Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

A whole cell model may be constructed and used to simulate cell behavior. The whole cell model may have a baseline cell state that can be perturbed by a user in order to understand the behavior and importance of various molecules, processes and/or sub-models within the whole cell model. The simulation data is evaluated according to a variety of heuristics. The simulation data is ranked within each heuristic. The heuristic evaluation of the simulation data is then compared to an input perturbation to determine the relative importance of the heuristics. The output is a visualization of the simulation data according to each heuristic within a dynamic ranked display.

A whole cell model may be constructed and used to simulate cell behavior. The whole cell model may have a baseline cell state that can be perturbed by a user in order to understand the behavior and importance of various molecules, processes and/or sub-models within the whole cell model. The simulation data is evaluated according to a variety of heuristics. The simulation data is ranked within each heuristic. The heuristic evaluation of the simulation data is then compared to an input perturbation to determine the relative importance of the heuristics. The output is a visualization of the simulation data according to each heuristic within a dynamic ranked display.

A computer distinguishes relationships among concepts of conveyance contained within delivered content. The computer receives several of concepts of conveyance and determines a first visual identification indicia for a first of the concepts, with the first visual identification indicia being characterized by a first combination of appearance style elements selected from a group of visual attribute categories. The appearance style elements have sufficient visual contrast relative to one another to represent distinguishable relationships among individual concepts of conveyance, pairs of concepts of conveyance and multiple concepts of conveyance that are overlapping. The computer iteratively determines a visual identification indicia for each concepts of conveyance. The visual identification indicia are characterized by a unique corresponding combination of appearance style elements selected from the visual attribute categories. The computer presents a visualization of concepts of conveyance in a manner that distinguishes relationships among the concepts of conveyance.

A computer distinguishes relationships among concepts of conveyance contained within delivered content. The computer receives several of concepts of conveyance and determines a first visual identification indicia for a first of the concepts, with the first visual identification indicia being characterized by a first combination of appearance style elements selected from a group of visual attribute categories. The appearance style elements have sufficient visual contrast relative to one another to represent distinguishable relationships among individual concepts of conveyance, pairs of concepts of conveyance and multiple concepts of conveyance that are overlapping. The computer iteratively determines a visual identification indicia for each concepts of conveyance. The visual identification indicia are characterized by a unique corresponding combination of appearance style elements selected from the visual attribute categories. The computer presents a visualization of concepts of conveyance in a manner that distinguishes relationships among the concepts of conveyance.

A computer-implemented method that is for assessing pathogenicity of a variant for a patient. Receive a variant. Determine at least one probability for the variant in relation to pathogenic metrics based on a collection of learned variants. The pathogenic metrics comprise a data representation of at least one genetic condition cluster for determining at least one probability for the variant. The combined representation of at least one probability of the variant for the patient is outputted.

A computer-implemented method that is for assessing pathogenicity of a variant for a patient. Receive a variant. Determine at least one probability for the variant in relation to pathogenic metrics based on a collection of learned variants. The pathogenic metrics comprise a data representation of at least one genetic condition cluster for determining at least one probability for the variant. The combined representation of at least one probability of the variant for the patient is outputted.

A method for genome analysis is provided. The method includes receiving a nucleotide string comprising a plurality of nucleotides from at least a portion of one or more individual patients' genome. The method also includes identifying a plurality of variants in said nucleotide string, assigning each identified variant a score based on a location of a variant and a predicted functional consequence, and determining a strength of a variation responsible for a trait or phenotypic manifestation of the variants. The method also includes identifying at least one phenotype, and displaying, in a graphic unit interface of a client device, said nucleotide string, the identified variants, and the at least one phenotype, in one or more genetic elements for one or more individual patients. A system and a non-transitory, computer-readable medium storing instructions to perform the above method are also provided.

A method for genome analysis is provided. The method includes receiving a nucleotide string comprising a plurality of nucleotides from at least a portion of one or more individual patients' genome. The method also includes identifying a plurality of variants in said nucleotide string, assigning each identified variant a score based on a location of a variant and a predicted functional consequence, and determining a strength of a variation responsible for a trait or phenotypic manifestation of the variants. The method also includes identifying at least one phenotype, and displaying, in a graphic unit interface of a client device, said nucleotide string, the identified variants, and the at least one phenotype, in one or more genetic elements for one or more individual patients. A system and a non-transitory, computer-readable medium storing instructions to perform the above method are also provided.