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 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.

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 method is provided for constructing an ancestral recombination graph. A value K is received representing K extant units. M non-mixing segments are also received. K vertices V are generated. K lineages for each of M trees are associated with each of the K vertices. An ancestral recombination graph is constructed. To construct the ancestral recombination graph, there is repeated, until only one lineage survives for each of the M trees, a process that includes the following. A tree is randomly selected tree. A first vertex v1 and a second vertex v2 are randomly selected. Two adjoining segments in the M non-mixing segments of the first and second vertices are combined together into a single vertex. A separate vertex is generated for at least one remaining segment in each of the M non-mixing segments of the first and second vertices. The vertices V are updated to be vertices that are non-interior vertices.

Abstract: A method, information processing system, and computer readable medium, are provided for analyzing a protein folding process. The method includes conducting an incremental pattern discovery process. The incremental pattern discovery process includes judging multidimensional data from a simulation of a protein folding process. The incremental pattern discovery process captures at least one intermediate data point in at least one pattern associated with the protein folding process.

Abstract: A method and system are disclosed for identifying partial order patterns of a set of motifs in a data sequence. The method comprises the steps of obtaining the data sequence, identifying a set of motifs in the data sequence, identifying a plurality of partial orders of the motifs in the data sequence, and using the identified partial orders to identify functions of the motifs. In the preferred embodiment of the invention, the step of identifying the plurality of partial orders of the motifs includes the step of converting the identified motifs to an (n×m) incidence matrix, I, of expressions. Also, in this preferred embodiment, the step of identifying the plurality of partial orders of the motifs includes the steps of computing a partial order description of each of the expressions, and computing a redescription of each of the partial order descriptions.

Abstract: A method is provided for constructing an ancestral recombination graph. A value K is received representing K extant units. M non-mixing segments are also received. K vertices V are generated. K lineages for each of M trees are associated with each of the K vertices. An ancestral recombination graph is constructed. To construct the ancestral recombination graph, there is repeated, until only one lineage survives for each of the M trees, a process that includes the following. A tree is randomly selected tree. A first vertex v1 and a second vertex v2 are randomly selected. Two adjoining segments in the M non-mixing segments of the first and second vertices are combined together into a single vertex. A separate vertex is generated for at least one remaining segment in each of the M non-mixing segments of the first and second vertices. The vertices V are updated to be vertices that are non-interior vertices.

Abstract: A method for detecting errors in genetic sequence assemblies including defining an assembly (A) of a sequence of genetic data, collecting read data into a library of reads (L), plotting histograms of sizes or reads versus a number of reads per size, normalizing a distribution (D) with a coverage C to obtain D? that has a mean (?) and standard deviation (?) and reserve positions (i) not used to obtain D?, collecting subset of reads (Si?L) using A and D?, computing mean (?i) and standard deviation (?ci·?i) using Si, outputting results to user on a display.

Abstract: A method for detecting errors in genetic sequence assemblies including defining an assembly (A) of a sequence of genetic data, collecting read data into a library of reads (L), plotting histograms of sizes or reads versus a number of reads per size, normalizing a distribution (D) with a coverage C to obtain D? that has a mean (?) and standard deviation (?) and reserve positions (i) not used to obtain D?, collecting subset of reads (Si ? L) using A and D?, computing mean (?i) and standard deviation (?ci·?i) using Si, outputting results to user on a display.

Abstract: Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs that are formed by determining sets for selected basis motifs. From these sets, unique intersection sets are determined. This process continues, by selecting additional basis motifs, until all basis motifs have been selected. An apparatus for performing the process is also disclosed.

Abstract: Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs that are formed by determining sets for selected basis motifs. From these sets, unique intersection sets are determined. This process continues, by selecting additional basis motifs, until all basis motifs have been selected. An apparatus for performing the process is also disclosed.

Abstract: Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected.

Abstract: Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected.

Abstract: Classification of objects using the best boolean expression that represents the most optimal combination of the underlying features is disclosed.

Abstract: The exemplary embodiments provide a computer implemented method, apparatus, and computer usable program code for calculating a probability. An input is received, wherein the input comprises a PQ tree. The leaf nodes of the PQ tree are counted to form a number of leaf nodes. A factorial value of the number of leaf nodes is calculated to form a denominator. A hash value of a frontier of all permutations of the PQ tree is calculated to form a numerator. A ratio of the numerator to the denominator is determined to form a result. The result is displayed to a user.

Abstract: Basis motifs are determined from an input sequence though an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs.

Abstract: Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected.

Abstract: Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected.

Abstract: Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected.

Abstract: A method and system are disclosed for identifying partial order patterns of a set of motifs in a data sequence. The method comprises the steps of obtaining the data sequence, identifying a set of motifs in the data sequence, identifying a plurality of partial orders of the motifs in the data sequence, and using the identified partial orders to identify functions of the motifs. In the preferred embodiment of the invention, the step of identifying the plurality of partial orders of the motifs includes the step of converting the identified motifs to an (n×m) incidence matrix, I, of expressions. Also, in this preferred embodiment, the step of identifying the plurality of partial orders of the motifs includes the steps of computing a partial order description of each of said expressions, and computing a redescription of each of said partial order descriptions.