# US 10,504,614 B2 · Systems and methods for determining an unknown characteristic of a sample > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,504,614 B2 - Document ID: US10504614B2 - Status: Granted - Kind: B2 - Application number: 15/027,868 - Filed: 2014-10-07 - Published: 2016-08-18 - Granted: 2019-12-10 - Earliest claimed: 2013-10-07 - Assignees: Rutgers, The State University of New Jersey; Trustees of Boston University; Massachusetts Institute of Technology - Applicants: Rutgers, The State University of New Jersey; Trustees of Boston University; Massachusetts Institute of Technology - Inventors: Catherine M. Grgicak; Desmond S. Lun; Muriel Medard; Harish Swaminathan - Web page: https://patentgrep.com/patent/US10504614B2 ## CPC - G16B 40/10: Signal processing, e.g. from mass spectrometry [MS] or from PCR - C12Q 1/6851: Quantitative amplification - C12Q 1/686: Polymerase chain reaction [PCR] - 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 - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - 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 ## Abstract Computerized analysis methods and systems to implement the computerized analysis methods are disclosed herein. Specifically, the present disclosure relates to systems and methods for determining an unknown characteristic of a sample. ## Claims ### Claim 1 A method for determining a number of contributors to a DNA mixture, the method comprising: analyzing each of a plurality of calibration samples at a plurality of concentrations to generate a calibration sample profile corresponding to each of the plurality of calibration samples wherein each calibration sample profile comprises a plurality of allele peaks, and wherein each of the calibration samples is a biological sample comprising DNA that is obtained from a single contributor; generating, by a processor, for each of the plurality of calibration samples, calibration data from the corresponding calibration sample profile by modeling heights of the plurality of allele peaks, wherein the calibration data models one or more variables as a function of input DNA mass in the corresponding calibration sample; analyzing a test sample to generate a test sample profile, wherein the test sample is a biological sample comprising a DNA mixture from one or more contributors, and wherein the number of contributors is unknown; analyzing, by the processor, the calibration data and the test sample profile to generate a probability distribution of a number of contributors to the test sample for estimating the number of contributors to the DNA sample; and using the estimated number of contributors to determine whether an individual should be included as a contributor to the DNA mixture during forensic analysis, wherein: the plurality of calibration sample and the test sample are analyzed using the same laboratory device under the same test conditions. ### Claim 2 (depends on 1) The method of claim 1, wherein generating, for each of the plurality of calibration samples, calibration data from the corresponding calibration sample profile by modeling heights of the plurality of allele peaks further comprises: identifying a type for each of the plurality of allele peaks in the calibration sample profile as at least one of the following types: a true peak, a reverse stutter peak, a forward stutter peak, or a noise peak; and for each allele peak type: generating a Gaussian distribution, and identifying a mean and a standard deviation for each of plurality of loci. ### Claim 3 (depends on 2) The method of claim 2, further comprising, for each of the plurality of loci, determining a dropout rate and a rate of occurrence of stutter. ### Claim 4 (depends on 2) The method of claim 2, wherein analyzing the calibration data and the test sample profile to generate the probability distribution of the number of contributors to the test sample comprises using means and standard deviations for the allele peak types in the calibration data to generate the probability distribution function. ### Claim 5 (depends on 1) The method of claim 1, wherein analyzing the calibration data and the test sample profile to generate the probability distribution of the number of contributors to the test sample comprises using a Monte Carlo sampling algorithm to generate the probability distribution. ### Claim 6 (depends on 1) The method of claim 1, further comprising determining the number of contributors to the test sample as a number of contributors with the highest probability on the probability distribution. ### Claim 7 (depends on 1) The method of claim 1, further comprising determining the number of contributors to the test sample as a range on the probability distribution within which the number of contributors is most likely to lie. ### Claim 8 (depends on 1) The method of claim 1, wherein the one or more variables are selected from at least one of the following: a baseline peak height, drop out, stutter, and presence of one or more artifacts. ### Claim 9 (depends on 1) The method of claim 1, further comprising: determining an amount of DNA in the test sample; and using the amount to generate the probability distribution. ### Claim 10 (depends on 1) The method of claim 1, wherein the plurality of calibration samples include a plurality of biological samples comprising DNA material from the same person but at different levels of DNA concentration. ### Claim 11 (depends on 1) The method of claim 1, wherein the DNA mixture is derived from a biological sample evidence collected at a crime scene and the estimated number of contributors is used to assess the weight of the biological sample evidence. ### Claim 12 A system for determining a number of contributors to a DNA mixture, the system comprising: a laboratory device configured to perform measurements under a set of test conditions; a processing device; and a non-transitory computer readable medium comprising one or more programing instructions which when executed by the processing device, cause the processing device to: receive, from the laboratory device, a plurality of calibration profiles corresponding to a plurality of calibration samples, wherein each of the plurality of calibration profiles comprises a plurality of allele peaks and is generated by the laboratory device by analyzing corresponding calibration sample at a plurality of concentrations under the set of test conditions, and wherein each of the calibration samples is a biological sample comprising DNA that is obtained from a single contributor, generate, for each of the plurality of calibration samples, calibration data from the corresponding calibration sample profile by modeling heights of the plurality of allele peaks, wherein the calibration data models one or more variables as a function of input DNA mass in the corresponding calibration sample, receive, from the laboratory device, a test sample profile generated by analyzing the test sample under the set of test conditions, wherein the test sample is a biological sample comprising a DNA mixture from one or more contributors and wherein the number of contributors is unknown, analyze the calibration data and the test sample profile to generate a probability distribution of a number of contributors to the test sample for estimating the number of contributors to the DNA sample; and using the estimated number of contributors to determine whether an individual should be included as a contributor to the DNA mixture during forensic analysis. ### Claim 13 (depends on 12) The system of claim 12, wherein the one or more programming instructions that cause the processing device to generate, for each of the plurality of calibration samples, calibration data from the corresponding calibration sample profile by modeling heights of the plurality of allele peaks further comprise one or more instructions that cause the processing device to: identify a type for each of the plurality of allele peaks in the calibration sample profile as at least one of the following types: a true peak, a reverse stutter peak, a forward stutter peak, or a noise peak; and for each allele peak type: generate a Gaussian distribution, and identify a mean and a standard deviation for each of plurality of loci. ### Claim 14 (depends on 13) The system of claim 13, further comprising one or more instructions that cause the processing device to, for each of the plurality of loci, determine a dropout rate and a rate of occurrence of stutter. ### Claim 15 (depends on 13) The system of claim 13, wherein the one or more programming instructions that cause the processing device to analyze the calibration data and the test sample profile to generate the probability distribution of the number of contributors to the test sample further comprise one or more instructions that cause the processing device to use means and standard deviations for the allele peak types in the calibration data to generate the probability distribution function. ### Claim 16 (depends on 12) The system of claim 12, wherein the one or more programming instructions that cause the processing device to analyze the calibration data and the test sample profile to generate the probability distribution of the number of contributors to the test sample further comprise one or more instructions that cause the processing device to use a Monte Carlo sampling algorithm to generate the probability distribution. ### Claim 17 (depends on 12) The system of claim 12, further comprising one or more instructions that cause the processing device to determine the number of contributors to the test sample as a number of contributors with the highest probability on the probability distribution. ### Claim 18 (depends on 12) The system of claim 12, further comprising one or more instructions that cause the processing device to determine the number of contributors to the test sample as a range on the probability distribution within which the number of contributors is most likely to lie. ### Claim 19 (depends on 12) The system of claim 12, wherein the one or more variables are selected from at least one of the following: a baseline peak height, drop out, stutter, and presence of one or more artifacts. ### Claim 20 (depends on 12) The system of claim 12, further comprising one or more instructions that cause the processing device to: determine an amount of DNA in the test sample; and use the amount to generate the probability distribution. ### Claim 21 (depends on 12) The system of claim 12, wherein the plurality of calibration samples include a plurality of biological samples comprising DNA material from the same person but at different levels of DNA concentration.