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Published in: Clinical and Translational Medicine 1/2019

Open Access 01-12-2019 | Short report

Debutant iOS app and gene-disease complexities in clinical genomics and precision medicine

Authors: Zeeshan Ahmed, Saman Zeeshan, Ruoyun Xiong, Bruce T. Liang

Published in: Clinical and Translational Medicine | Issue 1/2019

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Abstract

Background

The last decade has seen a dramatic increase in the availability of scientific data, where human-related biological databases have grown not only in count but also in volume, posing unprecedented challenges in data storage, processing, analysis, exchange, and curation. Next generation sequencing (NGS) advancements have facilitated and accelerated the process of identifying genetic variations. Adopting NGS with Whole-Genome and RNA sequencing in a diagnostic context has the potential to improve disease-risk detection in support of precision medicine and drug discovery. Several bioinformatics pipelines have been developed to strengthen variant interpretation by efficiently processing and analyzing sequence data, whereas many published results show how genomics data can be proactively incorporated into medical practices and improve utilization of clinical information. To utilize the wealth of genomics and health, there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern technology.

Results

Our focus here is to create a comprehensive database with mobile access to actionable genes and classified diseases, considered the foundation for clinical genomics and precision medicine. We present a publicly available iOS app, PAS-Gen, which invites global users to freely download it on iPhone and iPad devices, quickly adopt its easy to use interface, and search for genes and related diseases. PAS-Gen was developed using Swift, XCODE, and PHP scripting that uses Web and MySQL database servers, which includes over 59,000 protein-coding and non-coding genes, and over 90,000 classified gene-disease associations. PAS-Gen is founded on the clinical and scientific premise that easier healthcare and genomics data sharing will accelerate future medical discoveries.

Conclusions

We present a cutting-edge gene-disease database with a smart phone application, integrating information on classified diseases and related genes. The PAS-Gen app will assist researchers, medical practitioners, and pharmacists by providing a broad and view of genes that may be implicated in the likelihood of developing certain diseases. This tool with accelerate users’ abilities to understand the genetic basis of human complex diseases and by assimilating genomic and phenotypic data will support future work to identify gene-specific designer drugs, target precise molecular fingerprints for tumors, suggest appropriate drug therapies, predict individual susceptibility to disease, and diagnose and treat rare illnesses.
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Literature
2.
go back to reference Escalona M, Rocha S, Posada D (2016) A comparison of tools for the simulation of genomic next-generation sequencing data. Nat Rev Genet 17(8):459–469PubMedPubMedCentralCrossRef Escalona M, Rocha S, Posada D (2016) A comparison of tools for the simulation of genomic next-generation sequencing data. Nat Rev Genet 17(8):459–469PubMedPubMedCentralCrossRef
3.
go back to reference Miller HI, Konkel DA, Leder P (1978) An intervening sequence of the mouse beta-globin major gene shares extensive homology only with beta-globin genes. Nature 275:772–776PubMedCrossRef Miller HI, Konkel DA, Leder P (1978) An intervening sequence of the mouse beta-globin major gene shares extensive homology only with beta-globin genes. Nature 275:772–776PubMedCrossRef
7.
go back to reference Laird CD (1971) Chromatid structure: relationship between DNA content and nucleotide sequence diversity. Chromosoma 32:378–406PubMedCrossRef Laird CD (1971) Chromatid structure: relationship between DNA content and nucleotide sequence diversity. Chromosoma 32:378–406PubMedCrossRef
8.
go back to reference Alberts B, Johnson A, Lewis J et al (2003) Molecular biology of the cell. Ann Bot 91:401CrossRef Alberts B, Johnson A, Lewis J et al (2003) Molecular biology of the cell. Ann Bot 91:401CrossRef
9.
go back to reference Flavell RA, Glover DM, Jeffreys AJ (1978) Discontinuous genes. Trends Biochem Sci 3:241–244CrossRef Flavell RA, Glover DM, Jeffreys AJ (1978) Discontinuous genes. Trends Biochem Sci 3:241–244CrossRef
11.
16.
go back to reference Lobo I, Zhaurova K (2008) Birth defects: causes and statistics. Nat Educ. 1:18 Lobo I, Zhaurova K (2008) Birth defects: causes and statistics. Nat Educ. 1:18
17.
go back to reference Chial H (2008) Mendelian genetics: patterns of inheritance and single-gene disorders. Nat Educ. 1:63 Chial H (2008) Mendelian genetics: patterns of inheritance and single-gene disorders. Nat Educ. 1:63
18.
go back to reference Kibbe WA, Arze C, Felix V et al (2014) Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 43(Database issue):D1071–D1078PubMedPubMedCentral Kibbe WA, Arze C, Felix V et al (2014) Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 43(Database issue):D1071–D1078PubMedPubMedCentral
19.
go back to reference Zhang G, Shi J, Zhu S et al (2017) DiseaseEnhancer: a resource of human disease-associated enhancer catalog. Nucleic Acids Res 46(D1):D78–D84PubMedCentralCrossRef Zhang G, Shi J, Zhu S et al (2017) DiseaseEnhancer: a resource of human disease-associated enhancer catalog. Nucleic Acids Res 46(D1):D78–D84PubMedCentralCrossRef
20.
go back to reference Pletscher-Frankild S, Pallejà A, Tsafou K et al (2015) DISEASES: Text mining and data integration of disease-gene associations. Methods 74:83–89PubMedCrossRef Pletscher-Frankild S, Pallejà A, Tsafou K et al (2015) DISEASES: Text mining and data integration of disease-gene associations. Methods 74:83–89PubMedCrossRef
21.
go back to reference Piñero J, Pallejà A, Tsafou K et al (2017) DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 45:D833–D839PubMedCrossRef Piñero J, Pallejà A, Tsafou K et al (2017) DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 45:D833–D839PubMedCrossRef
22.
go back to reference Babbi G, Martelli PL, Profiti G et al (2017) eDGAR: A database of disease-gene associations with annotated relationships among genes. BMC Genomics. 18:554PubMedPubMedCentralCrossRef Babbi G, Martelli PL, Profiti G et al (2017) eDGAR: A database of disease-gene associations with annotated relationships among genes. BMC Genomics. 18:554PubMedPubMedCentralCrossRef
24.
go back to reference Rubinstein WS, Maglott DR, Lee JM et al (2012) The NIH genetic testing registry: a new, centralized database of genetic tests to enable access to comprehensive information and improve transparency. Nucleic Acids Res 41(Database issue):D925–D935PubMedPubMedCentralCrossRef Rubinstein WS, Maglott DR, Lee JM et al (2012) The NIH genetic testing registry: a new, centralized database of genetic tests to enable access to comprehensive information and improve transparency. Nucleic Acids Res 41(Database issue):D925–D935PubMedPubMedCentralCrossRef
25.
go back to reference Rappaport N, Twik M, Nativ N et al (2014) MalaCards: a comprehensive automatically-mined database of human diseases. Curr Protoc Bioinform. 47:1–24CrossRef Rappaport N, Twik M, Nativ N et al (2014) MalaCards: a comprehensive automatically-mined database of human diseases. Curr Protoc Bioinform. 47:1–24CrossRef
26.
go back to reference Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A (2014) OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res 43(Database issue):D789–D798PubMedPubMedCentral Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A (2014) OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res 43(Database issue):D789–D798PubMedPubMedCentral
27.
go back to reference Jiang Q, Wang Y, Hao Y et al (2008) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37(Database issue):D98–D104PubMedPubMedCentral Jiang Q, Wang Y, Hao Y et al (2008) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37(Database issue):D98–D104PubMedPubMedCentral
28.
go back to reference Stenson PD, Mort M, Ball EV et al (2017) The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum Genet 136(6):665–677PubMedPubMedCentralCrossRef Stenson PD, Mort M, Ball EV et al (2017) The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum Genet 136(6):665–677PubMedPubMedCentralCrossRef
29.
30.
go back to reference Landrum MJ, Lee JM, Benson M et al (2015) ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res 44(D1):D862–D868PubMedPubMedCentralCrossRef Landrum MJ, Lee JM, Benson M et al (2015) ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res 44(D1):D862–D868PubMedPubMedCentralCrossRef
31.
go back to reference Qiu F, Xu Y, Li K et al (2012) CNVD: Text mining-based copy number variation in disease database. Hum Mutat 33:E2375–E2381PubMedCrossRef Qiu F, Xu Y, Li K et al (2012) CNVD: Text mining-based copy number variation in disease database. Hum Mutat 33:E2375–E2381PubMedCrossRef
33.
go back to reference Frankish A, Diekhans M, Ferreira AM et al (2018) GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 47(D1):D766–D773PubMedCentralCrossRef Frankish A, Diekhans M, Ferreira AM et al (2018) GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 47(D1):D766–D773PubMedCentralCrossRef
34.
go back to reference Ahmed Z (2009) Proposing semantic oriented agent and knowledge base product data management. Inf Manag Comput Secur. 17(5):360–371CrossRef Ahmed Z (2009) Proposing semantic oriented agent and knowledge base product data management. Inf Manag Comput Secur. 17(5):360–371CrossRef
35.
go back to reference Ahmed Z (2010) Towards performance measurement and metrics based analysis of PLA applications. Int J Softw Eng Appl. 1(3):66–80 Ahmed Z (2010) Towards performance measurement and metrics based analysis of PLA applications. Int J Softw Eng Appl. 1(3):66–80
36.
go back to reference Ahmed Z (2011) Designing flexible GUI to increase the acceptance rate of product data management systems in industry. Int J Comput Sci Emerg Technol. 2:100–109 Ahmed Z (2011) Designing flexible GUI to increase the acceptance rate of product data management systems in industry. Int J Comput Sci Emerg Technol. 2:100–109
37.
go back to reference Ahmed Z, Zeeshan S, Dandekar T (2014) Developing sustainable software solutions for bioinformatics by the “Butterfly” paradigm. F1000Research. 7:54–66 Ahmed Z, Zeeshan S, Dandekar T (2014) Developing sustainable software solutions for bioinformatics by the “Butterfly” paradigm. F1000Research. 7:54–66
38.
go back to reference Ahmed Z, Zeeshan S (2014) Cultivating software solutions development in the scientific academia. Recent Patents Comput Sci. 7:54–66CrossRef Ahmed Z, Zeeshan S (2014) Cultivating software solutions development in the scientific academia. Recent Patents Comput Sci. 7:54–66CrossRef
40.
go back to reference Flannick J, Florez JC (2016) Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet 17:535–549PubMedCrossRef Flannick J, Florez JC (2016) Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet 17:535–549PubMedCrossRef
41.
42.
go back to reference Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510):421–427PubMedCentralCrossRef Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510):421–427PubMedCentralCrossRef
43.
go back to reference Fromer M, Roussos P, Sieberts SK et al (2016) Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci 19(11):1442–1453PubMedPubMedCentralCrossRef Fromer M, Roussos P, Sieberts SK et al (2016) Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci 19(11):1442–1453PubMedPubMedCentralCrossRef
45.
go back to reference Benjamin EJ, Blaha MJ, Chiuve SE et al (2017) Heart disease and stroke statistics—2017 update: a report from the American heart association. Circulation 135(10):e146–e603PubMedPubMedCentralCrossRef Benjamin EJ, Blaha MJ, Chiuve SE et al (2017) Heart disease and stroke statistics—2017 update: a report from the American heart association. Circulation 135(10):e146–e603PubMedPubMedCentralCrossRef
46.
go back to reference Stewart J, Manmathan G, Wilkinson P (2017) Primary prevention of cardiovascular disease: a review of contemporary guidance and literature. JRSM Cardiovasc Dis. 6:2048004016687211PubMedPubMedCentral Stewart J, Manmathan G, Wilkinson P (2017) Primary prevention of cardiovascular disease: a review of contemporary guidance and literature. JRSM Cardiovasc Dis. 6:2048004016687211PubMedPubMedCentral
51.
go back to reference Marengo-Rowe AJ (2007) The thalassemias and related disorders. Bayl Univ Med Cent Proc 20(1):27–31CrossRef Marengo-Rowe AJ (2007) The thalassemias and related disorders. Bayl Univ Med Cent Proc 20(1):27–31CrossRef
52.
go back to reference Kazemi M, Salehi M, Kheirollahi M (2016) Down syndrome: current status, challenges and future perspectives. Int J Mol Cell Med. 5(3):125–133PubMedPubMedCentral Kazemi M, Salehi M, Kheirollahi M (2016) Down syndrome: current status, challenges and future perspectives. Int J Mol Cell Med. 5(3):125–133PubMedPubMedCentral
54.
go back to reference Ilesanmi OO (2010) Pathological basis of symptoms and crises in sickle cell disorder: implications for counseling and psychotherapy. Hematol Rep. 2(1):e2PubMedPubMedCentralCrossRef Ilesanmi OO (2010) Pathological basis of symptoms and crises in sickle cell disorder: implications for counseling and psychotherapy. Hematol Rep. 2(1):e2PubMedPubMedCentralCrossRef
56.
go back to reference Saldarriaga W, Tassone F, González-Teshima LY et al (2014) Fragile X syndrome. Colomb Med 45(4):190–198 Saldarriaga W, Tassone F, González-Teshima LY et al (2014) Fragile X syndrome. Colomb Med 45(4):190–198
57.
59.
go back to reference van Driel MA, Bruggeman J, Vriend G et al (2006) A text-mining analysis of the human phenome. Eur J Hum Genet 14:535–542PubMedCrossRef van Driel MA, Bruggeman J, Vriend G et al (2006) A text-mining analysis of the human phenome. Eur J Hum Genet 14:535–542PubMedCrossRef
60.
go back to reference Lage K, Karlberg EO, Storling ZM et al (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25:309–316PubMedCrossRef Lage K, Karlberg EO, Storling ZM et al (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25:309–316PubMedCrossRef
61.
go back to reference Kohler S, Doelken SC, Mungall CJ et al (2014) The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 42:D966–D974PubMedCrossRef Kohler S, Doelken SC, Mungall CJ et al (2014) The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 42:D966–D974PubMedCrossRef
62.
go back to reference Zhou X, Menche J, Barabasi AL, Sharma A (2014) Human symptoms-disease network. Nat Commun. 5:4212PubMed Zhou X, Menche J, Barabasi AL, Sharma A (2014) Human symptoms-disease network. Nat Commun. 5:4212PubMed
63.
go back to reference Blair DR, Lyttle CS, Mortensen JM et al (2013) A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk. Cell 155:70–80PubMedCrossRef Blair DR, Lyttle CS, Mortensen JM et al (2013) A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk. Cell 155:70–80PubMedCrossRef
64.
go back to reference Jensen AB, Moseley PL, Oprea TI et al (2014) Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat Commun. 5:4022PubMedCrossRef Jensen AB, Moseley PL, Oprea TI et al (2014) Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat Commun. 5:4022PubMedCrossRef
65.
66.
68.
go back to reference Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR (2018) Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob Health. 3(4):e000798PubMedPubMedCentralCrossRef Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR (2018) Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob Health. 3(4):e000798PubMedPubMedCentralCrossRef
69.
70.
go back to reference Jones LD, Golan D, Hanna SA, Ramachandran M (2018) Artificial intelligence, machine learning and the evolution of healthcare: a bright future or cause for concern? Bone Jt Res. 7(3):223–225CrossRef Jones LD, Golan D, Hanna SA, Ramachandran M (2018) Artificial intelligence, machine learning and the evolution of healthcare: a bright future or cause for concern? Bone Jt Res. 7(3):223–225CrossRef
72.
go back to reference Liu YI, Wise PH, Butte AJ (2009) The “etiome”: identification and clustering of human disease etiological factors. BMC Bioinform 10(Suppl 2):S14CrossRef Liu YI, Wise PH, Butte AJ (2009) The “etiome”: identification and clustering of human disease etiological factors. BMC Bioinform 10(Suppl 2):S14CrossRef
75.
go back to reference Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, Barabasi AL (2008) The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci USA 105:9880–9885PubMedCrossRefPubMedCentral Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, Barabasi AL (2008) The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci USA 105:9880–9885PubMedCrossRefPubMedCentral
76.
go back to reference Lu M, Zhang Q, Deng M, Miao J, Guo Y, Gao W, Cui Q (2008) An analysis of human microRNA and disease associations. PLoS ONE 2008(3):e3420CrossRef Lu M, Zhang Q, Deng M, Miao J, Guo Y, Gao W, Cui Q (2008) An analysis of human microRNA and disease associations. PLoS ONE 2008(3):e3420CrossRef
77.
go back to reference Duran-Frigola M, Rossell D, Aloy P (2014) A chemo-centric view of human health and disease. Nat Commun. 5:5676PubMedCrossRef Duran-Frigola M, Rossell D, Aloy P (2014) A chemo-centric view of human health and disease. Nat Commun. 5:5676PubMedCrossRef
78.
go back to reference Ma W, Zhang L, Zeng P, Huang C, Li J, Geng B, Yang J, Kong W, Zhou X, Cui Q (2016) An analysis of human microbe-disease associations. Brief Bioinform. 18:85–97PubMedCrossRef Ma W, Zhang L, Zeng P, Huang C, Li J, Geng B, Yang J, Kong W, Zhou X, Cui Q (2016) An analysis of human microbe-disease associations. Brief Bioinform. 18:85–97PubMedCrossRef
79.
go back to reference Biesecker LG, Nussbaum RL, Rehm HL (2018) Distinguishing variant pathogenicity from genetic diagnosis: how to know whether a variant causes a condition. JAMA 320:1929–1930PubMedCrossRef Biesecker LG, Nussbaum RL, Rehm HL (2018) Distinguishing variant pathogenicity from genetic diagnosis: how to know whether a variant causes a condition. JAMA 320:1929–1930PubMedCrossRef
80.
go back to reference Richards S, Aziz N, Bale S et al (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 17(5):405–424PubMedPubMedCentralCrossRef Richards S, Aziz N, Bale S et al (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 17(5):405–424PubMedPubMedCentralCrossRef
81.
go back to reference Cheng DT, Mitchell TN, Zehir A et al (2015) Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J Mol Diagn. 17(3):251–264PubMedPubMedCentralCrossRef Cheng DT, Mitchell TN, Zehir A et al (2015) Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J Mol Diagn. 17(3):251–264PubMedPubMedCentralCrossRef
82.
go back to reference Ku CS et al (2010) The discovery of human genetic variations and their use as disease markers: past, present and future. J Hum Genet 55:403–415PubMedCrossRef Ku CS et al (2010) The discovery of human genetic variations and their use as disease markers: past, present and future. J Hum Genet 55:403–415PubMedCrossRef
Metadata
Title
Debutant iOS app and gene-disease complexities in clinical genomics and precision medicine
Authors
Zeeshan Ahmed
Saman Zeeshan
Ruoyun Xiong
Bruce T. Liang
Publication date
01-12-2019
Publisher
Springer Berlin Heidelberg
Published in
Clinical and Translational Medicine / Issue 1/2019
Electronic ISSN: 2001-1326
DOI
https://doi.org/10.1186/s40169-019-0243-8

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