DESC:CROSS-REFERENCES TO RELATED APPLICATIONS
[001] The present disclosure is a complete specification for the provisional application number: 201941054488, titled: SYSTEM AND METHOD FOR PROCESSING GENOMIC DATA USING CUSTOM DATABASE IN REAL-TIME; filed on: 30.12.2019.

TECHNICAL FIELD
[002] The disclosed subject matter relates generally to computer-based genomic data analysis systems. More particularly, the present disclosure relates to a system and method for processing genomic data using a custom database in real-time to predict medical conditions of a user.

BACKGROUND
[003] Genomics analysis provides diagnostic and prognostic information in clinical and research environments. The present disclosure deals with next-generation sequencing (NGS) technologies in genomics, with particular reference to currently available and possible future platforms and bioinformatics. NGS technologies have demonstrated the capacity to sequence deoxyribonucleic acid (DNA) at an unprecedented speed, thereby enabling previously unimaginable scientific achievements with far-reaching novel biological and medical applications, such as personalized medicine. Generally, to process the genomic sequence data, a specific computing device with a typical workflow of computer-implemented processing steps has been established.

[004] Major hurdle for streamlining genomic medicine into conventional medical practice is the complexity of genomic data representation and limited training in the genomic space of the practicing physicians. Although, a few existing systems provide genomics analysis, the amount of genomic sequence data curated is quite generic, thus precluding the most important aspect of personalization concerning a patient. This is the reason clinically significant information related to food, exercise, and medication is missing from the reports. In order to provide quick, accurate and high quality results, it is essential to eliminate false positives from an alignment result. Clinically relevant and viable DNA analysis for a patient is still immature or crude and not many tools exist to make such huge DNA data relevant to a person’s general health. Existing systems perform one or more functions for clinical data interpretation, but a comprehensive suite of tools and curated pipeline dedicated for general health of a patient is absent.

[005] There are multiple open source databases available for interpretation of the genomic data and provide relevant insights about disease prediction, risk stratification, pharmacogenomics, etc. Some examples of these open source databases are ClinVar, EssembL, PharmKGB. However, the enormous volume of these data sets is from disparate sources makes it very difficult to correlate efficiently and effectively to provide clinically actionable insights to the physician. To address the above mentioned problems, several attempts to create genomic databases like GenBank along with creating search tools like Medline to search a large number of scientific journals like science, nature, etc have been made. Searching these journals is a time-consuming, tedious process and also ineffective as there is a high probability to omit important articles. Despite completing this tedious process, the result is too complex to interpret in a clinical perspective for practicing physicians. To add to all aforementioned issues the results obtained are ambiguous. The markers used to identify the variant which is proposed to cause the disease might be significant in one study and not as important in a different one. There is also variation among subsets of the patient population. There is no uniformity in the collection of phenotypic and environmental factors which can lead to this ambiguity in correlating genotypic data with the disease. For example, if data pertaining to smoking history, psychological stress, and nutrition status of the subject is available before obtaining the genotypic data, the outcomes fail to address the effect of environmental factors at the genome level.

[006] In the light of the aforementioned discussion, there exists a need for a system with novel methodologies that would overcome the above-mentioned challenges. The above discussion is included solely for the purpose of providing a context for the present disclosure.

SUMMARY
[007] The following presents a simplified summary of the disclosure in order to provide a basic understanding of the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[008] Exemplary embodiments of the present disclosure are directed towards a system and method for processing genomic data using a custom database in real-time to predict medical conditions of a user.

[009] An objective of the present disclosure is directed towards practicing personalized medicine by any physician without the knowledge of genomics and utilizing the system of custom information database relating to simplified genomic to aid the physician to make management protocols for patients concerned.

[0010] Another objective of the present disclosure is directed towards providing comprehensive data entry points including detailed phenotypic data, environmental factors before obtaining genomic data so that the results can be personalized to an individual to come up with the best treatment technique.

[0011] Another objective of the present disclosure is directed towards incorporating the genomic knowledge into clinical practice by collecting phenotypic traits, environmental factors and the custom database to provide best treatment solutions for patients.

[0012] Another objective of the present disclosure is directed towards a system that provides a clinically significant pipeline which caters to regular human health by providing personalized DNA analysis reports to the patient’s current conditions and potential ailments that can pose significant health risks, information on adverse reactions to medications, etc.

[0013] Another objective of the present disclosure is directed towards the system to reduce the trial and error method of prescribing drugs currently followed by medical practitioners worldwide.

[0014] Another objective of the present disclosure is directed towards the system that provides a structured process eliminating ambiguity thus rendering efficient and effective care to the end user i.e., the patient.

[0015] Another objective of the present disclosure is directed towards the system brings the complex data into a structured format for bringing genomics to every patient, individual in the near future.

[0016] Another objective of the present disclosure is directed towards the system that bridges the gap between genomic medicine and conventional medical practice.

[0017] According to an exemplary aspect, the system comprising a sequencing module configured to sequence one or more raw files and subsequently send one or more sequenced files to a server.

[0018] According to another exemplary aspect, the system further comprising a genomic data processing module configured to collect the one or more sequenced files from the server and process the one or more sequenced files for clinical significance of variants identified using a custom database.

[0019] According to another exemplary aspect, the genomic data processing module processes the drug associations for the variants on a computing device and provides information regarding how effective associated drugs are for the user. Here, the custom database is a database comprising information on the regular drugs associated with the specific condition and helps in identifying the most effective drug for the user.

[0020] According to another exemplary aspect, the genomic data processing module gets precise information on how the genetics of a specific area differs in an individual. The genomic data processing module generates one or more preliminary reports on the computing device based on the precise information. Here, one or more preliminary reports help in curating a precise diet and lifestyle regimen, precautions to be taken for high risk individuals, changing medication regimen for maximum benefit with minimal to no side effects.

BRIEF DESCRIPTION OF THE DRAWINGS
[0021] FIG. 1 is a block diagram depicting a schematic representation of a system for analyzing genomic data to predict medical conditions of a user, in accordance with one or more exemplary embodiments.

[0022] FIG. 2 is a block diagram depicting the genomic data processing module shown in FIG. 1, in accordance with one or more exemplary embodiments.

[0023] FIG. 3 is a flowchart depicting an exemplary method for processing genomic data to predict the medical conditions of the user, in accordance with one or more exemplary embodiments.

[0024] FIG. 4 is a flowchart depicting an exemplary method for persisting the clinical significance data along with the VCF information in the transient database, in accordance with one or more exemplary embodiments.

[0025] FIG. 5 is a flowchart depicting an exemplary method of interacting with the custom database to generate demystified records by the demystification module, in accordance with one or more exemplary embodiments.

[0026] FIG. 6 is a flowchart depicting an exemplary method for sanitizing the data for report generation and updating the reports database, in accordance with one or more exemplary embodiments.

[0027] FIG. 7 is a flowchart depicting an exemplary method for sending the information of normal responses to downstream modules, in accordance with one or more exemplary embodiments.

[0028] FIG. 8 is a flowchart depicting an exemplary method for annotating the records with the responses and strengthening of the response of the drug by the drug response calculator, in accordance with one or more exemplary embodiments.

[0029] FIG. 9 is a flowchart depicting an exemplary method for sending the documented recommendations to the report generation module from the nutrigenomics module, in accordance with one or more exemplary embodiments.

[0030] FIG. 10 is a block diagram illustrating the details of a digital processing system in which various aspects of the present disclosure are operative by execution of appropriate software instructions.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
[0031] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0032] The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Further, the use of terms “first”, “second”, and “third”, and so forth, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0033] Referring to FIG. 1 is a block diagram 100 depicting a schematic representation of the system for analyzing genomic data to predict the medical conditions of a user, in accordance with one or more exemplary embodiments. The system 100 includes a first computing device 102, a second computing device 104 operatively coupled to each other through a network 106. The network 106 may include but not limited to, an Internet of things (IoT network devices), an Ethernet, a wireless local area network (WLAN), or a wide area network (WAN), a Bluetooth low energy network, a ZigBee network, a WIFI communication network e.g., the wireless high speed internet, or a combination of networks, a cellular service such as a 4G (e.g., LTE, mobile WiMAX) or 5G cellular data service, a RFID module, a NFC module, wired cables, such as the world-wide-web based Internet, or other types of networks may include Transport Control Protocol/Internet Protocol (TCP/IP) or device addresses (e.g. network-based MAC addresses, or those provided in a proprietary networking protocol, such as Modbus TCP, or by using appropriate data feeds to obtain data from various web services, including retrieving XML data from an HTTP address, then traversing the XML for a particular node) and so forth without limiting the scope of the present disclosure. The system 100 is preferably realized as a computer-implemented system in that the computing devices 102, 104 are configured as computer-based electronic devices.

[0034] Although the first and second computing devices 102, 104 are shown in FIG. 1, an embodiment of the system 100 may support any number of computing devices. The computing devices 102, 104 may include, but not limited to, a desktop computer, a personal mobile computing device such as a tablet computer, a laptop computer, or a netbook computer, a smartphone, a video game device, a digital media player, a piece of home entertainment equipment, backend servers hosting database and other software, and the like. Each computing device supported by the system 100 is realized as a computer-implemented or computer-based device having the hardware or firmware, software, and/or processing logic needed to carry out the intelligent messaging techniques and computer-implemented methodologies described in more detail herein. The computing devices 102, 104 may be configured to display features by the genomic data processing module 108. The genomic data processing module 108 may be configured to give genomic test mutation data, i.e. changes in one’s DNA when compared with Normal reference DNA. DNA information (mutated & your healthy DNA information) in understanding response to specific drugs. Every clinically significant study’s response may be assessed and based on collective information.

[0035] The genomic data processing module 108, which is accessed as mobile applications, web applications, software that offers the functionality of accessing mobile applications, and viewing/processing of interactive pages, for example, are implemented in the computing devices 102, 104 as will be apparent to one skilled in the relevant arts by reading the disclosure provided herein. The genomic data processing module 108 may be downloaded from the cloud server (not shown). For example, the genomic data processing module 108 may be any suitable applications downloaded from, GOOGLE PLAY® (for Google Android devices), Apple Inc.'s APP STORE® (for Apple devices, or any other suitable database). In some embodiments, the genomic data processing module 108 may be software, firmware, or hardware that is integrated into the computing devices 102, 104. The genomic data processing module 108 may be an artificial intelligence powered, needs-based, social networking service to provide the processing of genomic data to users. The users may include, but not limited to, patients, doctors, service providers, healthcare supporters, and so forth. The users may allow accessing the genomic data processing module 108 by entering login identity credentials at the computing devices 102, 104. The credentials may include a unique identifier or identifiers of the genomic data processing module 108. For example, identifiers may include, a username, an email address, an account identity, a mobile number, and so forth. A secured code associated with an identifier may include a password, a symmetric encryption key, biometric values, a passphrase, and so forth.

[0036] The genomic data processing module 108 may be configured to collect one or more raw files received from a sequencing module 110. The one or more raw files may be in FASTQ format, or SFF (Standard Flowgram Format) or the like. The one or more raw files may be general called FASTQ files. The sequencing module 110 may be installed on the first computing device 102 and/or on the second computing device 104. To perform the quality control of raw files we adopted an open source tool called FASTQC tool. FASTQC tool is most popular for quality check of high-throughput sequencing data. To get quality check report for FASTQ files, we run FASTQC workflow. The genomic data processing module 108 may include a TRIMMOMATIC (for example, version 0.36) tool configured to trim and crop the ILLUMINA raw reads (FASTQ format) and to remove adapter sequences from 5' end and 3' end. Using phred+33 or phred + 64 sequence quality scores may be considered depending on the ILLUMINA pipeline used. The trimming steps and their parameters may be given on the command line. The system 100 further includes a server 112 configured to supply data in response access from the genomic data processing module 108. The server 112 may be configured to receive one or more raw files from the sequencing module 110. The genomic data processing module 108 may also be configured to collect the one or more sequenced files from the server 112 and process the one or more sequenced files for clinical significance of variants identified using the custom database (shown in FIG. 2).

[0037] The genomic data processing module 108 may include an open source fast and memory-efficient tool (for example, Bowtie2) configured to map the trimmed smoothed reads to reference genome. The open source fast and memory-efficient tool runs on the command line under Windows, Mac OS X and Linux. The open source fast and memory-efficient tool allows gapped, local, and paired-end alignment modes. The first computing device 102 and/or the second computing device 104 may include processors configured to perform parallel mapping to reduce alignment time and increase alignment speed. The open source fast and memory-efficient tool may be configured give output of aligned reads in sequence alignment map(SAM) format, enable the operations with large number of other tools (e.g. SAM tools, GATK) which uses SAM files as input. The genomic data processing module 108 may further include a compressed columnar file format (for example, CRAM) configured to store biological sequence aligned to a reference sequence. Set of utilities for interacting with high-throughput sequencing data with processing of short DNA sequence read alignment in SAM tools. SAM files may be generated using read aligners like Bowtie2, Bowtie, BWA, STAR, HISAT, HISAT2, and the like. The genomic data processing module 108 may use Bowtie2 to align the reads to reference genome and generated the SAM files in previous step. Major applications of SAM tools like variant calling and alignment viewing as well as sorting, indexing, data extraction and format conversion.

[0038] The genomic data processing module 108 may further include a command line tool (VarScan) for variant calling; here the command line tool is configured to take a pileup file generated using SAM tools. The command line tool may be configured to employ a robust heuristic/statistic approach to call variants that meet desired thresholds for read depth, base quality, variant allele frequency, and statistical significance. The genomic data processing module 108 may also include Ensembl Variant Effect Predictor (VEP) configured to allow the users to annotate the predicted variants. VEP may be configured to determine the associated effect of corresponding Ensembl transcripts and proteins. VEP may work by considering the input coordinates of alleles (SNPs, CNVs, indels or structural variations) which are identified on a particular gene, sequence, protein, transcript or transcription factor. If an input variant causes a change in the protein sequence, the VEP may calculate the possible amino acids at that position and the variant may be given a consequence type of missense. VEP may be accessed through tools such as SNPsift or Annovar tool. The SNPsift tool may be a Java program used for annotating the VCF file generated by varscan with RSIDs for a database (shown in FIG. 2) called dbSNP ( which is an open source Database) and may be considered an authority on Genomics and genomics variants data.

[0039] Once a blood sample is collected, the first step is to extract DNA. The extracted DNA is then prepped and ready to be loaded for genomic analysis. The next-generation sequencing (NGS) machine 110 may be configured to conduct the genomic analysis on the computing device 102/104. The raw data from the sequencing module 110 may be run through a pipeline of open source computer tools and in-house custom tools, including a custom database (207 shown in FIG. 2). The resultant genetic data may be then cataloged based on the cumulative effect of the different gene variants for each condition. Finally, the genomic data processing module 108 may be configured to generate a report by considering the medical history, food habits, and lifestyle.

[0040] The report may include, but not limited to immunity profile, cancer risks, medical conditions- prognosis, pharmacogenomics such as hypertension/ high blood pressure, obesity/ metabolic syndrome (moderate risk), diabetes mellitus (moderate risk), dyslipidemia/ cholesterol disorders (moderate risk), inflammatory bowel disease (moderate risk), degenerative joint disease (moderate risk), cardiomyopathy/ heart failure (mild risk), asthma like respiratory illness (mild risk), Parkinson’s disease (mild risk), eye disorders (mild risk), coronary artery disease/ heart attack risk (low risk), thyroid disorders (low risk), female hormonal imbalance (low risk), gastritis/ reflux esophagitis, liver disorders (low risk), kidney disorders (low risk), osteoporosis (low risk), autoimmune disorders (low risk), anxiety/ depression/ schizophrenia (low risk), Alzheimer’s dementia (low risk), and so forth.

[0041] The genomic data processing module 108 may also be configured to generate lifestyle recommendations and nutrition recommendations on the computing device 102/104 based on the generated report. The lifestyle recommendations may include, but not limited to regular food timings, work out details, pre workout snack details, and meal details after workout, duration of meditation or breathing exercises, work out duration, and so forth. The nutrition recommendations may include, but not limited to, foods rich in Riboflavin- Dark green vegetables, eggs and buttermilk, variety of vegetables, avoidable foods, avoidable oils, avoidable cold foods, macronutrient distribution, number of meals, recommendations based on food groups, template for meal planning, and so forth.

[0042] Whole exome sequencing (WES) may be performed using the sequencing module 110. Huge quantities of the next-generation sequencing based genomic data for better clinical guidance. The test results and reports may be carefully reviewed by highly trained and experienced individuals. DNA is extracted from blood and next-generation sequence library is prepared using illumina’s TruSeq Exome Kit. The raw data may be checked for quality by fastQC and aligned to human reference genome GRCh38. Further VarScan tool may be used to call variants from the sequencing data. Single nucleotide variants (SNVs) and indels with higher confidence may be obtained by specific filtering options on the resultant variant calling files. Genomic analysis toolkit guidelines may be followed for variant identification. A variant may be defined as a change in the gene. It can be pathogenic (disease causing) or benign (doesn’t cause disease). Gene annotation of the variants and its clinical relevance was performed using Ensembl VEP version 98. Non-Synonymous, few associated synonymous and other splice site variants of the genome’s coding region may be considered for clinical correlation. The splice site variants may include, but not limited to, response to diabetic drugs, response to obesity drugs, response to arthritis drugs, response to cardiac & hypertension drugs, response to statins, response to antineoplastic agents, others, pathogenic and likely pathogenic variants, risk factors, protective variants, association, benign variants, indels, nutrigenomics profile, lipid metabolism, protein metabolism, vitamin disorders, fitness factors, immune fitness, the blood factors, and so forth.

For example,

PHARMACOGENOMIC ANALYSIS AND DRUG RESPONSE STATUS
(FDA, CPIC & PharmGkb Approved Biomarker evaluation)

No Risk of G6PD Deficiency.
DIABETIC DRUG RESPONSE Good response – Sufonyl ureas (Glimeperide, Glipizide, Glibenclamide, Gliclazide) Poor response – Metformin, Repaglinide

RESPONSE TO HYPERTENSION & CARDIAC DRUGS

Good response:
• ACE Inhibitors - Enalapril, Captopril, Benazepril , Imidapril, Ramipril
• Beta Blockers - Atenolol, Metoprolol
• Diuretics - Spironolactone, Amiloride+ Hydrochlorothiazide; Bumetanide, Furosemide, Torasemide
• Calcium Channel Blockers - Verapamil, Nitrendipine
• Potassium Sparing Diuretics - Spironolactone, Amiloride
• ARB’s - Telmisartan, Losartan
• Anti-Platelet Agents - Prasugrel
• Antiarrhythmic Drug - Amiodarone
Poor response:
• Calcium Channel Blocker - Amlodipine • ACE Inhibitors- Perindopril, Quinapril
• Beta Blockers- Atenolol + Verapamil Combination
• ARB’s - Irbesartan, Candesartan, Olmesartan
• Antiarrhythmic Drugs - Acetaminophen, Aspirin, Diclofenac , Propionic Acid Derivatives , Pyrazolones
• Anti-Platelet Agents - Aspirin, Aspirin + Clopidogrel, Ticagrelor.
Neutral Response:
• Hydrochlorothiazide, Anticoagulants – Dabigatran, Apixaban
• Warfarin- Normal dosage recommended.
• Cough Medicine – Codeine - Normal dosage recommended.

RESPONSE TO STATINS
Good response
• Lovastatin Poor response
• Atorvastatin, Fluvastatin, Rosuvastatin
Neutral response
• Pravastatin, Simvastatin. CPIC Guideline- Recommended dosing of simvastatin based on SLCO1B1 phenotype.

Phenotype Examples Of Diplotypes Genotype At rs4149056 Implications For Simvastatin Dosing Recommendations For Simvastatin Classification Of Recommendations
Normal function, Homozygous wild-type (two normal function alleles) *1a/*1a, *1a/*1b, *1b/*1b TT Normal myopathy risk Prescribe desired starting dose and adjust doses of simvastatin based on disease-specific guidelines. Strong

ANTI-CHOLESTEROL MEDS OTHER THAN STATINS
Neutral response
• Fenofibrate
PROTON PUMP INHIBITORS
Good response
• Omeprazole, Ondansetron
Neutral Response
• Lansoprazole, Omeprazole, Rabeprazole, Pantoprazole (In case of H. pylori infections)
Drug response data to Fentanyl, NSAIDS, cefotaxime, Pencillin molecules is also reported.
DETAILED REPORT
Note: A small value for Strength of the study means there is a high correlation between Good and Poor response studies. It also means that the numbers of drug response studies are sufficient to draw statistical significance.
P value < 0.5 is considered as statistically significant.
Hence, drug molecules which have P=0.05 or small value for strength of the study is to be considered while drug decision making. P=0.05 is considered to be statistically significant.

DIABETES MELLITUS DRUGS
Drug Molecule
% GOOD response studies
% POOR
response studies Response status
Strength of the study
(Smaller the value,
more the strength) P value

Repaglinide
33.33
66.67
Poor
11.11
0.00

Sufonyl ureas
85.71
14.29
Good
51.01
0.00

Metformin
44.00
56.00
Poor
1.44
0.23

Muraglitazar
0.00
100.00
Poor
100.00
0.00

Farglitazar And Glibenclamide
100.00
0.00
Good
100.00
0.00

HYPERTENSION AND CARDIAC DRUGS
Drug Molecule
% GOOD response studies
% POOR
response studies Response status
Strength of the study
(Smaller the value,
more the strength) P value

Calcium Channel Blockers

Calcium Channel Blockers, Nitrendipine
57.14
42.86
Good
2.04
0.15

Good

Nitrendipine
66.67
33.33
Good 11.11 0.00

Amlodipine
0.00
100.00
Poor
100.00
0.00

Amlodipine, Chlorthalidone, Lisinopril
0.00
100.00
Poor 100.00
0.00

Nifedipine
100.00
0.00
Good 100.00
0.00

Verapamil
66.67
33.33
Good 11.12
0.00

Verapamil, Trandolapril
100.00
0.00
Good 100.00
0.00

Loop Diuretics

Bumetanide, Furosemide, Torasemide
60.00
40.00
Good
4.00
0.05

Thiazide Diuretics

Hydrochlorothiazide
55.56
44.44
Good
1.23
0.27

Thiazides, Plain
100.00
0.00
Good
100.00
0.00

Potassium Sparing Diuretics

Spironolactone, Amiloride
80.00
20.00
Good
36.00
0.00

Ace Inhibitors

Perindopril
40.00
60.00
Poor
11.11
0.00

Captopril
100.00
0.00
Good
100.00
0.00
Enalapril
83.33
16.67
Good
44.44
0.00
Benazepril, Imidapril
62.50
37.50
Good
6.25
0.01

Ramipril
100.00
0.00
Good
100.00
0.00

Quinapril
0.00
100.00
Poor
100.00
0.00

Angiotensin 2 Receptor Blockers (ARB's)

Losartan
75.00
25.00
Good
25.00
0.00

Telmisartan
100.00
0.00
Good
100.00
0.00

Olmesartan
0.00
100.00
Poor
100.00
0.00

Candesartan 33.33 66.67 Poor 11.11 0.00
Irbesartan 33.33 66.67 Poor 11.11 0.00
Beta Blockers

Atenolol
73.33
26.67
Good
21.78
0.00

Atenolol, Verapamil 33.33 66.67 Poor 11.11 0.00
Atenolol, Hydrochlorothiazide 100.00 0.00 Good 100.00 0.00
Atenolol, Irbesartan 0.00 100.00 Poor 100.00 0.00
Atenolol, Beta Blocking Agents, Bucindolol, Carvedilol, Metoprolol 0.00 100.00 Poor 100.00 0.00
Atenolol, Metoprolol 100.00 0.00 Good 100.00 0.00
Metoprolol 33.33 66.67 Good 11.12 0.00
Anti-Arrhythmic Drugs

Amiodarone 100.00 0.00 Good 100.00 0.00

Acetaminophen, Aspirin ,Diclofenac, Propionic Acid Derivatives, Pyrazolones 0.00 100.00 Poor 100.00 0.00
Antiplatelet Agents

Aspirin 33.33 66.67 Poor 11.11 0.00
Aspirin, Clopidogrel 43.75 56.25 Poor 1.56 0.21
Ticagrelor 0.00 100.00 Poor 100.00 0.00
Prasugrel 66.67 33.33 Good 11.11 0.00
Anticoagulants

Dabigatran 50.00 50.00 Intermediate 0.00 1.00
Apixaban 50.00 50.00 Intermediate 0.00 1.00
Rivaroxaban 100.00 0.00 Good 100.00 0.00

STATINS
Drug Molecule
% GOOD response studies
% POOR
response studies Response status
Strength of the study
(Smaller the value,
more the strength) P value

Atorvastatin 35.90 64.10 Poor 7.96 0.00
Fluvastatin 37.50 62.50 Poor 6.25 0.01
Lovastatin 100.00 0.00 Good 100.00 0.00
Pravastatin 48.28 51.72 Intermediate 0.12 0.73
Simvastatin 48.84 51.16 Intermediate 0.05 0.82
Pitavastatin 50.00 50.00 Intermediate 0.00 1.00
Rosuvastatin 33.33 66.67 Poor 11.11 0.00
Anti-Cholesterol Med- Other Than Statins

Fenofibrate 46.67 53.33 Intermediate 0.44 0.50
Niacin 100.00 0.00 Good 100.00 0.00

RESPONSE TO FENTANYL, NSAID’S
Drug Molecule
% GOOD response studies
% POOR
response studies Response status
Strength of the study
(Smaller the value,
more the strength) P value

Analgesic

Fentanyl 100.00 0.00 Good 100.00 0.00
NSAIDS

Celecoxib 0.00 100.00 Poor 100.00 0.00
Antiinflammatory Agents,
Non-Steroids, Celecoxib , Diclofenac 0.00 100.00 Poor 100.00 0.00
Ibuprofen 0.00 100.00 Poor 100.00 0.00
Acetaminophen, Ibuprofen, Loxoprofen, Salicylamide 0.00 100.00 Poor 100.00 0.00
Sulindac 50.00 50.00 Intermediate 0.00 1.00

RESPONSE TO PROTON PUMP INHIBITORS, CEPHALOSPORIN, PENCILLINS
Drug Molecule
% GOOD response studies
% POOR
response studies Response status
Strength of the study
(Smaller the value,
more the strength) P value

PROTON PUMP INHIBITORS

Omeprazole 66.67 33.33 Good 11.12 0.00
Lansoprazole, Omeprazole, Rabeprazole, Pantoprazole
(In H. Pylori Infections) 50.00 50.00 Intermediate 0.00 1.00
Prochlorperazine 50.00 50.00 Intermediate 0.00 1.00
Ondansetron 75.00 25.00 Good 25.00 0.00

Drug Molecule
% GOOD response studies
% POOR
response studies Response status
Strength of the study
(Smaller the value,
more the strength) P value

CEPHALOSPORINS

Cefotaxime 0.00 100.00 Good 100.00 0.00
PENCILLINS

Amoxicillin Or Clavulanate 0.00 100.00 Poor 100.00 0.00
Cyclosporine, Dicloxacillin 100.00 0.00 Good 100.00 0.00
Dicloxacillin 100.00 0.00 Good 100.00 0.00
Dolasetron,Granisetron 0.00 100.00 Poor 100.00 0.00
Erythromycin 0.00 100.00 Poor 100.00 0.00

DOSAGE RECOMMENDATION FOR CODEINE AND WARFARIN
% Low Dose response studies
% High Dose response studies
Dosage Recommendation

COUGH MEDICINE

codeine
50.00 50.00 Normal Dosage Recommended
CARDIAC RELATED MOLECULES

warfarin 48.89 51.11 Normal Dosage Recommended warfarin

Immune Factors of KHGLBS22
Gene Name & rs ID Consequence Zygosity Condition Clinical Significance
AICDA
rs2028373, rs2518144,
rs1345004 Synonymous, Intron, 5 Prime UTR Variant Homozygous Immunodeficiency with hyper IgM type 2, Immunodeficiency with Hyper-IgM Benign
CD3G
rs3753059, rs3753058 3 Prime UTR, NMD Transcript Variant Heterozygous Immunodeficiency due to defect in CD3-gamma Benign/Likely benign
CD3E
rs1126924 Synonymous Variant Heterozygous Immunodeficiency 18, Severe Combined Immune Deficiency Benign
CD40
rs1883832 5 Prime UTR Variant Heterozygous Immunodeficiency with hyper IgM type 3, Immunodeficiency with Hyper-IgM Benign
IL17RA
rs2241046 Intron Variant Heterozygous Immunodeficiency 51, Familial Candidiasis Recessive Benign
DCLRE1C
rs7076862 Synonymous Variant Homozygous Severe combined immunodeficiency disease, Histiocytic medullary reticulosis Benign
JAK3
rs3008 3 Prime UTR Variant Heterozygous Severe Combined Immune Deficiency Benign
LOC105374724
rs1494558 Missense Variant Heterozygous Severe combined immunodeficiency autosomal recessive, T cell-negative B cell-positive NK cell-positive, Severe Combined Immune Deficiency Benign
IL7R
rs1494555, rs6897932 Missense Variant Heterozygous Severe combined immunodeficiency autosomal recessive, T cell-negative B cell-positive NK cell-positive, Severe Combined Immune Deficiency Benign
CD19
rs2904880 Missense Variant Homozygous Common Variable Immune Deficiency Recessive Benign
TNFRSF13B
rs8072293 Synonymous Variant Homozygous Common variable immunodeficiency 2, Common Variable Immune Deficiency Dominant Benign
ICOS
rs4264550, rs10183087 Splice Region, Intron, 3 Prime UTR Variant Heterozygous Common Variable Immune Deficiency Recessive Benign/Likely benign
FAS
rs2234978, rs1468063 3 Prime UTR, Synonymous Variant Heterozygous Autoimmune lymphoproliferative syndrome Benign
RAG1
rs1980131 Synonymous Variant Heterozygous Histiocytic medullary reticulosis, Severe Combined Immune Deficiency Benign/Likely benign
NFKBIA
rs8904, rs1957106 Downstream Gene, Synonymous Variant Variant Heterozygous Ectodermal dysplasia, anhidrotic with T-cell immunodeficiency autosomal dominant Benign
FOXN1
rs532648 Missense Variant Heterozygous T-cell immunodeficiency, congenital alopecia and nail dystrophy Benign
SMARCAL1
rs2066518, rs2066527 Missense, Splice Region, Synonymous Variant Heterozygous Schimke immunoosseous dysplasia Benign/Likely benign
SP110
rs1365776 Missense Variant Homozygous Hepatic venoocclusive disease with immunodeficiency Benign
SP110
rs3948463, rs35495464,
rs1135791, rs9061,
rs28930679, rs41309096,
rs11556887 Missense, Synonymous, Intron, Non Coding Transcript Variant Heterozygous Hepatic venoocclusive disease with immunodeficiency Benign/Likely benign
DNMT3B
rs910085, rs6058891,
rs2424922, rs1997797,
rs2424928 Intron, Synonymous, Splice Region Variant Homozygous Centromeric instability of chromosomes 1, 9 and 16 and immunodeficiency Benign
TLR3
rs3775291 Missense Variant Heterozygous Human immunodeficiency virus type 1, susceptibility to Herpes simplex encephalitis 2 Benign/Likely benign
NBN
rs1061302, rs2308962,
rs709816, rs1805794,
rs1063045 Missense, 3 Prime UTR, NMD Transcript, Splice Region, Synonymous Variant Heterozygous Hereditary cancer-predisposing syndrome, Microcephaly normal intelligence and immunodeficiency Benign
LOC101928462
rs1991517 Missense Variant Homozygous Congenital hypothyroidism, Hyperthyroidism nonautoimmune Benign/Likely benign
TG
rs2076740 Missense Variant Homozygous Thyroid dyshormonogenesis, Autoimmune thyroid disease 3 Benign/Likely benign
TG
rs180223, rs853326 Missense Variant Heterozygous Thyroid dyshormonogenesis, Autoimmune thyroid disease 3 Benign/Likely benign
DOCK8
rs529208 Non Coding Transcript Exon Variant Homozygous Hyperimmunoglobulin E recurrent infection syndrome autosomal recessive, Hyper-IgE syndrome Benign
DOCK8
rs506121, rs1887957 Intron, Non Coding Transcript, Synonymous Variant Heterozygous Hyperimmunoglobulin E recurrent infection syndrome autosomal recessive, Hyper-IgE syndrome Benign
LOC105371080
rs2228238, rs7197779 Missense, Synonymous Variant Homozygous Bare lymphocyte syndrome 2 Benign
NLRP12
rs4539722 5 Prime UTR Variant Heterozygous Familial cold autoinflammatory syndrome, Benign
SYN3
rs9862, rs11547635 Synonymous Variant Heterozygous Pseudoinflammatory fundus dystrophy Benign

[0043] Referring to FIG. 2 is a block diagram 200 depicting the genomic data processing module 108 shown in FIG. 1, in accordance with one or more exemplary embodiments. The genomic data processing module 108 may include a bus 201, a clinical significance module 203, a pharmacogenomics module 205, and a custom database 207, and a nutrition diet and exercise module 209. The bus 201 may include a path that permits communication among the modules of the genomic data processing module 108. The term “module” is used broadly herein and refers generally to a program resident in the memory of the computing device 102 or 104.
[0044] The clinical significance module 203 may be configured to receive files from an open source tool in the pipeline. The files may include, but not limited to a (Variant Call Format) VCF file. The open source tool may include, but not limited to, variant effect predictor tool. Once the file is received, all the records are processed, sanitized and classified in accordance with the clinical significance classes prescribed by a public database. The database may include, but not limited to a NCBI Clinvar Database. The classified records may include, but not limited to affects, associations, benign, likely benign, drug response, likely pathogenic, pathogenic, risk factors, protective, uncertain, and the like. The classified records may be then fully annotated with the public database and sent to a demystification module 211 to simplify the process of interpretation and evaluation of the record’s significance for the patient. The demystification module 211 may be configured to interact with the custom database 207 to simplify and personalize the genomic information in accordance with phenotypic information. The data abstraction for clinical interpretation is prepared by the clinical significance module 203 and sent to a report generation module 213. The report generation module 213 may be configured to sort and regularize the data to confirm with report generation and stores the partial report data (the clinical significance data is but a part of the report) in a report database (not shown).

[0045] The clinical significance data along with VCF information may persist in a transient database 215 which facilitates other modules like the pharmacogenomics module 205, and the nutrigenomics module 209 to work with clinically significant data, extracted in clinical significance module 203. However the data in the transient database 215 may be wiped out after a few samples are processed and the information is sent to an online analytical processing module (OLAP module) (not shown) data warehouse. The regular sanitization of transient database 215 helps in increasing the data retrieval speeds and improving the overall performance of the pipeline. While the process of demystification takes place, in a parallel manner, the records which have not yet been tested for any clinical significance are processed. The unclassified data along with the clinically relevant data may be then sent to the OLAP module. The OLAP module (not shown) may be configured to provide insights about the data, the correlation between genotypic phenotypic data, the significance of unclassified information which might play a significant role in the manifestation of diseases etc. The insights may be useful for further research and new developments in the clinical significance of genetic information for the demographics.

[0046] The computing device 102/104 may be configured to enable the clinical significance module 203 to work on the input file (the VCF file). Each record may be interpreted for the information on clinical significance and put into separate bins for further processing. Once the records are classified, the entire information regarding the record may be extracted from all the public databases available in the pipeline. These fully annotated records may the resultant output and may be served to the downstream module.

[0047] The demystification module 211 may be a generic module that takes a set of records fully annotated (for clinical significance, pharmacogenomics, nutrigenomics or immunogenomics). The general flow for the demystification module 211 starts with the input of fully annotated records which are processed (either in a parallel manner or in an sequential manner in accordance with the structure and mode of data demands) for the clinical interpretations curated by experts and stored in the custom database 207. The clinically interpreted data may be in medical parlance for the physicians to better understand the recommendation provided, without the hassle of diving into and dealing with plethora of information which might be confusing and lead to misinterpretation of recommendations made. However, a short description of the underlying reason is provided on which the recommendations stand.

[0048] The information is gathered from the transient database 215 for clinically significant information and also the VCF file for unclassified information. The records are then passed through the pharmacogenomics module 205, which annotated the records based on Phrmgkb and other pharma public databases. These annotated records may be classified according to drug classes these records belong to. Parallel to the classification the Normal medication response for the patient is gathered (the medications for which the patient does not have any genetic variations or mutations are considered as Normal medication response), which are documented in the custom database 207 (retrieved from a public database and sanitized/regularized for pipeline). The (classified single nucleotide polymorphisms) SNP (variant) data along with the normal response data is then sent to a drug response calculator 217. The input for the drug response calculator 217 may be classified as mutant and normal response records from the upstream. The response of a drug for specific ailment may be classified such as poor, good or intermediate. The score for the response may be calculated by the number of studies done and the response noted down. The information may be gathered and put into the custom database 207 by the literature search performed by our experts. Once a response score for each category is determined, the strength of response may be calculated using a chi-square technique. The records may be annotated with the responses and the strength of the response of the drug for the patient. This information may be sent to the downstream for further analysis.

[0049] The drug response calculator 217 accessed the custom database 207 to get the drug response and its strength of response indicated. The data thus annotated and processed is sent to the demystification module 211 for clinical interpretation of pharma data. The demystification module 211 then sends the data to the report generation module 213 to sanitize the data for report generation and updated the reports database. The data while being processed is parallelly/correspondingly transferred to the OLAP module. The records for pharmacogenomics may be classified into drug classes such as statins, antibiotics, hypertension, and diabetes, etc.

[0050] The input for the drug response calculator 217 is the records annotated by public databases. The RSIDs (the identifiers for the records) are extracted and the drug response calculator 217 gets all records for a specific category except the input RSIDs. All records present for a class of drugs except the input records are fetched. The reason for this being, normal response is the information for which the patient does not show any mutation (and the input data is all about mutations). The information of normal responses may be sent to the downstream modules for further processing.

[0051] The clinical significance module 203 may be a python module developed to process the variant calling file generated by an open source pipeline for clinical significance of variants identified using an open source database (not shown) and the custom database 207 which includes the information regarding the gene variant’s clinical statistics and the clinical statistics on diseases associated with the gene. The custom database 207 also includes copy number variants (CNVs) which give the information on how effective the variant identified is and how expressed the specific gene variant is. The pharmacogenomics module 205 may also be a python module developed to process the drug associations for the variants and how effective associated drugs are for the person. The custom database 207 may include information on the regular drugs associated with a specific condition and help in identifying the most effective drug for the user. The statistics collected over time helps identify which of these drugs are working for a specific demographic. The pharmacogenomics module 205 thus, gets precise information on how the genetics of a specific area are and if they have been modified due to environmental changes or nutrition that may be taken in general at these places.

[0052] The information may be gathered from the transient database 215 for clinical significance information along with the input file. The information about significant genes and records are queried, retrieved and matched with the patient’s genomics information. For nutrigenomics, the classification is based on molecules (carbs, proteins, unsaturated fats etc.) which there may be no classification for immunogenomics. The interpretations of the records and the phenotypic data of the patient is used by the nutrigenomics module 209 and recommendations are documented. These documented recommendations are then sent to the report generation module 213 which sanitizes the data for report creation and stores it in the reports database (not shown).

[0053] The nutrigenomics module 209 may be a python module that processes the data of the variants and the general body genomics present in the person to identify the right nutrition diet, nutrition the user may eat to get maximum benefit. The nutrigenomics module 209 may also be configured to suggest types of exercise that the user needs to avoid and exercises that benefit the maximum for the user using the custom database 207. The custom database 207 includes the clinical significance of many genes, statistics on the genes, drug related statistics and nutrition, diet and exercise information, and the like.

[0054] In some embodiments, the custom database 207 may take away multiple steps which are multifactorial in the tertiary analysis of the results. The custom database 207 may be configured to take away the most tedious process out of the equation. The information stored comprises of public sources of databases, scientific publications, and proprietary information. Each variant or gene of a question may be annotated with a simplified action which may be universally understood. In case of ambiguity of the data then the strength of the study is manually assessed which is defined by number of people involved in the study. For example, if we have the study of 25 people from a country using metformin and in Gene SLC22A1, did not report having any side effects and we have a meta-analysis done on 15 randomized controlled trials in 3000 patients using metformin reporting significant gastrointestinal side effects, then we assume that in first study there is no phenotypic data asking about gastrointestinal symptoms. Annotate the Gene SLC22A1 with single recommendation as “Don’t use Metformin” rather than giving detailed pathway and pathogenesis.
[0055] The process of obtaining phenotypic data may be standardized using a template questionnaire which leads to consistent results. The questionnaire may cover most of the environmental factors along with diet, mental health and lifestyle of the patient. In genomic data, once we have VCF file we run through open source PharmKGB database using our in-house built custom code for preliminary results of the pharmacogenomics. The results are again run through our custom database where we developed a risk score for each drug of interest depending on the phenotypic traits and the results are given in plain “best drug for the condition”, “Drugs to be used with caution”, “absolutely contraindicated” in this individual.

[0056] In one embodiment, for all pathogenic variants in the genomic reports, only relevant results that may help patient to modify or delay the condition are provided in the results. For example, a 55-year-old person with upstream variant GATA4 rs745379 which encodes for congenital heart disease, these results are omitted from the results as the assumption is the gene is not expressed.

[0057] In one embodiment, the custom database 207 may be configured to develop contingency plans for the patient to help in early intervention of all high-risk traits. For example, if a pathogenic missense variant PTPRJ rs1566734 for colon cancer is detected in a 36-year-old person, the result may recommend colonoscopy now and every 5 years for early detection of cancer along with dietary modifications with an anti-inflammatory diet.

[0058] In another embodiment, the custom database 207 may also be configured to help in developing the immunological profile of the patient. For example, if the patient has an upstream variant in gene BTK or splice region variant in gene CARD11, these patients may be advised extra caution in cases of a pandemic like SARS-CoV-2 as they are more prone to have severe complications because of dysfunction in developing host immunological response in developing immunoglobulins. Database of people may be created who are more prone to develop an infection in case of future pandemics.

[0059] In another embodiment, the custom database 207 may be configured to give a nutrition assessment of the individual about metabolism of macronutrients (proteins, carbohydrates, fats). Diet templates may be developed which may suit the optimal function of human body. Micronutrient requirements may be calculated and supplemented as needed. For instance, if the individual has variations in gene PPM1K, he/she needs additional replacement with magnesium and manganese for normal cellular function.

[0060] In another embodiment, the process may help in determining muscle endurance of the individual to help curate fitness regimen. For example, if the individual have multiple variants positive for pyruvate kinase deficiency, he/she might get tired with basic workout because the body lacks the ability to convert pyruvate to glucose, so the recommendations to these individuals may be to take a carbohydrate rich meal before starting work out.

[0061] In another embodiment, the process may be invaluable in discovering new markers to look for disease associations. This may be extremely invaluable for polygenic diseases like Diabetes, Hypertension, and Coronary Artery disease to name a few. Gene therapy is another evolving field where cure for these polygenic diseases may be invented rather than disease management as per current medical care.

[0062] The custom database 207 may be useful to develop new disease pathways and aid in drug discovery and drug repurposing. Future drug discovery is going to be through computer programs where 3D protein structures are developed based on the genome sequence, then thousands of small and big molecules are tested through AI algorithms/techniques to find novel drug targets. Genomic data is going to be a new arsenal for practicing medicine in near future.

[0063] The genomic data processing module 108 may be configured to generate the report of a personal genome analysis along with recommendations. The report of the report of a personal genome analysis may include decisions regarding diet, lifestyle, and disease monitoring and health checkups. The report may include indication of genetic pre-disposition to specific health conditions.

[0064] According to an exemplary embodiment of the present disclosure, the system for processing genomic data in real-time, comprising: a sequencing module 110 configured to sequence one or more input files on a computing device 102/104 and then send one or more sequenced files to a server 112, the one or more sequenced files processed through at least one of: a plurality of computer tools and a custom database 207. A genomic data processing module 108 is configured to analyse the one or more sequenced files on the computing device 102/104 for a plurality of preliminary results of pharmacogenomics, the plurality of preliminary results of pharmacogenomics processed through the custom database to develop a risk score for each drug depending on the one or more sequenced files, the genomic data processing module 108 is configured to get precise information from the custom database 207 on how the genetics of a specific area are occurred, the genomic data processing module 108 is also configured to process a plurality of variants and genomics present in a user to advise a plurality of nutritional recommendations on the computing device 102/104, the computing device 102/104 is configured to enable the genomic data processing module 108 to identify a plurality of exercises that the user needs to avoid and the plurality of exercises that benefit for the user using the custom database 207 in accordance with the processed data of the plurality of variants and genomics, the computing device 102/104 is configured to generate one or more preliminary reports by the genomic data processing module 108 based on the plurality of variants and genomics.

[0065] According to another exemplary embodiment of the present disclosure, a method for processing genomic data using custom database in real-time, comprising: sequencing one or more input files by a sequencing module 110 and then sending one or more sequenced files from the sequencing module 110 to a server 112, the one or more sequenced files processed through at least one of: a plurality of computer tools and a custom database 207, processing, sanitizing, classifying records as per a plurality of clinical significance classes prescribed by a public database, fully annotating classified records with public database information by a clinical significance module 203, the clinical significance module 203 is configured to send the classified records to a demystification module 211 to simplify the process of interpretation and evaluation of the record’s significance for a user, interacting the demystification module 211 with the custom database 207 to simplify and personalize the genomic information in accordance with phenotypic information, the demystification module 211 is configured to prepare data abstraction for clinical interpretation and send data abstraction to a report generation module 213, processing fully annotated records by the demystification module 211 as per the structure and mode of data demands for clinical interpretations and processed annotated records stored in the custom database 207, the clinical interpretations are in medical parlance for the user to better understand a recommendation provided, gathering information from a transient database 215 to a pharmacogenomics module 205 for clinically significant information and also the one or more input files for unclassified information, the pharmacogenomics module 205 is configured to annotate the records based on public databases, classifying a plurality of variants according to drug classes by the pharmacogenomics module 205, the pharmacogenomics module 205 is configured to document classified variants in the custom database 207, sending the plurality of variants along with normal response data to a drug response calculator 217 from the pharmacogenomics module 205, the drug response calculator 217 is configured to access the custom database 207 to get the drug response and its strength of response indicated, sending annotated and processed data to the demystification module 211 from the pharmacogenomics module 205 for clinical interpretation of data, the demystification module 211 is configured to send the data to the report generation module 213 to sanitize the data for report generation and updating the report database, gathering information from the transient database 215 to a nutrigenomics module 209 for clinically significant information and also the one or more input files, the nutrigenomics module 209 is configured to query, retrieve and match information of significant genes with a user’s genomics information, interpreting records and phenotypic data of the user by the nutrigenomics module 209 and documenting a plurality of nutritional recommendations; and sending the plurality of nutritional recommendations from the nutrigenomics module 209 to the report generation module, whereby the report generation module is configured to sanitize the plurality of nutritional recommendations for report creation and stores the plurality of nutritional recommendations in the reports database.

[0066] Referring to FIG. 3 is a flowchart 300 depicting an exemplary method for processing genomic data to predict the medical conditions of the user, in accordance with one or more exemplary embodiments. As an option, the method 300 is carried out in the context of the details of FIG. 1, and FIG. 2. However, the method 300 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0067] The method commences at step 302, the sequencing module sequences the input files and then sends the one or more sequenced files to the server from the sequencing module, the one or more sequenced files processed through computer tools and a custom database. The one or more input files may include, but not limited to input VCF (Variant Call Format) files. Thereafter, at step 304, a (NCBI) public database processes, sanitizes, classifies records as per clinical significance classes prescribed. Thereafter, at step 306, the clinical significance module fully annotates classified records with public database information. Thereafter, at step 308, the clinical significance module sends the classified records to a demystification module to simplify the process of interpretation and evaluation of the record’s significance for a doctor. Thereafter, at step 310, the demystification module interacts with the custom database to simplify and personalize the genomic information in accordance with phenotypic information. Thereafter, at step 312, prepare data abstraction for clinical interpretation and send data abstraction from the demystification module to the report generation module. Thereafter, at step 314, the demystification module processes fully annotated records by as per the structure and mode of data demands for clinical interpretations and processed annotated records stored in the custom database. Thereafter, at step 316, the pharmacogenomics module gathers information from a transient database for clinically significant information and also the one or more input files for unclassified information, the pharmacogenomics module configured to annotate the records based on public databases.

[0068] Thereafter, at step 318, the pharmacogenomics module classifies variants according to drug classes, the pharmacogenomics module configured to document classified variants in the custom database. Thereafter, at step 320, the pharmacogenomics module sends the variants along with normal response data to the drug response calculator. Thereafter, at step 322, the drug response calculator accesses the custom database to get the drug response and its strength of response indicated. Thereafter, at step 324, the pharmacogenomics module sends annotated and processed data to the demystification module for clinical interpretation of data. Thereafter, at step 326, the demystification module sends the data to the report generation module from to sanitize the data for report generation and updating the report database. Thereafter, at step 328, the nutrigenomics module gathers information from the transient database for clinically significant information and also the one or more input files. Thereafter, at step 330, the nutrigenomics module queries, retrieves and matches information of significant genes with a user’s genomics information by the nutrigenomics module. Thereafter, at step 332, the nutrigenomics module interprets records and phenotypic data of the user and documenting nutritional recommendations. Thereafter, at step 334, the nutrigenomics module sends the nutritional recommendations to the report generation module. Thereafter, at step 336, the report generation module sanitizes nutritional recommendations for report and stores the nutritional recommendations in the reports database.

[0069] Referring to FIG. 4 is a flowchart 400 depicting an exemplary method for persisting the clinical significance data along with the VCF information in the transient database, in accordance with one or more exemplary embodiments. As an option, the method 400 is carried out in the context of the details of FIG. 1, FIG. 2, and FIG. 3. However, the method 400 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0070] At step 402, the clinical significance module receives a VCF (Variant Call Format) file as an input from the open source tool in the pipeline. Thereafter, at step 404, the clinical significance module processes, sanitizes and classifies in the VCF file in accordance with clinical significance classes prescribed by a NCBI Clinvar database. Thereafter, at step 406, determine whether each record goes through the clinical significance module to be classified and fully annotated? If the answer to step 406 is NO, the exemplary method continues at step 408, searching for further insights about genomic/phenotypic data on the online analytics platform by the online analytical processing module. Thereafter, at step 410, the online analytical processing module provides insights about the data, the correlation between genotypic phenotypic data, and significance of unclassified information to the clinical significance module by the online analytical processing module and then sends the insights about the data, the correlation, and the significance of unclassified information to the database. If the answer to step 406 is YES, the exemplary method continues at step 412, store the classified records on clinical significance secured in the private database.

[0071] Thereafter, at step 414, the public database annotates the classified records with public database information and sent to the demystification module to simplify the process of interpretation and evaluation of the record’s significance for the user. Thereafter, at step 416, the demystification module interacts with the custom database to simplify and personalize the genomic information in accordance with phenotypic information. Thereafter, at step 418, the demystification module prepares data abstraction for clinical interpretation and send the prepared data abstraction to the report generation module. Thereafter, at step 420, the report generation module sorts and regularizes the data to confirm with report generation and stores the partial report data (the clinical significance data is but a part of the report) in the report database. Thereafter, at step 422, it persists the clinical significance data along with the VCF information in the transient database which facilitates other modules (for example, a pharmacogenomics module, a nutrigenomics module) to work with clinically significant data, extracted in the clinical significance module. Thereafter, at step 424, wipes out the data in the transient database after processing a few samples and sends the information to the OLAP module.

[0072] Referring to FIG. 5 is a flowchart 500 depicting an exemplary method of interacting the custom database to generate demystified records by the demystification module, in accordance with one or more exemplary embodiments. As an option, the method 500 is carried out in the context of the details of FIG. 1, FIG. 2, FIG. 3, and FIG. 4. However, the method 500 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0073] At step 502, the demystification module requests and pulls a set of medical records from the medical records database, the set of medical records is fully annotated (for Clinical Significance, Pharmacogenomics, Nutrigenomics or Immunogenomics). Thereafter, at step 504, Processes the fully annotated records (either parallelly or sequentially as per the structure and mode of data demands) for the clinical interpretations curated by the experts. Thereafter, at step 506, the demystification module generates demystified records with medical parlance as an output.

[0074] Referring to FIG. 6 is a flowchart 600 depicting an exemplary method for sanitizing the data for report generation and updating the reports database, in accordance with one or more exemplary embodiments. As an option, the method 600 is carried out in the context of the details of FIG. 1, FIG. 2, FIG. 3, and FIG. 4. However, the method 600 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0075] At step 602, the information gathers from the transient database for clinically significant information and also the VCF file for unclassified information. Thereafter, at step 604, the records passes through the pharmacogenomics module, which annotates the records based on pharmacogenomics knowledge base (phrmgkb) and other public pharma databases. Thereafter, at step 606, classify the annotated records according to drug classes and gather the normal medication response for the patient parallelly to the classification. Thereafter, at step 608, the sends the classified SNP variant (single nucleotide polymorphism variant) data along with the normal response data to the drug response calculator. Thereafter, at step 610, the drug response calculator accesses the custom database to get the drug response and its strength of response indicated. Thereafter, at step 612, the drug response calculator sends the annotated and processed data to the demystification module for clinical interpretation of pharma data. Thereafter, at step 614, the demystification module sends the data to the report generation module to sanitize the data for report generation and updating the reports database. Thereafter, at step 616, data while being processed is parallelly transferred to the online analytical processing module.

[0076] Referring to FIG. 7 is a flowchart 700 depicting an exemplary method for sending the information of normal responses to downstream modules, in accordance with one or more exemplary embodiments. As an option, the method 700 is carried out in the context of the details of FIG. 1, FIG. 2, FIG. 3, FIG. 4, and FIG. 5, and FIG. 6. However, the method 700 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0077] At step 702, the public databases annotate the input records for the pharmacogenomics module. Thereafter, at step 704, extracts normal response RSIDs from the custom database and gets information for each category excluding input RSIDs which are mutant response. Thereafter, at step, 706, the public databases send the information of normal responses to downstream modules for further processing.

[0078] Referring to FIG. 8 is a flowchart 800 depicting an exemplary method for annotating the records with the responses and strengthening of the response of the drug by the drug response calculator, in accordance with one or more exemplary embodiments. As an option, the method 800 is carried out in the context of the details of FIG. 1, FIG. 2, FIG. 3, FIG. 4, and FIG. 5, FIG. 6, and FIG. 7. However, the method 800 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0079] At step 802, provide the input such as the classified mutant and normal response records from the upstream to the drug response calculator. Thereafter, at step 804, the drug response calculator classifies the response of the drug for specific ailment into categories like poor, good or intermediate. Thereafter, at step 806, the drug response calculator calculates the score for the response by the number of studies done and the response noted down. Thereafter, at step 808, a chi-square technique calculates the strength of the response after determining the response score for each category. Thereafter, at step 810, annotates the records with the responses and strengthens the response of the drug for the patient. This information is sent to the downstream for further analysis.

[0080] Referring to FIG. 9 is a flowchart 900 depicting an exemplary method for sending the documented recommendations to the report generation module from the nutrigenomics module, in accordance with one or more exemplary embodiments. As an option, the method 900 is carried out in the context of the details of FIG. 1, FIG. 2, FIG. 3, FIG. 4, and FIG. 5, FIG. 6, FIG. 7, and FIG. 8. However, the method 900 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0081] At step 902, gather the information from the transient database for clinical significance information along with the VCF file, where the information about significant genes and records are queried, retrieved, and matched with the patient’s genomics information. Thereafter, at step 904, the nutrigenomics module uses the interpretations of the records and the phenotypic data of the patient and documents the recommendations. Thereafter, at step 906, the nutrigenomics module sends the documented recommendations to the report generation module which sanitizes the data for report creation and storing the data in the reports database.

[0082] Referring to FIG. 1000 is a block diagram 1000 illustrating the details of a digital processing system 1000 in which various aspects of the present disclosure are operative by execution of appropriate software instructions. The Digital processing system 1000 may correspond to the computing devices 102, 104 (or any other system in which the various features disclosed above can be implemented).

[0083] Digital processing system 1000 may contain one or more processors such as a central processing unit (CPU) 1010, random access memory (RAM) 1020, secondary memory 1027, graphics controller 1060, display unit 1070, network interface 1080, and input interface 1090. All the components except display unit 1070 may communicate with each other over communication path 1050, which may contain several buses as is well known in the relevant arts. The components of Figure 4 are described below in further detail.

[0084] CPU 1010 may execute instructions stored in RAM 1020 to provide several features of the present disclosure. CPU 1010 may contain multiple processing units, with each processing unit potentially being designed for a specific task. Alternatively, CPU 1010 may contain only a single general-purpose processing unit.

[0085] RAM 1020 may receive instructions from secondary memory 430 using communication path 1050. RAM 1020 is shown currently containing software instructions, such as those used in threads and stacks, constituting shared environment 1025 and/or user programs 1026. Shared environment 1025 includes operating systems, device drivers, virtual machines, etc., which provide a (common) run time environment for execution of user programs 1026.

[0086] Graphics controller 1060 generates display signals (e.g., in RGB format) to display unit 1070 based on data/instructions received from CPU 1010. Display unit 1070 contains a display screen to display the images defined by the display signals. Input interface 1090 may correspond to a keyboard and a pointing device (e.g., touch-pad, mouse) and may be used to provide inputs. Network interface 1080 provides connectivity to a network (e.g., using Internet Protocol), and may be used to communicate with other systems (such as those shown in Figure 1) connected to the network 106.

[0087] Secondary memory 1030 may contain hard drive 1035, flash memory 1036, and removable storage drive 1037. Secondary memory 1030 may store the data software instructions (e.g., for performing the actions noted above with respect to the Figures), which enable digital processing system 1000 to provide several features in accordance with the present disclosure.

[0088] Some or all of the data and instructions may be provided on removable storage unit 1040, and the data and instructions may be read and provided by removable storage drive 1037 to CPU 1010. Floppy drive, magnetic tape drive, CD-ROM drive, DVD Drive, Flash memory, removable memory chip (PCMCIA Card, EEPROM) are examples of such removable storage drive 1037.

[0089] Removable storage unit 1040 may be implemented using medium and storage format compatible with removable storage drive 1037 such that removable storage drive 1037 can read the data and instructions. Thus, removable storage unit 1040 includes a computer readable (storage) medium having stored therein computer software and/or data. However, the computer (or machine, in general) readable medium can be in other forms (e.g., non-removable, random access, etc.).

[0090] In this document, the term "computer program product" is used to generally refer to removable storage unit 1040 or hard disk installed in hard drive 1035. These computer program products are means for providing software to digital processing system 1000. CPU 1010 may retrieve the software instructions, and execute the instructions to provide various features of the present disclosure described above.

[0091] The term “storage media/medium” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, or solid-state drives, such as storage memory 1030. Volatile media includes dynamic memory, such as RAM 1020. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.

[0092] Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus (communication path) 1050. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

[0093] Reference throughout this specification to “one embodiment”, “an embodiment”, or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in one embodiment”, “in an embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
[0094] Furthermore, the described features, structures, or characteristics of the disclosure may be combined in any suitable manner in one or more embodiments. In the above description, numerous specific details are provided such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments of the disclosure.

[0095] Although the present disclosure has been described in terms of certain preferred embodiments and illustrations thereof, other embodiments and modifications to preferred embodiments may be possible that are within the principles and spirit of the invention. The above descriptions and figures are therefore to be regarded as illustrative and not restrictive.

[0096] Thus the scope of the present disclosure is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description. ,CLAIMS:1. A system for processing genomic data in real-time, comprising:

a sequencing module configured to sequence one or more input files on a computing device and then send one or more sequenced files to a server, whereby the one or more sequenced files processed through at least one of: a plurality of computer tools and a custom database; and

a genomic data processing module configured to analyse the one or more sequenced files on the computing device for a plurality of preliminary results of pharmacogenomics, whereby the plurality of preliminary results of pharmacogenomics processed through the custom database to develop a risk score for each drug depending on the one or more sequenced files, the genomic data processing module configured to get precise information from the custom database on how the genetics of a specific area are occurred, the genomic data processing module also configured to process a plurality of variants and genomics present in a user to advise a plurality of nutritional recommendations on the computing device, whereby the computing device configured to enable the genomic data processing module to identify a plurality of exercises that the user needs to avoid and the plurality of exercises that benefit for the user using the custom database in accordance with the processed data of the plurality of variants and genomics, the computing device configured to generate one or more preliminary reports by the genomic data processing module based on the plurality of variants and genomics.

2. The system of claim 1, wherein the genomic data processing module comprises a clinical significance module configured to receive files from the open source computer tools on the computing device.

3. The system of claim 2, wherein the clinical significance module configured to process, sanitize and classify records as per clinical significance classes prescribed by a public database.

4. The system of claim 3, wherein the clinical significance module configured to annotate classified records with the public database and send the classified records to a demystification module to simplify the process of interpretation and evaluation of the record’s significance for the user.

5. The system of claim 4, wherein the demystification module configured to prepare data abstraction for clinical interpretation and send data abstraction to a report generation module.

6. The system of claim 1, wherein the computing device configured to persist clinical significance data along with variant call format information in a transient database which facilitates other modules to work with clinically significant data, extracted in the clinical significance module.

7. The system of claim 1, wherein the genomic data processing module comprises a pharmacogenomics module configured to gather pharmacogenomics information from the transient database and annotate the records based on pharma public databases.

8. The system of claim 7, wherein the annotated records are classified according to drug classes belong to parallelly to a classification of a normal medication response for the user, which are documented in the custom database.

9. The system of claim 7, wherein the pharmacogenomics module is configured to send the plurality of variants along with the normal medication response to a drug response calculator.

10. The system of claim 9, wherein the drug response calculator is configured to access the custom database to get the drug response and its strength of response indicated.

11. The system of claim 9, wherein the drug response calculator is configured to annotate and process the drug response and then send the drug response to the demystification module for clinical interpretation of data.

12. The system of claim 11, wherein the demystification module is configured to send the data to the report generation module to sanitize the data for report generation and updated a reports database.

13. The system of claim 1, wherein the genomic data processing module comprises a nutrigenomics module configured to gather nutrigenomics and immunogenomics information from the transient database.

14. The system of claim 13, wherein the nutrigenomics module is configured to query, retrieve, and match the significant genes information and the records with the user’s genomics information.

15. The system of claim 13, wherein the nutrigenomics module is configured to interpret the records and the phenotypic data of the user and then document recommendations.

16. The system of claim 13, wherein the nutrigenomics module is configured to send documented recommendations to the report generation module which sanitizes the data for report generation and stores the report in the report database.

17. A method for processing genomic data using custom database in real-time, comprising:

sequencing one or more input files by a sequencing module and then sending one or more sequenced files from the sequencing module to a server, whereby the one or more sequenced files processed through at least one of: a plurality of computer tools and a custom database;

processing, sanitizing, classifying records as per a plurality of clinical significance classes prescribed by a (NCBI) public database;

fully annotating classified records with public database information by a clinical significance module, whereby the clinical significance module configured to send the classified records to a demystification module to simplify the process of interpretation and evaluation of the record’s significance for a user;

interacting the demystification module with the custom database to simplify and personalize the genomic information in accordance with phenotypic information, whereby the demystification module is configured to prepare data abstraction for clinical interpretation and send data abstraction to a report generation module;

processing fully annotated records by the demystification module as per the structure and mode of data demands for clinical interpretations and processed annotated records stored in the custom database, whereby the clinical interpretations are in medical parlance for the user to better understand a recommendation provided;

gathering information from a transient database to a pharmacogenomics module for clinically significant information and also the one or more input files for unclassified information, whereby the pharmacogenomics module configured to annotate the records based on public databases;

classifying a plurality of variants according to drug classes by the pharmacogenomics module, whereby the pharmacogenomics module configured to document classified variants in the custom database;

sending the plurality of variants along with normal response data to a drug response calculator from the pharmacogenomics module, whereby the drug response calculator configured to access the custom database to get the drug response and its strength of response indicated;

sending annotated and processed data to the demystification module from the pharmacogenomics module for clinical interpretation of data, whereby the demystification module configured to send the data to the report generation module to sanitize the data for report generation and updating the report database;

gathering information from the transient database to a nutrigenomics module for clinically significant information and also the one or more input files, whereby the nutrigenomics module configured to query, retrieve and match information of significant genes with a user’s genomics information;

interpreting records and phenotypic data of the user by the nutrigenomics module and documenting a plurality of nutritional recommendations; and

sending the plurality of nutritional recommendations from the nutrigenomics module to the report generation module, whereby the report generation module configured to sanitize the plurality of nutritional recommendations for report creation and stores the plurality of nutritional recommendations in the reports database.

18. A computer program product comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, said program code including instructions to:

sequence one or more input files by a sequencing module and then send one or more sequenced files from the sequencing module to a server, whereby the one or more sequenced files processed through at least one of: a plurality of computer tools and a custom database;

process, sanitize, classify records as per a plurality of clinical significance classes prescribed by a public database;

fully annotate classified records with public database information by a clinical significance module, whereby the clinical significance module configured to send the classified records to a demystification module to simplify the process of interpretation and evaluation of the record’s significance for a user;

interact the demystification module with the custom database to simplify and personalize the genomic information in accordance with phenotypic information, whereby the demystification module is configured to prepare data abstraction for clinical interpretation and send data abstraction to a report generation module;

process fully annotated records by the demystification module as per the structure and mode of data demands for clinical interpretations and processed annotated records stored in the custom database, whereby the clinical interpretations are in medical parlance for the user to better understand a recommendation provided;

gather information from a transient database to a pharmacogenomics module for clinically significant information and also the one or more input files for unclassified information, whereby the pharmacogenomics module configured to annotate the records based on public databases;

classify a plurality of variants according to drug classes by the pharmacogenomics module, whereby the pharmacogenomics module configured to document classified variants in the custom database;

send the plurality of variants along with normal response data to a drug response calculator from the pharmacogenomics module, whereby the drug response calculator configured to access the custom database to get the drug response and its strength of response indicated;

send annotated and processed data to the demystification module from the pharmacogenomics module for clinical interpretation of data, whereby the demystification module configured to send the data to the report generation module to sanitize the data for report generation and updating the report database;

gather information from the transient database to a nutrigenomics module for clinically significant information and also the one or more input files, whereby the nutrigenomics module configured to query, retrieve and match information of significant genes with a user’s genomics information;

interpret records and phenotypic data of the user by the nutrigenomics module and documenting a plurality of nutritional recommendations; and

send the plurality of nutritional recommendations from the nutrigenomics module to the report generation module, whereby the report generation module configured to sanitize the plurality of nutritional recommendations for report creation and stores the plurality of nutritional recommendations in the reports database.