Abstract: A method includes generating, by a processor system, a graph. The graph is based at least in part on a plurality of instances in which operational taxonomic units are identified as being represented within an environment. The method can also include determining, using the processor system, that at least one instance of the plurality of instances corresponds to a false-positive identification of an operational taxonomic unit. The determining is based on the properties of the graph. The method can also include reporting the determination.

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 select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold.

Abstract: Various embodiments select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold.

Abstract: Embodiments of the invention include systems and methods for protecting study participant data for aggregate analysis. Aspects include sending a broker encryption key to a plurality of subjects. Aspects also include receiving double-encrypted subject data from the plurality of subjects. Aspects also include decrypting the double-encrypted subject data with a broker decryption key to generate single-encrypted subject data for the plurality of subjects. Aspects also include aggregating the single-encrypted subject data for the plurality of subjects to generate an aggregated single-homomorphically encrypted data set. Aspects also include including a plurality of random factors in the aggregated single-encrypted data set. Aspects also include sending the aggregated single-homomorphically encrypted data set to a researcher.

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: A method includes generating, by a processor system, a graph. The graph is based at least in part on a plurality of instances in which operational taxonomic units are identified as being represented within an environment. The method can also include determining, using the processor system, that at least one instance of the plurality of instances corresponds to a false-positive identification of an operational taxonomic unit. The determining is based on the properties of the graph. The method can also include reporting the determination.

Abstract: Embodiments of the present invention include method, systems and computer program products for algebraic phasing of polyploids. Aspects of the invention include receiving a matrix including a set of two or more single-nucleotide poloymorphisms (SNPs) for two or more sample organisms. Each row of the matrix is set to a ploidy based on a number of ploidies present in the two or more sample organisms. Each allele in the set of two or more SNPs is represented as a binary number. A set of algebraic rules is received, wherein the set of algebraic rules include an algebraic phasing algorithm. And the set of algebraic rules are applied to the matrix to determine a haplotype of a parent of the two or more sample organisms.

Abstract: A method includes generating, by a processor system, a graph. The graph is based at least in part on a plurality of instances in which operational taxonomic units are identified as being represented within an environment. The method can also include determining, using the processor system, that at least one instance of the plurality of instances corresponds to a false-positive identification of an operational taxonomic unit. The determining is based on the properties of the graph. The method can also include reporting the determination.

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: Embodiments of the present invention include method, systems and computer program products for algebraic phasing of polyploids. Aspects of the invention include receiving a matrix including a set of two or more single-nucleotide poloymorphisms (SNPs) for two or more sample organisms. Each row of the matrix is set to a ploidy based on a number of ploidies present in the two or more sample organisms. Each allele in the set of two or more SNPs is represented as a binary number. A set of algebraic rules is received, wherein the set of algebraic rules include an algebraic phasing algorithm. And the set of algebraic rules are applied to the matrix to determine a haplotype of a parent of the two or more sample organisms.

Abstract: Embodiments of the invention include systems and methods for protecting study participant data for aggregate analysis. Aspects include sending a broker encryption key to a plurality of subjects. Aspects also include receiving double-encrypted subject data from the plurality of subjects. Aspects also include decrypting the double-encrypted subject data with a broker decryption key to generate single-encrypted subject data for the plurality of subjects. Aspects also include aggregating the single-encrypted subject data for the plurality of subjects to generate an aggregated single-homomorphically encrypted data set. Aspects also include including a plurality of random factors in the aggregated single-encrypted data set. Aspects also include sending the aggregated single-homomorphically encrypted data set to a researcher.

Abstract: Embodiments of the present invention include method, systems and computer program products for algebraic phasing of polyploids. Aspects of the invention include receiving a matrix including a set of two or more single-nucleotide poloymorphisms (SNPs) for two or more sample organisms. Each row of the matrix is set to a ploidy based on a number of ploidies present in the two or more sample organisms. Each allele in the set of two or more SNPs is represented as a binary number. A set of algebraic rules is received, wherein the set of algebraic rules include an algebraic phasing algorithm. And the set of algebraic rules are applied to the matrix to determine a haplotype of a parent of the two or more sample organisms.

Abstract: Embodiments include methods, systems, and computer program products for analyzing mutational evolution. Aspects include receiving a whole genome data set for a patient including a plurality of mutations. Aspects also include determining a variant allele frequency for each of the plurality of mutations. Aspects also include labeling each of the plurality of mutations with a gene set designation. Aspects also include constructing an evolution topology comprising an ordered representation of the plurality of mutations, wherein each of the plurality of mutations comprises one of the gene set designations.

Abstract: Embodiments include methods, systems, and computer program products for identifying content in a metagenomic sample. Aspects include receiving a plurality of metagenomic reads for a sample. Aspects also include comparing a portion of the plurality of metagenomic reads to a genomic database and identifying a plurality of associated nodes, wherein the genomic database includes a plurality of gene sequences having known taxonomies. Aspects also include generating a probabilistic score for each of the associated nodes per metagenomic read. Aspects also include generating an output including a plurality of identifications and a final probability score for each of the identifications based at least in part upon the probabilistic score for each of the associated nodes per metagenomic read.

Abstract: Embodiments of the present invention include method, systems and computer program products for algebraic phasing of polyploids. Aspects of the invention include receiving a matrix including a set of two or more single-nucleotide poloymorphisms (SNPs) for two or more sample organisms. Each row of the matrix is set to a ploidy based on a number of ploidies present in the two or more sample organisms. Each allele in the set of two or more SNPs is represented as a binary number. A set of algebraic rules is received, wherein the set of algebraic rules include an algebraic phasing algorithm. And the set of algebraic rules are applied to the matrix to determine a haplotype of a parent of the two or more sample organisms.

Abstract: Embodiments include methods, systems, and computer program products for identifying a metagenomic read. Aspects include receiving a metagenomic read of a sample. Aspects also include comparing the metagenomic read to a genomic database including a plurality of gene sequences. Aspects also include randomly fragmenting the metagenomic read into two read fragments. Aspects also include comparing the read fragments to the genomic database including a plurality of gene sequences. Aspects also include generating an identification based at least in part on a determination that a read fragment exactly matches a gene sequence.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for generating a list of genetic variants. A non-limiting example of the computer-implemented method includes receiving genetic and biological data. The exemplary method also includes generating data patterns from the genetic and biological data with data mining. The method also includes determining redescription distances between each of a plurality of data patterns. The method also includes generating computational homology filtrations from the redescription distances using a topological data analysis and homology groups including homology group elements based upon the computational homology filtrations. The method also includes generating a single nucleotide polymorphism combination list based upon the homology group elements and redescription clusters.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for generating a list of genetic variants. A non-limiting example of the computer-implemented method includes receiving genetic and biological data. The exemplary method also includes generating data patterns from the genetic and biological data with data mining. The method also includes determining redescription distances between each of a plurality of data patterns. The method also includes generating computational homology filtrations from the redescription distances using a topological data analysis and homology groups including homology group elements based upon the computational homology filtrations. The method also includes generating a single nucleotide polymorphism combination list based upon the homology group elements and redescription clusters.