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Bioinformatics Databases: Implications in Human Health

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Genome Analysis and Human Health
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Abstract

The emergence of bioinformatics has enriched the different dimensions of biological research. A sizable quantum of complex biological data can now be encapsulated into a more palatable form particularly in the context of human health. We provide a critical overview on such bioinformatic databases and softwares that enable a deeper insight into the human genome and proteome.

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References

  • Adams MD, et al. The genome sequence of Drosophila melanogaster. Science. 2000;287(5461):2185–95.

    Article  PubMed  Google Scholar 

  • Aloy P, Russell RB. Ten thousand domain-domain interactions for the molecular biologist. Nat Biotechnol. 2004;22(10):1317–21.

    Article  CAS  PubMed  Google Scholar 

  • Altshuler D, et al. An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature. 2000;407(6803):513–6.

    Article  CAS  PubMed  Google Scholar 

  • Bartel PL, Fields S (eds). The yeast two-hybrid system. In: Bartel PL, Fields S, editors, Advances in molecular biology. New York: Oxford University Press; 1997.

    Google Scholar 

  • Blackstock WP, Weir MP. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 1999;17(3):121–7.

    Article  CAS  PubMed  Google Scholar 

  • Bult CJ, et al. Complete genome sequence of the methanogenic archaeon, Methanococcus jannaschii. Science. 1996;273(5278):1058–73.

    Article  CAS  PubMed  Google Scholar 

  • C. elegans Sequencing Consortium. Genome sequence of the nematode C. elegans: a platform for investigating biology. Science. 1998;282(5396):2012–8.

    Article  Google Scholar 

  • Cambien F, et al. Deletion polymorphism in the gene for angiotensin-converting enzyme is a potent risk factor for myocardial infarction. Nature. 1992;359(6396):641–4.

    Article  CAS  PubMed  Google Scholar 

  • Chakrabarti P, Janin J. Dissecting protein–protein recognition sites. Proteins. 2002;47(3):334–43.

    Article  CAS  PubMed  Google Scholar 

  • Chanock S. Candidate genes and single nucleotide polymorphisms (SNPs) in the study of human disease. Dis Markers. 2001;17(2):89–98.

    Article  CAS  PubMed  Google Scholar 

  • Cheng I, et al. 8q24 and prostate cancer: association with advanced disease and meta-analysis. Eur J Hum Genet. 2008;16(4):496–505.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • de Bakker PI, Yelensky R, Pe’er I, Gabriel SB, Daly MJ, Altshuler D. Efficiency and power in genetic association studies. Nat Genet. 2005;37(11):1217–23.

    Article  PubMed  Google Scholar 

  • De Jager PL, et al. Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet. 2009;41(7):776–82.

    Article  PubMed  PubMed Central  Google Scholar 

  • Deng M, Mehta S, Sun F, Chen T. Inferring domain-domain interactions from protein-protein interactions. Genome Res. 2002;12:1540–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dove A. Proteomics: translating genes into products? Nat Biotechnol. 1999;17(3):233–6.

    Article  CAS  PubMed  Google Scholar 

  • Duerr RH, et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science. 2006;314(5804):1461–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Enright AJ, Ililopoulos I, Kyrpides NC, Ouzounis CA. Protein interaction maps for complete genomes based on gene fusion events. Nature. 1999;402(6757):86–90.

    Article  CAS  PubMed  Google Scholar 

  • Fields S, Song O. A novel genetic system to detect protein–protein interactions. Nature. 1989;340(6230):245–6.

    Article  CAS  PubMed  Google Scholar 

  • Fleischmann RD, et al. Whole genome random sequencing and assembly of haemophilus-influenzae Rd. Science. 1995;269:496–512.

    Article  CAS  PubMed  Google Scholar 

  • Fraser CM, et al. The minimal gene complement of mycoplasma-genitalium. Science. 1995;270(5235):397–403.

    Article  CAS  PubMed  Google Scholar 

  • Freedman ML, et al. Admixture mapping identifies 8q24 as a prostate cancer risk locus in African–American men. Proc Natl Acad Sci USA. 2006;103(38):14068–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Goffeau A, et al. Life with 6000 genes. Science. 1996;274(5287):546, 563–7.

    Article  Google Scholar 

  • Gratacòs M, et al. A polymorphic genomic duplication on human chromosome 15 is a susceptibility factor for panic and phobic disorders. Cell. 2001;106(3):367–79.

    Article  PubMed  Google Scholar 

  • Guimaraes KS, Jothi R, Zotenko E, Przytycka TM. Predicting domain-domain interactions using a parsimony approach. Genome Biol. 2006;7:R104.

    Article  PubMed  PubMed Central  Google Scholar 

  • Howard TD, et al. Gene-gene interaction in asthma: IL4RA and IL13 in a Dutch population with asthma. Am J Hum Genet. 2002;70(1):230–6.

    Article  CAS  PubMed  Google Scholar 

  • Hunter S, et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 2009;37(D):D211–5.

    Article  CAS  PubMed  Google Scholar 

  • Ingolfsson H, Yona G. Protein domain prediction. Methods Mol Biol. 2008;426:117–43.

    Article  CAS  PubMed  Google Scholar 

  • International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature. 2004;431(7011):931–45.

    Article  Google Scholar 

  • Jacques PF, et al. Relation between folate status, a common mutation in methylene tetrahydrofolate reductase, and plasma homocysteine concentrations. Circulation. 1996;93(1):7–9.

    Article  CAS  PubMed  Google Scholar 

  • Kortemme T, Baker D. Computational design of protein–protein interactions. Curr Opin Struct Biol. 2004;8(1):91–7.

    Article  CAS  Google Scholar 

  • Kraft P, Cox DG. Study designs for genome-wide association studies. Adv Genet. 2008;60:465–504.

    Google Scholar 

  • Lander ES, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860–921.

    Article  CAS  PubMed  Google Scholar 

  • Lee H, Deng M, Sun F, Chen T. An integrated approach to the prediction of domain-domain interactions. BMC Bioinformatics. 2006;7:269–27.

    Article  PubMed  PubMed Central  Google Scholar 

  • Libioulle C, et al. Novel Crohn disease locus identified by genome-wide association maps to a gene desert on 5p13.1 and modulates expression of PTGER4. PLoS Genet 2007, 3(4):e58.

    Google Scholar 

  • LoConte L, et al. The atomic structure of protein–protein recognition sites. J Mol Biol. 1999;285(5):2177–98.

    Article  CAS  Google Scholar 

  • Lu L, Lu H, Skolnick J. MULTIPROSPECTOR: an algorithm for the prediction of protein-protein interactions by multimeric threading. Proteins. 2002;49(3):350–64.

    Article  CAS  PubMed  Google Scholar 

  • Lykke-Andersen J. mRNA quality control: marking the message for life or death. Curr Biol. 2001;11(3):R88–91.

    Article  CAS  PubMed  Google Scholar 

  • Marchler-Bauer A, et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 2011;39(D):D225–9.

    Article  CAS  PubMed  Google Scholar 

  • Marcotte EM, et al. Detecting protein function and protein–protein interactions from genome sequences. Science. 1999;285(5428):751–3.

    Article  CAS  PubMed  Google Scholar 

  • Moore JH, Asselbergs FW, Williams SM. Bioinformatics challenges for genome-wide association studies. Bioinformatics. 2010;26(4):445–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mount DW, Pandey R. Using bioinformatics and genome analysis for new therapeutic interventions. Mol Cancer Ther. 2005;4(10):1636–43.

    Article  CAS  PubMed  Google Scholar 

  • Musani SK, et al. Detection of gene x gene interactions in genome-wide association studies of human population data. Hum Hered. 2007;63(2):67–84.

    Article  CAS  PubMed  Google Scholar 

  • Ng SK, Zhang Z, Tan SH, Lin K. InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes. Nucleic Acids Res. 2003;31(1):251–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pabinger S, Dander A, Fischer M, Snajder R, Sperk M, Efremova M, et al. A survey of tools for variant analysis of next-generation genome sequencing data. Brief Bioinform. 2014;15(2):256–78.

    Article  PubMed  Google Scholar 

  • Panwar D, Rawal L, Ali S. Molecular docking uncovers TSPY binds more efficiently with eEF1A2 compared to eEF1A1. J Biomol Struct Dyn. 2015;33(7):1412–23.

    Google Scholar 

  • Poort SR, Rosendaal FR, Reitsma PH, Bertina RM. A common genetic variation in the 3′-untranslated region of the prothrombin gene is associated with elevated plasma prothrombin levels and an increase in venous thrombosis. Blood. 1996;88(10):3698–703.

    CAS  PubMed  Google Scholar 

  • Reich DE, Lander ES. On the allelic spectrum of human disease. Trends Genet. 2001;17(9):502–10.

    Article  CAS  PubMed  Google Scholar 

  • Richards RI, Holman K, Yu S, Sutherland GR. Fragile X syndrome unstable element, p(CCG)n, and other simple tandem repeat sequences are binding sites for specific nuclear proteins. Hum Mol Genet. 1993;2(9):1429–35.

    Article  CAS  PubMed  Google Scholar 

  • Salwinski L, Eisenberg D. Computational methods of analysis of protein–protein interactions. Curr Opin Struct Biol. 2003;13(3):377–82.

    Article  CAS  PubMed  Google Scholar 

  • Schork NJ, Murray SS, Frazer KA, Topol EJ. Common vs. rare allele hypotheses for complex diseases. Curr Opin Genet Dev. 2009;19(3):212–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Seng KC, Seng CK. The success of the genome-wide association approach: a brief story of a long struggle. Eur J Hum Genet. 2008;16:554–64.

    Google Scholar 

  • Small KM, Wagoner LE, Levin AM, Kardia SL, Liggett SB. Synergistic polymorphisms of beta1- and alpha2C-adrenergic receptors and the risk of congestive heart failure. N Engl J Med. 2002;347(15):1135–42.

    Article  CAS  PubMed  Google Scholar 

  • Ta HX, Holm L. Evaluation of different domain-based methods in protein interaction prediction. Biochem Biophys Res Commun. 2009;390(3):357–62.

    Article  CAS  PubMed  Google Scholar 

  • Teichmann SA. Principles of protein-protein interactions. Bioinformatics. 2002;18(Suppl 2):S249.

    Article  PubMed  Google Scholar 

  • The International HapMap Consortium. The International HapMap Project. Nature. 2003;426(6968):789–96.

    Article  Google Scholar 

  • Uetz P, et al. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature. 2000;403(6770):623–7.

    Article  CAS  PubMed  Google Scholar 

  • Valencia A, Pazos F. Computational methods for the prediction of protein interactions. Curr Opin Struct Biol. 2002;12(3):368–73.

    Article  CAS  PubMed  Google Scholar 

  • Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, et al. The sequence of the human genome. Science. 2001;291:1304–51.

    Article  CAS  PubMed  Google Scholar 

  • Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–78.

    Article  Google Scholar 

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Competing Interests

No competing interests to be disclosed.

Dr. Leena Rawal

Dr. Leena Rawal is currently working as a scientist at the Department of Cytogenetics, Dr Lal PathLabs Limited, New Delhi. She holds a doctoral degree in molecular genetics from the National Institute of Immunology, New Delhi, and has significant years of experience in the field. Her expertise lies in the area of human and animal genetics, proteomics, gene regulation, cytogenetics, and bioinformatics. Earlier, she has headed the Molecular Diagnostics and Research Division of a renowned pharmacogenomics-based organization that provides state-of-the-art in vitro personalized diagnostic services to medical healthcare and allied communities.

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Rawal, L., Panwar, D., Ali, S. (2017). Bioinformatics Databases: Implications in Human Health. In: Rawal, L., Ali, S. (eds) Genome Analysis and Human Health. Springer, Singapore. https://doi.org/10.1007/978-981-10-4298-0_6

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