Abstract: An approach for differentially isolating eukaryotic (plant and animal) DNA from bacterial DNA prior to sequencing using a combination of size exclusion-based separation and differential cell lysis. The method of the present invention exploits the differences of the cellular size and components of each type of organism to be separated. The composition and nature of the cell wall of plant cells, enzymatic sensitivity of bacterial and animal cells and overall size difference of bacterial and plant/animal cells allows one portion of a mixed sample to be lysed while retaining the integrity of the remaining organisms. Separation of one phylogenetic component then permits the remaining components to be extracted with minimal contribution from the preceding component. The separation of DNAs from differing contributing kingdoms in an unknown sample increases interpretability through decreasing complexity in subsequent sequencing applications.

Abstract: A system configured to characterize a number of contributors to a DNA mixture within a sample, the system comprising: a sample preparation module configured to generate initial data about the DNA mixture within the sample; a processor comprising a number of contributors determination module comprising a machine-learning algorithm configured to: (i) receive the generated initial data; (ii) analyze the generated initial data to determine the number of contributors to the DNA mixture within the sample; and an output device configured to receive the determined number of contributors from the processor, and further configured output information about the received determined number of contributors.

Abstract: A system for evaluating a DNA sample and determining whether the sample contains related individuals and/or unrelated individuals with high levels of alleles sharing. Trained and pre-validated machine learning algorithms are to rapidly and probabilistically assess the presence of relatives in a DNA mixture. To make a probabilistic determination, the system evaluates aspect of the sample that have not be considered before, such as peak heights, peak height ratios, maximum peak heights, minimum peak heights, ratios of allele heights to one another, number of contributors using maximum allele count method, and quantitative measures of the amount of DNA contributed by the male and female organisms. The system identifies whether a DNA sample has contributors that are not readily identifiable based on the data and can thus improve downstream analysis.

Abstract: An approach for differentially isolating eukaryotic (plant and animal) DNA from bacterial DNA prior to sequencing using a combination of size exclusion-based separation and differential cell lysis. The method of the present invention exploits the differences of the cellular size and components of each type of organism to be separated. The composition and nature of the cell wall of plant cells, enzymatic sensitivity of bacterial and animal cells and overall size difference of bacterial and plant/animal cells allows one portion of a mixed sample to be lysed while retaining the integrity of the remaining organisms. Separation of one phylogenetic component then permits the remaining components to be extracted with minimal contribution from the preceding component. The separation of DNAs from differing contributing kingdoms in an unknown sample increases interpretability through decreasing complexity in subsequent sequencing applications.

Abstract: Methods and systems for characterizing two or more nucleic acids in a sample. The method can include the steps of providing a hybrid machine learning approach that enables rapid and automated deconvolution of DNA mixtures of multiple contributors. The input is analyzed by an expert system which is implemented in the form of a rule set. The rule set establishes requirements based on expectations on the biology and methods used. The methods and systems also include a machine learning algorithm that is either incorporated into the expert system, or utilizes the output of the expert system for analysis. The machine learning algorithm can be any of a variety of different algorithms or combinations of algorithms used to perform classification in a complex data environment.

Abstract: A system configured to characterize a ratio of contributors to a DNA mixture within a sample, the system including: a sample preparation module configured to generate initial data about the DNA mixture within the sample; a processor comprising a ratio of contributors determination module configured to: (i) receive the generated initial data; (ii) analyze the generated initial data to determine the ratio of contributors to the DNA mixture within the sample; and an output device configured to receive the determined ratio of contributors from the processor, and further configured to output information about the received determined ratio of contributors.

Abstract: A system configured to characterize a ratio of contributors to a DNA mixture within a sample, the system including: a sample preparation module configured to generate initial data about the DNA mixture within the sample; a processor comprising a ratio of contributors determination module configured to: (i) receive the generated initial data; (ii) analyze the generated initial data to determine the ratio of contributors to the DNA mixture within the sample; and an output device configured to receive the determined ratio of contributors from the processor, and further configured to output information about the received determined ratio of contributors.

Abstract: A system configured to characterize the probability of any allele dropout in the sequence of DNA extracted from a sample. The system includes a sample preparation module that can generate sequence data about any DNA within the sample, a processor that is programmed to receive the sequence data and determine the probability of allelic dropout in the sequence data, and an output device that provides the determination of allele dropout to a user of the system.

Abstract: A method for characterizing at least a portion of the biodiversity of a sample. The method includes the steps of: (i) obtaining a sample having nucleic acid from a plurality of different organisms; (ii) extracting at least a portion of the nucleic acid from the sample; (iii) optionally performing a whole-genome amplification of the extracted nucleic acid; (iv) optionally performing a second, targeted amplification; (v) sequencing the amplified nucleic acid to obtain sequence data comprising a nucleic acid sequence for at least some of the plurality of different organisms; (vi) querying, using the obtained sequence data, a sequence database, where querying the sequence database identifies one or more of the plurality of different organisms; and (vii) determining, using the identified one or more of the plurality of different organisms, a characteristic of the sample.

Abstract: A system for evaluating a DNA sample and determining whether the sample contains related individuals and/or unrelated individuals with high levels of alleles sharing. Trained and pre-validated machine learning algorithms are to rapidly and probabilistically assess the presence of relatives in a DNA mixture. To make a probabilistic determination, the system evaluates aspect of the sample that have not be considered before, such as peak heights, peak height ratios, maximum peak heights, minimum peak heights, ratios of allele heights to one another, number of contributors using maximum allele count method, and quantitative measures of the amount of DNA contributed by the male and female organisms. The system identifies whether a DNA sample has contributors that are not readily identifiable based on the data and can thus improve downstream analysis.

Abstract: A system configured to characterize a number of contributors to a DNA mixture within a sample, the system comprising: a sample preparation module configured to generate initial data about the DNA mixture within the sample; a processor comprising a number of contributors determination module comprising a machine-learning algorithm configured to: (i) receive the generated initial data; (ii) analyze the generated initial data to determine the number of contributors to the DNA mixture within the sample; and an output device configured to receive the determined number of contributors from the processor, and further configured output information about the received determined number of contributors.

Abstract: A method for determining the presence or absence of an illicit plant-derived compound in a sample. The method includes the steps of: (i) providing a sample potentially containing an illicit plant-derived compound; (ii) extracting DNA in the illicit plant-derived compound from the identified sample; (iii) amplifying, using PCR, the target plant DNA sequence from the extracted DNA; and (iv) detecting an amplified target plant DNA sequence, where detection of the amplified target plant DNA sequence indicates the presence of the illicit plant-derived compound.

Abstract: A system configured to characterize a ratio of contributors to a DNA mixture within a sample, the system including: a sample preparation module configured to generate initial data about the DNA mixture within the sample; a processor comprising a ratio of contributors determination module configured to: (i) receive the generated initial data; (ii) analyze the generated initial data to determine the ratio of contributors to the DNA mixture within the sample; and an output device configured to receive the determined ratio of contributors from the processor, and further configured to output information about the received determined ratio of contributors.

Abstract: A method for characterizing at least a portion of the biodiversity of a sample. The method includes the steps of: (i) obtaining a sample having nucleic acid from a plurality of different organisms; (ii) extracting at least a portion of the nucleic acid from the sample; (iii) optionally performing a whole-genome amplification of the extracted nucleic acid; (iv) optionally performing a second, targeted amplification; (v) sequencing the amplified nucleic acid to obtain sequence data comprising a nucleic acid sequence for at least some of the plurality of different organisms; (vi) querying, using the obtained sequence data, a sequence database, where querying the sequence database identifies one or more of the plurality of different organisms; and (vii) determining, using the identified one or more of the plurality of different organisms, a characteristic of the sample.

Abstract: Methods and systems for characterizing two or more nucleic acids in a sample. The method can include the steps of providing a hybrid machine learning approach that enables rapid and automated deconvolution of DNA mixtures of multiple contributors. The input is analyzed by an expert system which is implemented in the form of a rule set. The rule set establishes requirements based on expectations on the biology and methods used. The methods and systems also include a machine learning algorithm that is either incorporated into the expert system, or utilizes the output of the expert system for analysis. The machine learning algorithm can be any of a variety of different algorithms or combinations of algorithms used to perform classification in a complex data environment.

Abstract: A home security device is provided for simulating a television receiver which consists of a housing having an electrical circuit therein, a mechanism for electrically connecting the electrical circuit in the housing to a power source and a mechanism electrically connected to the electrical circuit in the housing for simulating the picture of the television receiver, so that a potential burglar will think someone is home watching the television receiver and not break into the home.