# US 2021/0340633 A1 · Methods and related aspects for quantitative polymerase chain reaction to determine fractional abundance

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- Number: US 2021/0340633 A1
- Document ID: US20210340633A1
- Status: Published application
- Kind: A1
- Application number: 17/239,211
- Filed: 2021-04-23
- Published: 2021-11-04
- Earliest claimed: 2020-04-24
- Assignees: The Johns Hopkins University; Pioneer Hi-Bred International, Inc.
- Applicants: The Johns Hopkins University; Pioneer Hi-Bred International, Inc.
- Inventors: Tza-Huei Jeff Wang; Alexander Y. Trick; Xiaoyi Sopko
- Web page: https://patentgrep.com/patent/US20210340633A1

## CPC

- C12Q 1/6895: for plants, fungi or algae
- C12Q 1/6851: Quantitative amplification
- C12Q 1/686: Polymerase chain reaction [PCR]
- G16B 5/00: ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
- G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
- G16B 25/20: Polymerase chain reaction [PCR]; Primer or probe design; Probe optimisation
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- C12Q 2600/13: Plant traits
- C12Q 2600/156: Polymorphic or mutational markers
- G16H 20/00: ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Provided herein are methods of processing grain samples to detect the fractional abundance of transgenic traits. Related systems and computer program products are also provided.

## Claims

### Claim 1

A method of processing a grain sample to detect the fractional abundance of a transgenic trait, comprising:
Grinding and mixing the grain sample,
Extracting and purifying DNA from the grain sample,
Running said purified DNA sample in a qPCR reaction, said qPCR reaction comprising at least two different fluorescent probes that competitively bind to the same DNA locus, wherein said DNA locus is indicative of the presence or absence of a transgenic trait,
Generating results of the qPCR reaction that characterize the competitive binding levels of each of the at least two different fluorescent probes during each cycle of the qPCR reaction, and
Interpreting said results with one or more of a machine learning model, ratiometric regression algorithm, or best fit model to determine the fractional abundance of the transgenic trait.

### Claim 2 (depends on 1)

The method of claim 1, wherein the results of the qPCR reaction that characterize the competitive binding levels of each of the at least two different fluorescent probes during each cycle of the qPCR reaction are at least one of a standard curve or an N-dimensional curve.

### Claim 3 (depends on 1)

The method of claim 1, wherein a machine learning model is used, and the machine learning model has been trained on a range of grain samples comprising levels from 0-100% of transgenic grain in increments of 30% less.

### Claim 4 (depends on 1)

The method of claim 1, wherein a machine learning model is used, and the machine learning model has been trained on a range of grain samples comprising levels from 0-100% of transgenic grain in 10% increments.

### Claim 5 (depends on 1)

The method of claim 1, wherein a machine learning model is used, and the machine learning model has been trained on a range of grain samples comprising levels from 0-100% of transgenic grain in 5% increments.

### Claim 6 (depends on 5)

The method of claim 5, wherein the machine learning model is a support vector machine model.

### Claim 7 (depends on 3)

The method of claim 3, wherein the results of the qPCR reaction input into the support vector machine model comprise the ratio of the fluorescence values of FAM/(FAM+VIC).

### Claim 8 (depends on 1)

The method of claim 1, wherein the transgenic trait is glyphosate resistance, and the method identifies the genetic sequence that confers glyphosate resistance.

### Claim 9 (depends on 1)

The method of claim 1, wherein the at least two different fluorescent probes are FAM and VIC.

### Claim 10 (depends on 9)

The method of claim 9, wherein the results of the qPCR reaction input into the support vector machine model comprise the ratio of the fluorescence values of FAM/(FAM+VIC).

### Claim 11 (depends on 1)

The method of claim 1, wherein the time elapsed from processing the grain sample to detecting the fractional abundance of the transgenic trait is 15 minutes or less.

### Claim 12 (depends on 1)

The method of claim 1, wherein the grain is soybean, corn, sunflower, wheat or canola.