High-Throughput Translational Medicine: Challenges and Solutions

  • Dinanath SulakheEmail author
  • Sandhya Balasubramanian
  • Bingqing Xie
  • Eduardo Berrocal
  • Bo Feng
  • Andrew Taylor
  • Bhadrachalam Chitturi
  • Utpal Dave
  • Gady Agam
  • Jinbo Xu
  • Daniela Börnigen
  • Inna Dubchak
  • T. Conrad Gilliam
  • Natalia MaltsevEmail author
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 799)


Recent technological advances in genomics now allow producing biological data at unprecedented tera- and petabyte scales. Yet, the extraction of useful knowledge from this voluminous data presents a significant challenge to a scientific community. Efficient mining of vast and complex data sets for the needs of biomedical research critically depends on seamless integration of clinical, genomic, and experimental information with prior knowledge about genotype–phenotype relationships accumulated in a plethora of publicly available databases. Furthermore, such experimental data should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining. Translational projects require sophisticated approaches that coordinate and perform various analytical steps involved in the extraction of useful knowledge from accumulated clinical and experimental data in an orderly semiautomated manner. It presents a number of challenges such as (1) high-throughput data management involving data transfer, data storage, and access control; (2) scalable computational infrastructure; and (3) analysis of large-scale multidimensional data for the extraction of actionable knowledge.

We present a scalable computational platform based on crosscutting requirements from multiple scientific groups for data integration, management, and analysis. The goal of this integrated platform is to address the challenges and to support the end-to-end analytical needs of various translational projects.


Spina Bifida Analytical Pipeline Disease Gene Association Gene Prioritization Gene Enrichment Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported in part by Mr. and Mrs. Lawrence Hilibrand, the Boler Family Foundation, and NIH/NINDS grant NS050375—The Genetic Basis of Mid-Hindbrain Malformations.


  1. 1.
    Ranganathan S, Schönbach C, Kelso J, Rost B, Nathan S, Tan TW (2011) Towards big data science in the decade ahead from ten years of InCoB and the 1st ISCB-Asia Joint Conference. BMC Bioinforma 12(Suppl 13):S1. doi: 10.1186/1471-2105-12-S13-S1 Google Scholar
  2. 2.
    Chen J, Qian F, Yan W, Shen B (2013) Translational biomedical informatics in the cloud: present and future. Biomed Res Int 2013:658925. doi: 10.1155/2013/658925 PubMedGoogle Scholar
  3. 3.
    Payne PR, Embi PJ, Sen CK (2009) Translational informatics: enabling high-throughput research paradigms. Physiol Genomics 39(3):131–140. doi: 10.1152/physiolgenomics.00050.2009 PubMedGoogle Scholar
  4. 4.
    Schuler R, Smith DE, Kumaraguruparan G, Chervenak A, Lewis AD, Hyde DM et al (2012) A flexible, open, decentralized system for digital pathology networks. Stud Health Technol Inform 175:29–38 [Research Support, N.I.H., Extramural]PubMedGoogle Scholar
  5. 5.
    Boyd LB, Hunicke-Smith SP, Stafford GA, Freund ET, Ehlman M, Chandran U, Dennis R, Fernandez AT, Goldstein S, Steffen D, Tycko B, Klemm JD (2011) The caBIG® life science business architecture model. Bioinformatics 27(10):1429–1435. doi: 10.1093/bioinformatics/btr141 PubMedGoogle Scholar
  6. 6.
    Hillman-Jackson J, Clements D, Blankenberg D, Taylor J, Nekrutenko A, Galaxy Team (2012) Using Galaxy to perform large-scale interactive data analyses. Curr Protoc Bioinformatics; Chapter 10:Unit10.5. doi: 10.1002/0471250953.bi1005s38
  7. 7.
    Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, De Smet F et al (2006) Gene prioritization through genomic data fusion. Nat Biotechnol 24(5):537–544 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  8. 8.
    Knaup P et al (2004) Towards clinical bioinformatics: advancing genomic medicine with informatics methods and tools. Methods Inf Med 43(3):302–307PubMedGoogle Scholar
  9. 9.
    Desai AN, Jere A (2012) Next-generation sequencing: ready for the clinics? Clin Genet 81(6):503–510PubMedGoogle Scholar
  10. 10.
    Bill BR, Geschwind DH (2009) Genetic advances in autism: heterogeneity and convergence on shared pathways. Curr Opin Genet Dev 19(3):271–278PubMedGoogle Scholar
  11. 11.
    Iossifov I, Zheng T, Baron M, Gilliam TC, Rzhetsky A (2008) Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network. Genome Res 18(7):1150–1162. doi: 10.1101/gr.075622.107, Epub 2008 Apr 16. PubMed PMID: 18417725; PubMed Central PMCID: PMC2493404PubMedGoogle Scholar
  12. 12.
    Sarnyai Z, Alsaif M, Bahn S, Ernst A, Guest PC, Hradetzky E, Kluge W, Stelzhammer V, Wesseling H (2011) Behavioral and molecular biomarkers in translational animal models for neuropsychiatric disorders. Int Rev Neurobiol 101:203–238. doi: 10.1016/B978-0-12-387718-5.00008-0, Review. PubMed PMID: 22050853PubMedGoogle Scholar
  13. 13.
    de Mooij-van Malsen AJ, Vinkers CH, Peterse DP, Olivier B, Kas MJ (2011) Cross-species behavioural genetics: a starting point for unravelling the neurobiology of human psychiatric disorders. Prog Neuropsychopharmacol Biol Psychiatry 35(6):1383–1390. doi: 10.1016/j.pnpbp.2010.10.003, Epub 2010 Oct 16. Review. PubMed PMID: 20955750PubMedGoogle Scholar
  14. 14.
    Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP (2010) Computational solutions to large-scale data management and analysis. Nat Rev Genet 11(9):647–657PubMedGoogle Scholar
  15. 15.
  16. 16.
    McKenna A et al (2010) The genome analysis toolkit: a map reduce framework for analyzing next-generation DNA sequencing data. Genome Res 20(9):1297–1303PubMedGoogle Scholar
  17. 17.
    Li H, Durbin R (2010) Fast and accurate long-read alignment with burrows–wheeler transform. Bioinformatics 26(5):589–595PubMedGoogle Scholar
  18. 18.
    Goecks J, Nekrutenko A, Taylor J, Galaxy Team (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 11(8):R86PubMedGoogle Scholar
  19. 19.
    Wolstencroft K, Haines R, Fellows D, Williams A, Withers D, Owen S, Soiland-Reyes S, Dunlop I, Nenadic A, Fisher P, Bhagat J, Belhajjame K, Bacall F, Hardisty A, Nieva de la Hidalga A, Balcazar Vargas MP, Sufi S, Goble C (2013) The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Res, First published online 2 May 2013. doi: 10.1093/nar/gkt328
  20. 20.
    Kulikova T et al (2007) EMBL Nucleotide Sequence Database in 2006. Nucleic Acids Res 35:D16–D20PubMedGoogle Scholar
  21. 21.
    Karolchik D, Hinrichs AS, Kent WJ (2012) The UCSC Genome Browser. Curr Protoc Bioinformatics;  Chapter 1:Unit1.4. doi: 10.1002/0471250953.bi0104s40. PubMed PMID: 23255150
  22. 22.
    Rosenbloom KR, Sloan CA, Malladi VS, Dreszer TR, Learned K, Kirkup VM, Wong MC, Maddren M, Fang R, Heitner SG, Lee BT, Barber GP, Harte RA, Diekhans M, Long JC, Wilder SP, Zweig AS, Karolchik D, Kuhn RM, Haussler D, Kent WJ (2013) ENCODE data in the UCSC genome browser: year 5 update. Nucleic Acids Res 41(Database issue):D56–D63. doi: 10.1093/nar/gks1172, Epub 2012 Nov 27. PubMed PMID: 23193274; PubMed Central PMCID: PMC3531152PubMedGoogle Scholar
  23. 23.
    NCBI Resource Coordinators (2013) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 41(Database issue):D8–D20. doi: 10.1093/nar/gks1189, Epub 2012 Nov 27. PubMed PMID: 23193264; PubMed Central PMCID: PMC3531099Google Scholar
  24. 24.
    UniProt Consortium (2013) Update on activities at the universal protein resource (UniProt) in 2013. Nucleic Acids Res 41(Database issue):D43–D47. doi: 10.1093/nar/gks1068, Epub 2012 Nov 17. PubMed PMID: 23161681; PubMed Central PMCID: PMC3531094Google Scholar
  25. 25.
    Pruitt KD, Tatusova T, Maglott DR (2007) NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35(Database issue):D61–D65, Epub 2006 Nov 27. PubMed PMID: 17130148; PubMed Central PMCID: PMC1716718PubMedGoogle Scholar
  26. 26.
    Hermjakob H et al (2004) The HUPO PSI’s molecular interaction format–a community standard for the representation of protein interaction data. Nat Biotechnol 22(2):177–183PubMedGoogle Scholar
  27. 27.
    Vastrik I, D’Eustachio P, Schmidt E, Gopinath G, Croft D, de Bono B, Gillespie M, Jassal B, Lewis S, Matthews L, Wu G, Birney E, Stein L (2007) Reactome: a knowledge base of biologic pathways and processes. Genome Biol 8(3):R39PubMedGoogle Scholar
  28. 28.
    BioCarta Pathways.
  29. 29.
    Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Yeast 17(1):48–55Google Scholar
  30. 30.
    Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH (2009) PID: the Pathway Interaction Database. Nucleic Acids Res 37:D674–D679PubMedGoogle Scholar
  31. 31.
    BioPAX-Consortium (2006) BioPAX: biological pathways exchange.
  32. 32.
    Online Mendelian Inheritance in Man (OMIM).
  33. 33.
    Mottaz A, Yip YL, Ruch P, Veuthey AL (2008) Mapping proteins to disease terminologies: from UniProt to MeSH. BMC Bioinforma 9(Suppl 5):S3Google Scholar
  34. 34.
  35. 35.
    Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S (2008) The HUMAN Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet 83(5):610–615PubMedGoogle Scholar
  36. 36.
    Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS (2011) DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res 39(Database issue):D1035–D1041, PMID: 21059682PubMedGoogle Scholar
  37. 37.
    Davis AP, Wiegers TC, Johnson RJ, Lay JM, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, Murphy CG, Mattingly CJ (2013) Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database. PLoS One 8(4):e58201. doi: 10.1371/journal.pone.0058201 PubMedGoogle Scholar
  38. 38.
    Kanehisa M (1997) Linking databases and organisms: GenomeNet resources in Japan. Trends Biochem Sci 22(11):442–444, Review. PubMed PMID: 9397687PubMedGoogle Scholar
  39. 39.
    Geer LY, Marchler-Bauer A, Geer RC, Han L, He J, He S, Liu C, Shi W, Bryant SH (2010) The NCBI BioSystems database. Nucleic Acids Res 38(Database issue):D492–D496. doi: 10.1093/nar/gkp858, Epub 2009 Oct 23. PubMed PMID: 19854944; PubMed Central PMCID: PMC2808896PubMedGoogle Scholar
  40. 40.
    Wilming LG, Gilbert JG, Howe K, Trevanion S, Hubbard T, Harrow JL (2008) The vertebrate genome annotation (Vega) database. Nucleic Acids Res 36(Database issue):D753–D760, Epub 2007 Nov 14. PubMed PMID: 18003653; PubMed Central PMCID: PMC2238886PubMedGoogle Scholar
  41. 41.
    Altshuler DM, Gibbs RA, Peltonen L et al (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467(7311):52–58. doi: 10.1038/nature09298, PubMed PMID: 20811451; PubMed Central PMCID: PMC3173859PubMedGoogle Scholar
  42. 42.
    Buchanan CC, Torstenson ES, Bush WS, Ritchie MD (2012) A comparison of cataloged variation between International HapMap Consortium and 1000 Genomes Project data. J Am Med Inform Assoc 19(2):289–294. doi: 10.1136/amiajnl-2011-000652, PubMed PMID: 22319179; PubMed Central PMCID: PMC3277631PubMedGoogle Scholar
  43. 43.
    Maher B (2012) ENCODE: the human encyclopaedia. Nature 489(7414):46–48, PubMed PMID: 22962707PubMedGoogle Scholar
  44. 44.
    ENCODE Project Consortium, Dunham I, Kundaje A, Aldred SF, Collins PJ et al (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414):57–74. doi: 10.1038/nature11247. PubMed PMID: 22955616; PubMed Central PMCID: PMC3439153Google Scholar
  45. 45.
    Pennisi E (2012) Genomics. ENCODE project writes eulogy for junk DNA. Science 337(6099):1159, 1161. doi: 10.1126/science.337.6099.1159. PubMed PMID: 22955811Google Scholar
  46. 46.
    Hardison RC (2003) Comparative genomics. PLoS Biol 1(2):E58PubMedGoogle Scholar
  47. 47.
    Cheng JF, Priest JR, Pennacchio LA (2007) Comparative genomics: a tool to functionally annotate human DNA. Methods Mol Biol 366:229–251PubMedGoogle Scholar
  48. 48.
    da Huang W, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13Google Scholar
  49. 49.
    Curtis RK, Oresic M, Vidal-Puig A (2005) Pathways to the analysis of microarray data. Trends Biotechnol 23(8):429–435PubMedGoogle Scholar
  50. 50.
    Khatri P, Draghici S (2005) Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21(18):3587–3595PubMedGoogle Scholar
  51. 51.
    Rivals I, Personnaz L, Taing L, Potier MC (2007) Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics 23(4):401–407 [Evaluation Studies]PubMedGoogle Scholar
  52. 52.
    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550PubMedGoogle Scholar
  53. 53.
    Doncheva NT, Kacprowski T, Albrecht M (2012) Recent approaches to the prioritization of candidate disease genes. Wiley Interdiscip Rev Syst Biol Med 4(5):429–442 [Research Support, Non-U.S. Gov’t Review]PubMedGoogle Scholar
  54. 54.
    Moreau Y, Tranchevent LC (2012) Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet 13(8):523–536PubMedGoogle Scholar
  55. 55.
    Tranchevent LC, Barriot R, Yu S, Van Vooren S, Van Loo P, Coessens B et al (2008) ENDEAVOUR update: a web resource for gene prioritization in multiple species. Nucleic Acids Res 36(Web Server issue):W377–W384 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  56. 56.
    Pers TH, Dworzyñski P, Thomas CE, Lage K, Brunak S (2013) MetaRanker 2.0: a web server for prioritization of genetic variation data. Nucleic Acids Res 41(Web Server issue):W104–W108PubMedGoogle Scholar
  57. 57.
    Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37(Web Server issue):W305–W311 [Research Support, N.I.H., Extramural]PubMedGoogle Scholar
  58. 58.
    Tranchevent LC, Capdevila FB, Nitsch D, De Moor B, De Causmaecker P, Moreau Y (2011) A guide to web tools to prioritize candidate genes. Brief Bioinform 12(1):22–32 [Research Support, Non-U.S. Gov’t Review]PubMedGoogle Scholar
  59. 59.
    Tiffin N, Adie E, Turner F, Brunner HG, van Driel MA, Oti M et al (2006) Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes. Nucleic Acids Res 34(10):3067–3081 [Evaluation Studies Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  60. 60.
    Tiffin N, Andrade-Navarro MA, Perez-Iratxeta C (2009) Linking genes to diseases: it’s all in the data. Genome Med 1(8):77PubMedGoogle Scholar
  61. 61.
    Kohler S, Bauer S, Horn D, Robinson PN (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82(4):949–958 [Evaluation Studies Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  62. 62.
    Börnigen D, Tranchevent LC, Bonachela-Capdevila F, Devriendt K, De Moor B, De Causmaecker P et al (2012) An unbiased evaluation of gene prioritization tools. Bioinformatics 28(23):3081–3088 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  63. 63.
    Foster I (2011) Globus online: accelerating and democratizing science through cloud-based services. IEEE Internet Comput 15:70–73Google Scholar
  64. 64.
    Dubey A, Wagle D (2007) Delivering software as a service. The McKinsey Quarterly 6:1–12Google Scholar
  65. 65.
    Waters B (2005) Software as a service: a look at the customer benefits. J Digit Asset Manag 1(1):32–39Google Scholar
  66. 66.
    Frazer KA, Pachter L, Poliakov A, Rubin EM, Dubchak I (2004) VISTA: computational tools for comparative genomics. Nucleic Acids Res 32(Web Server issue):W273–W279PubMedGoogle Scholar
  67. 67.
    Lukashin I, Novichkov P, Boffelli D, Paciorkowski AR, Minovitsky S, Yang S, Dubchak I (2011) VISTA Region Viewer (RViewer)–a computational system for prioritizing genomic intervals for biomedical studies. Bioinformatics 27(18):2595–2597PubMedGoogle Scholar
  68. 68.
    Visel A, Minovitsky S, Dubchak I, Pennacchio LA (2007) VISTA Enhancer Browser–a database of tissue-specific human enhancers. Nucleic Acids Res 35(Database issue):D88–D92PubMedGoogle Scholar
  69. 69.
    Foster I, Kesselman C, Tsudik G, Tuecke SA (1998) Security architecture for computational grids. 5th ACM conference on computer and communications security conference, 1998, San Francisco, CA, USA pp 83–92Google Scholar
  70. 70.
    Amazon Web Services.
  71. 71.
    Litzkow M, Livny M, Mutka M (1998) Condor – a hunter of idle workstations. Proceedings of the 8th international conference of distributed computing systems, June 1988, San Jose, CA, USA pp 104–111Google Scholar
  72. 72.
    Foster I, Kesselman C, Tuecke S (2001) The Anatomy of the Grid: Enabling Scalable Virtual Organizations. Int J Supercomput Appl 15(3):200–222Google Scholar
  73. 73.
    Gotea V, Visel A, Westlund JM, Nobrega MA, Pennacchio LA, Ovcharenko I (2010) Homotypic clusters of transcription factor binding sites are a key component of human promoters and enhancers. Genome Res 20(5):565–577PubMedGoogle Scholar
  74. 74.
    Gene Ontology Consortium (2006) The gene ontology (GO) project in 2006. Nucleic Acids Res 34:D322–D326Google Scholar
  75. 75.
    Rzhetsky A et al (2004) GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. J Biomed Inform 37(1):43–53PubMedGoogle Scholar
  76. 76.
    Nitsch D, Tranchevent LC, Goncalves JP, Vogt JK, Madeira SC, Moreau Y (2011) PINTA: a web server for network-based gene prioritization from expression data. Nucleic Acids Res 39(Web Server issue):W334–W338 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  77. 77.
    Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P et al (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39(Database issue):D561–D568 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  78. 78.
    Padmanabhan R (2006) Etiology, pathogenesis and prevention of neural tube defects. Congenital anomalies 46(2):55–67PubMedGoogle Scholar
  79. 79.
    Mitchell LE, Adzick NS, Melchionne J, Pasquariello PS, Sutton LN, Whitehead AS (2004) Spina bifida. Lancet 364(9448):1885–1895. doi: 10.1016/S0140-6736(04)17445-X, ISSN 0140-6736PubMedGoogle Scholar
  80. 80.
    Boyles AL, Billups AV, Deak KL, Siegel DG, Mehltretter L, Slifer SH et al (2006) Neural tube defects and folate pathway genes: family-based association tests of gene-gene and gene-environment interactions. Environ Health Perspect 114(10):1547–1552 [Research Support, N.I.H., Extramural]PubMedGoogle Scholar
  81. 81.
    Ross ME (2010) Gene-environment interactions, folate metabolism and the embryonic nervous system. Wiley Interdiscip Rev Syst Biol Med 2(4):471–480PubMedGoogle Scholar
  82. 82.
    Wu G, Huang X, Hua Y, Mu D (2011) Roles of planar cell polarity pathways in the development of neural [correction of neutral] tube defects. J Biomed Sci 18:66PubMedGoogle Scholar
  83. 83.
    Wen S, Zhu H, Lu W, Mitchell LE, Shaw GM, Lammer EJ et al (2010) Planar cell polarity pathway genes and risk for spina bifida. Am J Med Genet A 152A(2):299–304PubMedGoogle Scholar
  84. 84.
    Harris MJ, Juriloff DM (2007) Mouse mutants with neural tube closure defects and their role in understanding human neural tube defects. Birth Defects Res A Clin Mol Teratol 79(3):187–210PubMedGoogle Scholar
  85. 85.
    Harris MJ, Juriloff DM (2010) An update to the list of mouse mutants with neural tube closure defects and advances toward a complete genetic perspective of neural tube closure. Birth Defects Res A Clin Mol Teratol 88(8):653–669PubMedGoogle Scholar
  86. 86.
    Kozyraki R, Fyfe J, Kristiansen M, Gerdes C, Jacobsen C, Cui S et al (1999) The intrinsic factor-vitamin B12 receptor, cubilin, is a high-affinity apolipoprotein A-I receptor facilitating endocytosis of high-density lipoprotein. Nat Med 5(6):656–661 [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]PubMedGoogle Scholar
  87. 87.
    Wahlstedt-Froberg V, Pettersson T, Aminoff M, Dugue B, Grasbeck R (2003) Proteinuria in cubilin-deficient patients with selective vitamin B12 malabsorption. Pediatr Nephrol 18(5):417–421 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  88. 88.
    Franke B, Vermeulen SH, Steegers-Theunissen RP, Coenen MJ, Schijvenaars MM, Scheffer H et al (2009) An association study of 45 folate-related genes in spina bifida: Involvement of cubilin (CUBN) and tRNA aspartic acid methyltransferase 1 (TRDMT1). Birth Defects Res A Clin Mol Teratol 85(3):216–226 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  89. 89.
    Aminoff M, Carter JE, Chadwick RB, Johnson C, Grasbeck R, Abdelaal MA et al (1999) Mutations in CUBN, encoding the intrinsic factor-vitamin B12 receptor, cubilin, cause hereditary megaloblastic anaemia 1. Nat Genet 21(3):309–313 [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]PubMedGoogle Scholar
  90. 90.
    Whitehead VM (2006) Acquired and inherited disorders of cobalamin and folate in children. Br J Haematol 134(2):125–136PubMedGoogle Scholar
  91. 91.
    Andersson ER, Bryjova L, Biris K, Yamaguchi TP, Arenas E, Bryja V (2010) Genetic interaction between Lrp6 and Wnt5a during mouse development. Dev Dyn 239:237–245. doi: 10.1002/dvdy.22101 PubMedGoogle Scholar
  92. 92.
    Gray JD, Nakouzi G, Slowinska-Castaldo B, Dazard J-E, Sunil Rao J, Nadeau JH et al (2010) Functional interactions between the LRP6 WNT co-receptor and folate supplementation. Hum Mol Genet 19(23):4560–4572PubMedGoogle Scholar
  93. 93.
    Lefebvre C, Rieckhof G, Califano A (2012) Reverse-engineering human regulatory networks. Wiley Interdiscip Rev Syst Biol Med 4(4):311–325 [Review]PubMedGoogle Scholar
  94. 94.
    Tkacik G, Walczak AM (2011) Information transmission in genetic regulatory networks: a review. J Phys Condens Matter 23(15):153102 [Review]PubMedGoogle Scholar
  95. 95.
    Kirouac DC, Saez-Rodriguez J, Swantek J, Burke JM, Lauffenburger DA, Sorger PK (2012) Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks. BMC Syst Biol 6:29 [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  96. 96.
    Guan Y, Gorenshteyn D, Burmeister M, Wong AK, Schimenti JC, Handel MA et al (2012) Tissue-specific functional networks for prioritizing phenotype and disease genes. PLoS Comput Biol 8(9):e1002694 [Research Support, N.I.H., Extramural Research Support, U.S. Gov’t, Non-P.H.S.]PubMedGoogle Scholar
  97. 97.
    Rzhetsky A, Wajngurt D, Park N, Zheng T (2007) Probing genetic overlap among complex human phenotypes. Proc Natl Acad Sci U S A 104(28):11694–11699PubMedGoogle Scholar
  98. 98.
    Oti M, Brunner HG (2007) The modular nature of genetic diseases. Clin Genet 71(1):1–11 [Research Support, Non-U.S. Gov’t Review]PubMedGoogle Scholar
  99. 99.
    Oti M, Huynen MA, Brunner HG (2008) Phenome connections. Trends Genet 24(3):103–106PubMedGoogle Scholar
  100. 100.
    Vidal M, Cusick ME, Barabasi AL (2011) Interactome networks and human disease. Cell 144(6):986–998 [Research Support, N.I.H., Extramural Review]PubMedGoogle Scholar
  101. 101.
    Piro RM, Ala U, Molineris I, Grassi E, Bracco C, Perego GP et al (2011) An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction. Eur J Hum Genet 19(11):1173–1180 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  102. 102.
    Wysocki K, Ritter L (2011) Diseasome: an approach to understanding gene-disease interactions. Annu Rev Nurs Res 29:55–72 [Review]PubMedGoogle Scholar
  103. 103.
    Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC et al (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(5):P3 [Research Support, U.S. Gov’t, P.H.S.]PubMedGoogle Scholar
  104. 104.
    Kokocinski F, Delhomme N, Wrobel G, Hummerich L, Toedt G, Lichter P (2005) FACT–a framework for the functional interpretation of high-throughput experiments. BMC Bioinforma 6:161 [Evaluation Studies Research Support, Non-U.S. Gov’t]Google Scholar
  105. 105.
    Robinson MD, Grigull J, Mohammad N, Hughes TR (2002) FunSpec: a web-based cluster interpreter for yeast. BMC Bioinforma 3:35 [Research Support, Non-U.S. Gov’t]Google Scholar
  106. 106.
    Castillo-Davis CI, Hartl DL (2003) GeneMerge–post-genomic analysis, data mining, and hypothesis testing. Bioinformatics 19(7):891–892PubMedGoogle Scholar
  107. 107.
    Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M et al (2003) GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 4(4):R28 [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]PubMedGoogle Scholar
  108. 108.
    Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, Conklin BR (2003) MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 4(1):R7 [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]PubMedGoogle Scholar
  109. 109.
    Khatri P, Draghici S, Ostermeier GC, Krawetz SA (2002) Profiling gene expression using onto-express. Genomics 79(2):266–270 [Evaluation Studies Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]PubMedGoogle Scholar
  110. 110.
    Zhang B, Kirov S, Snoddy J (2005) WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res 33(Web Server issue):W741–W748 [Research Support, N.I.H., Extramural Research Support, U.S. Gov’t, Non-P.H.S. Research Support, U.S. Gov’t, P.H.S.]PubMedGoogle Scholar
  111. 111.
    Backes C, Keller A, Kuentzer J, Kneissl B, Comtesse N, Elnakady YA et al (2007) GeneTrail–advanced gene set enrichment analysis. Nucleic Acids Res 35(Web Server issue):W186–W192 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  112. 112.
    Gupta P, Yoshida R, Imoto S, Yamaguchi R, Miyano S (2007) Statistical absolute evaluation of gene ontology terms with gene expression data. In: MƒÉndoiu I, Zelikovsky A (eds) Bioinformatics research and applications. Springer, Berlin, pp 146–157Google Scholar
  113. 113.
    Bauer S, Grossmann S, Vingron M, Robinson PN (2008) Ontologizer 2.0 – a multifunctional tool for GO term enrichment analysis and data exploration. Bioinformatics 24(14):1650–1651PubMedGoogle Scholar
  114. 114.
    Antonov AV, Schmidt T, Wang Y, Mewes HW (2008) ProfCom: a web tool for profiling the complex functionality of gene groups identified from high-throughput data. Nucleic Acids Res 36(Web Server issue):W347–W351 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  115. 115.
    Alexa A, Rahnenfuhrer J, Lengauer T (2006) Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22(13):1600–1607 [Research Support, Non-U.S. Gov’t]PubMedGoogle Scholar
  116. 116.
    Naidoo N, Pawitan Y, Soong R, Cooper DN, Ku CS (2011) Human genetics and genomics a decade after the release of the draft sequence of the human genome. Hum Genomics 5(6):577–622, Review. PubMed PMID: 22155605; PubMed Central PMCID: PMC3525251PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Dinanath Sulakhe
    • 1
    Email author
  • Sandhya Balasubramanian
    • 2
  • Bingqing Xie
    • 2
    • 3
  • Eduardo Berrocal
    • 2
    • 3
  • Bo Feng
    • 2
    • 3
  • Andrew Taylor
    • 2
  • Bhadrachalam Chitturi
    • 4
  • Utpal Dave
    • 5
  • Gady Agam
    • 3
  • Jinbo Xu
    • 6
  • Daniela Börnigen
    • 2
    • 6
  • Inna Dubchak
    • 7
  • T. Conrad Gilliam
    • 2
    • 5
  • Natalia Maltsev
    • 1
    • 2
    Email author
  1. 1.Computation InstituteUniversity of Chicago/Argonne National LaboratoryChicagoUSA
  2. 2.Department of Human GeneticsUniversity of ChicagoChicagoUSA
  3. 3.Department of Computer ScienceIllinois Institute of TechnologyChicagoUSA
  4. 4.Department of Computer ScienceAmrita Vishwa Vidyapeetham UniversityKollamIndia
  5. 5.Computation InstituteUniversity of Chicago/Argonne National LaboratoryChicagoUSA
  6. 6.Toyota Technological Institute at ChicagoChicagoUSA
  7. 7.Genomics DivisionBerkley National LaboratoryWalnut CreekUSA

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