A 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.

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 one or more individual patients 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 poly-A addition site or signal, or a cryptic version thereof, from a known protein coding gene and a non-protein coding RNA gene, and within the 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, wherein at least one of the variants is in at least one of the 5′-UTR, the promoter, the enhancer, the silencer, the exon, the intron, the non-protein coding RNA, the splice acceptor, the splice donor, the branch point site, the 3′-UTR, the poly-A addition site or signal, or the cryptic version thereof;   assigning each identified variant a score based on a location of a variant and a predicted functional consequence;   determining a strength of a variant responsible for a trait or phenotypic manifestation based on a similarity score by executing instructions from an algorithm such as Shapiro-Senapathy algorithm, a MaxEntScan algorithm, and NNSplice algorithm, stored in a memory;   identifying at least one phenotype such as a disease, a drug response, a therapeutic indications, and a harmful side effects of a medication or substance, based on the strength of the variation identified in one or more patients; and   displaying, in a graphical user 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.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein determining the deleteriousness or the alteration in strength of a variant by comparing its strength with the strength of the reference sequence. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein determining a strength of a variant based on a similarity score by executing instructions from modifications of algorithm such as Shapiro-Senapathy algorithm, a MaxEntScan algorithm, and NNSplice algorithm, stored in a memory, based on the length and variability of sequence signals. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein determining the similarity score by executing instructions from an algorithm selected from a group consisting of algorithms such as Shapiro-Senapathy algorithm, a MaxEntScan algorithm, and NNSplice algorithm, stored in a memory, and further determining a combined score of these algorithms based on their average or differentially weighted scores. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein displaying, in a graphical user interface of a client device, said nucleotide string, the identified variants, their altered strengths and deleteriousness, their biological effects and consequences, in one or more genetic elements leading to at least one phenotype, in one or more individual patients. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein determining a strength of a variation responsible for a trait or phenotypic manifestation of the variants comprises determining a deleteriousness of the variation. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein determining a strength of a variation responsible for a trait or phenotypic manifestation of the variants comprises determining that the variation responsible for a trait or phenotypic manifestation is a non-deleterious variation. 
     
     
         8 . The computer-implemented method of  claim 1 , wherein identifying the plurality of variants comprises identifying a copy number variants, structural variants and gene fusion in the plurality of nucleotides. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein determining the a strength of a variation responsible for a trait or phenotypic manifestation comprises determining a zygosity and mode of inheritance for the identified variants. 
     
     
         10 . The computer-implemented method of  claim 1 , further comprising identifying a protein-coding and non-coding RNA genes from a dark matter genome region within the plurality of nucleotides using algorithms such as GenScan, GeneID, and Augustus. 
     
     
         11 . The computer-implemented method of  claim 1 , further comprising, determining an exon score as an average of the scores or differentially weighted scores of acceptor and donor of that exon. 
     
     
         12 . The computer-implemented method of  claim 1 , further comprising, determining an exon score as an average of the scores or differentially weighted scores of acceptor, donor, and branch point site of that exon. 
     
     
         13 . The computer-implemented method of  claim 1 , further comprising, determining an exon score as an average of the scores or differentially weighted scores of acceptor, donor, branch point site, and splicing enhancers of that exon. 
     
     
         14 . The computer-implemented method of  claim 1 , further comprising, determining an exon score as an average of the scores or differentially weighted scores of acceptor, donor, branch point site, and splicing enhancers, and subtracting the average of the scores or differentially weighted scores of splicing silencers of that exon. 
     
     
         15 . The computer-implemented method of  claim 1 , further comprising, determining a promoter score as an average of the scores or differentially weighted scores of different promoter elements such as TATA box, GC box, CAAT box, initiator box, enhancers, and subtracting the average of the scores or differentially weighted scores of silencers of that promoter site. 
     
     
         16 . The computer-implemented method of  claim 1 , further comprising, determining a poly-A score as an average of the scores or differentially weighted scores of poly-A elements such as poly-A signal, site, enhancers, and subtracting the average of the scores or differentially weighted scores of silencers of that poly-A site. 
     
     
         17 . The computer-implemented method of  claim 1 , further comprising, determining an effect of a mutation in a true acceptor or a true donor, based on a position of a cryptic acceptor or donor within the exon or intron, as exon skipping or intron retention. 
     
     
         18 . The computer-implemented method of  claim 1 , further comprising, determining an effects of a mutation in a cryptic acceptor or donor, based on its position to a real acceptor or donor within the exon or intron, as exon skipping or intron retention. 
     
     
         19 . The computer-implemented method of  claim 1 , further comprising, determining an effect of a mutation in a cryptic acceptor or donor as cryptic exon creation causing intron retention, wherein a cryptic exon score is higher than or closely comparable to the scores of a true exon bordering it. 
     
     
         20 . The computer-implemented method of  claim 1 , further comprising, determining an effect of a mutation in a true acceptor or donor, wherein a mutated exon score is lower than an adjacent true exon on one or both sides below a score threshold as “exon skipping.” 
     
     
         21 . The computer-implemented method of  claim 1 , further comprising, graphically displaying a mutated gene and showing an effect of splicing aberrations such as exon skipping or intron retention in animation. 
     
     
         22 . The computer-implemented method of  claim 1 , further comprising, graphically depicting an effect of a splicing aberration such as frameshift of a codon, premature termination codon, and amino acid deletion or insertion, in structure and sequence views. 
     
     
         23 . The computer-implemented method of  claim 1 , further comprising, graphically displaying a comparison of an effect of an exonic coding region mutation determined to be a true or cryptic splice site, branch point site, splicing enhancer and silencer mutation. 
     
     
         24 . The computer-implemented method of  claim 1 , further comprising, identifying one, multiple or all genes with splicing aberrations in one or more patients and displaying them graphically. 
     
     
         25 . The computer-implemented method of  claim 1 , further comprising, tabulating a statistics of a splicing aberration in one or more genes in one or more patients. 
     
     
         26 . The computer-implemented method of  claim 1 , further comprising, graphically displaying the genes with splicing aberrations and a relevant statistics drawn to gene or genome scale in a single line for one or more genes in one or more patients. 
     
     
         27 - 97 . (canceled)