Methods and systems for using integrated multi-omic data analysis for assessing functional impact of genomic variants

Abstract

A method (100) for characterizing functional impact of a plurality of variants, comprising: obtaining (110) information including at least a plurality of variants, gene expression information, copy number variations, and epigenetic effects; determining (120) a splice state for a variant; determining (130) a variant-based expression regulation state including whether the variant has an effect on gene expression; determining (140) a gene-based expression regulation state including an indication of whether the variant has a functional impact on a target gene; determining (150) a gene-based copy number variation (CNV) and epigenetic impact state including whether one or both has an impact on expression of a gene; adjusting (160) the variant-based and / or gene-based expression regulation state based on the CNV and epigenetic impact state; and reporting (170) at least the adjusted variant-based expression regulation state and / or the adjusted gene-based expression regulation state for each variant and / or gene from the plurality of variants and / or genes of a genomic sample.

Description

Technical Field

[0001] This disclosure generally relates to methods and systems for improved characterization of the functional effects of genomic variants. Background Technology

[0002] As technologies for utilizing different types of molecular information become more readily available and at lower cost, it is becoming more common to generate multiple types of omics data (e.g., genomic, transcriptomic, proteomic, and epigenomic) from the same sample. This allows scientists to better understand the workings of potentially complex biological systems. Commercial assays (such as NanoString) are becoming increasingly common. 3D and The initiation of 170) is based on... The development of next-generation sequencing (NGS) technology, which supports the simultaneous extraction of DNA and RNA, further drives the demand for multi-omics data analysis. While different types of omics data can be analyzed in separate silos through various bioinformatics pipelines, this mainstream approach cannot leverage potential interrelationships across data modalities to construct evidence of the functional impacts of genomic variants, nor can it generate insights into the work at the molecular level. It also cannot generate new insights into the functional or even pathological effects of individual aberrations. Summary of the Invention

[0003] There is a persistent need for methods and systems to assess and characterize functional evidence of genomic variants at different levels to provide multi-level evidence of their functional impact. This disclosure relates to inventive methods and systems for characterizing the functional impact of genomic variants. Various embodiments and implementations herein relate to a system and method that creates multiple states based on data about variants, gene expression, and other omics data received by the system, including mutation states, splice variant states, variant-based expression regulation states, gene-based expression regulation states, and gene-based CNV and epigenetic effect states. The system utilizes gene-based CNV and epigenetic effect states to modulate the variant-based expression regulation states and gene-based expression regulation states for received variants to generate a final list of variants and associated information by filtering and sorting based on one or more of the generated states and scores. A report is generated including the final list of variants / genes and associated information, including the functional impact of each variant / gene in the final list.

[0004] Typically, in one aspect, there is a method for characterizing the functional effects of multiple variants identified from a genomic sample using a variant analysis system. The method includes: (i) obtaining genomic sample information, which includes at least multiple variants identified in the genomic sample, gene expression information obtained from the genomic sample, copy number variations for one or more genes in the genomic sample, and epigenetic effects of one or more genes in the genomic sample; (ii) determining the splicing state for a variant, including an indication of whether the variant has an effect on gene splicing; (iii) determining the expression regulation state based on the variant, including an indication of whether the variant has an effect on gene expression; and (iv) determining the gene expression regulation state based on the variant. (v) Indicating whether the variant has a functional effect on the target gene in a pathway associated with the target gene; (vi) Determining the gene-based copy number variation (CNV) and epigenetic effect status, including an indication of whether the CNV and / or epigenetic effect have an effect on gene expression; (vii) Regulating variant-based expression regulation status and / or gene-based expression regulation status based on gene-based CNV and epigenetic effect status; (vii) Reporting at least the regulated variant-based expression regulation status and / or regulated gene-based expression regulation status for each of the multiple variants and / or genes identified in the genomic sample.

[0005] According to one embodiment, the method includes the step of filtering and / or sorting a plurality of variants and / or genes based at least in part on at least regulated variant-based expression regulatory states and / or regulated gene-based expression regulatory state information.

[0006] According to one embodiment, the splicing status further includes an indication of the strength of splicing evidence for the splicing action of the gene.

[0007] According to one embodiment, the expression regulatory state based on the variant further includes an indication that the affected gene is local or distant.

[0008] According to one embodiment, the gene expression regulation status further includes an indication of upregulation or downregulation of the target gene.

[0009] According to one embodiment, the copy number variation (CNV) and epigenetic effect status of a gene further include an indication of whether the copy number variation (CNV) and / or epigenetic effect lead to a potential upregulation or downregulation of the gene.

[0010] According to one embodiment, for one or more of a plurality of residual variants, the functional effect information includes an indication of the effect of the variant on the expression of one or more genes.

[0011] One aspect is a system used to characterize the functional effects of multiple variants identified from genomic samples. The system includes: genomic sample information, which includes at least multiple variants identified in the genomic sample, gene expression information obtained from the genomic sample, copy number variations for one or more genes in the genomic sample, and epigenetic effects on one or more genes in the genomic sample; and a processor configured to: (i) determine the splicing state of a variant, including an indication of whether the variant has an effect on gene splicing; (ii) determine the expression regulation state based on the variant, including an indication of whether the variant has an effect on gene expression; (iii) determine the expression regulation state based on the gene, including an indication of whether the variant has a functional effect on a target gene in the pathway; (iv) determine the copy number variation (CNV) and epigenetic effect state based on the gene, including an indication of whether the CNV and / or epigenetic effect have an effect on gene expression; and (v) regulate the expression regulation state based on the CNV and epigenetic effect state and / or the gene expression regulation state based on the CNV and epigenetic effect state.

[0012] According to one embodiment, the system further includes a user interface configured to report at least a regulated variant-based expression regulation state and / or a regulated gene-based expression regulation state for each of a plurality of variants and / or genes identified in the genomic sample.

[0013] According to one embodiment, the system further includes a database that operatively correlates regulated variant-based expression regulation states with responses to treatment, diagnosis, and / or prognostic outcomes of patient cases.

[0014] According to one embodiment, the system further includes a matching algorithm that compares and / or identifies one or more associations between a patient's genomic profile and a regulated, variant-based expression regulatory state and the response to treatment, diagnosis, or prognosis of the patient's case. According to one embodiment, the system also includes a user interface that reports one or more matching associations related to the patient at the point of care in a patient context, wherein healthcare professionals can use these associations to automatically generate clinical reports.

[0015] In various embodiments, the processor or controller may be associated with one or more storage media (generally referred to herein as "memory," such as volatile and non-volatile computer memories like RAM, PROM, EPROM and EEPROM, floppy disks, compact disks, optical disks, magnetic tapes, etc.). In some embodiments, the storage media may be encoded with one or more programs that, when executed on one or more processors and / or controllers, perform at least some of the functions discussed herein. Various storage media may be fixed within the processor or controller, or may be transferable, such that one or more programs stored thereon may be loaded into the processor or controller to implement the various functions discussed herein. The terms "program" or "computer program" are used herein in a general sense to refer to any type of computer code (e.g., software or microcode) that can be used to program one or more processors or controllers.

[0016] It should be understood that all combinations of the foregoing concepts and the additional concepts discussed in more detail below (provided these concepts are not inconsistent with each other) are considered part of the inventive solutions disclosed herein. In particular, all combinations of the claimed technical solutions appearing at the end of this disclosure are considered part of the inventive solutions disclosed herein. It should also be understood that terms expressly used herein and that may also appear in any disclosure incorporated by reference should be given the meaning most consistent with the specific concepts disclosed herein.

[0017] These and other aspects of the various embodiments will become apparent and clarified with reference to the embodiments described below. Attached Figure Description

[0018] In the accompanying drawings, the same reference numerals generally refer to the same parts in different views. Furthermore, the drawings are not necessarily drawn to scale, but rather typically focus on illustrating the principles of the various embodiments.

[0019] Figure 1 This is a flowchart of a method for characterizing the functional effects of variants in a genome sample, according to one embodiment.

[0020] Figure 2 This is a flowchart of a method for determining the splicing state according to one embodiment.

[0021] Figure 3 This is a flowchart of a method for determining the expression regulation state and / or fraction based on a variant, according to one embodiment.

[0022] Figure 4 This is a flowchart of a method for determining gene-based expression regulatory states and / or fractions, according to one embodiment.

[0023] Figure 5 This is a flowchart of a method for determining gene-based CNV and epigenetic influence status and / or fractions, according to one embodiment.

[0024] Figure 6 This is a flowchart of a method for regulating the expression regulation state and / or fraction based on variants and the expression regulation state and / or fraction based on genes, according to one embodiment.

[0025] Figure 7 This is a flowchart of a method for characterizing the functional effects of variants in a genome sample, according to one embodiment.

[0026] Figure 8 This is a schematic representation of a variant analysis system according to one embodiment. Detailed Implementation

[0027] This disclosure describes various embodiments of systems and methods for more accurately determining the functional effects of identified variants and genes in a sample on gene expression. More generally, the applicant has acknowledged and recognized that it would be beneficial to provide a method for characterizing the functional effects of variants in detail. The system determines: (i) the splice status of the variant; (ii) the variant-based expression regulation status, including an indication of whether the variant has an effect on gene expression; and (iii) the gene-based expression regulation status, including an indication of whether the variant has a functional effect on a target gene in the pathway. The system also determines gene-based copy number variation (CNV) and epigenetic effect status, including an indication of whether the CNV and / or epigenetic effect have an effect on gene expression. The system uses gene-based CNV and epigenetic effect status to modulate variant-based expression regulation status and / or gene-based expression regulation status. The modulated variant-based expression regulation status and the modulated gene-based expression regulation status include information about the functional effects of the variant and gene. This information on functional effects and other information for one or more variants and / or genes can then be reported.

[0028] refer to Figure 1 In one embodiment, this is a flowchart of a method 100 for characterizing the variant expression state of variants in a genomic sample using a variant analysis system. The variant analysis system may be any system described herein or otherwise contemplated, and may include any components described herein or otherwise contemplated.

[0029] At step 110 of the method, the variant analysis system generates and / or receives DNA and RNA sequencing data for the genetic sample. The genetic sample can be any genetic sample from any organism, including humans, pathogenic organisms, and non-pathogenic organisms, among many others. It is recognized that there are no restrictions on the source of the genetic sample.

[0030] According to one embodiment, the variant analysis system includes a DNA and / or RNA sequencing platform configured to obtain sequencing data from a genetic sample. The sequencing platform can be any sequencing platform, including but not limited to any system described or contemplated herein. Any preparation method can be used to prepare the sample and / or the nucleic acids therein for sequencing, which may depend at least in part on the sequencing platform. According to one embodiment, nucleic acids can be extracted, purified, and / or amplified in many other preparations or processes. For some platforms, nucleic acids can be fragmented using any method for nucleic acid fragmentation, such as shearing, sonication, enzymatic fragmentation, and / or chemical fragmentation, and can be ligated to sequencing adapters or any other molecules or linker bodies. According to one embodiment, the variant analysis system receives DNA and / or RNA sequencing data for a genetic sample. For example, the variant analysis system may be communicative or otherwise receive DNA and / or RNA sequencing data from a database comprising one or more genetic samples.

[0031] The generated and / or received DNA and / or RNA sequencing data can be stored in local or remote databases for use by the variant analysis system. For example, the variant analysis system may include a database for storing DNA and / or RNA sequencing data of genetic samples, and / or may communicate with a database storing sequencing data. These databases may be located with or within the variant analysis system, or they may be located remotely, such as in cloud storage and / or other remote storage.

[0032] The generated and / or received DNA and / or RNA sequencing data may contain a complete or mostly complete genome, or it may be a partial genome, or a small portion of the genome. For example, the generated and / or received sequencing data may be an assembly, a whole genome construct, an incomplete genome, a partial genome, an exome, and / or any other sequencing data.

[0033] The generated and / or received DNA and / or RNA sequencing data each include multiple different variant types, including but not limited to single nucleotide variants, insertions, deletions, copy number variants, and gene fusions. Many other variant types are also possible. Gene fusions can be detected using a variety of systems, including but not limited to dRanger with a breakpointer, FusionMap, and / or other tools. Various systems, including but not limited to SVDetect, BreakDancer, and / or other tools, can be used to detect variants such as inversions, translocations, and other structures.

[0034] The generated and / or received RNA sequencing data also includes expression data for each variant, including but not limited to gene expression data, transcriptional expression data, exon expression data, splicing data, and / or allele-specific expression data. Expression data may be obtained, analyzed, reported, and / or stored from the RNA sequencing data using any method. Expression data may include information on allele-specific expression (ASE); allele-specific splicing (ASS); exon, transcription, and gene (including long non-coding RNA, i.e., lncRNA) expression; differential exon, transcription, and gene (including lncRNA) expression, based on comparisons with matched normal samples and / or mean expression and its standard deviation in unrelated normal tissues; and / or prediction of gene pathway activity by running methods such as Philips OncoSignal and others for gene expression and other desired data.

[0035] If the source is a germline, the data obtained may include genotype (such as homozygous major, heterozygous, homozygous minor), copy number (which can be compared with a healthy population of the same background), and / or other information. If the source is a somatic cell, the data obtained may include variant allele frequency (VAF), differential copy number variation (compared to matched or unrelated normal tissue), and / or other information.

[0036] At step 120 of the method, the system generates a splice state for the variant, which includes the type of splicing action of the variant. This is actually based on the splicing regulatory state and fraction of the variant. For example, the splice state may include predefined or user-defined variables indicating that the variant has no splicing action, or that the splicing action indicates that the variant only affects the splicing of genes local to the variant, where "local" can be, for example, a predefined or user-defined range using centimoles or megabase pairs, as well as other ranges or location-based definitions. For example, local can be defined as one or more genes immediately adjacent to either side of the variant, or, if the variant is within a gene, as the gene in which the variant is located. The splice state may include a splicing effect indicating that the variant only affects the splicing of distant genes, where "distant" can be, for example, a predefined or user-defined range using centimoles or megabase pairs, as well as other ranges or location-based definitions. For example, distant can be defined as one or more genes not directly located on either side of the variant, or, if the variant is within a gene, as a gene different from the gene in which the variant is located. The splicing state can include cis and trans splicing actions and can indicate whether variants affect the splicing of local and distant genes.

[0037] According to one embodiment, the splicing status also includes an indication of the strength of the splicing evidence. For example, the indication of the strength of the splicing evidence may include the type of supporting evidence for the splicing action, which could be “allele-specific splicing,” “differential exon expression,” “differential transcriptional expression,” or other applicable types. The indication may be a fraction indicating the strength of the splicing evidence, such as a log2 fold change between the allele-carrying read and the wild-type read, a fold change in exon / transcriptional expression, or other indicators.

[0038] According to one embodiment, the splicing status includes charts, tables, or other information summaries, including variant identification, type of splicing action against the variant, type of supporting evidence for the splicing action generated from the variant, and / or a score of the strength of supporting evidence for the splicing action.

[0039] refer to Figure 2 This is a flowchart of a method for generating splice status for variants. At step 210 of the method, the system generates or receives a list of variants identified in a genomic sample, along with associated expression information, including one or more of the following: differential exon / transcriptional expression (or changes in splice ratio, or other similar measures) between allele carriers and non-carriers, and allele-specific splice data.

[0040] At step 220 of the method, the system determines whether one or more variants in the list of variants are located within a defined flanking distance between the 5' and 3' ends of the i-th exon (exon_i) of a gene (gene_x), wherein the defined flanking distance is predefined or user-defined. For example, the user can define the flanking distance based on preferences and / or experiments; the flanking distance can be determined by the programmer; or the flanking distance can be defined using any other process or setting.

[0041] At step 230 of the method, the system analyzes the received differential exon expression, differential transcriptional expression, and / or allele-specific splicing data to determine whether the variant affects the expression of a local gene. For example, if the variant shows allele-specific splicing of a local gene, the system records this indication at step 240. As an example only, the system may register the presence of a local gene in a table or other data entry format (such as "cis", "gene_x:exon_i", "allele_specific_splicing", and values, although many other variations are possible). If the variant results in differential exon expression, the system records this indication at step 240. As an example only, the system may register the presence of a local gene in a table or other data entry format (such as "cis", "gene_x:exon_i", "differential_exon_expression", and values, although many other variations are possible). If the variant results in differential transcriptional expression, the system records this indication at step 240. As just one example, the system may register the presence of differentially transcribed or local genes in tables or other data entry formats (such as “cis”, “gene_x:exon_i”, “differential_transcription_expression” and values, although many other variations are possible).

[0042] At step 250 of the method, the system determines whether the variant affects the exon / transcriptional expression of a distal gene. To do this, the system searches databases such as sQTL (specimen-based quantitative trait loci) databases to determine if the variant is associated with an effect on the distal gene. The database may include cis information (cis-regulation of variable splicing in nearby genes) and / or trans information (trans-regulation of variable splicing in distal genes). If a variant is found to be associated with an effect on the distal gene, this association is recorded at step 260 in a table or other data entry table (e.g., “trans”, “target_gene_x”, “differential_transcription_expression”, and values, although many other variations are possible).

[0043] At step 270 of the method, a score is determined. If no indication exists that the variant has an effect on splicing, a score such as "none" or "0" is recorded, or alternatively, no score is recorded. If there is an indication that the variant does have an effect on splicing, a score is calculated for the strength of the evidence supporting the splicing effect. For example, the score may include a log2 fold change between allele-carrying and wild-type reads, a fold change in exon / transcriptional expression, or any other indication of splicing effect caused by the variant.

[0044] At step 280 of the method, the splicing status and / or splicing score are reported, such as via a data table or other data format, or via a printed or displayed report. According to one embodiment, the report includes one or more of the following:

[0045] The splice status is a categorical variable indicating the splice action type of the indicator variant as "cis" (affects splicing of local genes only), "trans" (affects splicing of distant genes only), "cis and trans" (affects splicing of both local and distant genes), and / or "none" (no splicing action).

[0046] The splice score is a score that measures the strength of spliced ​​evidence. The splice score can be a function of splice_results, such as selecting the maximum normalized evidence value; and...

[0047] Splice data structures (splice_results) include tables or other data structures or formats that summarize the splicing of variants with one or more of the following fields, as well as other possible fields:

[0048] type – the type of splicing action, which can be “cis” (on local genes) or “trans-sQTL” (on distant genes, based on splicing sites reported in sQTL databases);

[0049] target – the target of splicing behavior, which can be a specific gene exon for cis-splicing or a target gene for trans-splicing only.

[0050] evidence_type – The type of supporting evidence for splicing, which can be “allele-specific splicing,” “differential exon expression,” “differential transcriptional expression,” or other applicable types; and / or

[0051] evidence_value – A value that measures the strength of supporting evidence. Depending on the type of evidence, it can be a fold change in log2 between allele-carrying and wild-type reads, a fold change in exon / transcriptional expression, etc.

[0052] exist Figure 1 At step 130 of the method shown, the system generates a variant-based expression regulatory state. This effectively analyzes the regulation of expression of one or more local and / or distant genes by the variant. For example, the variant-based expression state may include predefined or user-defined variables indicating the variant's effect on expression regulation, upregulation of local and / or distant genes, and / or downregulation of local and / or distant genes, and other indicators that have no effect.

[0053] According to one embodiment, the goal is to assess functional evidence of expression regulation for each genomic variant, whether it resides in a gene's promoter / enhancer (cis-acting – promoter / enhancer) or is reported in an external eQTL database to regulate the expression of local / distant genes (cis-acting / trans-acting – eQTL). Another example of a database is EPDnew (a eukaryotic promoter database), although other sources are also possible.

[0054] refer to Figure 3 This is a flowchart of a method for generating expression regulatory states based on variants. At step 310 of the method, the system generates or receives a list of variants identified in the genomic sample. The system also generates or receives differentially expressed gene (optionally including lncRNA) information.

[0055] At step 320 of the method, the system first determines whether a variant in the variant list is located within the promoter region of a gene (gene_x), wherein the location of the promoter region can be predefined or user-defined. For example, a user-defined region may include a user-defined upstream distance from the transcription start site. Optionally, the predefined region may be based on known / predicted promoters in a database. Therefore, the system may include or be associated with a promoter database.

[0056] If the variant is not located within the promoter region, the system determines whether the variant is located within the enhancer region of the gene (gene_x), where the location of the enhancer region can be predefined in enhancer databases such as FANTOM5 (functional nomenclature of mammalian genomes), although other sources are also possible.

[0057] At step 330 of the method, the system uses received or generated differentially expressed gene (optionally including lncRNA) information to determine whether differential expression of a gene (gene_x) exists between allele vectors and non-vectors. If differential expression of a gene (gene_x) exists and the variant is located in a promoter region and / or enhancer region, the system records the indication at step 340. As an example only, the system may register the indication in a table or other data entry format var_reg_results (such as "cis-promoter", "gene_x", "differential_gene_expression" and values, but many other variations are possible).

[0058] At step 350 of the method, the system further determines whether the variant is known to be associated with differential expression of one or more target genes (gene_x) and the direction of differential expression (reg_dir_x) (upregulation or downregulation). For example, the system may utilize quantitative trait locus (eQTL) databases, such as the GTEx (genotype-tissue expression) eQTL database, and other sources are also possible.

[0059] At step 360 of the method, the system uses received or generated gene (optionally including lncRNA) expression information to determine whether there is observable differential expression of a gene (gene_x) between allele vectors and non-vectors, in the same direction (reg_dir_x) as the direction from the expression database. If differential expression of the target gene (gene_x) is present in the same direction (reg_dir_x) as the direction from the expression database, the system records this indication at step 370. As an example only, the system may register the indication in a table or other data entry var_reg_results (such as “[cis / trans]-eQTL”, “gene_x:reg_dir_x”, “differential_gene_expression”, and values, although many other variations are possible).

[0060] At step 380 of the method, the expression regulation state and score based on the variant are determined. If there is no indication that the variant has any effect on expression regulation, the state can be recorded as "None" and the score can be recorded as "0". If there is an indication that the variant does have an effect on expression regulation, a score is calculated for the strength of evidence supporting the variant's effect on expression regulation. For example, the score can be based on the target gene with the largest magnitude of expression change caused by regulation, regardless of the sign / direction of the expression change.

[0061] At step 390 of the method, a report is made based on the expression regulation status of the variant and / or the expression regulation score of the variant, such as via a data table or other data format, or via a printed or displayed report. According to one embodiment, the report includes one or more of the following:

[0062] Based on the expression regulation status of variants (var_reg_status), which is a categorical variable indicating the type of expression regulation effect of variants, such as "cis-promoter" (cis-acting and in the promoter region of local genes), "cis-enhancer" (cis-acting and in the enhancer of one or more genes), "trans-eQTL" (trans-acting as defined in the eQTL database), "cis and trans" (both cis-acting and trans-acting gene expression regulation) and / or "none" (no expression regulation effect);

[0063] The variant-based expression regulation state score (var_reg_score) measures the strength of evidence for gene expression regulation. The score can be a function of var_reg_results, such as selecting the evidence_value with the largest amplitude (regardless of sign / direction); and...

[0064] A variant-based data structure for gene expression regulation (var_reg_results) containing tables or other data structures or formats summarizing the gene expression regulatory effects of variants, having one or more of the following fields and other possible fields:

[0065] type – the type of regulatory action, which can be “cis-promoter”, “cis-enhancer”, “trans-eQTL”, “cis and trans”, or “none”;

[0066] target – the target of the regulatory behavior, which can be a symbol of the affected gene, optionally connected by “:”, followed by the direction of regulation (up / down) (if any);

[0067] evidence_type – The type of supporting evidence for the regulatory effect, which can be “differential_gene_expression” or other applicable types; and / or

[0068] evidence_value – A value that measures the strength of supporting evidence. For evidence based on differential expression, it can be, among other values, a log2-fold change in expression level in a case relative to a control.

[0069] Many other fields and data are possible.

[0070] exist Figure 1 At step 140 of the method shown, the system generates a gene-based expression regulatory state. This is an efficient analysis of gene-gene interactions to determine whether a gene has a functional role in a target gene within a pathway. For example, the gene-based expression regulatory state may include variables indicating the strongest type of expression regulation of a gene on its direct gene targets defined in a pathway database. According to one embodiment, the goal is to evaluate functional evidence for each gene-gene interaction identified by differential expression between cases and controls, or between disease samples and normal tissue samples, either jointly or in each individual matched sample pair and as defined in an external pathway database.

[0071] Figure 4 This is a flowchart of a method for generating gene-based expression regulatory states. At step 410 of the method, the system generates or receives differentially expressed gene (optionally including lncRNA) information and / or differentially expressed protein information.

[0072] At step 420 of the method, the system identifies one or more genes with differential gene expression based on generated or received RNA-seq and / or identifies differential protein expression based on proteomics data. Different selection strategies can be applied depending on the research hypothesis. For example, genes showing significant differential expression in common between disease and normal sample groups, or genes showing significant differential expression (in both upward and downward directions) in disease-normal sample pairs matched with more than a certain number / percentage of individuals, can be selected. In the following discussion, all examples are given based on a first scenario involving common differential expression, although this does not limit the different scenarios or selection strategies that can be used according to the method.

[0073] At step 430 of the method, the system identifies the associated pathway and corresponding gene target for each identified gene from one or more pathway databases. The pathway database can be any database containing gene pathway information, including but not limited to KEGG, Reactome, Pathway Commons, etc.

[0074] At step 440, a gene-gene regulation table, gene_reg_results, is generated to capture information such as accessory pathways in the data, reported regulatory directions, and observed differential gene expression. For example, gene_1 and gene_2 can be tags for genes “from” (gene_1) “to” (gene_2) at the edge of a pathway found in the pathway database (path_db). Furthermore, de_1 (or de_2) and de_status_1 (or de_status_2) can be the differential expression value (e.g., log2 fold change) and differential expression status (up, down, or none) for gene_1 (or gene_2), respectively.

[0075] At step 450, the system determines the expression regulatory status of each gene-gene interaction. If the downstream gene is differentially expressed while the upstream gene (gene_1) is not differentially expressed, the status is recorded as non-differential expression. For example, a tag such as "Non-DE" indicates the presence of expression regulation originating from non-differential expression. In fact, if the protein function of a non-differentially expressed gene is altered, it can still act on its downstream target.

[0076] Similarly, if it is not specified in the pathway database or is an unknown state, the status is recorded as "unknown direction." For example, a label such as "unknown direction" indicates the existence of an unknown control direction.

[0077] If the differential expression of the upstream gene (gene_1) and the downstream gene (gene_2) is consistent with the predefined regulatory direction in the database, then the status is recorded accordingly. For example, a label such as "consistent direction" for the status indicates that the differential expression of the two genes is consistent with known information.

[0078] If the differential expression direction of the upstream gene (gene_1) and the downstream gene (gene_2) is inconsistent with the predefined regulatory direction in the database, then the status is recorded accordingly. For example, a label such as "opposite direction" for the status indicates that the differential expression of one or two genes is inconsistent with known information.

[0079] According to one embodiment, gene-gene regulatory status is recorded in a table or other data format or structure. For example, status may include formats such as ("path_db:path", gene_1, gene_2, status, de_1, de_status_1, de_2, de_status_2) and many other possible formats.

[0080] At step 460, based on the gene-gene regulation table `gene_reg_results` generated in steps 440 and 450, the overall expression regulation status for each identified gene is determined. According to one embodiment, for gene g, the system finds all entries in `gene_reg_results` that are matched by gene_1. If no matching entry exists, then `gene_reg_status = "No evidence". Otherwise, if any matching entry exists with the status "Conventional direction", then `gene_reg_status = "Conventional direction". Otherwise, if any matching entry exists with the status "Unknown direction", then `gene_reg_status = "Unknown direction". If any matching entry exists with the status "Non-DE", then `gene_reg_status = "Non-DE". Otherwise, `gene_reg_status = "Opposite direction".

[0081] At step 470 of the method, the system generates one or more gene-based expression regulation scores. For example, the system may generate a score or score vector (gene_reg_score_close) that quantifies evidence of the gene's regulatory effect on its direct or proximate targets. According to one embodiment, the system may use the number of direct targets of the gene in each type of regulatory state, i.e., "conventional direction," "unknown direction," "Non-DE," and "opposite direction" recorded in the gene-gene regulation table. As another example, the system may generate a score or score vector (gene_reg_score_ext) that quantifies evidence of the gene's regulatory effect on its extended downstream targets up to a user-defined distance d (in terms of the number of genes). According to one embodiment, the system may use the number of extended targets in each type of regulatory state, i.e., "conventional direction," "unknown direction," "Non-DE," and "opposite direction."

[0082] According to one embodiment, the number of these direct targets and / or the number of extended targets can be determined by the following method, although other methods are possible:

[0083] Let g_fr and p be the vectors of the gene and the corresponding pathway, respectively;

[0084] g_fr=g;p=null;tab=null;

[0085] For i = 1 to d:

[0086] If p == null, then t = all entries in gene_reg_results, where gene_1 matches any element in g_fr;

[0087] If p<>null, then t = all entries in gene_reg_results, where both gene_1 and path match g_fr and p at the same vector position;

[0088] Remove any entries that are already in the tab (t);

[0089] g_fr=gene_2in t; p=path in t;;

[0090] Adding 't' to tab; then

[0091] n_agr_e, n_unk_e, n_nde_e, and n_opp_e are calculated by counting the number of entries in the tab for the states "conventional direction", "unknown direction", "Non-DE", and "opposite direction".

[0092] At step 480 of the method, gene expression regulation status and / or gene expression regulation score are reported, such as via a data table or other data format, or via a printed or displayed report. According to one embodiment, the report includes one or more of the following:

[0093] Based on gene expression regulatory status (gene_reg_status), which is a categorical variable, it indicates the strongest type of regulatory effect a gene has on its direct gene targets defined in the pathway database. Possible categories include, but are not limited to:

[0094] "Prescribed direction" - the observed differential expression of genes and their downstream targets are consistent with the prescribed regulatory direction;

[0095] "Unknown direction" - Both the gene and its downstream target are expressed differentially, but the direction of regulation is not specified;

[0096] "Non-DE" means differential expression of the target gene rather than differential expression of the upstream gene;

[0097] "Opposite direction"—observed differential expression of upstream genes and downstream genes is opposite to the defined regulatory direction; and / or

[0098] "No evidence" - No differential expression was observed in any target gene.

[0099] The gene-reg score close is a score or score vector that quantifies evidence of a gene’s regulatory effect on its immediate or near target expression.

[0100] Gene-based expression regulation score for extended downstream targets (gene_reg_score_ext) is a score or score vector that quantifies the evidence of a gene’s regulatory role in the expression of its extended downstream targets up to a user-defined distance d (in terms of the number of genes).

[0101] Gene-based expression regulation state data structures (gene_reg_results) include tables or other data structures or formats that summarize gene expression regulation states, having one or more of the following fields, as well as other possible fields:

[0102] path_db: path - a path database and defined genes - gene pathways that regulate genes;

[0103] gene_1 – upstream gene;

[0104] gene_2 – a direct downstream target gene;

[0105] status – the type of evidence for gene regulation;

[0106] de_1 – differential expression value of gene_1, such as a log2-fold change;

[0107] de_status_1 – the differential expression status of gene 1, i.e., up / down;

[0108] de_2 – differential expression value of gene_2, such as log2 fold change; and / or

[0109] de_status_2 – the differential expression status of gene 2, i.e., up / down.

[0110] Many other fields and data are possible.

[0111] exist Figure 1 At step 150 of the method shown, the system generates copy number variation (CNV) and / or epigenetic activity status and / or score based on genes. This is an analysis of CNV and epigenetic activity for each gene. For example, the copy number variation (CNV) and epigenetic activity status based on genes may include a categorical value that indicates a combination of CNV and epigenetic activity at gene expression. According to one embodiment, the objective is to evaluate CNV and epigenetic activity at each gene.

[0112] Although only three factors (CNV, methylation, and transcriptional factor binding) are discussed below, this method can include other epigenetic factors or any other factors by weighting their effects in a similar manner.

[0113] Figure 5 This is a flowchart of a method for generating gene-based copy number variations (CNVs) and epigenetic status and / or fractions. At step 510 of the method, the system generates or receives a list of variants identified in the genomic sample. The system also generates or receives information on copy number variations, differential methylation at gene promoters, and / or differential binding at transcription factor binding sites (TFBSs) (e.g., read-enrichment fold changes). Information on CNVs, epigenetic factors, and differential bindings is obtained through any CNVs and epigenetic analyses known now or in the future. The analyses are performed from the same genomic source as the variants identified in the provided genomic sample. The system also generates or receives information on differential gene expression and / or differential protein expression.

[0114] According to one embodiment, CNVs, epigenetic factors, and / or differentially bounded genes at TFBS can be identified by comparing the results of genomic source analysis with databases of known CNVs, databases of epigenetic factors, and / or databases of differentially bound genes at TFBS (such as GTRD (Gene Transcription Regulation Database) and / or by comparing the results of genomic source analysis with comparative genomic sources or samples. For example, the original genomic source could be a tumor sample, while the comparative source or sample could be a non-tumor sample from the same individual.

[0115] At step 520 of the method, the system maps one or more CNVs received in step 510 to corresponding genes affected by the CNVs based on the genomic coordinates of the identified CNVs. For example, the corresponding gene may be the gene containing the CNV. Alternatively or additionally, the system maps the epigenetic factor received in step 510 to the corresponding gene affected by the epigenetic factor based on the genomic coordinates of the identified epigenetic factor. For example, the corresponding gene may be a gene with a promoter having a differentially methylated site. Alternatively or additionally, the system maps differentially binding TFBSs to corresponding genes affected by the differential binding based on the genomic coordinates of the identified TFBSs. For example, the corresponding gene may be a gene with TFBSs that overlap with differentially binding sites obtained by peak modulation in ChIP-Seq data.

[0116] At step 530, each gene is analyzed to determine gene-based CNV, epigenetic, or TFBS activity status and / or score. For example, genes identified as being affected by CNVs are analyzed to determine gene-based CNV status and / or score. Genes identified as being affected by identified epigenetic factors are analyzed to determine gene-based epigenetic factor status and / or score. Genes identified as having identified differentially binding effects of TFBSs are analyzed to determine gene-based TFBS differentially binding status and / or score. According to one embodiment, each gene identified as being affected by CNVs is analyzed to determine whether CNV expression is upregulated, downregulated, or neutral relative to a comparative source or sample or database. Similarly, each gene identified as being affected by epigenetic factors is analyzed to determine whether epigenetic modifications are upregulated, downregulated, or neutral relative to a comparative source or sample or database. Similarly, each gene identified as having differentially binding effects of TFBSs is analyzed to determine whether TFBS differential binding is upregulated, downregulated, or neutral relative to a comparative source or sample or database. Many mechanisms, processes, and algorithms can be used to determine gene-based CNV, epigenetic, or TFBS action status and / or fraction.

[0117] According to one embodiment, the gene-based CNV, epigenetic or TFBS effect status and / or score is determined according to the following steps. The method is provided by way of example only, without limiting the scope of the method. According to this method, one or more of the following parameters are defined:

[0118] defining cnv_hi and cnv_lo as user-defined upper and lower limits for differential copy number values (which can be absolute values or percentile (reference background) values);

[0119] defining meth_hi and meth_lo as user-defined upper and lower limits for differential methylation values (which can be absolute values or percentile (reference background) values);

[0120] defining bind_hi and bind_lo as user-defined upper and lower limits for TFBS differential binding values (which can be absolute values or percentile (reference background) values);

[0121] defining k_cnv, k_meth and k_bind respectively as user-defined weights for the effects on gene expression due to CNV, methylation and transcription factor binding; and

[0122] defining cnv_epi_hi and cnv_epi_lo as user-defined upper and lower limits for the combined CNV and epigenetic effect on gene expression (which can be absolute values or percentile (reference background) values).

[0123] Using these parameters, according to this embodiment, the system can use the following or similar steps to determine the status and / or score of genes affected by CNV, epigenetic factors and / or TFBS differential binding:

[0124] assigning or calculating values for cnv_value, meth_value and bind_value, such as by averaging the log2 fold changes across multiple loci;

[0125] For a gene affected by CNV, if cnv_value>cnv_hi, then cnv_effect = "up"; otherwise if cnv_value<cnv_lo, then cnv_effect = "down"; otherwise cnv_effect = "neutral";

[0126] For genes affected by epigenetic factors, if meth_value<meth_lo, then meth_effect="up"; otherwise, if meth_value>meth_hi, then meth_effect="down"; otherwise, meth_effect="neutral" (it is worth noting that since DNA methylation represses transcription, low (high) differential methylation means upregulation (downregulation) of gene expression); and

[0127] For genes affected by differential TFBS binding, if bind_value>bind_hi, then bind_effect="up"; otherwise, if bind_value<bind_lo, then bind_effect="down"; otherwise, bind_effect="neutral".

[0128] At step 540 of the method, the system records the status and / or score in a table or other data entry form.

[0129] According to one embodiment, the status can lead to activation of oncogenes or inactivation of tumor suppressor genes. In cancer samples, any of these outcomes may be significant, as they may be associated with information regarding diagnosis, prognosis, or response to therapy. In germline samples, the information may relate to cancer susceptibility.

[0130] Oncogenes encode proteins that drive cell proliferation and programmed cell death. Oncogenes fall into six distinct classes: transcription factors, proteins that remodel chromatin structure, growth factors, growth factor receptors, signal transducers of signal transduction pathways, and apoptosis regulators. Oncogenes can be activated by mutation, amplification, or rearrangement (fusion).

[0131] Such an example is the epidermal growth factor receptor (EGFR), which is a receptor tyrosine kinase belonging to the HER family of receptor tyrosine kinases. Receptor activation upon ligand binding leads to downstream activation of the PI3K / AKT, RAS / RAF / MEK / ERK, and PLCγ / PKC pathways. These pathways play functional roles in cell proliferation, survival, and metastatic potential of tumor cells. Increased activation via gene amplification, protein overexpression, or EGFR mutation has been identified as an etiological factor in many human epithelial cancers, including non-small cell lung cancer, colorectal cancer, glioblastoma, and breast cancer. For example, if a copy number variation that leads to ERBB2 amplification is present, it can result in activation, and this active form is associated with response to trastuzumab or pertuzumab. Many drugs target these activated forms, and published clinical evidence exists for conferring response to these drugs when activating mutations or amplifications are detected.

[0132]

[0133] It is known that CNV is associated with many diseases. By way of example only, oncogenes with more copies can increase the risk of disease. However, if the promoter of the oncogene is hypermethylated, the risk may be counteracted by the inhibitory effect on the oncogene. Similarly, tumor suppressor genes with more copies can reduce the risk of disease. However, if the promoter of the tumor suppressor gene is hypermethylated, the protective effect can be counteracted. Therefore, it is important to consider the effect of the combination of CNV and epigenetic factors in clinical applications, as supported by the methods and systems for multi-omics data analysis described herein or otherwise envisioned.

[0134] Another application of the methods and systems for multi-omics data analysis described herein or otherwise envisioned is the use of SNV data to confirm copy-neutral loss of heterozygosity. The normal copy number of these segments is 2. However, the two copies are identical to each other, resulting in the same clinical effect as copy number loss. This type of event can only be detected by jointly analyzing SNV and CNV data.

[0135] At optional step 550 of the method, the system determines the cumulative status and / or score of the gene-based CNV, epigenetic factors and / or TFBS differential binding influences or effects. This can be achieved, for example, by summing, combining or processing the status or values of CNV effects, epigenetic factor effects and / or TFBS differential binding influences or effects. The cumulative score can be any combination of two or more of CNV effect, epigenetic factor effect, and TFBS differential binding.

[0136] According to one non-limiting example, the system uses the following equation or algorithm to determine the cumulative status and / or score of CNV and epigenetic factor influences:

[0137] cnv_epi_value=k_cnv*cnv_value-k_meth*meth_value+k_bind*bind_value;

[0138] and

[0139] If the calculated cnv_epi_bind_value>cnv_epi_hi, then cnv_epi_effect="up"; otherwise if cnv_epi_value<cnv_epi_lo, then cnv_epi_effect="down"; otherwise cnv_epi_effect="neutral".

[0140] At step 560 of the method, the system records the determined cumulative status and / or score in a table or other data entry form.

[0141] At step 570 of the method, the determined state and / or score is reported, such as by being stored in a data table or other data format, or via a printed or displayed report. According to one embodiment, the report for each gene in the analysis includes one or more of the following:

[0142] The CNV effect status (cnv_effect) can be a categorical value indicating the effect of CNV on gene expression. Possible categories or values ​​include, but are not limited to: "up" indicating upregulation of gene expression, "down" indicating downregulation of gene expression, and "neutral" indicating no significant change in gene expression.

[0143] The score (cnv_value) for CNV effect can be a differential copy value (log2-fold change relative to a matched normal tissue and / or a healthy population of the same race / universal baseline 2);

[0144] The state of epigenetic effect (meth_effect) can be a classification value indicating the effect of epigenetic factors on gene expression. Possible categories or values ​​include, but are not limited to: "up" indicating upregulation of gene expression, "down" indicating downregulation of gene expression, and "neutral" indicating no significant change in gene expression.

[0145] The fraction of epigenetic effects (meth_value) can be the differential methylation value (log2 fold change). Although methylation has been described, other epigenetic factors are also possible;

[0146] The TFBS binding status (bind_effect) can be a categorical value indicating the effect of transcriptional factor binding on gene expression (e.g., due to histone modification). Possible categories or values ​​include, but are not limited to: "up" indicating upregulation of gene expression, "down" indicating downregulation of gene expression, and "intermediate" indicating no significant change in gene expression.

[0147] The TFBS binding score (bind_value) can be a difference binding value (log2 multiple change).

[0148] The cumulative state of the effect on gene expression (e.g., cnv_epi_effect) is a classification value indicating the combined effects of CNV, epigenetics, and / or TFBS on gene expression. Possible categories or values ​​include, but are not limited to: "up" indicating upregulation of gene expression, "down" indicating downregulation of gene expression, "intermediate" indicating no significant change in gene expression; and / or

[0149] The cumulative quantitative score (e.g., cnv_epi_value) of the effect of CNV, epigenetics, and / or TFBS on gene expression is measured.

[0150] Many other fields and data are possible.

[0151] exist Figure 1 At step 160 of the method shown, the system generates CNVs and expression regulation states and / or scores regulated by epigenetic factors. This is a reassessment of the variant-based expression regulation states and scores from step 130 of the method and / or a reassessment of the gene-based expression regulation states and scores from step 140 of the method by adjusting CNVs and epigenetic factors from step 150 of the method.

[0152] Figure 6 This is a flowchart of a method for generating CNVs and expression regulation states and / or fractions regulated by epigenetic factors. At step 610 of the method, the system receives or otherwise retrieves one or more of the following: (1) the variant-target regulation table var_reg_results generated in steps 330–360 of the method; (2) the gene-gene regulation table gene_reg_results generated in steps 440–450 of the method; and (3) the gene-based copy number variant (CNV) and epigenetic effect state from step 150 of the method. For example, the gene-based CNV and epigenetic effect state from step 150 of the method could be the cnv_epi_effect value.

[0153] At step 620 of the method, the system compares the variant-target expression regulation information var_reg_results generated in steps 330-360 of the method with the gene-based CNV and epigenetic action status from step 150 of the method. If the regulatory direction of the gene affected by the variant is the same as the regulatory direction of the gene from the CNV and the epigenetic action status from step 150, the variant-gene regulation entry is removed from the data structure var_reg_results.

[0154] At step 630 of the method, the same procedure 300 is then applied to the updated data structure var_reg_results to compute the adjusted variant-based expression-controlled state and score.

[0155] At step 640 of the method, the system compares the gene-gene regulation information gene_reg_results generated in steps 440-450 of the method with the gene-based CNV and epigenetic action status from step 150 of the method. If the regulatory direction of the gene affected by the variant is the same as the regulatory direction of the gene from the CNV and the epigenetic action status from step 150, then the gene-gene regulation entry is removed from the data structure gene_reg_results.

[0156] At step 650 of the method, the same procedure 400 is then applied to the updated data structure gene_reg_results to calculate the regulated gene-based expression regulation state and score.

[0157] At step 660 of the method, the system generates a report that includes a final list of variants / genes and information relating them to the regulatory role of expression following regulation of relevant CNVs and epigenetic factors. This may include storing the information in a data table or other data format, or via a printed or displayed report. According to one embodiment, the report for each variant includes one or more of the following:

[0158] adj_var_reg_status - a categorical variable that indicates the overall type of expression regulation of the variant, such as "cis-promoter", "cis-enhancer", "trans-eQTL", "cis and trans" or "none".

[0159] `adj_var_reg_score` – A score that measures the strength of evidence for gene expression regulation. It should be a function of `var_reg_results`, for example, selecting the value of evidence with the greatest amplitude (regardless of sign / direction); and

[0160] adj_var_reg_results – A table summarizing the target regulatory effects of specific variants with the following fields:

[0161] type – the type of regulatory action, which can be “cis-promoter”, “cis-enhancer”, “trans-eQTL”, “cis and trans”, or “none”;

[0162] target – the target of the regulatory behavior, which can be a symbol of the affected gene, optionally followed by a colon (:) and then the direction of regulation (up / down), if present;

[0163] evidence_type – The type of supporting evidence for the regulatory effect, which can be “differential_gene_expression” or other applicable types; and / or

[0164] evidence_value – A value that measures the strength of supporting evidence. For evidence based on differential expression, it can be the log2-fold change in expression level of the case relative to the control, as well as other values.

[0165] And the following information on the expression regulation of each gene:

[0166] `adj_gene_reg_status` – A categorical variable indicating the strongest type of regulatory effect of a gene on the expression of its direct gene targets defined in the pathway database. Possible categories include “conventional direction,” “unknown direction,” “Non-DE,” “opposite direction,” and “no evidence.”

[0167] gene_reg_score_close - A score or score vector that quantifies the overall evidence of a gene’s regulatory role in the expression of its direct or near-target.

[0168] `gene_reg_score_ext` – A score or score vector quantifying the overall evidence of a gene's regulatory role in the expression of its extended downstream targets, up to a user-defined distance `d` (in terms of gene number); and

[0169] gene_reg_results - A table summarizing the regulatory effects of a specific gene using the following fields:

[0170] path_db: a database of gene pathways and pathways that define gene regulation;

[0171] gene_1 – upstream gene;

[0172] gene_2 – Direct downstream target gene;

[0173] status – the type of evidence for gene-gene regulation;

[0174] The differential expression value of de_1-gene_1, for example, a log2-fold change;

[0175] de_status_1 – the differential expression status of gene_1, i.e., up / down;

[0176] Differential expression values ​​of de_2-gene_2, such as log2 fold change; and / or

[0177] de_status_2 – the differential expression status of gene_2, i.e., up / down.

[0178] Many other fields are possible. Although these values ​​are associated with specific tags, it should be understood that these tags can be any tags. Information may include... Figure 1 The system generates all or part of the report at point 170 of the method.

[0179] At optional step 180 of the method, the system may filter and / or sort multiple variants and / or genes based at least in part on at least regulated variant-based expression regulatory states and / or information on such regulated gene-based expression regulatory states. For example, the system may use these or any other scores or states generated by the system to sort or score variants and / or genes. For example, the system may create and report a list of genes and / or variants identified as having specific functions, and sort them according to the probability of the potential strength of that function. For example, the system may create and report a list of variants or genes that have only epigenetic functions among many other possible lists or sorts.

[0180] refer to Figure 7 In one embodiment, this is a flowchart of a method 700 for characterizing the variant expression state of variants in a genomic sample using a variant analysis system. The variant analysis system can be any system described herein or otherwise contemplated, and can include any elements described or otherwise contemplated.

[0181] At 710, the system receives information such as variant information, expression information, CNV information, epigenetic information, proteomic information, and / or any other information described or conjectured herein.

[0182] At 730, the system generates a splicing state for the variant, including the type of splicing action of the variant, and generates a variant-based expression regulation state and / or fraction. At 740, the system generates a gene-based expression regulation state and / or fraction. At 740, the system determines the gene-based copy number variant (CNV) and / or epigenetic action state and / or fraction. The system then uses the gene-based copy number variant (CNV) and / or epigenetic action state and / or fraction to regulate the variant-based expression regulation state and / or fraction, as well as the gene-based expression regulation state and / or fraction, as shown by the dashed lines.

[0183] refer to Figure 8 In one embodiment, a schematic diagram of a variant analysis system 800 is provided, configured to characterize the functional role of genomic variants identified from a genomic sample. System 800 may be any system described herein or otherwise contemplated, and may include any elements described herein or otherwise contemplated.

[0184] According to one embodiment, system 800 includes one or more of the following interconnected via one or more system buses 812: processor 820, memory 830, user interface 840, communication interface 850, and storage device 860. In some embodiments, such as where the system includes or directly implements a DNA and / or RNA sequencer or sequencing platform, the hardware may include additional sequencing hardware 815. It should be understood that... Figure 8 In some respects, it constitutes an abstraction, and the actual organization of the components of system 500 can be different from and more complex than that shown.

[0185] According to one embodiment, system 800 includes processor 820, which is capable of executing instructions stored in memory 830 or storage device 860, or otherwise processing data, for example, to perform one or more steps of the method. Processor 820 may be composed of one or more modules. Processor 820 may take any suitable form, including but not limited to microprocessors, microcontrollers, multiple microcontrollers, circuits, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), single processors, or multiple processors.

[0186] Memory 830 can take any suitable form, including non-volatile memory and / or RAM. Memory 830 may include various types of memory such as, for example, L1, L2, or L3 caches or system memory. Thus, memory 830 may include static random access memory (SRAM), dynamic RAM (DRAM), flash memory, read-only memory (ROM), or other similar memory devices. In particular, the memory may store an operating system. RAM is used by the processor for temporary data storage. According to one embodiment, the operating system may contain code that, when executed by the processor, controls the operation of one or more elements of system 800. Obviously, in embodiments where the processor implements one or more of the functions described herein in hardware, the software described in other embodiments corresponding to such functions may be omitted.

[0187] User interface 840 may include one or more devices for enabling communication with a user. The user interface may be any device or system that allows information to be transmitted and / or received, and may include a display, mouse, and / or keyboard for receiving user commands. In some embodiments, user interface 840 may include a command-line interface or graphical user interface that can be presented to a remote end via communication interface 850. The user interface may be located with one or more other components of the system, or it may be located remotely from the system and communicate via wired and / or wireless communication networks.

[0188] The communication interface 850 may include one or more devices for enabling communication with other hardware devices. For example, the communication interface 850 may include a network interface card (NIC) configured to communicate according to the Ethernet protocol. Alternatively, the communication interface 850 may implement a TCP / IP stack for communication according to the TCP / IP protocol. Various alternative or additional hardware or configurations for the communication interface 850 will be apparent.

[0189] Storage device 860 may include one or more machine-readable storage media, such as read-only memory (ROM), random access memory (RAM), disk storage media, optical storage media, flash memory devices, or similar storage media. In various embodiments, storage device 860 may store instructions executed by processor 820 or data operable thereon by processor 820. For example, storage device 860 may store operating system 861 for various operations of control system 800. Where system 800 implements a sequencer and includes sequencing hardware 815, storage device 860 may include sequencing instructions 862 for operating sequencing hardware 815, and sequencing data 863 obtained by sequencing hardware 815, although sequencing data 863 may be obtained from sources other than the associated sequencing platform.

[0190] Clearly, the various information described as being stored in storage device 860 may additionally or alternatively be stored in memory 830. In this respect, memory 830 may also be considered as constituting a storage device, while storage device 860 may be considered as memory. Various other arrangements will be apparent. Furthermore, both memory 830 and storage device 860 may be considered as non-transient machine-readable media. As used herein, the term non-transient will be understood to exclude transient signals but include all forms of storage devices, including both volatile and non-volatile memory.

[0191] Although the variant analysis system 800 is shown as including one element of each described element, various elements may be replicated in various embodiments. For example, processor 820 may include multiple microprocessors configured to independently execute the methods described herein or configured to execute steps or subroutines of the methods described herein, such that multiple processors cooperate to achieve the functions described herein. Furthermore, in the case of implementing one or more elements of system 800 in a cloud computing system, various hardware elements may belong to separate physical systems. For example, processor 820 may include a first processor in a first server and a second processor in a second server. Many other variations and configurations are possible.

[0192] According to one embodiment, the storage device 860 of the variant analysis system 800 may store one or more algorithms and / or instructions to perform one or more functions or steps of the methods described herein or otherwise contemplated. For example, the processor 820 may include splicing state indicator or software 864, variant-based expression regulation state indicator or software 865, gene-based expression regulation state indicator or software 866, gene-based CNV epigenetic action state indicator or software 867, and / or report generation indicator or software 868, as well as many other algorithms and / or indicators to perform one or more functions or steps of the methods described or contemplated herein.

[0193] According to one embodiment, a splice state indicator or software 864 guides a system to generate splice states for one or more variants, the splice states including the type of splicing action of the variant. This is actually based on the splice regulatory state and fraction of the variant. The splice states can include cis-splicing action and trans-splicing action, indicating that the variant acts on the splicing of local and distant genes.

[0194] According to one embodiment, a variant-based expression regulation state indicator or software 865 guides a system to generate variant-based expression regulation states. This is an efficient analysis of variants regulating the expression of one or more cis- and / or trans-genes. For example, the variant-based expression state may include predefined or user-defined variables indicating that the variant has no effect on the regulation of expression, upregulation of cis- and / or trans-genes, and / or downregulation of cis- or trans-genes, and other indicators that are ineffective.

[0195] According to one embodiment, a gene-based expression regulatory state indicator or software 866 guides a system to generate gene-based expression regulatory states. This is an efficient analysis of gene-gene interactions to determine whether variants have a functional effect on target genes in a pathway. For example, gene-based expression regulatory states may include variables indicating the strongest type of regulatory effect of a gene on the expression of its direct gene targets as defined in a pathway database. According to one embodiment, the objective is to evaluate functional evidence for each gene-gene interaction, as identified by differential expression and as defined in an external pathway database.

[0196] According to one embodiment, a gene-based CNV epigenetic effect status indicator or software 868 guidance system generates gene-based copy number variation (CNV) and / or epigenetic effect status and / or scores. This is an analysis of CNVs and the epigenetic effect on each gene. For example, the gene-based copy number variation (CNV) and epigenetic effect status may include a categorical value that indicates the combination of CNVs and epigenetic effects in gene expression. According to one embodiment, the objective is CNVs and the epigenetic effect on each gene.

[0197] According to one embodiment, the report generation instruction or software 869 guides the system to generate a user report that includes information about the analysis performed by the system. For example, the report may include a final list of variants and associated information generated by the methods and system. The report can be generated for any format or output method, such as a file format, visual display, or any other format. The report may include a text-based file or other formats that include the reported information.

[0198] The report generation instruction or software 868 can instruct the system to store the generated report or information in temporary and / or long-term storage or other storage devices. This can be local storage within or associated with system 800, or it can be remote storage that receives the report or information from or via system 800. Additionally and / or alternatively, the report or information can be transmitted or otherwise transferred to another system, recipient, process, device, and / or other local or remote location.

[0199] Report generation instructions or software 868 can guide the system to provide generated reports to users or other systems. For example, the system can visually display information about one or more variants on a user interface, which can be a screen or other display. Clinicians or researchers may only be interested in one or a few variants, so the variant analysis system can be instructed or otherwise designed or programmed to display information obtained only for one or a few variants.

[0200] A major challenge in genomics research and precision medicine is identifying the mutations and / or genes that actually cause disease symptoms among hundreds or thousands of candidate variants, essential for scientific discovery or identification of potential therapeutic targets. While standard variant filtering methods based on call quality, population allele frequencies, gene model annotation, known disease associations, and predicted pathogenicity can narrow down the set of candidate variants, multi-series data analysis of gene expression, CNV, epigenetics, and other data is crucial for further elucidating the molecular mechanisms of disease, thus clarifying disease etiology and treatment options.

[0201] One use case for the multigenomic data analysis framework described herein or otherwise envisioned is to facilitate the discovery of disease-causing variants by analyzing whole-genome sequencing (WES) data of DNA and RNA from hundreds of samples in genomic studies. By comparing exon / gene / transcriptional expression between vectors and non-vectors for each candidate variant, and using external databases (e.g., expression / assembly quantitative trait loci, promoter / enhancer maps, etc.), our framework can assess whether variants have any impact on allele-specific expression, alternative splicing, regulation of target genes, etc. As described herein, the generated variant-based states and scores can then be used to filter and rank variants based on their potential functional impact.

[0202] Beyond variant-based functional impact assessments, scientists can gain insights into the functional effects of individual genes. This can be done using frameworks described herein or otherwise envisioned to analyze differential gene expression between case and control samples. Referencing pathway definitions in external databases such as KEGG, Reactome, and Pathway Commons, the framework can assess whether a gene has any impact on its immediate / near downstream target genes or overall pathway activity. If CNV, methylation, or other epigenetic data are available, the framework can assess the combined CNV and epigenetic effects on each gene. This, combined with gene expression results, can further indicate that differential gene expression or any regulatory role is indeed driven by CNV or epigenetic factors. By carefully and systematically considering multi-layered evidence from different omics data, scientists can use it to identify causal mutations by interpreting their potential roles against gene targets and pathways.

[0203] In a similar manner, clinicians can use the framework described herein or other hypothetical frameworks to analyze DNA and RNA WES data to identify pathogenic mutations or genes in patients. When assessing the functional impact of variants, if data for a single patient is insufficient, vector gene expression data and non-vector gene expression data from other studies can be used. Using the framework described herein or other hypothetical frameworks, clinicians can pinpoint pathogenic mutations and genes based on an interpretation of the molecular mechanisms. For example, if a disease is found to be caused by a gene mutation that upregulates the activity of a pathway, the patient can be given drugs known to inhibit the activity of the pathway in an attempt to cure the disease or alleviate symptoms.

[0204] Therefore, according to one embodiment, the methods and systems described herein or otherwise envisioned encompass a wide range of practical applications. For example, the output of the system or method may be a report comprising one or more of a plurality of characterized states and / or scores, including splice states, variant-based expression regulation states and / or scores, gene-based expression regulation states and / or scores, and gene-based CNV and epigenetic effect states and / or scores, as well as other reports, states, and information. The report has numerous uses, including being used by physicians or other healthcare professionals or researchers to identify genes and / or variants involved in the phenotype of a particular individual among many other possible individuals, such as cancer patients or patients with rare genetic diseases. The system can generate reports that include not only a list of genes and / or variants that may be involved in the phenotype of a particular individual, but also a ranking of the most likely genes and / or variants, and / or a ranking of the most likely genes and / or variants with the greatest effect, and / or a ranking of genes and / or variants with the most supporting evidence for the effect.

[0205] Therefore, the methods and systems described herein or otherwise envisioned further include the step of receiving: a report from a scientist, healthcare professional, or other individual, generated by the system and including any information described herein or otherwise envisioned. The received individual's examination report and identification report identify one or more genes and / or variants that may be involved in testing the recipient's phenotype and are therefore potential targets for treatment and / or intervention. According to one embodiment, once identification is performed, the receiving individual or a person acting on behalf of the receiving individual implements a treatment or intervention to treat the phenotype. For example, this may include specific medical treatments based on a known association between the identified variants and / or genes and a specific drug or intervention. According to another embodiment, once identification is performed, the receiving individual or a person acting on behalf of the receiving individual may utilize information for research purposes to identify potential treatments and / or interventions. Therefore, there may be a direct relationship between variants and genes, the output of analytical methods and systems for examining variants and genes, and the individual's treatment or research.

[0206] The methods and systems described herein include several limitations, each encompassing and analyzing millions of pieces of information. For example, the variant information and associated expression (and potentially other) information received or generated by the system may include many 1000s of potential variants, genes, and other analytical data points for analysis. Similarly, each step of the process involves analyzing these 1000s of potential variants, genes, and other data points, thus constituting millions of computations. This is something the human brain cannot accomplish, even with a pen and pencil.

[0207] All qualifiers as defined and used herein should be understood as controllable dictionary qualifiers, qualifiers cited in incorporated literature, and / or the general meaning of the qualified terms.

[0208] The indefinite articles “a” and “an” used in the specification and claims, unless expressly indicated otherwise, shall be understood to mean “at least one”.

[0209] As used herein in the specification and claims, the phrase “and / or” should be understood to mean “any one or both” of the elements so combined, i.e., elements that exist together in some cases and separately in others. Multiple elements listed with “and / or” should be interpreted in the same way, i.e., “one or more” of the elements so combined. Other elements, whether related to or unrelated to the elements specifically identified by the “and / or” clause, may optionally exist in addition to those specifically identified.

[0210] As used herein in the specification and claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating items in a list, “or” or “and / or” should be interpreted as inclusive, that is, including at least one of a plurality of elements or a list of elements, but also including more than one, and alternatively, additional unlisted items. Only terms that clearly identify the opposite, such as “only one” or “exactly one”, or when used in the claims, “comprising of…” will mean including an exact one of a plurality of elements or a list of elements. In general, the term “or” as used herein should be interpreted only as indicating an exclusive substitution (i.e., “one or the other but not both”), preceded by an exclusive term such as “any one,” “one of,” “only one of,” or “exactly one of.”

[0211] As used herein in the specification and claims, the phrase "at least one" relating to a list of one or more elements should be understood to mean at least one element selected from any one or more elements in the list of elements, but does not necessarily include at least one element from each element specifically listed in the list of elements, and does not exclude any combination of elements in the list of elements. This limitation also allows for the optional presence of elements other than those specifically identified in the list of elements referred to by the phrase "at least one," whether related to or unrelated to the specifically identified elements.

[0212] It should also be understood that, unless expressly indicated to the contrary, in any method claimed herein that includes more than one step or action, the order of the steps or actions of the method is not necessarily limited to the order of the steps or actions of the method.

[0213] In the claims and the above description, all transitional phrases such as "comprising," "including," "carrying," "having," "containing," "involving," "holding," "consisting of," etc., should be understood as open-ended, meaning including but not limited to. Only the transitional phrases "consisting of" and "substantially consisting of" should be closed or semi-closed transitional phrases, respectively.

[0214] While several embodiments of the invention have been described and illustrated herein, those skilled in the art will readily foresee numerous other means and / or structures for performing the functions described herein and / or obtaining the results and / or one or more advantages, and each of these variations and / or modifications is considered to be within the scope of the embodiments of the invention described herein. More generally, those skilled in the art will readily understand that all parameters, dimensions, materials, and configurations described herein are exemplary, and actual parameters, dimensions, materials, and / or configurations will depend on the specific application or the application for which the teachings of this invention are applied. Those skilled in the art will recognize or be able to determine many equivalents of the specific embodiments of the invention described herein using only conventional experimentation. Therefore, it should be understood that the foregoing embodiments are presented by way of example only, and embodiments of the invention may be practiced in ways different from those specifically described and claimed within the scope of the appended claims and their equivalents. The embodiments of the invention disclosed herein relate to each individual feature, system, article of manufacture, material, tool, and / or method described herein. Furthermore, any combination of two or more such features, systems, articles of manufacture, materials, tools, and / or methods, provided that such features, systems, articles of manufacture, materials, tools, and / or methods do not contradict each other, is included within the scope of the invention disclosed herein.

Claims

1. A method for characterizing the functional effects of multiple variants identified from a genomic sample using a variant analysis system, comprising: Obtain genome sample information, which includes at least multiple variants identified in the genome sample, gene expression information obtained from the genome sample, copy number variations of one or more genes in the genome sample, and epigenetic effects on one or more genes in the genome sample. Determine the splicing status for the variant, the splicing status including an indication of whether the variant has an effect on gene splicing; Determine the expression regulation state based on variants, wherein the expression regulation state based on variants includes an indication of whether the variant has an effect on gene expression; Determine the gene-based expression regulatory state, which includes an indication of whether the variant has a functional effect on target genes in the pathway; Determine gene-based copy number variation (CNV) and epigenetic effect status, wherein the gene-based CNV and epigenetic effect status include an indication of whether the CNV and / or the epigenetic effect have an impact on gene expression; Based on the gene-based CNV and epigenetic influence status, regulate the expression regulation status based on variants and / or the expression regulation status based on genes; as well as Report at least the regulated variant-based expression regulation state and / or the regulated gene-based expression regulation state for each of the multiple variants and / or genes in the genomic sample.

2. The method according to claim 1, further comprising: The step of filtering at least some of the plurality of variants or genes based at least on the regulated variant-based expression regulation state and / or the regulated gene-based expression regulation state associated with each corresponding variant and / or gene.

3. The method according to claim 1, further comprising: The step of sorting at least some of the plurality of variants or genes.

4. The method of claim 1, wherein the splicing state further includes an indication of the strength of splicing evidence for the splicing action of the gene.

5. The method of claim 1, wherein the variant-based expression regulatory state further includes an indication that the affected gene is local or distant.

6. The method of claim 1, wherein the gene-based expression regulation state further includes an indication of upregulation or downregulation of the target gene.

7. The method of claim 1, wherein the gene-based copy number variation (CNV) and epigenetic effect status further comprises: The copy number variation (CNV) and / or the epigenetic effect indicate that the gene is upregulated or downregulated.

8. The method of claim 7, wherein the report includes a table or other data structure, the table or other data structure including a list of variants and / or genes and functional impact information associated with each variant and / or gene.

9. The method of claim 8, wherein, for one or more of a plurality of remaining variants, the functional effect information includes an indication of the effect of the variant on the expression of one or more genes.

10. A system for characterizing the functional effects of multiple variants identified from a genomic sample, comprising: Genomic sample information, which includes at least multiple variants identified in the genomic sample, gene expression information obtained from the genomic sample, copy number variations of one or more genes in the genomic sample, and epigenetic effects on one or more genes in the genomic sample. as well as A processor configured to: (i) determine a splicing state for the variant, the splicing state including an indication of whether the variant has an effect on gene splicing; (ii) determine a variant-based expression regulation state, the variant-based expression regulation state including an indication of whether the variant has an effect on gene expression; (iii) determine a gene-based expression regulation state, the gene-based expression regulation state including an indication of whether the variant has a functional effect on a target gene in the pathway; (iv) determine a gene-based copy number variation (CNV) and epigenetic effect state, the gene-based CNV and epigenetic effect state including an indication of whether the CNV and / or the epigenetic effect have an effect on gene expression; and (v) regulate the variant-based expression regulation state and / or the gene-based expression regulation state based on the gene-based CNV and the epigenetic effect state. as well as A user interface configured to report at least the regulated variant-based expression regulation state and / or the regulated gene-based expression regulation state for each of the plurality of variants and / or genes in the genome sample.

11. The system of claim 10, wherein the processor is further configured to filter at least some of the plurality of variants or genes based at least on the regulated variant-based expression regulation state and / or the regulated gene-based expression regulation state associated with each respective variant and / or gene.

12. The system of claim 10, wherein the regulated variant-based expression regulation state and / or gene-based expression regulation state comprises a table or other data structure, the table or other data structure comprising a list of variants and / or genes and functional effect information associated with each variant and / or gene.

13. The system of claim 12, wherein, for one or more of a plurality of remaining variants, the functional effect information includes an indication of the effect of the variant on the expression of one or more genes.

14. The system of claim 10, wherein the variant-based expression regulatory state further includes an indication that the affected gene is local or distant.

15. The system of claim 10, wherein the gene-based expression regulation state further includes an indication of upregulation or downregulation of the target gene.

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