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 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 of electronically separating host and non-host sequence data (sequenced DNA, RNA, and/or proteins) utilizes electronic host filters, which can be generated on a just-in-time basis by a cloud-based software service. Host reads and non-host reads of a given sample are separated and stored in separate data repositories. Also disclosed is a cloud-based software service utilizing the method. The non-host reads resulting from the host filtration process can then be profiled more accurately for the microorganism content therein.

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: 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 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: A semiconductor structure and a method for fabricating the same. The semiconductor structure includes a gate cut mask having one cut window exposing one or more portions of multiple sacrificial gate structures of the at least one plurality of sacrificial gate structures. The multiple sacrificial gate structures having been formed over portions of in structures. The method comprises forming a gate cut mask a plurality of semiconductor fins and a plurality of sacrificial gate structures. The gate cut mask being formed with one cut window exposing one or more portions of multiple sacrificial gate structures of the plurality of sacrificial gate structures. At least the portion of multiple sacrificial gate structures and one or more portions of each semiconductor fin of the plurality of semiconductor fins underlying the one or more portions of one of the multiple sacrificial gate structures are removed.

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: Embodiments of the present invention are directed to a computer-implemented method for positive OTU identification. A non-limiting example of the computer-implemented method includes receiving, by a processor, a plurality of sequencing reads for a metagenome sample and, for each of the plurality of sequencing reads, a corresponding OTU set comprising a plurality of OTUs. The method also includes determining, by the processor, a true positive score for each of the plurality of OTUs based upon a C?ech Complex and generating a plurality of preliminary OTUs. The method also includes determining a threshold score for the preliminary OTUs. The method also includes removing one of the preliminary OTUs based at least in part upon a determination that the true positive score is less than a threshold. The method also includes retaining one of the preliminary OTUs based at least in part upon a determination that the true positive score is greater than or equal to the threshold.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for positive OTU identification. A non-limiting example of the computer-implemented method includes receiving, by a processor, a plurality of sequencing reads for a metagenome sample and, for each of the plurality of sequencing reads, a corresponding OTU set comprising a plurality of OTUs. The method also includes determining, by the processor, a true positive score for each of the plurality of OTUs based upon a ?ech Complex and generating a plurality of preliminary OTUs. The method also includes determining a threshold score for the preliminary OTUs. The method also includes removing one of the preliminary OTUs based at least in part upon a determination that the true positive score is less than a threshold. The method also includes retaining one of the preliminary OTUs based at least in part upon a determination that the true positive score is greater than or equal to the threshold.

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: 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: 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: Embodiments include method, systems and computer program products for metagenome mapping. Aspects include receiving a plurality of operational taxonomic unit (OTU) identifications from a sample. Aspects also include calculating rank distributions for OTU identifications, ranking OTU identifications, and retaining or discarding OTU identifications based on the rankings. Aspects also include calculating a promiscuity score for each of the operational taxonomic unit identifications, wherein the promiscuity score is a number that reflects a likelihood of a false positive. Aspects also include ranking or discarding OTU identifications based on the rankings.

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.

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.