide is capable of hybridizing to the nucleic acid amplification product.
  • a signal-generating oligonucleotide e.g., a TaqMan detection probe oligonucleotide, a molecular beacon detection probe oligonucleotide, or a molecular torch detection probe
  • the instrument is a mass spectrometer.
  • mass spectrometers can be used to detect target analytes.
  • Several types of mass spectrometers are available or can be produced with various configurations.
  • a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and instrument-control system, and a data system. Difference in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities.
  • an inlet can be a capillary-column liquid chromatography source or can be a direct probe or stage such as used in matrix-associated laser desorption.
  • Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-associated laser desorption.
  • Common mass analyzers include a quadrupole mass filter, ion trap mass analyzer and time-of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70:647 R- 716R (1998); Kinter and Sherman, New York (2000)).
  • Protein biomarkers and biomarker values can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-associated laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q- TOF), tandem time-of-flight (TOF/TOF) technology, called ultraflex III TOF/TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS).sup.N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS
  • the instrument is configured to perform an immunoassay or immunodetection assay.
  • Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect the analyte in a sample depending on the specific assay format.
  • Immunoassays have been designed for use with a wide range of biological sample matrices.
  • Immunoassay formats have been designed to provide qualitative, semi-quantitative, and quantitative results. Numerous immunoassay formats have been designed.
  • ELISA or EIA can be quantitative for the detection of a target analyte. This method relies on attachment of a label to either the analyte or the antibody and the label component includes, either directly or indirectly, an enzyme.
  • ELISA tests may be formatted for direct, indirect, competitive, or sandwich detection of the analyte.
  • Other methods rely on labels such as, for example, radioisotopes (I125) or fluorescence.
  • Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and others (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).
  • the instrument is a nucleic acid sequencing platform.
  • the sequencing system may be any sequencing system of interest, including a Sanger sequencing system, a next generation sequencing (NGS) system, or the like. In certain aspects the sequencing system is an NGS system.
  • Suitable flow cytometry systems may include, but are not limited to those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et at (2012) Ann Clin Biochem. Jan; 49(pt 1):17-28; Linden, et.
  • flow cytometry systems of interest include BD Biosciences FACSCantoTM II flow cytometer, BD AccuriTM flow cytometer, BD Biosciences FACSCelestaTM flow cytometer, BD Biosciences FACSLyricTM flow cytometer, BD Biosciences FACSVerseTM flow cytometer, BD Biosciences FACSymphonyTM flow cytometer BD Biosciences LSRFortessaTM flow cytometer, BD Biosciences LSRFortessTM X-20 flow cytometer and BD Biosciences FACSCaliburTM cell sorter, a BD Biosciences FACSCountTM cell sorter, BD Biosciences FACSLyricTM cell sorter and BD Biosciences ViaTM cell sorter BD Biosciences InfluxTM cell sorter, BD Biosciences jazzTM cell sorter, BD Biosciences AriaTM cell sorters and BD Biosciences FACSMelodyTM cell sorter, or the like.
  • the subject particle sorting systems are flow cytometric systems, such those described in U.S. Pat. Nos. 9,952,076; 9 933,341; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9,095,494; 9,092,034; 8,975,595; 8,753,573; 8,233,146; 8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039; the disclosure of which are herein incorporated by reference in their entirety.
  • systems e.g., isothermal amplification systems, flow cytometry systems, nucleic acid sequencing systems
  • systems additionally include a processor having memory operably coupled to the processor wherein the memory includes instructions stored thereon, which when executed by the processor, cause the processor to perform the real-time calling methods provided herein.
  • time series data e.g., molecular diagnostic signals
  • the processor is configured to perform the real-time calling methods provided herein.
  • FIG. 1 shows a functional block diagram for one example of a processor 100, for analyzing and displaying data.
  • a processor 100 can be configured to implement a variety of processes for controlling graphic display of time series data (e.g., molecular diagnostic signals).
  • An instrument 102 can be configured to acquire data by analyzing a biological sample (e.g., as described above).
  • the instrument can be configured to provide time series data (e.g., molecular diagnostic signals) to the processor 100.
  • a data communication channel can be included between the instrument 102 and the processor 100.
  • the data can be provided to the processor 100 via the data communication channel.
  • the processor 100 can be configured to provide a graphical display including heatmaps and/or plots to display 106.
  • the display device 106 can be implemented as a monitor, a tablet computer, a smartphone, or other electronic device configured to present graphical interfaces.
  • the processor 100 can be connected to a storage device 104.
  • the storage device 104 can be configured to receive and store data from the processor 100.
  • the storage device 104 can be further configured to allow retrieval of data, such as time series data (e.g., molecular diagnostic signals), by the processor 100.
  • a display device 106 can be configured to receive display data from the processor 100.
  • the display data can comprise plots of time series data (e.g., molecular diagnostic signals).
  • the display device 106 can be further configured to alter the information presented according to input received from the processor 100 in conjunction with input from instrument 102, the storage device 104, the keyboard 108, and/or the mouse 110.
  • Computer Systems for Real-time Calling include systems comprising a computer having a computer readable storage medium with a computer program stored thereon, where the computer program when loaded on the computer includes instructions for performing the real-time calling methods provided herein. Aspects of the present disclosure further include computer-controlled systems, where the systems further include one or more computers for complete automation or partial automation. [0137] In embodiments, the system includes an input module, a processing module and an output module.
  • the subject systems may include both hardware and software components, where the hardware components may take the form of one or more platforms, e.g., in the form of servers, such that the functional elements, i.e., those elements of the system that carry out specific tasks (such as managing input and output of information, processing information, etc.) of the system may be carried out by the execution of software applications on and across the one or more computer platforms represented of the system.
  • Systems may include a display and operator input device. Operator input devices may, for example, be a keyboard, mouse, or the like.
  • the processing module includes a processor which has access to a memory having instructions stored thereon for performing the steps of the subject methods.
  • the processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, memory storage devices, and input- output controllers, cache memory, a data backup unit, and many other devices.
  • the processor may be a commercially available processor or it may be one of other processors that are or will become available.
  • the processor executes the operating system and the operating system interfaces with firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages, such as Java, Perl, C++, other high level or low level languages, as well as combinations thereof, as is known in the art.
  • the operating system typically in cooperation with the processor, coordinates and executes functions of the other components of the computer.
  • the operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.
  • the processor may be any suitable analog or digital system.
  • the system memory may be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic medium such as a resident hard disk or tape, an optical medium such as a read and write compact disc, flash memory devices, or other memory storage device.
  • RAM random access memory
  • the memory storage device may be any of a variety of known or future devices, including a compact disk drive, a tape drive, a removable hard disk drive, or a diskette drive.
  • systems according to the present disclosure may be configured to include a communication interface.
  • the communication interface includes a receiver and/or transmitter for communicating with a network and/or another device.
  • the communication interface can be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., Radio- Frequency Identification (RFID), Zigbee communication protocols, WiFi, infrared, wireless Universal Serial Bus (USB), Ultra Wide Band (UWB), Bluetooth® communication protocols, and cellular communication, such as code division multiple access (CDMA) or Global System for Mobile communications (GSM).
  • RF radio frequency
  • the output manager may also provide information generated by the processing module to a user at a remote location, e.g., over the Internet, phone or satellite network, in accordance with known techniques.
  • the presentation of data by the output manager may be implemented in accordance with a variety of known techniques.
  • data may include SQL, HTML or XML documents, email or other files, or data in other forms.
  • the data may include Internet URL addresses so that a user may retrieve additional SQL, HTML, XML, or other documents or data from remote sources.
  • the one or more platforms present in the subject systems may be any type of known computer platform or a type to be developed in the future, although they typically will be of a class of computer commonly referred to as servers.
  • the computing device 200 includes a processing unit 210, a network interface 220, a computer readable medium drive 230, an input/output device interface 240, a display 250, and an input device 260, all of which may communicate with one another by way of a communication bus.
  • the network interface 220 may provide connectivity to one or more networks or computing systems.
  • the processing unit 210 may thus receive information and instructions from other computing systems or services via a network.
  • the processing unit 210 may also communicate to and from memory 270 and further provide output information for an optional display 250 via the input/output device interface 240.
  • an analysis software stored as executable instructions in the non-transitory memory of the analysis system can display time series data (e.g., molecular diagnostic signals) to a user.
  • the input/output device interface 240 may also accept input from the optional input device 260, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.
  • the memory 270 may contain computer program instructions (grouped as modules or components in some embodiments) that the processing unit 210 executes in order to implement one or more embodiments of the real-time calling methods provided herein.
  • the memory 270 generally includes RAM, ROM and/or other persistent, auxiliary or non-transitory computer-readable media.
  • the memory 270 may store an operating system 272 that provides computer program instructions for use by the processing unit 210 in the general administration and operation of the computing device 200. Data may be stored in data storage device 290.
  • the memory 270 may further include computer program instructions and other information for implementing aspects of the present disclosure.
  • Computer-Readable Storage Medium [0151] Aspects of the present disclosure further include non-transitory computer readable storage mediums having instructions for practicing the disclosed methods of real-time calling. Computer readable storage media may be employed on one or more computers for complete automation or partial automation of a system for practicing methods described herein.
  • instructions in accordance with the method described herein can be coded onto a computer-readable medium in the form of “programming”, where the term “computer readable medium” as used herein refers to any non-transitory storage medium that participates in providing instructions and data to a computer for execution and processing.
  • suitable non-transitory storage media include a floppy disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD-R, magnetic tape, non-volatile memory card, ROM, DVD- ROM, Blue-ray disk, solid state disk, and network attached storage (NAS), whether or not such devices are internal or external to the computer.
  • instructions may be provided on an integrated circuit device.
  • Integrated circuit devices of interest may include, in certain instances, a reconfigurable field programmable gate array (FPGA), an application specific integrated circuit (ASIC) or a complex programmable logic device (CPLD).
  • FPGA reconfigurable field programmable gate array
  • ASIC application specific integrated circuit
  • CPLD complex programmable logic device
  • a file containing information can be “stored” on computer readable medium, where “storing” means recording information such that it is accessible and retrievable at a later date by a computer.
  • the computer- implemented method described herein can be executed using programming that can be written in one or more of any number of computer programming languages. Such languages include, for example, Java (Sun Microsystems, Inc., Santa Clara, Calif.), Visual Basic (Microsoft Corp., Redmond, Wash.), and C++ (AT&T Corp., Bedminster, N.J.), as well as any many others.
  • computer readable storage media of interest include a computer program stored thereon, where the computer program when loaded on the computer includes instructions for performing the real-time calling methods provided herein.
  • the system is configured to analyze the data within a software or an analysis tool for analyzing time series data (e.g., molecular diagnostic signals).
  • the initial data can be analyzed within the data analysis software or tool (e.g., FlowJo®, SeqGeq®) by appropriate means, such as manual gating, cluster analysis, or other computational techniques.
  • the instant systems, or a portion thereof can be implemented as software components of a software for analyzing data, such as FlowJo® or SeqGeq®.
  • the computer readable storage medium may be employed on one or more computer systems having a display and operator input device. Operator input devices may, for example, be a keyboard, mouse, or the like.
  • the processing module includes a processor which has access to a memory having instructions stored thereon for performing the steps of the subject methods.
  • the processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, memory storage devices, and input-output controllers, cache memory, a data backup unit, and many other devices.
  • GUI graphical user interface
  • the processor may be a commercially available processor, or it may be one of other processors that are or will become available.
  • the processor executes the operating system and the operating system interfaces with firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages, such as Java, Perl, Python, C++, other high level or low level languages, as well as combinations thereof, as is known in the art.
  • the operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.
  • ADF Assays and methods described herein can be defined in an ADF, which may include information that describes how to process results, what process steps are executed, the order they are executed, interpretations generated, etc.
  • the ADF can comprise one or more of defined classifiers, a defined LR threshold, t1, t2, Signalmin, Signalmax, a defined step dislocation threshold, a defined Minimum Call Cycle, and a defined span half window, for use in a method provided herein.
  • the ADF may include software code modules for performing one or more steps of the methods provided herein.
  • the server system software may support an encapsulated assay configuration that includes assay name, assay type, panel, hotspot file if any, reference name, control names if any, quality control QC thresholds, assay description if any, data analysis parameters and values, instrument run script names and other configurations that define the assay.
  • the entire set of the information is called an assay definition.
  • the assay configuration content and corresponding workflows may be delivered to the user as modular software components in an assay definition file (ADF).
  • the server system software may import an assay definition file that contains the assay configuration.
  • the import process may be initiated by zip file import which includes an encrypted Debian file and triggers an installation process.
  • the user interface may provide a page for the user to select an ADF for import.
  • the instructions contained on computer readable media provided in the subject kits, or a portion thereof, can be implemented as software components of a software for analyzing data, such as, for example, FlowJo® or SeqGeq®.
  • the subject kits may further include (in some embodiments) instructions, e.g., for installing the plugin to the existing software package such as, for example, FlowJo® and SeqGeq®. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit.
  • these instructions may be present in printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, and the like.
  • a suitable medium or substrate e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, and the like.
  • a computer readable medium e.g., diskette, compact disk (CD), portable flash drive, and the like, on which the information has been recorded.
  • Yet another form of these instructions that may be present is a website address which may be used via the internet to access the information at a removed site.

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Abstract

Disclosed herein include methods, systems, compositions, algorithms, and kits suitable for use in categorizing molecular diagnostic signals in real time. The method can comprise receiving time series data from an instrument in real time. The time series data comprises a plurality of data points forming a sample curve. The method can comprise calculating two or more quantitative metrics for each data point in real time. The method can comprise calculating a likelihood ratio (LR) at each data point in real time. Said calculating step can comprise employing defined classifiers for each of the two or more quantitative metrics derived from (i) a reference population of positive curves and (ii) a reference population of negative curves. The method can comprise calling the sample curve in real time. A sample curve can be called positive in real time once the LR calculated for a data point exceeds a defined LR threshold.

Description

P-27930.WO01 / 68EB-317362-WO PATENT METHOD FOR CATEGORIZING MOLECULAR DIAGNOSTIC SIGNALS IN REAL TIME RELATED APPLICATIONS [0001] This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application Ser. No. 63/496,001, filed April 13, 2023, the content of this related application is incorporated herein by reference in its entirety for all purposes. BACKGROUND Field [0002] The present disclosure relates generally to the field of decision algorithms. Description of the Related Art [0003] Existing molecular diagnostic technologies either employ end-state processing of data or use relatively simplistic real-time methods for generating Positive/Negative calls. The use of end-point processing inherently prevents a system from making a call until all data are collected, extending the time required to report a result. Within the point-of-care diagnostic market, for which rapid reporting of results is highly valued, end- point processing can put products at a serious disadvantage. Real-time methods that make use of a single metric, such as the Amplitude or Gradient of a fluorescence data point, often fail to capture the essential signal characteristics that define amplified vs non-amplified behaviors. Even when multiple metrics are employed in a real-time context, they are typically evaluated in isolation with independent thresholds for each, limiting the potential sensitivity and specificity of a test. There is a need for methods, systems, algorithms, compositions, and kits for categorizing molecular diagnostic signals in real time. SUMMARY [0004] There are provided, in some embodiments, real-time calling methods. In some embodiments, the method comprises: receiving time series data from an instrument in real time, wherein the time series data comprises a plurality of data points forming a sample curve. The method can comprise: calculating two or more quantitative metrics for each data point in real time. The method can comprise: calculating a likelihood ratio (LR) at each data point in real time. Said calculating step can comprise employing defined classifiers for each of the two or more quantitative metrics derived from (i) a reference population of positive curves and (ii) a reference population of negative curves, optionally derived via Quadratic Discriminant Analysis (QDA). The method can comprise: calling the sample curve in real time. In some embodiments, a sample curve is called positive in real time once the LR calculated for a data point exceeds a defined LR threshold. In some embodiments, a sample curve is called negative in real time if the LR calculated for all data points of the sample curve falls at or below the defined LR threshold. [0005] In some embodiments, the quantitative metrics comprise amplitude (A), gradient (G), and/or span (S), optionally S is the change in A over a defined window. In some embodiments, the time series data are derived from instrument analysis of a sample, optionally a sample suspected of comprising a target analyte. In some embodiments, the instrument is configured to produce time series data by analyzing the sample, optionally the instrument is configured to perform a molecular diagnostic assay. In some embodiments, instrument analysis of the sample comprises subjecting said sample to one or more reaction(s), optionally said reaction(s) are configured to detect the presence and/or amount of a target analyte in the sample. In some embodiments, the instrument comprises one or more sensor(s) configured to detect signals derived from the sample, and wherein the time series data comprises time series signal data, optionally said signals are generated from said one or more reaction(s). In some embodiments, the signal is a calorimetric signal, a potentiometric signal, an amperometric signal, an optical signal (e.g., a fluorescent signal and/or colorimetric signal), a piezo-electric signal, or any combination thereof. In some embodiments, signals are generated in the presence of the target analyte. In some embodiments, signals are generated in the absence of the target analyte. [0006] In some embodiments, instrument analysis comprises one or more of spectrometry, Raman spectroscopy, FFT (Fast-Fourier Transform) spectroscopy, Fourier- Transform Infrared Spectroscopy (FTIR), infrared spectrometry, Nuclear Magnetic Resonance (NMR) spectrometry, electrochemical detection, polynucleotide detection, volatile organic compound methods, fluorescence anisotropy, fluorescence resonance energy transfer, electron transfer, enzyme assay, magnetism, electrical conductivity, electrochemical detection, isoelectric focusing, lateral flow assay (LFA), microfluidics, amino acid sequencing, nucleic acid sequencing, flow cytometry, chromatography, immunoprecipitation, immunoseparation, aptamer binding, filtration, electrophoresis, use of a CCD camera, immunoassay, enzyme-linked immunosorbent assay (ELISA), Gram staining, immunostaining, microscopy, immunofluorescence, size/weight/charge detection, western blotting, polymerase chain reaction (PCR), RT-PCR, isothermal amplification, sequencing, fluorescence in situ hybridization, mass spectrometry, Surface Plasmon Resonance (SPR), and Localized Surface Plasmon Resonance (LSPR). In some embodiments, the instrument comprises a thermocycler, such as, for example, a thermocycler configured for real-time PCR amplification and fluorescence monitoring. [0007] In some embodiments, a sample curve being called positive indicates the presence of a target analyte in the sample. In some embodiments, a sample curve being called negative indicates the absence of the target analyte in the sample. In some embodiments, the target analyte is a target nucleic acid sequence. In some embodiments, the one or more reaction(s) comprise nucleic acid detection reaction(s). In some embodiments, the sample curve comprises a nucleic acid amplification curve, and wherein the signals comprise fluorescence signals indicative of amplification of the target nucleic acid sequence. In some embodiments, a sample curve being called positive indicates the presence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the presence and/or amount of a target nucleic acid sequence in the sample. In some embodiments, a sample curve being called negative indicates the absence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the absence of a target nucleic acid sequence in the sample. [0008] In some embodiments, the step of calculating two or more quantitative metrics comprises applying a median filter (e.g., a 3-point median filter) to the time series data to generate median-filtered data. In some embodiments, said 3-point median filter comprises: ^^ெ^ௗ^^^^ ^^^ ൌ ^^ோ^௪^ ^^^, ^^ ൌ 1, ^^ ^^ெ^ௗ^^^^ ^^^ ൌ ^^ ^^ ^^ ^^ ^^ ^^^ ^^ோ^௪^ ^^ െ 1^, ^^ோ^௪^ ^^^, ^^ோ^௪^ ^^ ^ 1^^, ^^ ൌ 2, … , ^^ െ 1 [0009] In some embodiments, the median filter provides smoothing, removal of single-point spikes, and/or removal of system noises. In some embodiments, the step of calculating two or more quantitative metrics comprises applying a Savitzky-Golay (SG) filter (e.g., a 7-point SG filter) to the time series data and/or median-filtered data to generate a smoothed amplitude value (SGAmp) and smoothed gradient value (SGGrad). In some embodiments, application of the SG filter comprises a sliding window being moved through the time series data and/or median-filtered data and a second order linear regression applied to each window: ^^ ^^ ^^^ ൌ ^^^ ^ ^^^ ^^ ^ ^^ଶ ^^ ଶ [0010] In some embodiments, the x values in each window are coded as {-3,…3} and the regression fit uses as inputs: ^^^ ^^^ ൌ ^െ3, … ,3^ ^^^ ^^^ ൌ ^ ^^ெ^ௗ^^^^ ^^ െ 3^, … , ^^ெ^ௗ^^^^ ^^ ^ 3^^ [0011] In some embodiments, the smoothed amplitude value (SGAmp) and smoothed gradient value (SGGrad) are calculated for the center point (k = 0) of each window as follows: 1 ^^ ^^ ^^^ ൌ ^^ ^ ൌ ^^ ^ ⋅ ^^^ ^் ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ℎ ^^ ^^ ^^ ^^ ^ ൌ [0012] In some embodiments, the method comprises: calculating the average signals values (e.g., SGAmp values) between a defined time window of t1 and t2 to generate an initial signal average. In some embodiments, the calling step comprises calling the sample curve as invalid if: the initial signal average does not fall within the defined signal window of Signalmin and Signalmax. In some embodiments, Signalmin and Signalmax are identified based on the distribution of initial signal average values between the defined time window of t1 and t2 for a reference population comprising positive curves and negative curves. In some embodiments, Signalmin and Signalmax values are set equal to the mean ± 3.6σ bounds of said reference population. [0013] In some embodiments, the step of calculating two or more quantitative metrics comprises calculating the span (S) value by: employing the formula ^ା^ ^^^ ^^^ ൌ ^^ ^^^^^^ ^^ ^ ^^^ െ ^^ ^^^^^^ ^^ െ ^^^ െ ^ ^^^ [0014] of points, and wherein ΣJi is the sum of the step the interval if any step dislocations are identified in said internal. In some embodiments, the defined span half window is assay-specific. In some embodiments, the defined span half window is about 6 points to about 12 points, optionally about 10 points. In some embodiments, if a boundary point of a defined span window is located within a defined number of points surrounding a step dislocation, the step of calculating two or more quantitative metrics comprises calculating the span (S) value by: employing the median-filtered signal values ( ^^ெ^ௗ^^^) in the formula: ^ା^ ^^^ ^^^ ൌ ^^ெ^ௗ^^^^ ^^ ^ ^^^ െ ^^ ^^^^^^ ^^ െ ^^^ െ ^ ^^^ ^ୀ^ି^ [0015] wherein a boundary point of a defined span window is either k + a or k – a. [0016] In some embodiments, the method comprises providing defined classifiers for each of the two or more quantitative metrics. In some embodiments, said defined classifiers are provided via an assay definition file (ADF). In some embodiments, said defined classifiers comprise Quadratic Discriminant Analysis (QDA) coefficients. In some embodiments, said defined classifiers comprise Linear Discriminant Analysis (LDA) coefficients. In some embodiments, said defined classifiers are assay-specific and/or are defined for each curve. In some embodiments, the defined classifiers comprise two or more of ^^̄^^^, ^^̄ே^^, ^^^^^, and ^^ே^^, and wherein: ^^̄^^^ ൌ ^ ^̅^^^ோ,^^^, ^̅^^^ோ,^^^, ^^^̅^ோ,^^^^ ^^ ^^ ^^^ ^^^^ோ,^^^^ ^^ ^^ ^^^ ^^^^ோ,^^^, ^^^^ோ,^^^^ ^^ ^^ ^^^ ^^^^ோ,^^^, ^^^^ோ,^^^^ ^^^^^ ൌ ^ ^^ ^^ ^^^ ^^^^ோ,^^^, ^^^^ோ,^^^^ ^^ ^^ ^^^ ^^^^ோ,^^^^ ^^ ^^ ^^^ ^^^^ோ,^^^, ^^^^ோ,^^^^ ^ ^ the some embodiments, the reference population of positive curves and the reference population of negative curves are generated using representative target analyte concentrations in appropriate sample types. In some embodiments, providing the defined classifiers comprises, for each curve in the reference population of positive curves and the reference population of negative curves: identifying via an iterative process the characteristic peak yPLR (the point where the LR is maximized) that optimally discriminates between positive reference curves and negative reference curves; and identifying the A, G, and S metrics at the characteristic peak yPLR. In some embodiments, the providing further comprises calculating the means and covariance matrices for said metrics. [0018] In some embodiments, providing the defined classifiers comprises: (a) estimating initial coefficients ^^̄ே^^,ூ^^௧ and ^^ே^^,ூ^^௧ from the distributions of A, G, and S values measured at all points among the reference population of negative curves; (b) estimating initial coefficients ^^̄^^^,ூ^^௧ and ^^^^^,ூ^^௧ from the distributions of A, G, and S values measured at the point yMpeak in each positive reference curve, wherein yMpeak is the point having the largest Mahalanobis distance relative to ^^̄ே^^,ூ^^௧ and ^^ே^^,ூ^^௧, subject to the requirement that the A, G, and S at yMpeak must all be greater than 0; (c) calculating preliminary LRs for all points in the negative reference curves using input coefficients ^ ^^̄ே^^,ூ^^௧ , ^^̄^^^,ூ^^௧ , ^^ே^^,ூ^^௧, ^^^^^,ூ^^௧^ ; (d) calculating ^^̄ே^^ and ^^ே^^ from the distributions of A, G, and S values measured at the point yPLR in each negative reference curve; (e) calculating preliminary LRs for all points in the positive reference curves using input coefficients ^ ^^̄ே^^, ^^̄^^^,ூ^^௧ , ^^ே^^, ^^^^^,ூ^^௧^; (f) calculating ^^̄^^^ and ^^^^^ from the distributions of A, G, and S values measured at the point yPLR in each positive reference curve, wherein yPLR is the point where the LR is maximized; and (g1) repeating steps (c)-(f) with the output coefficients ^ ^^̄ே^^, ^^̄^^^, ^^ே^^, ^^^^^^ from one iteration used as input coefficients ^ ^^̄ே^^,ூ^^௧, ^^̄^^^,ூ^^௧ , ^^ே^^,ூ^^௧ , ^^^^^,ூ^^௧^ in the next until values of coefficients converge, or (g2) repeating steps (e)-(f) with the output coefficients ^ ^^̄ே^^, ^^̄^^^, ^^ே^^, ^^^^^^ from one iteration used as input coefficients ^ ^^̄ ே^^ , ^^̄ ^^^,ூ^^௧ , ^^ ே^^ , ^^ ^^^,ூ^^௧^ in the next until values of coefficients converge. [0019] In some embodiments, calculating the LR comprises for each data point ytest = {Atest, Gtest, Stest} calculating Q0/Q1, wherein: ^^^ ൌ ^^^ ଶ ^^ ^ ln ^ ^^ ^^ ^^൫ ^^ே^^൯^ [0020] and ^^^ ଶ ^^ Distances to each reference population and are given by: ^^^ ଶ ^^ ൌ ^ ^^௧^^௧ െ ^^̄ே^^^் ^^ே ି ^^ ^ ^ ^^௧^^௧ െ ^^̄ே^^^ ^ ^ [0021] In each data point in real time comprises calculating embodiments, the LR is calculated at point ytest provided the following conditions are satisfied: ytest occurs at or after a defined Minimum Call Cycle; Atest, Gtest, and Stest are all > 0; and no step dislocations occur within 3 points of ytest. In some embodiments, the method comprises detecting step dislocations, and wherein step dislocations are detected by: measuring the pairwise differences between adjacent median-filtered points: ^^ ൌ ^ ^^ெ^ௗ,ଶ െ ^^ெ^ௗ,^, ^^ெ^ௗ,ଷ െ ^^ெ^ௗ,ଶ … , ^^ெ^ௗ,^ െ ^^ெ^ௗ,^ି^൧ [0022] curves; subtracting smoothed differences Ys from the unsmoothed differences; and identifying any point in Y – Ys with a value above a defined step dislocation threshold as a step dislocation with a magnitude J. In some embodiments, step dislocations represent macroscopic system noises. [0023] In some embodiments, the calling step comprises calling the sample curve as invalid if: there are two or more contiguous missing points within the time series data prior to the sample curve being called positive; and/or there are more than two missing points within the time series data prior to the sample curve being called positive, optionally said missing points are either contiguous or non-contiguous. In some embodiments, one or more of the defined classifiers, the defined LR threshold, t1, t2, Signalmin, Signalmax, the defined step dislocation threshold, the defined Minimum Call Cycle, and the defined span half window are provided via an assay definition file (ADF). In some embodiments, the method comprises multiplexed calling comprising: receiving two or more sets of time series data from the instrument in real time, wherein each set of time series data comprises a plurality of data points forming a sample curve; and calling each of the two or more sample curves in real time, optionally said each of the sample curves are nucleic acid amplification curves related to different target nucleic acid sequences. [0024] In some embodiments, the method is capable of calling a curve as positive at least about 1 minute, about 2 minutes, about 5 minutes, about 10 minutes, about 15 minutes, about 20 minutes, about 25 minutes, about 30 minutes, about 35 minutes, about 40 minutes, about 45 minutes, about 50 minutes, about 55 minutes, or about 60 minutes, earlier than a method employing end-state processing of the time series data. In some embodiments, the step- detection and correction does not make any assumptions regarding the correct and/or absolute signal baseline. [0025] In some embodiments, the instrument is capable of: amplifying a target nucleic acid sequence in an amplification reaction mixture, thereby generating a nucleic acid amplification product, optionally the nucleic acid amplification product is generated at detectable levels within about 20 minutes, about 15 minutes, or about 10 minutes; and detecting the nucleic acid amplification product with a signal-generating oligonucleotide, wherein the signal-generating oligonucleotide is capable of hybridizing to the nucleic acid amplification product, optionally the signal-generating oligonucleotide is a TaqMan detection probe oligonucleotide, a molecular beacon detection probe oligonucleotide, or a molecular torch detection probe oligonucleotide. [0026] In some embodiments, the signal-generating oligonucleotide comprises a label, optionally the label comprises a quenchable label, further optionally the quenchable label is a fluorophore, optionally the signal-generating oligonucleotide comprises a quencher capable of quenching a signal generated by the label when the quencher and the label are in close proximity. In some embodiments, the label is capable of generating a detectable signal upon: (i) the signal-generating oligonucleotide hybridizing the nucleic acid amplification product; and/or (ii) the nucleic acid amplification product being extended to generate an extended nucleic acid amplification product hybridized to the signal-generating oligonucleotide, optionally the signal is fluorescence. In some embodiments, amplifying the target nucleic acid sequence comprises generating the nucleic acid amplification product at detectable levels within about 20 minutes, about 15 minutes, or about 10 minutes. [0027] The method can comprise: contacting a sample comprising biological entities with a lysis buffer to generate a treated sample, wherein the lysis buffer comprises one or more lytic agents capable of lysing biological entities to release sample nucleic acids comprised therein, and wherein the sample nucleic acids are suspected of comprising the target nucleic acid sequence; and contacting a reagent composition with the treated sample to generate the amplification reaction mixture, wherein the reagent composition comprises one or more amplification reagents. [0028] In some embodiments, the method: is performed in a single reaction vessel; does not comprise using any enzymes other than the reverse transcriptase and the enzyme having a hyperthermophile polymerase activity; does not comprise using any enzyme other than the enzyme having a hyperthermophile polymerase activity; does not comprise heat denaturing and/or enzymatic denaturing the nucleic acid during the amplification step; and/or does not comprise contacting the nucleic acid with a single-stranded DNA binding protein. [0029] In some embodiments, the amplifying is performed: for a period of about 5 minutes to about 60 minutes, optionally the amplifying is performed for a period of about 15 minutes; and/or in helicase-free, single-stranded binding protein-free, cleavage agent-free, and recombinase-free, isothermal amplification conditions. In some embodiments, the amplifying is carried out using a method selected from the group consisting of polymerase chain reaction (PCR), ligase chain reaction (LCR), loop-mediated isothermal amplification (LAMP), strand displacement amplification (SDA), replicase-mediated amplification, Immuno-amplification, nucleic acid sequence based amplification (NASBA), self-sustained sequence replication (3SR), rolling circle amplification, and transcription-mediated amplification (TMA), optionally the PCR is real-time PCR and/or quantitative real-time PCR (QRT-PCR). [0030] In some embodiments, the biological entities comprise one or more of prokaryotic cells, eukaryotic cells, viral particles, exosomes, protoplasts, and microvesicles. In some embodiments, the biological entities comprise a virus, a bacteria, a fungi, a protozoa, portions thereof, or any combination thereof. In some embodiments, the target nucleic acid sequence is a nucleic acid sequence of a virus, bacteria, fungi, or protozoa, optionally the sample nucleic acids are derived from a virus, bacteria, fungi, or protozoa. In some embodiments, the virus is SARS-CoV-2, Human Immunodeficiency Virus Type 1 (HIV-1), Human T-Cell Lymphotrophic Virus Type 1 (HTLV-1), Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Herpes Simplex, Herpesvirus 6, Herpesvirus 7, Epstein-Barr Virus, Respiratory Syncytial Virus (RSV), Cytomegalo-virus, Varicella-Zoster Virus, JC Virus, Parvovirus B19, Influenza A, Influenza B, Influenza C, Rotavirus, Human Adenovirus, Rubella Virus, Human Enteroviruses, Genital Human Papillomavirus (HPV), or Hantavirus. In some embodiments, the bacteria comprises one or more of Mycobacteria tuberculosis, Rickettsia rickettsii, Ehrlichia chaffeensis, Borrelia burgdorferi, Yersinia pestis, Treponema pallidum, Chlamydia trachomatis, Chlamydia pneumoniae, Mycoplasma pneumoniae, Mycoplasma sp., Legionella pneumophila, Legionella dumoffii, Mycoplasma fermentans, Ehrlichia sp., Haemophilus influenzae, Neisseria meningitidis, Neisseria gonorrhoeae, Streptococcus pneumonia, S. agalactiae, and Listeria monocytogenes. In some embodiments, the fungi comprises one or more of Cryptococcus neoformans, Pneumocystis carinii, Histoplasma capsulatum, Blastomyces dermatitidis, Coccidioides immitis, and Trichophyton rubrum. In some embodiments, the protozoa comprises one or more of Trypanosoma cruzi, Leishmania sp., Plasmodium, Entamoeba histolytica, Babesia microti, Giardia lamblia, Cyclospora sp., and Eimeria sp. [0031] In some embodiments, the sample is a biological sample or an environmental sample. In some embodiments, the environmental sample is, or is obtained from, a food sample, a beverage sample, a paper surface, a fabric surface, a metal surface, a wood surface, a plastic surface, a soil sample, a fresh water sample, a waste water sample, a saline water sample, exposure to atmospheric air or other gas sample, cultures thereof, or any combination thereof. In some embodiments, the biological sample is, or is obtained from, a tissue sample, saliva, blood, plasma, sera, stool, urine, sputum, mucous, lymph, synovial fluid, cerebrospinal fluid, ascites, pleural effusion, seroma, pus, swab of skin or a mucosal membrane surface, cultures thereof, or any combination thereof. [0032] In some embodiments, the amplifying does not comprise one or more of the following: Archaeal Polymerase Amplification (APA), loop-mediated isothermal Amplification (LAMP), helicase-dependent Amplification (HDA), recombinase polymerase amplification (RPA), strand displacement amplification (SDA), nucleic acid sequence-based amplification (NASBA), transcription mediated amplification (TMA), nicking enzyme amplification reaction (NEAR), rolling circle amplification (RCA), multiple displacement amplification (MDA), Ramification (RAM), circular helicase-dependent amplification (cHDA), single primer isothermal amplification (SPIA), signal mediated amplification of RNA technology (SMART), self-sustained sequence replication (3SR), genome exponential amplification reaction (GEAR) and isothermal multiple displacement amplification (IMDA), optionally the amplifying does not comprise LAMP. In some embodiments, the amplifying comprises one or more of the following: APA, LAMP, HDA, RPA, SDA, NASBA, TMA, NEAR, RCA, MDA, RAM, cHDA, SPIA, SMART, 3SR, GEAR and IMDA, optionally the amplifying does not comprise LAMP. In some embodiments, the method comprises and/or does not comprise one or more of the following: (i) dilution of the treated sample; (ii) dilution of the amplification reaction mixture; (iii) heat denaturation of the treated sample; (iv) sonication of the treated sample; (v) sonication of the amplification reaction mixture; (vi) the addition of ribonuclease inhibitors to the treated sample; (vii) the addition of ribonuclease inhibitors to the amplification reaction mixture; (viii) purification of the sample; (ix) purification of the sample nucleic acids; (x) purification of the nucleic acid amplification product; (xi) removal of the one or more lytic agents from the treated sample or the amplification reaction mixture; (xii) heat denaturing and/or enzymatic denaturing of the sample nucleic acids prior to and/or during amplification; and (xiii) the addition of ribonuclease H to the treated sample or amplification reaction mixture. [0033] There are provided, in some embodiments, systems for real-time calling. The system can comprise: an instrument configured to produce time series data by analyzing a sample. The system can comprise: a processor comprising memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to perform a method provided herein. [0034] There are provided, in some embodiments, computer systems for real-time calling. The computer system can comprise: a hardware processor; and non-transitory memory having instructions stored thereon, which when executed by the hardware processor causes the processor to perform a method provided herein. [0035] There are provided, in some embodiments, computer readable medium comprising code for performing a method provided herein. There are provided, in some embodiments, an assay definition file (ADF), comprising one or more of defined classifiers, a defined LR threshold, t1, t2, Signalmin, Signalmax, a defined step dislocation threshold, a defined Minimum Call Cycle, and a defined span half window, for use in a method provided herein. BRIEF DESCRIPTION OF THE DRAWINGS [0036] FIG. 1 depicts a non-limiting exemplary functional block diagram for an exemplary processor. [0037] FIG. 2 depicts a non-limiting exemplary block diagram of an exemplary computing system. [0038] FIGS. 3A-3B depict non-limiting exemplary schematics related to the Calling Algorithm provided herein: Initial Average Test (is the curve valid; FIG. 3A), and Assertion Test (is the curve amplifying; FIG.3B)). [0039] FIG. 4 depicts a non-limiting exemplary Initial Average and Assertion Test flow chart. Nodes correspond to relevant calling algorithm data and/or metrics. Annotations in black are functions used to calculate metrics from data or other metrics. Annotations in blue correspond to ADF inputs used within these functions/calculations. [0040] FIGS. 5A-5B depict a non-limiting exemplary application of median filter to raw fluorescence data. Raw fluorescence data (FIG. 5A) and median-filtered data (FIG. 5B) are shown. The median filter can be particularly useful for removing transient fluorescence spikes, representing system noises, from raw fluorescence data. [0041] FIG. 6 depicts a non-limiting exemplary distribution of Initial Average Values. Dataset is comprised of 100 asserted and 41 non-asserted curves. Initial Average measured between 75 and 120 seconds. [0042] FIG.7 depicts a non-limiting exemplary Step Dislocation Curve. [0043] FIGS. 8A-8F depict non-limiting exemplary Step Detection Functions. Pairwise difference calculations (FIG. 8C, FIG. 8D; Blue Curves), median-filtering of differences (FIG. 8C, FIG. 8D; Red Curves), and subtraction of smoothed from unsmoothed data (FIG. 8E, FIG. 8F) are shown for a true amplification (FIG. 8A) and a step dislocation (FIG.8B). [0044] FIG. 9 depicts non-limiting exemplary Asserted versus Non-Asserted Curves. LR peaks in Reference Data are plotted in 3 Dimensions, with the Amplitude, Gradient, and Span values of reference positive (blue) and reference negative (red) curves at their Likelihood Ratio peaks in a three-dimensional plot. The hyperplane separating these populations (green) corresponds to a defined Likelihood Ratio threshold within this space. [0045] FIGS. 10A-10B depict non-limiting exemplary higher decision logic for FluA/B Test. Higher decision logic for FluA (FIG. 10A) and FluB (FIG. 10B) are shown. FluA and Internal Control test are duplexed. [0046] FIG. 11 depicts a non-limiting exemplary plot of Initial Likelihood Ratio Peaks. Amplitude, Gradient, and Span values for the yPLR points in the curve population are plotted, one point per curve. Peaks for amplified curves are in blue and peaks for non-amplified curves in red. [0047] FIGS. 12A-12B depict non-limiting exemplary plots of Refined Likelihood Ratio Peaks. Populations of amplified curve PLR peaks have nearly collapsed to single dimension after 3 rounds of refinement (FIG. 12A). From the distributions of Amplitude, Gradient, and Span values at these points, it is possible to obtain final values for ^ ^^̄ே^^, ^^̄^^^, ^^ே^^, ^^^^^^. A hyperplane (FIG.12B) is drawn at the LR = 8 threshold (green). DETAILED DESCRIPTION [0048] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the Figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein and made part of the disclosure herein. [0049] All patents, published patent applications, other publications, and sequences from GenBank, and other databases referred to herein are incorporated by reference in their entirety with respect to the related technology. [0050] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. See, e.g. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, NY 1994); Sambrook et al., Molecular Cloning, A Laboratory Manual, Cold Spring Harbor Press (Cold Spring Harbor, NY 1989). For purposes of the present disclosure, the following terms are defined below. [0051] There are provided, in some embodiments, real-time calling methods. In some embodiments, the method comprises: receiving time series data from an instrument in real time, wherein the time series data comprises a plurality of data points forming a sample curve. The method can comprise: calculating two or more quantitative metrics for each data point in real time. The method can comprise: calculating a likelihood ratio (LR) at each data point in real time. Said calculating step can comprise employing defined classifiers for each of the two or more quantitative metrics derived from (i) a reference population of positive curves and (ii) a reference population of negative curves, optionally derived via Quadratic Discriminant Analysis (QDA). The method can comprise: calling the sample curve in real time. In some embodiments, a sample curve is called positive in real time once the LR calculated for a data point exceeds a defined LR threshold. In some embodiments, a sample curve is called negative in real time if the LR calculated for all data points of the sample curve falls at or below defined LR threshold. [0052] There are provided, in some embodiments, systems for real-time calling. The system can comprise: an instrument configured to produce time series data by analyzing a sample. The system can comprise: a processor comprising memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to perform a method provided herein. [0053] There are provided, in some embodiments, computer systems for real-time calling. The computer system can comprise: a hardware processor; and non-transitory memory having instructions stored thereon, which when executed by the hardware processor causes the processor to perform a method provided herein. [0054] There are provided, in some embodiments, computer readable medium comprising code for performing a method provided herein. There are provided, in some embodiments, an assay definition file (ADF), comprising one or more of defined classifiers, a defined LR threshold, t1, t2, Signalmin, Signalmax, a defined step dislocation threshold, a defined Minimum Call Cycle, and a defined span half window, for use in a method provided herein. Methods for Categorizing Molecular Diagnostic Signals in Real Time [0055] There are provided, in some embodiments, methods, systems, compositions, algorithms, and kits for categorizing molecular diagnostic signals in real time via an iteratively refined Quadratic Discriminant Analysis–based algorithm employing localized step detection and correction. Provided herein include robust methods for evaluating fluorescence signals generated by molecular diagnostic tests. There are provided herein decision algorithms that, when employed by diagnostic platforms, can be used to report positive versus negative results. Methods provided herein can detect amplification of target DNA in real time and can reliably differentiate true amplification from background noise. [0056] Methods, systems, compositions, algorithms, and kits provided herein can integrate multiple quantitative metrics to provide a comprehensive analysis of a signal (e.g., a fluorescence signal). Three metrics in particular – the Amplitude, Gradient, and Span (the change in Amplitude over a defined window) – can be calculated at each point in real time and compared to values derived from reference data sets of known Positives and Negatives as part of a Likelihood Ratio test (also known as a Wilks Test). If a valid curve contains at least one point with a Likelihood Ratio above a defined threshold, that curve can be called as asserted (amplified). But if all points within a valid curve fall below the threshold, then the curve can be called as not asserted (non-amplified). [0057] Likelihood Ratios can be calculated at each point according to classifiers derived from reference populations of known Positives and Negatives. These classifiers can be obtained via Quadratic Discriminant Analysis (QDA), a statistical method for binning multi- dimensional data into two or more groups. To classify entire curves comprised of multiple data points, QDA can be applied to metrics at characteristic peaks that optimally discriminate between amplified and non-amplified curves. These characteristic peaks can be identified using an iterative approach, with the point in each curve corresponding to the Peak (largest) Likelihood Ratio in one round selected as input for the next round of refinement. In this iterative approach, QDA coefficients for the non-amplified reference population can be initially defined according to the distribution of metrics observed over the set of all negative points, rather than at any specific peak. Corresponding inputs for the amplified population can be initially calculated based on the point in each curve that is most statistically distinct (has the largest Mahalanobis Distance (a multi-dimensional generalization of a standard deviation)) from the population of non-amplified points. Using this preliminary set of coefficients, initial Likelihood Ratios can be calculated for all points in the non-amplified and amplified training sets. The points corresponding to the Peak Likelihood Ratio in each curve can then be used to define revised QDA coefficients for the amplified and (optionally) non-amplified populations, which can in turn be used to generate revised Likelihood Ratios for each point. Multiple iterations of this process – calculation of Peak Likelihood Ratios to generate revised sets of QDA coefficients – can be performed until the coefficients converge to their final values. This iterative process can yield a set of model parameters optimized for discriminating between amplified and non- amplified curves according to the Likelihood Ratio metric. And since the Likelihood Ratio can be calculated for individual points, the approach can enable calls to be made in real time where a single point above a defined cutoff is sufficient for a positive call. [0058] Methods, systems, compositions, algorithms, and kits provided herein can separately make use of a real-time method for detection and correction of step dislocations – sudden shifts in baseline fluorescence unrelated to true amplification. Step dislocations can represent macroscopic system noises, such as bubbles obstructing the optical path or condensed drops of fluid falling into the reaction, that create a risk of false-positive calls if left uncorrected. As provided herein, step dislocations can be detected by measuring the pairwise differences between adjacent median-filtered points. Smoothed differences can be then subtracted from the raw differences and the resulting values can be compared to a defined threshold to identify sudden jumps in signal. Notably, this approach applies the correction for these jumps in real- time, reducing the Span metric by the magnitude of any steps detected within a locally defined window. [0059] Likelihood Ratio Tests have never previously been used or considered as a method for making molecular diagnostic calls in real time. Real-time implementations have proven challenging because QDA coefficients must be generated from objects described by single (multi-dimensional) metrics, which typically can only be calculated for a signal once all points have been measured. The methods provided herein can sidestep this issue by applying QDA to each point relative to characteristic peaks in reference populations. In some embodiments, and without being bound by any particular theory, by using the rule that the existence of a single point above a defined threshold is sufficient to assign a curve to a population, Likelihood Ratio Tests can be applied in a real-time context. [0060] The characteristic peaks used to generate the QDA coefficients can be obtained through an iterative process that has never previously been considered or employed for discriminating populations. The approach can enable optimization of QDA coefficients based on the specific data points within reference signals – the Likelihood Ratio Peaks – that are most relevant for classifying test signals as Positive or Negative. [0061] The real-time method for step correction represents an improvement over prior art in that it makes no assumptions regarding the correct (absolute) fluorescence baseline. Previous approaches either assume the baseline prior to the jump is “correct” and adjust all subsequent points, or that the baseline following the jump represents the true baseline and that the signal prior to the jump requires correction. Since, in some embodiments, the methods provided herein only correct the total difference in amplitude over a defined window, no assumptions concerning the true baseline are needed. [0062] The methods described herein can permit detection of DNA amplification in real time by integrating multiple metrics into a single score, the Likelihood Ratio, in a statistically sound manner. With this approach, a user is able to more fully capture the wide variety of fluorescence signals and behaviors present in true clinical data and robustly discriminate between Positive and Negative results in real time. [0063] There are provided, in some embodiments, algorithms for classifying signals into two or more populations in real time when reference populations of known types are available for training. There are provided, in some embodiments, calling algorithms for rapid molecular diagnostic tests, including tests which are subject to significant noises that limit the sensitivity and specificity of simpler approaches. [0064] In some embodiments, metrics other than Amplitude, Gradient, and Span can be used for describing points within a real-time curve. Additionally, in some embodiments, such as for signal processing applications where populations are expected to have similar variances and covariances, Linear Discriminant Analysis (LDA) can be used in place of Quadratic Discriminant Analysis (QDA) for simplicity. [0065] Disclosed herein include real-time calling methods. In some embodiments, the method comprises: receiving time series data from an instrument in real time, wherein the time series data comprises a plurality of data points forming a sample curve. The method can comprise: calculating two or more quantitative metrics for each data point in real time. The method can comprise: calculating a likelihood ratio (LR) at each data point in real time. Said calculating step can comprise employing defined classifiers for each of the two or more quantitative metrics derived from (i) a reference population of positive curves and (ii) a reference population of negative curves, optionally derived via Quadratic Discriminant Analysis (QDA). The method can comprise: calling the sample curve in real time. In some embodiments, a sample curve is called positive in real time once the LR calculated for a data point exceeds a defined LR threshold. In some embodiments, a sample curve is called negative in real time if the LR calculated for all data points of the sample curve falls at or below defined LR threshold. [0066] In some embodiments, the methods (e.g., Calling Algorithm) provided herein operates on-board the instrument (e.g., clinical instrument) to determine whether a result is Positive, Negative, or Invalid in real time. In some embodiments, it applies both an Initial Average Test and an Assertion Test to each fluorescence curve (FIGS. 3A-3B). Assertions obtained for Test and Internal Control curves can be used as inputs into the higher-level calling logic that determines whether a result is Positive, Negative, or Invalid. [0067] The Assertion Test, which lies at the core of the decision algorithm, relies on metrics derived from reference data sets of known Positive and Negative results. Three metrics in particular – the Amplitude, Gradient, and Span – can be calculated at each point in real time. A Likelihood Ratio Test (also called a Wilks Test), can be then applied to the real-time values to discriminate between amplified and non-amplified reactions. Specifically, measured parameters for each metric derived from reference populations of known positives and negatives via Quadratic Discriminant Analysis (QDA) can be supplied via the ADF to calculate a Likelihood Ratio at each point. The Likelihood Ratio represents the relative likelihood of a point occurring in amplified vs non-amplified populations. If a valid curve contains at least one point with a Likelihood Ratio above a defined threshold (specified in the ADF), that curve can be called as asserted. But if all points within a valid curve fall below the threshold, then the curve can be called as not asserted. [0068] FIG. 4 depicts a flow chart outlining the Initial Average and Assertion Test calculations used to make real-time assertions. Descriptions of the metrics and the functions used to generate these metrics are presented in the following sections, followed by a discussion of the higher-level logic used to make Positive, Negative, or Invalid calls. Initial Processing of Raw Signal Data [0069] A 3-point median filter (FIGS. 5A-5B) can be first applied to the raw signal data (e.g., raw fluorescence data) to provide smoothing and removal of single-point spikes: ^^ெ^ௗ^^^^ ^^^ ൌ ^^ோ^௪^ ^^^, ^^ ൌ 1, ^^ ^^ெ^ௗ^^^^ ^^^ ൌ ^^ ^^ ^^ 1^^, ^^ ൌ 2, … , ^^ െ 1 [0070] A 7-point Savitzky-Golay (SG) filter can be then applied to the median- filtered data to provide low-variance estimates of the amplitude and gradient values at each point. In some embodiments, and without being bound by any particular theory, a basic principle behind the SG filter is that a sliding window is moved through the data and a 2nd order linear regression applied to each window: ^^ ^^ ^^^ ൌ ^^^ ^ ^^^ ^^ ^ ^^ଶ ^^ ଶ [0071] In some embodiments, the x values in each window are coded as {-3,…3}. That is, the regression fit uses as inputs: ^^ ^ ^^ ^ ൌ ^ െ3, … ,3 ^ ^^ ^ ^^ ^ ൌ ^ ^^ெ^ௗ^^^ ^ ^^ െ 3 ^ , … , ^^ெ^ௗ^^^ ^ ^^ ^ 3 ^^ [0072] The smoothed amplitude and gradient values can be calculated for the center point (k = 0) of each window as follows: ^^ ^^ ^^^ ^0^ ൌ ^^ ^ ൌ ^^ ^ ⋅ ^^^0^், ^^ℎ ^^ ^^ ^^ ^^ 1 ^ ൌ 21 ^െ2,3,6,7,6,3,െ2^ ^^ ^^ீ^^ௗ 0 ൌ ^^ ∆ ^^ ൌ ^^ ⋅ ^ ^் ^ ^ ^ ^ ^^ 0 ∆ ^^ , ^^ℎ ^^ ^^ ^^ ^^ 1 ^ ൌ 28 ^െ3,െ2,െ1,0,1,2,3^ Initial algorithm can apply an Initial Average Test to each curve (FIG. 3A). This test can confirm that the signal (e.g., fluorescence signal) starts within a specified range and guards against gross system failures (e.g., dispense failures, presence of foreign objects, etc.). The Initial Average Test can use four ADF-defined inputs – t1, t2, Signalmin, and Signalmax – and can apply these to the ^^ ^^^^^ signal. If the average signal within the time window falls within the specified range, then the curve passes the Average Check. But if the average signal falls outside the specified range, then the curve fails the test and is called as Invalid. [0074] When designing the ADF, appropriate Signalmin, and Signalmax inputs can be identified based on the distribution of Initial Average values (bounded by t1 and t2) within a reference data set (FIG. 6). Signalmin, and Signalmax can be set equal to the Mean ± 3.6σ bounds of this reference population. Assuming the Initial Average values are normally distributed, these cutoff values are expected to yield an Invalid call only once every 3000 runs in the absence of a system failure. Since a typical assay comprises three curves (two Tests and one Internal Control), this translates to a 1 in 1000 chance of a clinical test yielding an Invalid assertion in the absence of a true system failure. The Span Metric [0075] The Span metric describes the net gain in amplitude over a defined interval. Except for the special cases noted below, the Span can be defined as: ^ା^ ^^^ ^^^ ^^ ^^ ^ ^^ ^ ^^^ ^^ ^^ ^ ^^ ^^^ ^ ^ [0076] and ΣJi is the sum of the magnitudes of all step dislocations (if any) identified within the interval (See Section Step Detection). The value of a should be of comparable duration to the “rise” portion of an amplified curve. Span windows that are too short may have reduced capability for discriminating amplified vs non-amplified signals, while those that are too long may delay a call without providing discriminatory benefit. The Span Half Window can be set within the ADF, with a typical value of 10 points but with permitted values ranging from 6 to 12. Assuming a sampling duration of 5 seconds, these correspond to full widths of 1 to 2 minutes. [0077] In the special circumstance where a boundary point of a Span window (either k + a or k – a) is located near a step dislocation (See Section Step Detection), the median-filtered fluorescence can be used instead of the Savitzky-Golay amplitude estimate. For example, if point k + a were located near a step dislocation, the Span could be calculated as: ^ା^ ^^^ ^^^ ൌ ^^ெ^ௗ^^^^ ^^ ^ ^^^ െ ^^ ^^^^^^ ^^ െ ^^^ െ ^ ^^^ The Likelihood ADF Inputs [0078] The assertion test can be viewed as a binary hypothesis test applied to each point within a curve. It draws upon the distribution of Amplitude, Gradient, and Span values at characteristic peaks in known positive and negative curves to determine the relative likelihood of a test curve belonging to each population. Typically, an SME-curated set of at least 100 amplified and 100 non-amplified curves serves as an appropriate reference. To ensure optimal sensitivity and specificity in a clinical setting, the reference curves can be collected using representative target concentrations in appropriate sample types. [0079] For each curve in the reference population, the Amplitude, Gradient, and Span can be identified at a characteristic peak yPLR, which can be identified through an iterative process (See Section Derivation of Calling Algorithm Input Parameters) for maximally separating known positives and negatives within the A,G,S, space. The means and covariance matrices for these metrics can be then calculated for the amplified and non-amplified populations: ^^̄^^^ ൌ ^ ^̅^^^ோ,^^^, ^̅^^^ோ,^^^, ^^^̅^ோ,^^^^ ^ can be used to distinguish between amplified and non-amplified curves via a Likelihood Ratio Test. These coefficients, which together comprise a total of 18 inputs – three for ^^̄ே^^, three for ^^̄^^^, and six for each (symmetric) covariance matrix ^^ – can be assay specific and defined for each test and internal control curve within an Assay Definition File (ADF). Calculating the Likelihood Ratio [0081] The calling algorithm can calculate the Mahalanobis Distance separating each point in a test curve from the distribution of known amplified and non-amplified curves. This is a multi-dimensional generalization of the number of standard deviations separating a point from the mean of a distribution. Here, the calculation can be performed over three dimensions, corresponding to the Amplitude, Gradient, and Span. The method assumes these metrics are normally distributed across the amplified and non-amplified populations, but is relatively robust to deviations from normality. [0082] The relative log likelihood of a point ytest = {Atest, Gtest, Stest} belonging to the negative or the positive populations can be given by Q0 and Q1 respectively: ^^^ ൌ ^^^ ଶ ^^ ^ ln ^ ^^ ^^ ^^൫ ^^ே^^൯^ [0083] where ^^^ ଶ ^^ Mahalanobis Distances to each reference population and can ^^^ ଶ ^^ ൌ ^ ^^௧^^௧ െ ^^̄ே^^^் ^^ே ି ^^ ^ ^ ^^௧^^௧ െ ^^̄ே^^^ ^ ^ [0084] The point in a test curve as Q0/Q1 provided the are ytest occurs at or after the Minimum Call Cycle, as defined within the ADF; (b) Atest, Gtest, and Stest are all > 0; and (c) no step dislocations occur within 3 points of ytest (See Section Step Detection). If these conditions are not satisfied, then the LR of ytest can be defined to be zero. Step Detection [0085] Curves collected on the system may occasionally exhibit step dislocations, sudden shifts in baseline signal unrelated to true amplification (FIG. 7). These can represent macroscopic system noises, such as bubbles in a detection window or condensed drops of fluid falling into a reaction. To prevent step dislocations from triggering false assertion calls, there are provided, in some embodiments, methods to identify and correct for these signal noises. [0086] Step dislocations can be detected by measuring the pairwise differences between adjacent median-filtered points. ^^ ൌ ^ ^^ ெ^ௗ,ଶ െ ^^ ெ^ௗ,^ , ^^ ெ^ௗ,ଷ െ ^^ ெ^ௗ,ଶ … , ^^ ெ^ௗ,^ െ ^^ ெ^ௗ,^ି^൧ [0087] with broad peaks that correspond to steady increases in signal over multiple time points (FIG. 8A and FIG. 8C). By contrast, step dislocations can be characterized by a sharp, single-point peak above baseline (FIG.8B and FIG.8D) due to the (nearly) instantaneous shift in signal. [0088] To identify step dislocations, a 3-point median filter can be applied to the pairwise-difference curves (FIG. 8C and FIG. 8D - red). The smoothed differences Ys can be then subtracted from the unsmoothed differences such that only the “sharp” peaks remain (FIG. 8E and FIG. 8F). Any points in Y – Ys with a value above the corresponding threshold defined in the ADF can be identified as step dislocations and the magnitude J recorded. Detection of a step dislocation alters the Span and Likelihood Ratio calculations as discussed above in Sections The Span Metric and Calculating the Likelihood Ratio. The Curve-Level Call [0089] Individual curves can be called in real time as Asserted, Not Asserted, or Invalid depending on the outcome of the Initial Average Test, the Likelihood Ratio at each point, and the LR Threshold defined within the ADF. [0090] In some embodiments the Initial Average Test is completed prior to the Assertion test, which implies that the value of t2 for the former is less than or equal to the Minimum Call Cycle. If the curve fails the Initial Average Test, the curve can be called as Invalid. Curves may also be called as Invalid in other rare circumstance such as: (a) there are ≥ 2 contiguous missing points within Fraw prior to an Asserted call; and/or (b) there are >2 missing points (either contiguous or non-contiguous) within Fraw prior to an Asserted call. [0091] Provided the curve passes the Initial Average Test (i.e., the curve is valid), the curve can be Asserted as soon as a point is determined to have a Likelihood Ratio above the threshold defined in the ADF. A single point with an LR above the threshold can be sufficient to trigger the Asserted call. If the run completes without the LR of any point exceeding the defined threshold, then the curve can be called as Not Asserted. [0092] FIG. 9 provides a more intuitive look at the math governing the assertion call. The figure shows the Amplitude, Gradient, and Span values of reference positive (blue) and reference negative (red) curves at their Likelihood Ratio peaks in a three-dimensional plot. The hyperplane separating these populations (green) corresponds to a defined Likelihood Ratio threshold within this space. The calling algorithm can make an Asserted call when a point is identified in this three-dimensional space that crosses the LR threshold. Algorithm Output Metrics [0093] The following curve-level algorithm metrics can be tracked and recorded by the instrument in raw output files. [0094] Initial Average. The mean signal recorded by the instrument between times t1 and t2 as part of the Initial Average Test. [0095] Initial Average Assertion. The result of the Initial Average Test. In a typical implementation, a value of 1 corresponds to a success and a value of 0 to a failure of the test. [0096] QDA Assertion. The result of the Assertion Test. In a typical implementation, a value of 1 corresponds to Asserted, a value of 0 to Not Asserted, and a value of “!” to Invalid. [0097] QDA Assertion Time. The time at which the assertion is made during the run. In some embodiments, since the Likelihood Ratio is calculated from data collected over a time window, there can be a delay between the time a point is measured and the time at which an LR can be assigned. The QDA Assertion Time can correspond to the latter, the real time at which the system actually makes the assertion. [0098] Example: A system collects data at 5 second intervals and employs a calling algorithm with an 8 point Span Half Window. The system “looks ahead” by 8 + 3 + 1 = 12 points (Span Half Window + Savitzky-Golay half window + Median Filter half window) to assign a Likelihood Ratio. Therefore, in some embodiments, if a curve were to cross the LR threshold at the 3 minute timepoint, the assertion would not be made until an additional 12 points (1 minute) of data could be collected. The QDA Assertion time would thus occur at 4 minutes in some embodiments. [0099] As used herein, the term “real time” shall be given its ordinary meaning, and shall also refer to processing and providing information within a time interval brief enough to not be discernable by a user. In some embodiments, real time means “near real time.” [0100] Peak Likelihood Ratio (PLR). The maximum value for the Likelihood Ratio calculated over all points within a curve. Since the LR is defined to be zero prior to the Minimum Call Cycle, the PLR occurs afterwards. [0101] Occasionally, all points within a valid curve may have Likelihood Ratios equal to zero. In these circumstances, the PLR is assigned a value of zero and the corresponding PLR Time, PLR Amplitude, PLR Gradient, and PLR Span values are undefined. [0102] PLR Time. The time at which the point with the Peak Likelihood Ratio is measured. [0103] PLR Amplitude. The value of SGAmp at the point where the Peak Likelihood Ratio is measured. [0104] PLR Gradient. The value of SGGrad at the point where the Peak Likelihood Ratio is measured. [0105] PLR Span. The Span value at the point where the Peak Likelihood Ratio is measured. [0106] Step Dislocations. The (integer) total number of Step Dislocations above the ADF-defined cutoff that are detected within a curve. Test Decision Logic [0107] FIGS. 10A-10B depict non-limiting exemplary higher decision logic for FluA/B Test. Higher decision logic for FluA (FIG. 10A) and FluB (FIG. 10B) are shown. FluA and Internal Control tests are duplexed in this example. Derivation of Calling Algorithm Input Parameters [0108] The assertion test inputs that comprise ^ ^^̄ே^^, ^^̄^^^, ^^ே^^, ^^^^^^ can be obtained from reference populations of known amplified and non-amplified curves. They can be derived through an approach intended to maximize the ability of the algorithm to classify curves according to the LR metric, with the requirement that a single point above a defined LR threshold shall be considered sufficient for a curve to be asserted. Briefly, calculation of these inputs can be performed as follows: [0109] Step 1: Estimation of initial coefficients ^^̄ே^^,ூ^^௧ and ^^ே^^,ூ^^௧ from the distributions of Amplitude, Gradient, and Span values measured at all points among the non- amplified population of curves. [0110] Step 2: Estimation of initial coefficients ^^̄^^^,ூ^^௧ and ^^^^^,ூ^^௧ from the distributions of Amplitude, Gradient, and Span values measured at the point yMpeak in each amplified curve, where yMpeak is the point having the largest Mahalanobis distance relative to ^^̄ே^^,ூ^^௧ and ^^ே^^,ூ^^௧ , subject to the requirement that the Amplitude, Gradient, and Span at yMpeak must all be greater than 0. [0111] Step 3: Calculation of preliminary Likelihood Ratios for all points in the non- amplified reference curves using input coefficients ^ ^^̄ே^^,ூ^^௧ , ^^̄^^^,ூ^^௧ , ^^ே^^,ூ^^௧, ^^^^^,ூ^^௧^. [0112] Step 4: Calculation of ^^̄ே^^ and ^^ே^^ from the distributions of Amplitude, Gradient, and Span values measured at the point yPLR in each non-amplified curve, where yPLR is the point where the Likelihood Ratio is maximized. [0113] Step 5: Calculation of preliminary Likelihood Ratios for all points in the amplified reference curves using input coefficients ^ ^^̄ே^^, ^^̄^^^,ூ^^௧ , ^^ே^^, ^^^^^,ூ^^௧^. [0114] Step 6: Calculation of ^^̄^^^ and ^^^^^ from the distributions of Amplitude, Gradient, and Span values measured at the point yPLR in each amplified curve, where yPLR is the point where the Likelihood Ratio is maximized. [0115] Step 7: Additional iterations (as needed) of steps #3-#6, with the output coefficients ^ ^^̄ே^^, ^^̄^^^, ^^ே^^, ^^^^^^ from one iteration used as input coefficients ^ ^^̄ே^^,ூ^^௧ , ^^̄^^^,ூ^^௧ , ^^ே^^,ூ^^௧, ^^^^^,ூ^^௧^ in the next. Alternatively, additional iterations (as needed) of steps #5-#6, with the output coefficients ^ ^^̄ே^^, ^^̄^^^, ^^ே^^, ^^^^^^ from one iteration used as input coefficients ^ ^^̄ே^^, ^^̄^^^,ூ^^௧ , ^^ே^^, ^^^^^,ூ^^௧^ in the next. can be repeated until values of coefficients converge. Estimation of Initial Coefficients [0116] Non-amplified curves are, by definition, curves having an absence of signal. In some embodiments, and without being bound by any particular theory, the expected value of each point within these curves should be the same, with variability in Amplitude, Gradient, and Span directly attributable to system noise. [0117] In order to fully capture the noise from which a signal must be discerned, the initial coefficients ^^̄ே^^,ூ^^௧ and ^^ே^^,ூ^^௧ can be obtained by calculating the mean, variance, and covariance values for the Amplitude, Gradient, and Span at all points in the set of non-amplified curves. [0118] Amplified curves contain a specific signal relative to background, that a subject-matter expert has determined to be distinct from the non-amplified curves and of a type that should be asserted by the calling algorithm. The initial coefficients ^^̄^^^,ூ^^௧ and ^^^^^,ூ^^௧ can be calculated by first identifying the point in each curve that is most statistically distinct from the population of non-amplified curves. This point, yMpeak, is the point in each curve with the largest Mahalanobis Distance (d) relative to the population of non-amplified curves, subject to the requirement that the Amplitude, Gradient, and Span must all be positive at yMpeak: ^^ଶ ൌ ^ ^^ െ ^^̄ே^^,ூ^^௧^் ^^ே ି ^^ ^,ூ^^௧ ^ ^^ െ ^^̄ே^^,ூ^^௧^ [0119] The be determined from the means, variances, and Span values observed at yMpeak over the set of amplified curves. Calculation of Preliminary Likelihood Ratios [0120] With the coefficients ^ ^^̄ே^^,ூ^^௧ , ^^̄^^^,ூ^^௧, ^^ே^^,ூ^^௧ , ^^^^^,ூ^^௧^ in hand, it is possible to assign an initial Likelihood Ratio to all points in the amplified and non-amplified reference curves using the method outlined in the Section Calculating the Likelihood Ratio. From this, the “most positive” point in each curve – the point with the Peak Likelihood Ratio (yPLR) – can be identified. A three-dimensional plot of the Amplitude, Gradient, and Span values for the yPLR points is presented in FIG. 11, with the population of amplified curves in blue and the non-amplified curves in red. Iterative Refinement of Algorithm Coefficients [0121] The means, variances, and covariances of the yPLR points in the amplified and non-amplified populations can be used to generate a refined set of ^ ^^̄ே^^, ^^̄^^^, ^^ே^^, ^^^^^^ coefficients. Importantly, refining the model based on the Peak Likelihood Ratio has the effect of maximally separating the amplified and non-amplified populations according to the metric ultimately used for making a call. Given the requirement that a single point above an LR threshold is sufficient for a curve to be asserted, in some embodiments, and without being bound by any particular theory, it is better to use the yPLR distribution for the non-amplified curves (the points most likely to trigger an assertion) than to use all points as was done originally to generate ^^̄ே^^,ூ^^௧ and ^^ே^^,ூ^^௧. For the amplified curves, refinement according to the likelihood ratio, rather than the absolute Mahalanobis distance, has the effect of generating a tighter distribution of ^^^^^ coefficients, enhancing the discriminatory power of the assertion algorithm. Multiple iterations of this process – calculation of a new set of yPLR values from a refined set of ^ ^^̄ே^^, ^^̄^^^, ^^ே^^, ^^^^^^ coefficients – may be used to additionally refine the assertion algorithm inputs to ensure optimal discrimination. [0122] FIGS. 12A-12B show the Amplitude, Gradient, and Span values of the yPLR points identified in the same data set used to generate FIG. 11, but after three rounds of refinement. In the refined model, the yPLR points have collapsed to an almost-linear distribution within the 3D space. FIG.12B shows the hyperplane (green) defined by these populations for an assertion algorithm having an LR cutoff of 8. For this algorithm, “asserted” calls occur when a single point in a test curve can be mapped to the “blue” side of the hyperplane. “Non-asserted” calls occur when all points can be mapped to the “red” side. Systems for Real-time Calling [0123] Aspects of the present disclosure include systems for real-time calling. In some embodiments, systems include an instrument (e.g., an apparatus) configured to produce data, and a processor configured to analyze the data. [0124] The systems disclosed herein can beneficially provide rapid target detection. In the clinical setting, the methods provided herein can further help to avoid the delays of conventional nucleic acid testing thereby enabling clinicians to determine diagnoses within the typical timeframe of a patient's office visit. As such, the disclosed systems enable clinicians to develop treatment plans for patients during their initial office visit, rather than requiring the clinician to wait for hours or even days to receive test results back from a laboratory. For example, when a patient visits a clinic a nurse or other healthcare practitioner can collect a sample from the patient and begin testing using the described system. The system can provide the test result by the time the patient consults with their doctor or clinician to determine a treatment plan. Particularly when used to diagnose pathologies that progress quickly, the disclosed systems can avoid the delays associated with laboratory testing that can negatively impact the treatment and outcome of the patient. [0125] In some embodiments, disclosed systems can be used outside of the clinical setting (e.g., in the field, in rural settings without easy access to an established healthcare clinic) to detect health conditions such as contagious diseases (e.g., Ebola), thus enabling the appropriate personnel to take immediate action to prevent or mitigate the spread of a contagious disease. Similarly, the disclosed systems can be used in the field or at the site of a suspected hazardous contaminant (e.g., anthrax) to quickly determine whether a sample contains the hazardous contaminant, thus enabling the appropriate personnel to take immediate action to prevent or mitigate human exposure to the contaminant. Additionally, the disclosed systems can be used to detect contaminants in the blood or plasma supply or in the food industry. It will be appreciated that the disclosed systems can provide similar benefits in other scenarios in which real-time detection of a target analyte enables more effective action than delayed detection through sending a sample to an off-site laboratory. Instrument [0126] There are provided, in some embodiments, instruments configured to produce time series data by analyzing a sample. Time series data can be derived from instrument analysis of a sample, such as, for example, a sample suspected of comprising a target analyte. The instrument can be configured to produce time series data by analyzing the sample. The instrument can be configured to perform a molecular diagnostic assay. Instrument analysis of the sample can comprise subjecting said sample to one or more reaction(s), and said reaction(s) can be configured to detect the presence and/or amount of a target analyte in the sample. The instrument can comprise one or more sensor(s) configured to detect signals derived from the sample. Time series data can comprise time series signal data, and said signals can be generated from said one or more reaction(s). The signal may be a calorimetric signal, a potentiometric signal, an amperometric signal, an optical signal (e.g., a fluorescent signal and/or colorimetric signal), a piezo-electric signal, or any combination thereof. Depending on the embodiment, signals are generated in the presence of the target analyte or in the absence of the target analyte. The real-time calling methods, compositions, systems, and kits provided herein can advantageously detect a target analyte with a low threshold of detection. The term "threshold of detection" is used herein to describe the minimal amount of target analyte (e.g., nucleic acid comprising a target nucleic acid sequence) that must be present in a sample in order for detection to occur. For example, when a threshold of detection is 10 nM, then a signal can be detected when a target nucleic acid is present in the sample at a concentration of 10 nM or more. In some cases, the threshold of detection is less than or equal to 5 nM, 1 nM, 0.5 nM, 0.1 nM, 0.05 nM, 0.01 nM, 0.005 nM, 0.001 nM, 0.0005 nM, 0.0001 nM, 0.00005 nM, 0.00001 nM, 10 pM, 1 pM, 500 fM, 25004, 10004, 50 fM, 10 fM, 5 fM, 104, 500 attomole (aM), 100 aM, 50 aM, 10 aM, or 1 aM. In some cases, the threshold of detection is in a range of from 1 aM to 1 nM, 1 aM to 500 pM, 1 aM to 200 pM, 1 aM to 100 pM, 1 aM to 10 pM, 1 aM to 1 pM, 1 aM to 50004, 1 aM to 100 fM, 1 aM to 1 fM, 1 aM to 500 aM, 1 aM to 100 aM, 1 aM to 50 aM, 1 aM to 10 aM, 10 aM to 1 nM, 10 aM to 500 pM, 10 aM to 200 pM, 10 aM to 100 pM, 10 aM to 10 pM, 10 aM to 1 pM, 10 aM to 500 fM, 10 aM to 100 fM, 10 aM to 1 fM, 10 aM to 500 aM, 10 aM to 100 aM, 10 aM to 50 aM, 100 aM to 1 nM, 100 aM to 500 pM, 100 aM to 200 pM, 100 aM to 100 pM, 100 aM to 10 pM, 100 aM to 1 pM, 100 aM to 50004, 100 aM to 100 fM, 100 aM to 1 fM, 100 aM to 500 aM, 500 aM to 1 nM, 500 aM to 500 pM, 500 aM to 200 pM, 500 aM to 100 pM, 500 aM to 10 pM, 500 aM to 1 pM, 500 aM to 500 fM, 500 aM to 10004, 500 aM to 1 fM, 1 fM to 1 nM, 1 fM to 500 pM, 1 fM to 200 pM, 1 fM to 100 pM, 104 to 10 pM, 1 fM to 1 pM, 10 fM to 1 nM, 10 fM to 500 pM, 1004 to 200 pM, 10 fM to 100 pM, 10 fM to 10 pM, 10 fM to 1 pM, 500 fM to 1 nM, 500 fM to 500 pM, 500 fM to 200 pM, 500 fM to 100 pM, 500 fM to 10 pM, 500 fM to 1 pM, 800 fM to 1 nM, 800 fM to 500 pM, 800 fM to 200 pM, 800 04 to 100 pM, 80004 to 10 pM, 80004 to 1 pM, fom 1 pM to 1 nM, 1 pM to 500 pM, 1 pM to 200 pM, 1 pM to 100 pM, or 1 pM to 10 pM. In some cases, the threshold of detection in a range of from 800 fM to 100 pM, 1 pM to 10 pM, 10 fM to 500 fM, 10 fM to 50 fM, 50 fM to 100 fM, 10004 to 250 fM, or 25004 to 500 fM. [0127] The instrument can comprise a spectrometer, an electrochemical detection device, a polynucleotide detection device, a fluorescence anisotropy device, a fluorescence resonance energy transfer device, an electron transfer device, an enzyme assay, a lateral flow assay, a magnetism device, an electrical conductivity device, an isoelectric focusing device, a chromatograph, an immunoprecipitation device, an immunoseparation device, an aptamer binding device, a filtration device, electrophoresis device, a CCD camera, an immunoassay, an ELISA, a Gram staining device, an immunostaining device, a flow cytometer, a microscope, an immunofluorescence device, a western blot device, a polymerase chain reaction (PCR) device, RT-PCR device, an isothermal amplification device, a fluorescence in situ hybridization device, a sequencing device, a next gen sequencing device, a mass spectrometer, an ion mobility spectrometer, a surface plasmon resonance device, and a localized-surface plasmon resonance device, or any combination thereof. [0128] Instrument analysis can comprise one or more of spectrometry, Raman spectroscopy, FFT (Fast-Fourier Transform) spectroscopy, Fourier-Transform Infrared Spectroscopy (FTIR), infrared spectrometry, Nuclear Magnetic Resonance (NMR) spectrometry, electrochemical detection, polynucleotide detection, volatile organic compound methods, fluorescence anisotropy, fluorescence resonance energy transfer, electron transfer, enzyme assay, magnetism, electrical conductivity, electrochemical detection, isoelectric focusing, lateral flow assay (LFA), microfluidics, amino acid sequencing, nucleic acid sequencing, flow cytometry, chromatography, immunoprecipitation, immunoseparation, aptamer binding, filtration, electrophoresis, use of a CCD camera, immunoassay, enzyme-linked immunosorbent assay (ELISA), Gram staining, immunostaining, microscopy, immunofluorescence, size/weight/charge detection, western blotting, polymerase chain reaction (PCR), RT-PCR, isothermal amplification, sequencing, fluorescence in situ hybridization, mass spectrometry, Surface Plasmon Resonance (SPR), and Localized Surface Plasmon Resonance (LSPR). The target analyte can be a cell, a cancer cell, a virus, a bacterium, a fungus, a protein, a nucleic acid, a DNA molecule, an RNA molecule, an miRNA molecule, an mRNA molecule, a peptide, a polypeptide, an antibody, a tissue, a nanoparticle, a drug metabolite, a lipid, a carbohydrate, a hormone, a vitamin, a fragment thereof, or any combination thereof. The target analyte can be detected using a label that is selected from the group comprising a light-emitting label, a fluorescent label, a dye, a quantum dot, a luminescent label, electro-luminescent label, a chemi- luminescent label, a bead, an electromagnetic radiation emitter, an optical label, an electric label, enzymes that can be used to generate an optical or electrical signal, a nanoparticle, a colorimetric label, an enzyme-linked reagent, a multicolor reagent, and an avidin-streptavidin associated detection reagent. A sample curve being called positive can indicate the presence of a target analyte in the sample. A sample curve being called negative can indicate the absence of the target analyte in the sample. [0129] The instrument can comprise a thermocycler, such as, for example, a thermocycler configured for real-time PCR amplification and fluorescence monitoring. The target analyte is a target nucleic acid sequence. The one or more reaction(s) can comprise nucleic acid detection reaction(s). The sample curve can comprise a nucleic acid amplification curve, and the signals can comprise fluorescence signals indicative of amplification of the target nucleic acid sequence. In some embodiments, a sample curve being called positive indicates the presence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the presence and/or amount of a target nucleic acid sequence in the sample. In some embodiments, a sample curve being called negative indicates the absence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the absence of a target nucleic acid sequence in the sample. In some embodiments the one or more reaction(s) can comprise an isothermal amplification reaction. The term “isothermal amplification reaction” shall be given its ordinary meaning and shall also include reactions wherein the temperature does not significantly change during the reaction. In some embodiments, the temperature of the isothermal amplification reaction does not deviate by more than 10°C, for example by not more than 5°C or by not more than 2°C during the main enzymatic reaction step where amplification takes place. Depending on the method of isothermal amplification of nucleic acids, different enzymes can be used for amplification. Isothermal amplification compositions and methods are described in WO2017176404, the content of which is incorporated herein by reference in its entirety. The instrument can be capable of amplifying a target nucleic acid sequence in an amplification reaction mixture, thereby generating a nucleic acid amplification product, optionally the nucleic acid amplification product is generated at detectable levels within about 20 minutes, about 15 minutes, or about 10 minutes. The instrument can be capable of detecting the nucleic acid amplification product with a signal-generating oligonucleotide (e.g., a TaqMan detection probe oligonucleotide, a molecular beacon detection probe oligonucleotide, or a molecular torch detection probe oligonucleotide), wherein the signal-generating oligonucleotide is capable of hybridizing to the nucleic acid amplification product. [0130] In some embodiments, the instrument is a mass spectrometer. A variety of configurations of mass spectrometers can be used to detect target analytes. Several types of mass spectrometers are available or can be produced with various configurations. In general, a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and instrument-control system, and a data system. Difference in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, an inlet can be a capillary-column liquid chromatography source or can be a direct probe or stage such as used in matrix-associated laser desorption. Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-associated laser desorption. Common mass analyzers include a quadrupole mass filter, ion trap mass analyzer and time-of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70:647 R- 716R (1998); Kinter and Sherman, New York (2000)). Protein biomarkers and biomarker values can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-associated laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q- TOF), tandem time-of-flight (TOF/TOF) technology, called ultraflex III TOF/TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS).sup.N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI- (MS).sup.N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry. [0131] In some embodiments, the instrument is configured to perform an immunoassay or immunodetection assay. Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect the analyte in a sample depending on the specific assay format. Immunoassays have been designed for use with a wide range of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semi-quantitative, and quantitative results. Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of a target analyte. This method relies on attachment of a label to either the analyte or the antibody and the label component includes, either directly or indirectly, an enzyme. ELISA tests may be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods rely on labels such as, for example, radioisotopes (I125) or fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and others (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition). Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescent, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time resolved-FRET (TR-FRET) immunoassays. [0132] In some embodiments, the instrument is a nucleic acid sequencing platform. The sequencing system may be any sequencing system of interest, including a Sanger sequencing system, a next generation sequencing (NGS) system, or the like. In certain aspects the sequencing system is an NGS system. NGS systems of interest include, but are not limited to, a sequencing system provided by Illumina® (e.g., the HiSeq™, MiSeq™ and/or Genome Analyzer™ sequencing systems); Ion Torrent™ (e.g., the Ion PGM™ and/or Ion Proton™ sequencing systems); Pacific Biosciences (e.g., the PACBIO RS II sequencing system); Life Technologies™ (e.g., a SOLiD sequencing system); Roche (e.g., the 454 GS FLX+ and/or GS Junior sequencing systems), or any other suitable NGS systems. [0133] In some embodiments, the instrument is a flow cytometer. Suitable flow cytometry systems may include, but are not limited to those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et at (2012) Ann Clin Biochem. Jan; 49(pt 1):17-28; Linden, et. al., Semin Throm Hemost.2004 Oct;30(5):502-11; Alison, et al, J Pathol, 2010 Dec; 222(4):335-344; and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Cyst.24(3):203-255; the disclosures of which are incorporated herein by reference. In certain instances, flow cytometry systems of interest include BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortess™ X-20 flow cytometer and BD Biosciences FACSCalibur™ cell sorter, a BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter and BD Biosciences Via™ cell sorter BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorters and BD Biosciences FACSMelody™ cell sorter, or the like. In some embodiments, the subject particle sorting systems are flow cytometric systems, such those described in U.S. Pat. Nos. 9,952,076; 9 933,341; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9,095,494; 9,092,034; 8,975,595; 8,753,573; 8,233,146; 8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039; the disclosure of which are herein incorporated by reference in their entirety. Processors [0134] In certain embodiments, systems (e.g., isothermal amplification systems, flow cytometry systems, nucleic acid sequencing systems) additionally include a processor having memory operably coupled to the processor wherein the memory includes instructions stored thereon, which when executed by the processor, cause the processor to perform the real-time calling methods provided herein. [0135] In embodiments, after time series data (e.g., molecular diagnostic signals) are produced by an instrument (e.g., by an isothermal amplification device, by a flow cytometer, by a nucleic acid sequencing platform), the processor is configured to perform the real-time calling methods provided herein. FIG. 1 shows a functional block diagram for one example of a processor 100, for analyzing and displaying data. A processor 100 can be configured to implement a variety of processes for controlling graphic display of time series data (e.g., molecular diagnostic signals). An instrument 102 can be configured to acquire data by analyzing a biological sample (e.g., as described above). The instrument can be configured to provide time series data (e.g., molecular diagnostic signals) to the processor 100. A data communication channel can be included between the instrument 102 and the processor 100. The data can be provided to the processor 100 via the data communication channel. The processor 100 can be configured to provide a graphical display including heatmaps and/or plots to display 106. The display device 106 can be implemented as a monitor, a tablet computer, a smartphone, or other electronic device configured to present graphical interfaces. The processor 100 can be connected to a storage device 104. The storage device 104 can be configured to receive and store data from the processor 100. The storage device 104 can be further configured to allow retrieval of data, such as time series data (e.g., molecular diagnostic signals), by the processor 100. A display device 106 can be configured to receive display data from the processor 100. The display data can comprise plots of time series data (e.g., molecular diagnostic signals). The display device 106 can be further configured to alter the information presented according to input received from the processor 100 in conjunction with input from instrument 102, the storage device 104, the keyboard 108, and/or the mouse 110. Computer Systems for Real-time Calling [0136] Provided herein include systems comprising a computer having a computer readable storage medium with a computer program stored thereon, where the computer program when loaded on the computer includes instructions for performing the real-time calling methods provided herein. Aspects of the present disclosure further include computer-controlled systems, where the systems further include one or more computers for complete automation or partial automation. [0137] In embodiments, the system includes an input module, a processing module and an output module. The subject systems may include both hardware and software components, where the hardware components may take the form of one or more platforms, e.g., in the form of servers, such that the functional elements, i.e., those elements of the system that carry out specific tasks (such as managing input and output of information, processing information, etc.) of the system may be carried out by the execution of software applications on and across the one or more computer platforms represented of the system. [0138] Systems may include a display and operator input device. Operator input devices may, for example, be a keyboard, mouse, or the like. The processing module includes a processor which has access to a memory having instructions stored thereon for performing the steps of the subject methods. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, memory storage devices, and input- output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or it may be one of other processors that are or will become available. The processor executes the operating system and the operating system interfaces with firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages, such as Java, Perl, C++, other high level or low level languages, as well as combinations thereof, as is known in the art. The operating system, typically in cooperation with the processor, coordinates and executes functions of the other components of the computer. The operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. [0139] The system memory may be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic medium such as a resident hard disk or tape, an optical medium such as a read and write compact disc, flash memory devices, or other memory storage device. The memory storage device may be any of a variety of known or future devices, including a compact disk drive, a tape drive, a removable hard disk drive, or a diskette drive. Such types of memory storage devices typically read from, and/or write to, a program storage medium (not shown) such as, respectively, a compact disk, magnetic tape, removable hard disk, or floppy diskette. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store a computer software program and/or data Computer software programs, also called computer control logic, typically are stored in system memory and/or the program storage device used in conjunction with the memory storage device. [0140] In some embodiments, a computer program product is described comprising a computer usable medium having control logic (computer software program, including program code) stored therein. The control logic, when executed by the processor the computer, causes the processor to perform functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementation of the hardware state machine so as to perform the functions described herein will be apparent to those skilled in the relevant arts. [0141] Memory may be any suitable device in which the processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including magnetic or optical disks or tape or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor suitably programmed from a computer readable medium carrying necessary program code. Programming can be provided remotely to processor through a communication channel, or previously saved in a computer program product such as memory or some other portable or fixed computer readable storage medium using any of those devices in connection with memory. For example, a magnetic or optical disk may carry the programming, and can be read by a disk writer/reader. Systems provided herein also include programming, e.g., in the form of computer program products, algorithms for use in practicing the methods as described above. Programming provided herein can be recorded on computer readable media, e.g., any medium that can be read and accessed directly by a computer, Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD- ROM; electrical storage media such as RAM and ROM; portable flash drive; and hybrids of these categories such as magnetic/optical storage media. [0142] The processor may also have access to a communication channel to communicate with a user at a remote location. By remote location is meant the user is not directly in contact with the system and relays input information to an input manager from an external device, such as a computer connected to a Wide Area Network (“WAN”), telephone network, satellite network, or any other suitable communication channel, including a mobile telephone (i.e., smartphone). [0143] In some embodiments, systems according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and/or transmitter for communicating with a network and/or another device. The communication interface can be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., Radio- Frequency Identification (RFID), Zigbee communication protocols, WiFi, infrared, wireless Universal Serial Bus (USB), Ultra Wide Band (UWB), Bluetooth® communication protocols, and cellular communication, such as code division multiple access (CDMA) or Global System for Mobile communications (GSM). [0144] In one embodiment, the communication interface is configured to include one or more communication ports, e.g., physical ports or interfaces such as a USB port, an RS-232 port, or any other suitable electrical connection port to allow data communication between the subject systems and other external devices such as a computer terminal (for example, at a physician's office or in hospital environment) that is configured for similar complementary data communication. [0145] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the subject systems to communicate with other devices such as computer terminals and/or networks, communication enabled mobile telephones, personal digital assistants, or any other communication devices which the user may use in conjunction. [0146] In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing Internet Protocol (IP) through a cell phone network, Short Message Service (SMS), wireless connection to a personal computer (PC) on a Local Area Network (LAN) which is connected to the internet, or WiFi connection to the internet at a WiFi hotspot. [0147] In one embodiment, the subject systems are configured to wirelessly communicate with a server device via the communication interface, e.g., using a common standard such as 802.11 or Bluetooth® RF protocol, or an IrDA infrared protocol. The server device may be another portable device, such as a smart phone, Personal Digital Assistant (PDA) or notebook computer; or a larger device such as a desktop computer, appliance, etc, In some embodiments, the server device has a display, such as a liquid crystal display (LCD), as well as an input device, such as buttons, a keyboard, mouse or touch-screen. [0148] In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored in the subject systems, e.g., in an optional data storage unit, with a network or server device using one or more of the communication protocols and/or mechanisms described above. [0149] Output controllers may include controllers for any of a variety of known display devices for presenting information to a user, whether a human or a machine, whether local or remote. If one of the display devices provides visual information, this information typically may be logically and/or physically organized as an array of picture elements. A graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing graphical input and output interfaces between the system and a user, and for processing user inputs. The functional elements of the computer may communicate with each other via system bus. Some of these communications may be accomplished in alternative embodiments using network or other types of remote communications. The output manager may also provide information generated by the processing module to a user at a remote location, e.g., over the Internet, phone or satellite network, in accordance with known techniques. The presentation of data by the output manager may be implemented in accordance with a variety of known techniques. As some examples, data may include SQL, HTML or XML documents, email or other files, or data in other forms. The data may include Internet URL addresses so that a user may retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject systems may be any type of known computer platform or a type to be developed in the future, although they typically will be of a class of computer commonly referred to as servers. However, they may also be a main- frame computer, a work station, or other computer type. They may be connected via any known or future type of cabling or other communication system including wireless systems, either networked or otherwise. They may be co-located or they may be physically separated. Various operating systems may be employed on any of the computer platforms, possibly depending on the type and/or make of computer platform chosen. Appropriate operating systems include Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS/400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, and others. [0150] FIG. 2 depicts a general architecture of an example computing device 200 according to certain embodiments. The general architecture of the computing device 200 depicted in FIG. 2 includes an arrangement of computer hardware and software components. It is not necessary, however, that all of these generally conventional elements be shown in order to provide an enabling disclosure. As illustrated, the computing device 200 includes a processing unit 210, a network interface 220, a computer readable medium drive 230, an input/output device interface 240, a display 250, and an input device 260, all of which may communicate with one another by way of a communication bus. The network interface 220 may provide connectivity to one or more networks or computing systems. The processing unit 210 may thus receive information and instructions from other computing systems or services via a network. The processing unit 210 may also communicate to and from memory 270 and further provide output information for an optional display 250 via the input/output device interface 240. For example, an analysis software (e.g., data analysis software or program) stored as executable instructions in the non-transitory memory of the analysis system can display time series data (e.g., molecular diagnostic signals) to a user. The input/output device interface 240 may also accept input from the optional input device 260, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device. The memory 270 may contain computer program instructions (grouped as modules or components in some embodiments) that the processing unit 210 executes in order to implement one or more embodiments of the real-time calling methods provided herein. The memory 270 generally includes RAM, ROM and/or other persistent, auxiliary or non-transitory computer-readable media. The memory 270 may store an operating system 272 that provides computer program instructions for use by the processing unit 210 in the general administration and operation of the computing device 200. Data may be stored in data storage device 290. The memory 270 may further include computer program instructions and other information for implementing aspects of the present disclosure. Computer-Readable Storage Medium [0151] Aspects of the present disclosure further include non-transitory computer readable storage mediums having instructions for practicing the disclosed methods of real-time calling. Computer readable storage media may be employed on one or more computers for complete automation or partial automation of a system for practicing methods described herein. In some embodiments, instructions in accordance with the method described herein can be coded onto a computer-readable medium in the form of “programming”, where the term “computer readable medium” as used herein refers to any non-transitory storage medium that participates in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include a floppy disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD-R, magnetic tape, non-volatile memory card, ROM, DVD- ROM, Blue-ray disk, solid state disk, and network attached storage (NAS), whether or not such devices are internal or external to the computer. In some instances, instructions may be provided on an integrated circuit device. Integrated circuit devices of interest may include, in certain instances, a reconfigurable field programmable gate array (FPGA), an application specific integrated circuit (ASIC) or a complex programmable logic device (CPLD). A file containing information can be “stored” on computer readable medium, where “storing” means recording information such that it is accessible and retrievable at a later date by a computer. The computer- implemented method described herein can be executed using programming that can be written in one or more of any number of computer programming languages. Such languages include, for example, Java (Sun Microsystems, Inc., Santa Clara, Calif.), Visual Basic (Microsoft Corp., Redmond, Wash.), and C++ (AT&T Corp., Bedminster, N.J.), as well as any many others. In some embodiments, computer readable storage media of interest include a computer program stored thereon, where the computer program when loaded on the computer includes instructions for performing the real-time calling methods provided herein. [0152] In embodiments, the system is configured to analyze the data within a software or an analysis tool for analyzing time series data (e.g., molecular diagnostic signals). The initial data can be analyzed within the data analysis software or tool (e.g., FlowJo®, SeqGeq®) by appropriate means, such as manual gating, cluster analysis, or other computational techniques. The instant systems, or a portion thereof, can be implemented as software components of a software for analyzing data, such as FlowJo® or SeqGeq®. [0153] The computer readable storage medium may be employed on one or more computer systems having a display and operator input device. Operator input devices may, for example, be a keyboard, mouse, or the like. The processing module includes a processor which has access to a memory having instructions stored thereon for performing the steps of the subject methods. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, memory storage devices, and input-output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor, or it may be one of other processors that are or will become available. The processor executes the operating system and the operating system interfaces with firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages, such as Java, Perl, Python, C++, other high level or low level languages, as well as combinations thereof, as is known in the art. The operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. Assay Definition Files [0154] An assay definition file (ADF) is an encapsulated file that can define configurations for the molecular test or assay, including assay name, technology platform configuration (for example, next generation sequencing (NGS), chip type, chemistry type), workflow steps (sample prep, instrument scripts, analytics, reporting), analysis algorithms, regulatory labels (for example, research use only (RUO), in vitro diagnostics (IVD), Central Europe in vitro diagnostics (CE-IVD, internal use only (IUO), etc.), targeted markers (panel), reference genome version, consumables, controls, QC thresholds, reporting genes and variants. Assays and methods described herein can be defined in an ADF, which may include information that describes how to process results, what process steps are executed, the order they are executed, interpretations generated, etc. The ADF can comprise one or more of defined classifiers, a defined LR threshold, t1, t2, Signalmin, Signalmax, a defined step dislocation threshold, a defined Minimum Call Cycle, and a defined span half window, for use in a method provided herein. In some embodiments, the ADF may include software code modules for performing one or more steps of the methods provided herein. [0155] In some embodiments, the server system software may support an encapsulated assay configuration that includes assay name, assay type, panel, hotspot file if any, reference name, control names if any, quality control QC thresholds, assay description if any, data analysis parameters and values, instrument run script names and other configurations that define the assay. The entire set of the information is called an assay definition. The assay configuration content and corresponding workflows may be delivered to the user as modular software components in an assay definition file (ADF). The server system software may import an assay definition file that contains the assay configuration. The import process may be initiated by zip file import which includes an encrypted Debian file and triggers an installation process. The user interface may provide a page for the user to select an ADF for import. An application store in the cloud-based support and resource system may store ADFs supporting various assays, panels, and workflows available for selection by the user for download to the user's local server system. Kits [0156] Aspects of the present disclosure further include kits, where kits include storage media such as a floppy disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD-R, magnetic tape, non-volatile memory card, ROM, DVD-ROM, Blue-ray disk, solid state disk, and network attached storage (NAS). Any of these program storage media, or others now in use or that may later be developed, may be included in the subject kits. In embodiments, the program storage media include instructions for performing the real-time calling methods provided herein. In embodiments, the instructions contained on computer readable media provided in the subject kits, or a portion thereof, can be implemented as software components of a software for analyzing data, such as, for example, FlowJo® or SeqGeq®. [0157] In addition to the above components, the subject kits may further include (in some embodiments) instructions, e.g., for installing the plugin to the existing software package such as, for example, FlowJo® and SeqGeq®. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, and the like. Yet another form of these instructions is a computer readable medium, e.g., diskette, compact disk (CD), portable flash drive, and the like, on which the information has been recorded. Yet another form of these instructions that may be present is a website address which may be used via the internet to access the information at a removed site. [0158] In at least some of the previously described embodiments, one or more elements used in an embodiment can interchangeably be used in another embodiment unless such a replacement is not technically feasible. It will be appreciated by those skilled in the art that various other omissions, additions and modifications may be made to the methods and structures described above without departing from the scope of the claimed subject matter. All such modifications and changes are intended to fall within the scope of the subject matter, as defined by the appended claims. [0159] With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and/or” unless otherwise stated. [0160] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “ a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “ a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. [0161] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group. [0162] As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible sub-ranges and combinations of sub-ranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as “up to,” “at least,” “greater than,” “less than,” and the like include the number recited and refer to ranges which can be subsequently broken down into sub-ranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 articles refers to groups having 1, 2, or 3 articles. Similarly, a group having 1-5 articles refers to groups having 1, 2, 3, 4, or 5 articles, and so forth. [0163] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Claims

WHAT IS CLAIMED IS: 1. A real-time calling method, comprising: receiving time series data from an instrument in real time, wherein the time series data comprise a plurality of data points forming a sample curve; calculating two or more quantitative metrics for each data point in real time; calculating a likelihood ratio (LR) at each data point in real time, wherein said calculating step comprises employing defined classifiers for each of the two or more quantitative metrics derived from (i) a reference population of positive curves and (ii) a reference population of negative curves, optionally derived via Quadratic Discriminant Analysis (QDA); and calling the sample curve in real time, wherein a sample curve is called positive in real time once the LR calculated for a data point exceeds a defined LR threshold, and wherein a sample curve is called negative in real time if the LR calculated for all data points of the sample curve falls at or below defined LR threshold.
2. The method of claim 1, wherein the quantitative metrics comprise amplitude (A), gradient (G), and/or span (S), optionally S is the change in A over a defined window.
3. The method of any one of claims 1-2, wherein the time series data is derived from instrument analysis of a sample, optionally a sample suspected of comprising a target analyte.
4. The method of any one of claims 1-3, wherein the instrument configured to produce time series data by analyzing the sample, optionally the instrument is configured to perform a molecular diagnostic assay.
5. The method of any one of claims 1-4, wherein instrument analysis of the sample comprises subjecting said sample to one or more reaction(s), optionally said reaction(s) are configured to detect the presence and/or or amount of a target analyte in the sample.
6. The method of any one of claims 1-5, wherein the instrument comprises one or more sensor(s) configured to detect signals derived from the sample, and wherein the time series data comprises time series signal data, optionally said signals are generated from said one or more reaction(s).
7. The method of any one of claims 1-6, wherein the signal is a calorimetric signal, a potentiometric signal, an amperometric signal, an optical signal, a piezo-electric signal, or any combination thereof.
8. The method of claim 7, wherein the optical signal is a fluorescent signal and/or colorimetric signal.
9. The method of any one of claims 1-8, wherein signals are generated in the presence of the target analyte; or wherein signals are generated in the absence of the target analyte.
10. The method of any one of claims 1-9, wherein said instrument analysis comprises one or more of spectrometry, Raman spectroscopy, FFT (Fast-Fourier Transform) spectroscopy, Fourier-Transform Infrared Spectroscopy (FTIR), infrared spectrometry, Nuclear Magnetic Resonance (NMR) spectrometry, electrochemical detection, polynucleotide detection, volatile organic compound methods, fluorescence anisotropy, fluorescence resonance energy transfer, electron transfer, enzyme assay, magnetism, electrical conductivity, electrochemical detection, isoelectric focusing, lateral flow assay (LFA), microfluidics, amino acid sequencing, nucleic acid sequencing, flow cytometry, chromatography, immunoprecipitation, immunoseparation, aptamer binding, filtration, electrophoresis, use of a CCD camera, immunoassay, enzyme-linked immunosorbent assay (ELISA), Gram staining, immunostaining, microscopy, immunofluorescence, size/weight/charge detection, western blotting, polymerase chain reaction (PCR), RT-PCR, isothermal amplification, sequencing, fluorescence in situ hybridization, mass spectrometry, Surface Plasmon Resonance (SPR), and Localized Surface Plasmon Resonance (LSPR), optionally the instrument comprises a thermocycler, further optionally the instrument comprises a thermocycler configured for real-time PCR amplification and fluorescence monitoring.
11. The method of any one of claims 1-10, wherein a sample curve being called positive indicates the presence of a target analyte in the sample; and wherein a sample curve being called negative indicates the absence of the target analyte in the sample.
12. The method of any one of claims 1-11, wherein the target analyte is a target nucleic acid sequence.
13. The method of any one of claims 1-12, wherein the one or more reaction(s) comprise nucleic acid detection reaction(s).
14. The method of any one of claims 1-13, wherein the sample curve comprises a nucleic acid amplification curve, and wherein the signals comprise fluorescence signals indicative of amplification of the target nucleic acid sequence.
15. The method of any one of claims 1-14, wherein a sample curve being called positive indicates the presence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the presence and/or amount of a target nucleic acid sequence in the sample; and wherein a sample curve being called negative indicates the absence of target nucleic acid amplification in the nucleic acid detection reaction(s), and thereby indicates the absence of a target nucleic acid sequence in the sample.
16. The method of any one of claims 1-15, wherein the step of calculating two or more quantitative metrics comprises applying a median filter to the time series data to generate median-filtered data, optionally a 3-point median filter, further optionally said 3-point median filter comprises: ^^ெ^ௗ^^^^ ^^^ ൌ ^^ோ^௪^ ^^^, ^^ ൌ 1, ^^ ^^ெ^ௗ^^^^ ^^^ ൌ ^^ ^^ ^^ ^^ ^^ ^^^ ^^ோ^௪^ ^^ െ 1^, ^^ோ^௪^ ^^^, ^^ோ^௪^ ^^ ^ 1^^, ^^ ൌ 2, … , ^^ െ 1 the median filter removal of spikes, 17. The method of any one of claims 1-16, wherein the step of calculating two or more quantitative metrics comprises applying a Savitzky-Golay (SG) filter to the time series data and/or median-filtered data to generate a smoothed amplitude value (SGAmp) and smoothed gradient value (SGGrad), optionally the SG filter is a 7-point SG filter. 18. The method of any one of claims 1-17, wherein application of the SG filter comprises a sliding window being moved through the time series data and/or median-filtered data and a second order linear regression applied to each window: ^^ ^ ^ ^^ ^ ൌ ^^^ ^ ^^^ ^^ ^ ^^ଶ ^^ ଶ optionally the x coded as {-3,…3} and the regression fit uses as inputs: ^^^ ^^^ ൌ ^െ3, … ,3^ ^^^ ^^^ ൌ ^ ^^ெ^ௗ^^^^ ^^ െ 3^, … , ^^ெ^ௗ^^^^ ^^ ^ 3^^ 19. The smoothed amplitude value (SGAmp) and smoothed gradient value (SGGrad) are calculated for the center point (k = 0) of each window as follows: ^^ ^^ ் 1 ^^^ ^0^ ൌ ^^^ ൌ ^^^ ⋅ ^^^0^ , ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ℎ ^^ ^^ ^^ ^^^ ൌ ^െ2,3,6,7,6,3,െ2^ calculating the average signals values between a defined time window of t1 and t2 to generate an initial signal average, optionally the signal values are SGAmp values. 21. The method of any one of claims 1-20, wherein the calling step comprises calling the sample curve as invalid if: the initial signal average does not fall within the defined signal window of Signalmin and Signalmax, and optionally Signalmin and Signalmax are identified based on the distribution of initial signal average values between the defined time window of t1 and t2 for a reference population comprising positive curves and negative curves, further optionally Signalmin and Signalmax values are set equal to the mean ± 3.6σ bounds of said reference population. 22. The method of any one of claims 1-21, wherein the step of calculating two or more quantitative metrics comprises calculating the span (S) value by: employing the formula ^ା^ ^^^ ^^^ ൌ ^^ ^^ ^^^ ^ ^^ ^ ^^^ െ ^^ ^^ ^^^ ^ ^^ െ ^^^ െ ^ ^^ ^ of points, and sum of all step dislocations identified within the interval if any step dislocations are identified in said internal, optionally the defined span half window is assay-specific, further optionally the defined span half window is about 6 points to about 12 points, optionally about 10 points; or if a boundary point of a defined span window is located within an about ±1 point to an about ±10 point exclusion zone surrounding a step dislocation, the median-filtered signal values ( ^^ெ^ௗ^^^) is employed in the formula: ^ା^ ^^^ ^^^ ൌ ^^ ெ^ௗ^^^ ^ ^^ ^ ^^^ െ ^^ ^^ ^^^ ^ ^^ െ ^^^ െ ^ ^^ ^ k + a or k – a. 23. The method of any one of claims 1-22, wherein the method comprises providing defined classifiers for each of the two or more quantitative metrics, optionally said defined classifiers are provided via an assay definition file (ADF). 24. The method of any one of claims 1-23, wherein said defined classifiers comprise Quadratic Discriminant Analysis (QDA) coefficients. 25. The method of any one of claims 1-24, wherein said defined classifiers comprise Linear Discriminant Analysis (LDA) coefficients. 26. The method of any one of claims 1-25, wherein said defined classifiers are assay- specific and/or are defined for each curve, optionally a test curve and/or an internal control curve. 27. The method of any one of claims 1-26, wherein the defined classifiers comprise two or more of ^^̄^^^, ^^̄ே^^, ^^^^^, and ^^ே^^, and wherein: ^^̄^^^ ൌ ^ ^̅^^^ோ,^^^, ^̅^^^ோ,^^^, ^^^̅^ோ,^^^^ ൌ ^ ^ ^ of curves curves 10 curves. 29. The method of any one of claims 1-28, wherein the reference population of positive curves and the reference population of negative curves are generated using representative target analyte concentrations in appropriate sample types. 30. The method of any one of claims 1-29, wherein providing the defined classifiers comprises, for each curve in the reference population of positive curves and the reference population of negative curves: identifying via an iterative process the characteristic peak yPLR that optimally discriminates between positive reference curves and negative reference curves; and identifying the A, G, and S metrics at the characteristic peak yPLR, optionally the providing further comprises calculating the means and covariance matrices for said metrics. 31. The method of any one of claims 1-30, wherein providing the defined classifiers comprises: (a) estimating initial coefficients ^^̄ே^^,ூ^^௧ and ^^ே^^,ூ^^௧ from the distributions of A, G, and S values measured at all points among the reference population of negative curves; (b) estimating initial coefficients ^^̄^^^,ூ^^௧ and ^^^^^,ூ^^௧ from the distributions of A, G, and S values measured at the point yMpeak in each positive reference curve, wherein yMpeak is the point having the largest Mahalanobis distance relative to ^^̄ே^^,ூ^^௧ and ^^ே^^,ூ^^௧, subject to the requirement that the A, G, and S at yMpeak must all be greater than 0; (c) calculating preliminary LRs for all points in the negative reference curves using input coefficients ^ ^^̄ே^^,ூ^^௧, ^^̄^^^,ூ^^௧, ^^ே^^,ூ^^௧ , ^^^^^,ூ^^௧^; (d) calculating ^^̄ே^^ and ^^ே^^ from the distributions of A, G, and S values measured at the point yPLR in each negative reference curve, wherein yPLR is the point where the LR is maximized; (e) calculating preliminary LRs for all points in the positive reference curves using input coefficients ^ ^^̄ே^^, ^^̄^^^,ூ^^௧ , ^^ே^^, ^^^^^,ூ^^௧^; (f) calculating ^^̄^^^ and ^^^^^ from the distributions of A, G, and S values measured at the point yPLR in each positive reference curve, wherein yPLR is the point where the LR is maximized; and (g1) repeating steps (c)-(f) with the output coefficients ^ ^^̄ே^^, ^^̄^^^, ^^ே^^, ^^^^^^ from one iteration used as input coefficients ^ ^^̄ ே^^,ூ^^௧ , ^^̄ ^^^,ூ^^௧ , ^^ ே^^,ூ^^௧ , ^^ ^^^,ூ^^௧^ in the next until values of coefficients converge, or (g2) repeating steps (e)-(f) with the output coefficients ^ ^^̄ ே^^ , ^^̄ ^^^ , ^^ ே^^ , ^^ ^^^^ from one iteration used as input coefficients ^ ^^̄ே^^, ^^̄^^^,ூ^^௧, ^^ே^^, ^^^^^,ூ^^௧^ in the next until values of coefficients converge. 32. The method of any one of claims 1-31, wherein calculating the LR comprises for each data point ytest = {Atest, Gtest, Stest} calculating Q0/Q1, wherein: ^^^ ൌ ^^^ ଶ ^^ ^ ln ^ ^^ ^^ ^^൫ ^^ே^^൯^ ^^^ ൌ ^^^ ଶ ^^ ^ ln൫ ^^ ^^ ^^ ^ ^^^^^ ^ ൯ and wherein ^^^ ଶ ^^ and ^^^ ଶ ^^ correspond to the Mahalanobis Distances to each reference population and are given by: ^^^ ଶ ^^ ൌ ^ ^^௧^^௧ െ ^^̄ே^^^் ^^ே ି ^^ ^ ^ ^^௧^^௧ െ ^^̄ே^^^ 33. The the LR at each data point in real time comprises calculating the LR at each data point following t2. 34. The method of any one of claims 1-33, wherein the LR is calculated at point ytest provided the following conditions are satisfied: ytest occurs at or after a defined Minimum Call Cycle; Atest, Gtest, and Stest are all > 0; and no step dislocations occur within 3 points of ytest. 35. The method of any one of claims 1-34, wherein the method comprises detecting step dislocations, and wherein step dislocations are detected by: measuring the pairwise differences between adjacent median-filtered points: ^^ ൌ ^ ^^ ெ^ௗ,ଶ െ ^^ ெ^ௗ,^ , ^^ ெ^ௗ,ଷ െ ^^ ெ^ௗ,ଶ … , ^^ ெ^ௗ,^ െ ^^ ெ^ௗ,^ି^൧ a 3- median filter to the difference curves; and identifying any point in Y – Ys with a value above a defined step dislocation threshold as a step dislocation with a magnitude J, optionally step dislocations represent macroscopic system noises. 36. The method of any one of claims 1-35, wherein the calling step comprises calling the sample curve as invalid if: there are two or more contiguous missing points within the time series data prior to the sample curve being called positive; and/or there are more than two missing points within the time series data prior to the sample curve being called positive, optionally said missing points are either contiguous or non-contiguous. 37. The method of any one of claims 1-36, wherein one or more of the defined classifiers, the defined LR threshold, t1, t2, Signalmin, Signalmax, the defined step dislocation threshold, the defined Minimum Call Cycle, and the defined span half window are provided via an assay definition file (ADF). 38. The method of any one of claims 1-37, wherein the method comprises multiplexed calling comprising: receiving two or more sets of time series data from the instrument in real time, wherein each set of time series data comprises a plurality of data points forming a sample curve; and calling each of the two or more sample curves in real time, optionally said each of the sample curves are nucleic acid amplification curves related to different target nucleic acid sequences. 39. The method of any one of claims 1-38, wherein the method is capable of calling a curve as positive at least about 1 minute, about 2 minutes, about 5 minutes, about 10 minutes, about 15 minutes, about 20 minutes, about 25 minutes, about 30 minutes, about 35 minutes, about 40 minutes, about 45 minutes, about 50 minutes, about 55 minutes, or about 60 minutes, earlier than a method employing end-state processing of the time series data. 40. The method of any one of claims 1-39, wherein the step-detection and correction does not make any assumptions regarding the correct and/or absolute signal baseline. 41. The method of any one of claims 1-40, wherein the instrument is capable of: amplifying a target nucleic acid sequence in an amplification reaction mixture, thereby generating a nucleic acid amplification product, optionally the nucleic acid amplification product is generated at detectable levels within about 20 minutes, about 15 minutes, or about 10 minutes; and detecting the nucleic acid amplification product with a signal-generating oligonucleotide, wherein the signal-generating oligonucleotide is capable of hybridizing to the nucleic acid amplification product, optionally the signal-generating oligonucleotide is a TaqMan detection probe oligonucleotide, a molecular beacon detection probe oligonucleotide, or a molecular torch detection probe oligonucleotide. 42. The method of any one of claims 1-41, wherein the signal-generating oligonucleotide comprises a label, optionally the label comprises a quenchable label, further optionally the quenchable label is a fluorophore, optionally the signal-generating oligonucleotide comprises a quencher capable of quenching a signal generated by the label when the quencher and the label are in close proximity. 43. The method of any one of claims 1-42, wherein the label is capable of generating a detectable signal upon: (i) the signal-generating oligonucleotide hybridizing the nucleic acid amplification product; and/or (ii) the nucleic acid amplification product being extended to generate an extended nucleic acid amplification product hybridized to the signal-generating oligonucleotide, optionally the signal is fluorescence. 44. The method of any one of claims 1-43, wherein amplifying the target nucleic acid sequence comprises generating the nucleic acid amplification product at detectable levels within about 20 minutes, about 15 minutes, or about 10 minutes. 45. The method of any one of claims 1-44, comprising: contacting a sample comprising biological entities with a lysis buffer to generate a treated sample, wherein the lysis buffer comprises one or more lytic agents capable of lysing biological entities to release sample nucleic acids comprised therein, and wherein the sample nucleic acids are suspected of comprising the target nucleic acid sequence; and contacting a reagent composition with the treated sample to generate the amplification reaction mixture, wherein the reagent composition comprises one or more amplification reagents. 46. The method of any one of claims 1-45, wherein the method: is performed in a single reaction vessel; does not comprise using any enzymes other than the reverse transcriptase and the enzyme having a hyperthermophile polymerase activity; does not comprise using any enzyme other than the enzyme having a hyperthermophile polymerase activity; does not comprise heat denaturing and/or enzymatic denaturing the nucleic acid during the amplification step; and/or does not comprise contacting the nucleic acid with a single-stranded DNA binding protein. 47. The method of any one of claims 1-46, wherein the amplifying is performed: for a period of about 5 minutes to about 60 minutes, optionally the amplifying is performed for a period of about 15 minutes; and/or in helicase-free, single-stranded binding protein-free, cleavage agent-free, and recombinase-free, isothermal amplification conditions. 48. The method of any one of claims 1-47, wherein the amplifying is carried out using a method selected from the group consisting of polymerase chain reaction (PCR), ligase chain reaction (LCR), loop-mediated isothermal amplification (LAMP), strand displacement amplification (SDA), replicase-mediated amplification, Immuno-amplification, nucleic acid sequence based amplification (NASBA), self-sustained sequence replication (3SR), rolling circle amplification, and transcription-mediated amplification (TMA), optionally the PCR is real-time PCR and/or quantitative real-time PCR (QRT-PCR). 49. The method of any one of claims 1-48, wherein: the biological entities comprise one or more of prokaryotic cells, eukaryotic cells, viral particles, exosomes, protoplasts, and microvesicles; the biological entities comprise a virus, a bacteria, a fungi, a protozoa, portions thereof, or any combination thereof; and/or the target nucleic acid sequence is a nucleic acid sequence of a virus, bacteria, fungi, or protozoa, optionally the sample nucleic acids are derived from a virus, bacteria, fungi, or protozoa. 50. The method of any one of claims 1-49, wherein: the virus is SARS-CoV-2, Human Immunodeficiency Virus Type 1 (HIV-1), Human T-Cell Lymphotrophic Virus Type 1 (HTLV-1), Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Herpes Simplex, Herpesvirus 6, Herpesvirus 7, Epstein-Barr Virus, Respiratory Syncytial Virus (RSV), Cytomegalo-virus, Varicella-Zoster Virus, JC Virus, Parvovirus B19, Influenza A, Influenza B, Influenza C, Rotavirus, Human Adenovirus, Rubella Virus, Human Enteroviruses, Genital Human Papillomavirus (HPV), or Hantavirus; the bacteria comprises one or more of Mycobacteria tuberculosis, Rickettsia rickettsii, Ehrlichia chaffeensis, Borrelia burgdorferi, Yersinia pestis, Treponema pallidum, Chlamydia trachomatis, Chlamydia pneumoniae, Mycoplasma pneumoniae, Mycoplasma sp., Legionella pneumophila, Legionella dumoffii, Mycoplasma fermentans, Ehrlichia sp., Haemophilus influenzae, Neisseria meningitidis, Neisseria gonorrhoeae, Streptococcus pneumonia, S. agalactiae, and Listeria monocytogenes; the fungi comprises one or more of Cryptococcus neoformans, Pneumocystis carinii, Histoplasma capsulatum, Blastomyces dermatitidis, Coccidioides immitis, and Trichophyton rubrum; and/or the protozoa comprises one or more of Trypanosoma cruzi, Leishmania sp., Plasmodium, Entamoeba histolytica, Babesia microti, Giardia lamblia, Cyclospora sp., and Eimeria sp. 51. The method of any one of claims 1-50, wherein the sample is a biological sample or an environmental sample, wherein the environmental sample is, or is obtained from, a food sample, a beverage sample, a paper surface, a fabric surface, a metal surface, a wood surface, a plastic surface, a soil sample, a fresh water sample, a waste water sample, a saline water sample, exposure to atmospheric air or other gas sample, cultures thereof, or any combination thereof; and/or wherein the biological sample is, or is obtained from, a tissue sample, saliva, blood, plasma, sera, stool, urine, sputum, mucous, lymph, synovial fluid, cerebrospinal fluid, ascites, pleural effusion, seroma, pus, swab of skin or a mucosal membrane surface, cultures thereof, or any combination thereof. 52. The method of any one of claims 1-51, wherein the amplifying does not comprise one or more of the following: Archaeal Polymerase Amplification (APA), loop-mediated isothermal Amplification (LAMP), helicase-dependent Amplification (HDA), recombinase polymerase amplification (RPA), strand displacement amplification (SDA), nucleic acid sequence-based amplification (NASBA), transcription mediated amplification (TMA), nicking enzyme amplification reaction (NEAR), rolling circle amplification (RCA), multiple displacement amplification (MDA), Ramification (RAM), circular helicase-dependent amplification (cHDA), single primer isothermal amplification (SPIA), signal mediated amplification of RNA technology (SMART), self-sustained sequence replication (3SR), genome exponential amplification reaction (GEAR) and isothermal multiple displacement amplification (IMDA), optionally the amplifying does not comprise LAMP. 53. The method of any one of claims 1-52, wherein the amplifying comprises one or more of the following: APA, LAMP, HDA, RPA, SDA, NASBA, TMA, NEAR, RCA, MDA, RAM, cHDA, SPIA, SMART, 3SR, GEAR and IMDA, optionally the amplifying does not comprise LAMP. 54. The method of any one of claims 1-53, wherein the method comprises and/or does not comprise one or more of the following: (i) dilution of the treated sample; (ii) dilution of the amplification reaction mixture; (iii) heat denaturation of the treated sample; (iv) sonication of the treated sample; (v) sonication of the amplification reaction mixture; (vi) the addition of ribonuclease inhibitors to the treated sample; (vii) the addition of ribonuclease inhibitors to the amplification reaction mixture; (viii) purification of the sample; (ix) purification of the sample nucleic acids; (x) purification of the nucleic acid amplification product; (xi) removal of the one or more lytic agents from the treated sample or the amplification reaction mixture; (xii) heat denaturing and/or enzymatic denaturing of the sample nucleic acids prior to and/or during amplification; and (xiii) the addition of ribonuclease H to the treated sample or amplification reaction mixture. 55. A system for real-time calling, comprising: an instrument configured to produce time series data by analyzing a sample; and a processor comprising memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to perform the method of any one of claims 1-54. 56. A computer system for real-time calling, comprising: a hardware processor; and non-transitory memory having instructions stored thereon, which when executed by the hardware processor causes the processor to perform the method of any one of claims 1- 54. 57. A computer readable medium comprising code for performing the method of any one of claims 1-54. 58. An assay definition file (ADF), comprising: one or more of defined classifiers, a defined LR threshold, t1, t2, Signalmin, Signalmax, a defined step dislocation threshold, a defined Minimum Call Cycle, and a defined span half window, for use in the method of any one of claims 1-54.
EP24723392.7A 2023-04-13 2024-04-12 Method for categorizing molecular diagnostic signals in real time Pending EP4695825A1 (en)

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US202363496001P 2023-04-13 2023-04-13
PCT/US2024/024336 WO2024216077A1 (en) 2023-04-13 2024-04-12 Method for categorizing molecular diagnostic signals in real time

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