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;   determining a pathogenic mutation from a plurality of variants in a therapeutic indicator gene; and   predicting that the therapeutic gene with the pathogenic mutation is indicative of a therapeutically effective drug.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising,
 determining a pathogenic or strength altering effect of a variant of the plurality of variants causing an aberration such as abolition, increase, or decrease in rate, kinetics, or quantity of transcription, splicing, or translation, or the biochemical activity of the protein, as an indicator of therapeutic efficacy of a drug.   
     
     
         3 . The computer-implemented method of  claim 1 , wherein
 determining that any pathogenic mutation in a therapeutic indicator gene indicates that a drug is effective for a disease.   
     
     
         4 . The computer-implemented method of  claim 1 , further comprising,
 determining that, if a pathogenic variant of the plurality of variants in a gene causes a particular disease or drug-response phenotype, such as indicative of a disease, therapeutic drug, or ADR, then any other similar pathogenic variation in the same gene is indicative of the particular disease or drug-response phenotype.   
     
     
         5 . The computer-implemented method of  claim 1 , further comprising,
 determining that, if a strength altering variant of the plurality of variants in a gene causes a particular disease or drug-response phenotype, such as indicative of a disease, therapeutic drug, or ADR, then a similar strength altering variation causing a similar effect in the same gene is indicative of the particular disease or drug-response phenotype.   
     
     
         6 . The computer-implemented method of  claim 1 , further comprising,
 determining that a fraction of actionable mutations in a gene that are indicative of the therapeutically effective drug are pathogenic;   determining that a remainder of the actionable mutations in the same gene are strength altering variations indicative of improved therapeutics, and,   predicting, based on the fraction and the remainder, that other genes of a biochemically or biologically similar type to the therapeutic indicator gene, with similar pathogenic or strength altering mutations, indicates that the drug will be therapeutically effective for the disease.   
     
     
         7 . The computer-implemented method of  claim 1 , further comprising,
 determining that a fraction of causal mutations in genes that indicate a particular disease are pathogenic (deleterious, making the protein defective), and determining that a remainder of the causal mutations are strength altering variations (overexpress or under express a gene, over splice or under splice in a splicing reaction, or making a protein overactive or underactive) indicative of the particular disease.   
     
     
         8 . The computer-implemented method of  claim 1 , further comprising:
 determining a strength of the pathogenic mutation, or the extent of deleteriousness, in a therapeutic indicator gene to determine an efficacy or a dosage of the therapeutically effective drug, based on a molecular effect of the pathogenic mutation leading to an alteration in a coding, regulatory or splicing process, in at least one genetic element, in the one or more individual patients genome.   
     
     
         9 . The computer-implemented method of  claim 1 , further comprising:
 determining the dosage of the therapeutically effective drug, based on overexpression or underexpression of therapeutic drug response genes by strength altering mutations in regulatory elements such as a promoter, polyA site, or splicing elements, as an increased, normal, or decreased dose compared with a standard dose, based on if the therapeutically effective drug has an enhancing or inhibiting biochemical or biological effect or based on the zygosity of the pathogenic mutation.   
     
     
         10 . The computer-implemented method of  claim 1 , further comprising:
 using a combination of information of strength altering or pathogenic mutation and a zygosity of the therapeutic indicator gene to determine a therapeutic status, efficacy or dose of the therapeutically effective drug for a disease;   determining an efficacy of a therapeutic drug response gene, based on strengthening or weakening of a therapeutic drug response gene by strength altering mutations in at least one genetic element, including coding, regulatory or splicing elements, or cryptic versions, as a poor, intermediate or high efficacy response; and,   determining a recommendation for a drug or disease response, wherein a pathogenic mutation or strength altering variation in a gene, is used to predict the disease, drug response, or a severity of a disease, level of drug response, or drug dosage, in one or more patients.   
     
     
         11 . 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 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 the nucleotide string by comparing a sequence of the nucleotide string with at least one reference genome;   determining a pathogenic mutation from the plurality of variants in a drug metabolizing gene; and   predicting that the drug metabolizing gene with the pathogenic mutation is indicative of a harmful side effect for a drug.   
     
     
         12 . The computer-implemented method of  claim 11 , wherein,
 determining that a pathogenic or strength altering mutation of the drug metabolizing gene causes a protein encoded by the drug metabolizing gene to be defective, change activity level, or change metabolism level, wherein the pathogenic or strength altering mutation occurs in the coding, regulatory, or splicing elements or the cryptic versions thereof;   determining a pharmacogenomic status of the drug metabolizing gene based on the effects of the pathogenic or strength altering mutations; and   predicting, based on the pharmacogenomic status, that the pathogenic or strength altering mutation in the drug metabolizing gene indicates a side effect for a drug associated with the drug metabolizing gene.   
     
     
         13 . The computer-implemented method of  claim 11 , further comprising:
 determining the drug metabolizing status of a mutation, biomarker, or the drug metabolizing gene based on the mutation or zygosity, wherein deleterious mutation in one allele is an intermediate metabolizer, and in two alleles is a non-metabolizer;   determining the drug metabolizing status of the mutation, biomarker, or drug metabolizing gene, based on zygosity of strength altering mutations in the regulatory element such as a promoter, a splicing element or a coding sequence, wherein a combination of strengths in at least one allele indicates a low, intermediate, or high metabolizer or a non-metabolizer status; and   determining the drug metabolizer status of the drug metabolizing gene in a range spanning a poor metabolizer to a rapid metabolizer from the pathogenic or strength altering mutations in a genetic element in a combination of different pharmacogenes that metabolize the drug.   
     
     
         14 . The computer-implemented method of  claim 11 , further comprising,
 determining the drug metabolizing status of one or more PGx genes, based on an overexpression or underexpression of the drug metabolizing gene by strength altering mutations in regulatory elements such as a promoter or polyA site, or splicing elements, as a rapid metabolizer, intermediate metabolizer, or non-metabolizer; and   determining the drug metabolizing status of a PGx gene, based on strengthening or weakening of the biochemical function of the drug metabolizing gene by the strength altering mutations in the coding sequence as a poor, intermediate, or rapid metabolizer.   
     
     
         15 . The computer-implemented method of  claim 11 , wherein predicting that the pathogenic or strength altering mutation indicates the side effect comprises:
 determining that a fraction of the plurality of mutations in the drug metabolizing gene that indicates harmful side effects of the drug are pathogenic;   determining that a remainder of the plurality of mutations are strength altering variations indicative of various levels of side effects for the drug; and   predicting, based on the fraction and the remainder, that other genes of a biochemically or biologically similar type to the drug metabolizing gene, with similar pathogenic or strength altering mutations, indicates similar side effects for the same drug.   
     
     
         16 . The computer-implemented method of  claim 11 , further comprising:
 determining a dose of the drug based on a drug metabolizing status of a combination of one or more drug metabolizing genes that metabolizes the drug and variant zygosity or gene zygosity; and   calculating the dose of the drug based on a number of pathogenic mutated or strength altering mutated genes, enzymatic efficacy of the pathogenic or strength altering mutated genes, and zygosity, such that a number of mutated alleles and the enzymatic efficacy of genes metabolizing the drug is inversely proportional to the dose of the drug based on whether the drug is active, prodrug or postdrug, and such that the dose varies between low and high dosage.   
     
     
         17 . 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 regulatory, splicing, or functional element of a non-protein coding RNA gene, and within genes not yet identified in a Dark Matter genome;   identifying pharmacogene biomarkers or therapeutic gene biomarkers corresponding to the plurality of nucleotides, wherein the pharmacogene biomarkers and therapeutic gene biomarkers are present in coding sequence, regulatory element, splicing element, or cryptic version thereof;   predicting a drug and dosage recommendation based on a combination of efficacy and side effects based on the therapeutic gene biomarkers and the pharmacogene biomarkers; and   graphically displaying the drug recommendations for the disease, illustrating the biomarkers on the gene structure or sequence view, and correlating with the genetic, biological and biochemical effects of the mutations of the gene with the mechanism of drug action.   
     
     
         18 . The computer-implemented method of  claim 17 , wherein graphically illustrating the biomarkers comprises depicting a pathogenic or strength altering mechanism of imparting therapeutic or harmful side effects by the therapeutic gene biomarkers, pharmacogene biomarkers, and genes. 
     
     
         19 . The computer-implemented method of  claim 17 , wherein predicting the drug recommendation comprises:
 predicting one or more therapeutic drugs based on at least one therapeutic gene biomarker in an individual;   predicting at least one pharmacogene biomarker, indicative of adverse drug reactions (ADRs), for recommendation of an effective drug from the one or more therapeutic drugs, with lowest side effects; and   selecting the drug recommendation based on various combinations of drug response phenotypes such as drug efficacy and side effects from mutations in one or more genetic elements, in one or more drug response genes.   
     
     
         20 . The computer-implemented method of  claim 17 , wherein predicting the drug recommendation comprises:
 calculating an efficacy score or a side effect score from the therapeutic gene biomarkers or pharmacogene biomarkers, in one or more patients for a particular drug, prodrug or postdrug, wherein the efficacy score or a side effect score is assigned a quantitative value based on zero, low, medium, or high therapeutics or side effects;   determining, based on the efficacy score or the side effect score, a drug recommendation score by subtracting the side effect score from the efficacy score based on the pharmacogene biomarkers or therapeutic gene biomarkers; and,   providing a summary recommendation of the particular drug or prodrug for a disease in the one or more patients by determining a most effective drug with lowest side effects, based on a combination of mutations in various drug response, therapeutic, drug metabolizing or transporter genes.   
     
     
         21 . The computer-implemented method of  claim 17 , further comprising:
 determining if a mutation in a gene is actionable to indicate a therapeutic drug or to avoid drugs with harmful side effects for a disease, based on a reference data set with known details of the disease, mutation, gene, or therapeutic drug; and   graphically representing the disease, mutation, gene, or therapeutic drug or the drug recommendations, or biological or biochemical effect, on the gene structure or sequence views, tables and various graphical blocks with color codes and graphical notations.