Abstract: Embodiments are directed to a computer-based system for processing data of a sample. The system includes a memory and a processor system communicatively coupled to the memory. The processor system is configured to receive, from a sample analysis system, observed data of at least one element in the sample. The processor system is further configured to receive actual data of the at least one element, and identify error data of the observed data of the at least one element, wherein identifying the error data comprises running a simulation model that models the sample analysis system to identify properties of a relationship between the observed data of the at least one element in the sample and the actual data of the at least one element.

Abstract: Embodiments are directed to a computer-based system for processing data of a sample. The system includes a memory and a processor system communicatively coupled to the memory. The processor system is configured to receive, from a sample analysis system, observed data of at least one element in the sample. The processor system is further configured to receive actual data of the at least one element, and identify error data of the observed data of the at least one element, wherein identifying the error data comprises running a simulation model that models the sample analysis system to identify properties of a relationship between the observed data of the at least one element in the sample and the actual data of the at least one element.

Abstract: Embodiments are directed to a computer-based simulation system including an input circuit, a memory and a processor system communicatively coupled to the memory and the input circuit. The input circuit is configured to receive an input distribution. The processor system is configured to assign, for each marker of a simulated population matrix, a minor allele frequency. The processor system is further configured to assign, for each marker and each distance of the simulated population matrix, a linkage disequilibrium (LD).

Abstract: Embodiments are directed to a computer-based simulation system including an input circuit, a memory and a processor system communicatively coupled to the memory and the input circuit. The input circuit is configured to receive an input distribution. The processor system is configured to assign, for each marker of a simulated population matrix, a minor allele frequency. The processor system is further configured to assign, for each marker and each distance of the simulated population matrix, a linkage disequilibrium (LD).

Abstract: A nanodevice includes a nanochannel disposed through a dielectric material. A first electrode is disposed on a first side of the nanochannel, is formed within the dielectric material and has a surface exposed within the nanochannel. A second electrode is disposed on a second side of the nanochannel, is formed within the dielectric material and has a surface exposed within the nanochannel opposite the first electrode. A power circuit is connected between the first and second electrodes to create a potential difference between the first and second electrodes such that portions of a molecule can be identified by a change in electrical properties across the first and second electrodes as the molecule passes.

Abstract: A nanodevice includes a nanochannel disposed through a dielectric material. A first electrode is disposed on a first side of the nanochannel, is formed within the dielectric material and has a surface exposed within the nanochannel. A second electrode is disposed on a second side of the nanochannel, is formed within the dielectric material and has a surface exposed within the nanochannel opposite the first electrode. A power circuit is connected between the first and second electrodes to create a potential difference between the first and second electrodes such that portions of a molecule can be identified by a change in electrical properties across the first and second electrodes as the molecule passes.

Abstract: Various embodiments perform stable gene analysis of transcriptome sequencing data. In one embodiment, a plurality of datasets each including transcriptome sequencing data are received by a processor. Each of the plurality of datasets includes a plurality of genes and a respective ranking value for each of the plurality of genes. A plurality of rank normalized input datasets is generated based on assigning, for each of the plurality of datasets, a rank to each of the plurality of genes. One or more longest increasing subsequence (LIS) of ranks are identified between each pair of the plurality of rank normalized input datasets. A set of stable genes from the plurality of genes is identified based on each of the one or more LIS of ranks across the plurality of rank normalized input datasets.

Abstract: Various embodiments perform stable gene analysis of transcriptome sequencing data. In one embodiment, a plurality of datasets each including transcriptome sequencing data are received by a processor. Each of the plurality of datasets includes a plurality of genes and a respective ranking value for each of the plurality of genes. A plurality of rank normalized input datasets is generated based on assigning, for each of the plurality of datasets, a rank to each of the plurality of genes. One or more longest increasing subsequence (LIS) of ranks are identified between each pair of the plurality of rank normalized input datasets. A set of stable genes from the plurality of genes is identified based on each of the one or more LIS of ranks across the plurality of rank normalized input datasets.

Abstract: Various embodiments simulate crossover events on a chromosome. In one embodiment, a number Y of positions to be selected on a simulated chromosome is determined. Y positions j1, . . . , jy on the simulated chromosome are selected. A crossover event is placed at one or more of the positions j1, . . . , jy based on Y>0. An additional number Y? of positions j?1, . . . , j?y to be selected on the simulated chromosome is determined. Y? additional positions j?1, . . . , j?y on the simulated chromosome are selected. An additional crossover event is placed at one or more of the additional positions j?1, . . . , j?y based on Y?>0 and a neighborhood t associated with the one or more of the additional positions j?1, . . . , j?y being free of crossover events. A set of crossover event locations is identified based on the one or more of the positions j1, . . . , jy and additional positions j?1, . . . , j?y at which a crossover event has been placed.

Abstract: Various embodiments simulate crossover events on a chromosome. In one embodiment, a number Y of positions to be selected on a simulated chromosome is determined. Y positions j1, . . . , jy on the simulated chromosome are selected. A crossover event is placed at one or more of the positions j1, . . . , jy based on Y>0. An additional number Y? of positions j?1, . . . , j?y to be selected on the simulated chromosome is determined. Y? additional positions j?1, . . . , j?y on the simulated chromosome are selected. An additional crossover event is placed at one or more of the additional positions j?1, . . . , j?y based on Y?>0 and a neighborhood t associated with the one or more of the additional positions j?1, . . . , j?y being free of crossover events. A set of crossover event locations is identified based on the one or more of the positions j1, . . . , jy and additional positions j?1, . . . , j?y at which a crossover event has been placed.

Abstract: Various embodiments reconstruct haplotypes from genotype data. In one embodiment, a set of progeny genotype data comprising n progenies encoded with m genetic markers is accessed. A first set of parent haplotypes associated with a first parent of the n progenies and a second set of parent haplotypes associated with a second parent of the n progenies are identified based on at least the set of progeny genotype data. A total minimum number of observable crossovers in the n progenies is determined. An agglomerate data structure comprising a collection of sets of haplotype sequences characterizing the n progenies is constructed based on the set of progeny genotype data and the first and second sets of parent haplotypes. Each set of haplotype sequences includes a number of crossovers equal to the total minimum number of observable crossovers in the n progenies.

Abstract: A system for reconstructing haplotypes from genotype data includes a memory, a processor, and a reconstruction module. The reconstruction module is configured to access a set of progeny genotype data including n progenies encoded with m genetic markers. A first set of parent haplotypes associated with a first parent of the n progenies and a second set of parent haplotypes associated with a second parent of the n progenies are identified based on at least the set of progeny genotype data. An agglomerate data structure including a collection of sets of haplotype sequences characterizing the n progenies is constructed based on the set of progeny genotype data and the first and second sets of parent haplotypes. Each set of haplotype sequences includes a number of crossovers equal to a total minimum number of observable crossovers in the n progenies.

Abstract: A computer-implemented method for rank normalization for differential expression analysis of transcriptome sequencing data includes receiving, by a computer, a first dataset comprising transcriptome sequencing data, the first dataset comprising a plurality of genes, and further comprising a respective ranking value associated with each of the plurality of genes; assigning a rank to each of the genes of the plurality of genes based on the ranking value to produce a first rank normalized dataset; determining a change between a first rank of a particular gene in the first rank normalized dataset, and a second rank of the particular gene in a second rank normalized dataset, the second rank normalized dataset being based on a second dataset comprising transcriptome sequencing data; and determining whether the particular gene is differentially expressed between the first dataset and the second dataset based on the determined change in rank.

Abstract: A computer system for rank normalization for differential expression analysis of transcriptome sequencing data includes a processor; and a memory comprising a first dataset comprising transcriptome sequencing data, the first dataset comprising a plurality of genes and a respective ranking value associated with each of the plurality of genes, the system configured to perform a method including assigning a rank to each of the genes of the plurality of genes based on the ranking value to produce a first rank normalized dataset; determining a change between a first rank of a particular gene in the first rank normalized dataset, and a second rank of the particular gene in a second rank normalized dataset, the second rank normalized dataset being based on a second dataset comprising transcriptome sequencing data; and determining whether the particular gene is differentially expressed between the first and second datasets based on the determined change in rank.