Precision medicine portal for human diseases

Abstract

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.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 receiving a nucleotide string comprising a plurality of nucleotides from at least a portion of one or more individual patients genome, wherein the portion of the genome includes at least one genetic element of: a 5′-UTR, a promoter, an enhancer, a silencer, an exon, an intron, a coding sequence, a splice acceptor, a splice donor, a branch point site, a 3′-UTR, a Kozak sequence, a poly-A addition site or signal, or a cryptic version thereof, from a known protein coding gene or a regulatory, splicing, or functional element of a non-protein coding RNA gene, and within genes not yet identified in a Dark Matter genome;   identifying a plurality of variants in said nucleotide string by comparing a sequence of said nucleotide string with at least one reference genome;   identifying mutations in the portion of the genome across a plurality of genetic elements comprising coding sequences, regulatory elements, splicing elements, and cryptic versions thereof;   determining a mutation in one or more genetic elements in a gene, wherein the gene mutation is indicative of a disease, therapeutic or pharmacogenomic phenotype in the one or more individual patients; and   providing, based on the determined gene mutation, a recommendation of diagnosis, therapeutic drugs or drugs to avoid.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising:
 providing pharmacogenomic recommendations, such as use as directed, use with caution, avoid use, or other relevant recommendations, for the drugs reported in an electronic health record (EHR) of a patient, as a drug alert utility.   
     
     
         3 . The computer-implemented method of  claim 1 , further comprising:
 enabling voice navigation within an application to display, with voiceover, details of genes, mutations, genetic elements, biochemical functions, domains, gene families, disease, drugs, therapeutics, or harmful side effects, or a clinically relevant detail such as alleles, mode of inheritance or other genetic and clinical features, in tables, graphical illustrations and reports, by implementing clinic centric, genome centric, patient centric, clinician centric questions and answers through voice; and   enabling a user to pause voice navigation or ask for more details, and obtain relevant answers.   
     
     
         4 . The computer-implemented method of  claim 1 , further comprising,
 enabling various automated summary voice over and visualization reports, wherein key features, tables, graphical illustrations and reports are automatically displayed in animation with voice over to provide a quick summary overview, for a patient, of disease causing genes, genes that are associated with actionable drugs, or drugs to avoid, or other genetically or clinically relevant detail.   
     
     
         5 . A computer-implemented method, comprising:
 receiving a nucleotide string comprising a plurality of nucleotides from at least a portion of two or more individual patients genome, wherein the portion of the genome includes at least one of: a 5′-UTR, a promoter, an enhancer, a silencer, an exon, an intron, a coding sequence, a non-protein coding RNA, a splice acceptor, a splice donor, a branch point site, a 3′-UTR, a Kozak sequence, a poly-A addition site or signal, or a cryptic version thereof, from a known protein coding gene or a regulatory, splicing, or functional element of a non-protein coding RNA gene, and within genes not yet identified in a Dark Matter genome;   determining, via disease cohorts or clinical trial cohorts, one or more groups of the two or more individual patients with similar phenotypes such as a disease or drug response including efficacy or side effects of a therapeutic drug, wherein each group exhibit different levels of phenotypes;   determining mutated genes from individuals of a group, with the similar phenotypes; and   identifying frequently mutated genes of the mutated genes that are common across the individuals of a disease cohort or clinical trial cohort that are causal of a similar phenotype.   
     
     
         6 . The computer-implemented method of  claim 5 , further comprising,
 assessing the effects of mutations across a plurality of genetic elements on gene functions, zygosity, biochemical functions, pathways, or protein domains in the portion of the genome comprising pathogenic or strength altering mutations in protein coding and non-coding RNA genes, in the plurality of groups; and,   determining, based on comparing the plurality of groups, a plurality of genes, alleles, mutations, domains, or biochemical functions that are indicative of a drug response phenotype corresponding to high efficacy or low side effects of the therapeutic drug.   
     
     
         7 . The computer-implemented method of  claim 5 , wherein determining the causal genes comprises determining the genes that contain pathogenic or strength altering mutations in the highest number of types of genetic elements in the highest number of patients as diagnostic, therapeutic, or PGx genes. 
     
     
         8 . The computer-implemented method of  claim 5 , wherein identifying the genes responsible for a particular phenotype comprises identifying the frequently mutated genes, alleles, biochemical functions, pathways, or protein domains in the portion of the genome across a plurality of genetic elements comprising pathogenic or strength altering mutations in protein coding and non-coding RNA genes. 
     
     
         9 . The computer-implemented method of  claim 5 , wherein identifying the frequently mutated genes comprises determining a highest frequency of mutated genes across different genetic elements as an indicator of disease, therapeutics or harmful side effects, in a sub-cohort of the disease cohorts, exhibiting a specific disease, treated with a drug, or exhibiting a drug response phenotype, from an electronic health record (EHR). 
     
     
         10 . The computer-implemented method of  claim 5 , further comprising:
 depicting statistics of the frequently mutated genes comprising mutations in different elements across the disease cohorts or clinical trial cohorts in tabular or graphical illustrations on a gene structure or sequence view, with details of number or frequency of patients, genes, mutations, mutated position, zygosity, alleles, pathogenicity status, or score or strength of the different elements based on one or more scoring algorithms, biochemical functions, or domains.   
     
     
         11 . The computer-implemented method of  claim 5 , further comprising:
 graphically representing a clinical summary of findings from an analysis of the disease cohorts or clinical trial cohorts, such as diagnostic genes responsible for disease causation, therapeutic or PGx genes and drug recommendations, based on mutations in at least one of the plurality of genetic elements in coding or non-coding genes from the patients in the disease cohorts or clinical trial cohorts, in various blocks.   
     
     
         12 . The computer-implemented method of  claim 5 , further comprising:
 graphically depicting a frequency of mutations, patients with different zygosities within a selected protein coding or non-coding RNA gene in various genetic elements drawn to scale on a gene structure or sequence, and displaying a detail of a mutation on mouse hover and popover.   
     
     
         13 . The computer-implemented method of  claim 5 , further comprising:
 determining biochemical pathways, protein domains, or gene families contributing to a disease causation, therapeutics, or harmful side effects, by identifying genes that contain pathogenic or strength altering mutations in a highest number of genetic elements in a highest number of patients, which contribute to a highest frequency of affected biochemical pathways, domains, or gene families in the highest number of patients in the disease cohort or a clinical trial cohort.   
     
     
         14 . The computer-implemented method of  claim 5 , further comprising:
 determining frequencies of various genomic structural variations (GSVs), such as copy number variants, gene fusions, rearrangement, or other structural variants in the disease cohort or clinical trial cohort, to discover a highest frequency of GSVs that are causal of the disease, or therapeutic or pharmacogenomic phenotypes; and   depicting the frequencies of GSVs in tabular or graphical illustrations.   
     
     
         15 . The computer-implemented method of  claim 5 , further comprising:
 discovering biochemical causes for the drug response phenotypes, by identifying the genes with highest number of pathogenic or strength altering mutations across various genetic elements leading to the affected features such as biochemical functions, domains, or pathways across individuals in each of the plurality of groups, and comparing frequently affected features in the plurality of groups exhibiting the various drug response phenotypes.   
     
     
         16 . The computer-implemented method of  claim 5 , further comprising:
 depicting frequently occurring features such as the number of individuals, frequently mutated genes, zygosity, biochemical functions, pathways, or domains, in the individuals of the plurality of groups, in graphical blocks with sub sections for genes with mutations in genetic elements such as coding, regulatory and splicing elements, genomic structural variants, or non-coding RNA genes, in different graphical notations and color codes, and using scroll bars, blocks and sub blocks.   
     
     
         17 . The computer-implemented method of  claim 5 , further comprising:
 graphically depicting the frequently mutated genes in each of the one or more groups on a single line representing the genome drawn to scale of a genome length, within the view of a computer screen, along with a frequency of the frequently occurring features such as various genetic elements, biochemical pathways, domains, mutations, patients, disease or drug response genes, using various graphical notations and toggle buttons.   
     
     
         18 . The computer-implemented method of  claim 5 , further comprising:
 providing multiple single lines representing the genome, or toggled using the toggle buttons, to depict the frequently mutated genes, associated with different features such as pathogenicity, mutated genetic elements, mutational consequence, biochemical functions, domains, pathways, gene families, therapeutic or pharmacogenomic drug responses, treatment outcomes, or other clinical parameters, in each group of individuals or a combination of groups of individuals exhibiting varied levels of disease, side effects or efficacy for a drug.   
     
     
         19 . The computer-implemented method of  claim 5 , further comprising:
 analyzing clinical genomic data from an electronic health record (EHR), or a combination of multiple EHRs, using one or more patient data comprising disease, drugs, treatment outcomes, or other clinical parameters, based on the mutations across a plurality of various genetic elements, in protein-coding or the non-coding RNA genes, in a static mode, or non-static realtime mode, to determine genetic causes of different clinical phenotypes.   
     
     
         20 . A computer-implemented method comprising:
 receiving a nucleotide string comprising a plurality of nucleotides from at least a portion of one or more individual patients genome, wherein the portion of the genome includes at least one of: a 5′-UTR, a promoter, an enhancer, a silencer, an exon, an intron, a coding sequence, a splice acceptor, a splice donor, a branch point site, a 3′-UTR, a Kozak sequence, a poly-A addition site or signal, or a cryptic version thereof, from a known protein coding gene or a non-protein coding RNA gene, and within genes not yet identified in a Dark Matter genome;   identifying mutations and zygosity in genes within the portion of the genome across a plurality of genetic elements comprising coding sequences, regulatory elements, splicing elements, or cryptic versions thereof, wherein the one or more individuals correspond to a family; and,   determining, based on the mutations and zygosity, a gene mutation or a gene affected by any pathogenic mutation, in the portion of the genome, wherein the gene mutation or the affected gene is indicative of a disease, therapeutic or pharmacogenomic response in the one or more individual patients that is inherited from or related to a family member.   
     
     
         21 . The computer-implemented method of  claim 20 , further comprising,
 depicting a feature such as genes, genetic elements, alleles, domains, biochemical functions, or mutations on a single line representing the genome of an individual, siblings, parents, or grandparents, each on a different line, or toggled using toggle buttons, to enable tracing of inheritance of the feature from the individual's genome to any member of the family using graphical notations, color codes, and line navigators.   
     
     
         22 . The computer-implemented method of  claim 20 , further comprising,
 determining a probability of any disease or drug response phenotype that an individual exhibits within a lifetime, based on an occurrence of features such as genes, genetic elements, mutations, biochemical functions, domains, alleles, mode of inheritance, genes from gene panels for diseases or drug responses, or a combination of these features in one or more individuals of the family.   
     
     
         23 . The computer-implemented method of  claim 20 , further comprising:
 determining causal genes for a rare or undiagnosed disease in an individual by tracing mutations, zygosity, affected genes, genetic elements, alleles, mode of inheritance, domains, biochemical functions, or diseases across members of the family in various generations; and,   conducting a similar analysis in a cohort of families to detect inherited mutated genes, commonly mutated or causal genes, the mutations, the zygosity, alleles, affected domains, or biochemical functions across the cohort of families.   
     
     
         24 . The computer-implemented method of  claim 20 , further comprising:
 determining or depicting a pedigree chart illustrating an affected or carrier status of the disease, inherited or un-inherited genes, genetic elements, mutations, alleles, biochemical functions or domains in various members in a family; and   producing an inheritance tracing report of causal genes, genetic elements, mutations, and other genetic and clinical features for the disease, using toggle buttons, lists, dropdowns, or other graphical notations or navigators.   
     
     
         25 . The computer-implemented method of  claim 20 , further comprising:
 reducing a number of potential causal genes or alleles with pathogenic or strength altering mutations from the one or more individual patients genome, based on whether the disease or phenotype is inherited from a single parent; and,   enabling subsequent reduction of mutated genes for every generation or sibling the disease has been inherited or traced from, based on if a causal gene for the disease or a drug response phenotype is present, to determine the causal gene for the phenotype.   
     
     
         26 . The computer-implemented method of  claim 20 , further comprising:
 determining an allelic status or zygosity of a gene based on the occurrence of a pathogenic or strength altering mutation anywhere within the gene in a genetic element including coding, regulatory or splicing elements, rather than, based on a particular mutation occurring at a same chromosomal position in one or two alleles of the gene.   
     
     
         27 . The computer-implemented method of  claim 20 , further comprising:
 determining the genes and mutations in the plurality of genetic elements that are causal of a phenotype in each of the family members and representing them in different blocks for different individuals to compare the genes, mutations, zygosity or any other genetic or clinical features, with toggle buttons to view individual or combinations of genetic elements, protein coding genes, non-coding RNA genes or genomic structural variations in various blocks, sub-blocks in various graphical notations and color codes.