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Bioinformatics and Omics

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Part of the book series: Molecular Pathology Library ((MPLB,volume 2))

Abstract

Bioinformatics has become an essential part of omics research and requires unique practical and analytical skills for appropriate results interpretation. Bioinformatics uses computers and statistics to perform extensive omics-related research by searching biological databases and comparing gene sequences and protein on a vast scale to identify sequences or proteins that differ between diseased and healthy tissues, or between different phenotypes of the same disease.3037 The techniques used in omics are called high throughput because they involve analysis of very large numbers of genes, gene expression, or proteins in one procedure or combination of procedures. The vast amounts of data generated by these high-throughput studies typically require computers for analysis and comparison of differences between diseased and physiological cells and tissues, a key feature of bioinformatics. Omics and bioinformatics are used not only for the study of the genes and signaling pathways involved in human diseases, but also for identifying potential targets of therapy and the design of therapeutic drugs.

Omics – a suffix signifying the measurement of the entire complement of a given level of biological molecules and information – today encompasses a variety of new technologies that can help explain normal and abnormal cell pathways, networks, and processes via the simultaneous monitoring of thousands of molecular components.6,7

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References

  1. Biron DG, Brun C, Lefevre T, et al. The pitfalls of proteonomics experiments without the correct use of bioinformatics tools. Proteomics. 2006;20:5577–5596.

    Article  CAS  Google Scholar 

  2. McKusick VA. Genomics: structure and functional studies of genomes. Genomics. 1997;45:244–249.

    Article  PubMed  CAS  Google Scholar 

  3. Palagi PM, Hernandez P, Walther D, Appel RD. Proteome informatics I: bioinformatics tools for processing experimental data. Proteomics. 2006;6:5435–5444.

    Article  PubMed  CAS  Google Scholar 

  4. Lisacek F, Cohen-Boulakia S, Appel RD. Proteome informatics II: bioinformatics for comparative proteomics. Proteomics. 2006;6: 5445–5466.

    Article  PubMed  Google Scholar 

  5. Maojo V, Martin-Sanchez F. Bioinformatics: towards new directions for public health. Methods Inf Med. 2004;43:208–214.

    PubMed  CAS  Google Scholar 

  6. Bilello JA. The agony and ecstasy of “OMIC” technologies in drug development. Curr Mol Med. 2005;5:39–52.

    Article  PubMed  CAS  Google Scholar 

  7. Morel NM, Holland JM, van der Greef P, et al. Primer on medial genomics, part. XIV Introduction to systems biology: a new approach to understanding disease and treatment. Mayo Clin Proc. 2004;79:651–658.

    Article  PubMed  CAS  Google Scholar 

  8. Provart NJ, McCourt P. Systems approaches to understanding cell signaling and gene regulation. Curr Opin Plant Biol. 2004;7:605–609.

    Article  PubMed  CAS  Google Scholar 

  9. Wheelock AM, Goto S. Effects of post-electrophoretic analysis on variance in gel-based proteomics. Expert Rev Proteomics. 2006;3:129–142.

    Article  PubMed  CAS  Google Scholar 

  10. Debouck C, Metcalf B. The impact of genomics on drug discovery. Annu Rev Pharmacol Toxicol. 2000;40:193–207.

    Article  PubMed  CAS  Google Scholar 

  11. Ghosh D. High throughput and global approaches to gene expression. Comb Chem High Throughput Screen. 2000;3:411–420.

    PubMed  CAS  Google Scholar 

  12. Hanke J. Genomics and new technologies as catalysts for change in the drug discovery paradigm. J Law Med Ethics. 2000;28(4 suppl):15–22.

    PubMed  CAS  Google Scholar 

  13. Harris T. Genetics, genomics, and drug discovery. Med Res Rev. 2000;20:203–211.

    Article  PubMed  CAS  Google Scholar 

  14. Rudert F. Genomics and proteomics tools for the clinic. Curr Opin Mol Ther. 2000;2:633–642.

    PubMed  CAS  Google Scholar 

  15. Merrick BA, Bruno ME. Genomic and proteomic profiling for biomarkers and signature profiles of toxicity. Curr Opin Mol Ther. 2004;6:600–667.

    PubMed  CAS  Google Scholar 

  16. Chalkley RJ, Hansen KC, Baldwin MA. Bioinformatic methods to exploit mass spectrometric data for proteomic applications. Methods Enzymol. 2005;402:289–312.

    Article  PubMed  CAS  Google Scholar 

  17. Dennis JL, Oien KA. Hunting the primary: novel strategies for defining the origin of tumours. J Pathol. 2005;205:236–247.

    Article  PubMed  CAS  Google Scholar 

  18. Englbrecht CC, Facius A. Bioinformatics challenges in proteomics. Comb Chem High Throughput Screen. 2005;8:705–715.

    Article  PubMed  CAS  Google Scholar 

  19. Fung ET, Weinberger SR, Gavin E, Zhang F. Bioinformatics approaches in clinical proteomics. Expert Rev Proteomics. 2005;2:847–862.

    Article  PubMed  CAS  Google Scholar 

  20. Kremer A, Schneider R, Terstappen GC. A bioinformatics perspective on proteomics: data storage, analysis, and integration. Biosci Rep. 2005;25:95–106.

    Article  PubMed  CAS  Google Scholar 

  21. Mount DW, Pandey R. Using bioinformatics and genome analysis for new therapeutic interventions. Mol Cancer Ther. 2005;4:1636–1643.

    Article  PubMed  CAS  Google Scholar 

  22. Nishio K, Arao T, Shimoyama T, et al. Translational studies for target-based drugs. Cancer Chemother Pharmacol. 2005;56(suppl 1):90–93.

    Article  PubMed  CAS  Google Scholar 

  23. Katoh M, Katoh M. Bioinformatics for cancer management in the post-genome era. Technol Cancer Res Treat. 2006;5:169–175.

    PubMed  CAS  Google Scholar 

  24. Miles AK, Matharoo-Ball B, Li G, Ahmad M, Rees RC. The identification of human tumour antigens: current status and future developments. Cancer Immunol Immunother. 2006;55:996–1003.

    Article  PubMed  CAS  Google Scholar 

  25. Quackenbush J. Microarray analysis and tumor classification. N Engl J Med. 2006;354:2463–2472.

    Article  PubMed  CAS  Google Scholar 

  26. Redfern O, Grant A, Maibaum M, Orengo C. Survey of current protein family databases and their application in comparative, structural and functional genomics. J Chromatogr B Anal Technol Biomed Life Sci. 2005;815:97–107.

    Article  CAS  Google Scholar 

  27. Iqbal O, Fareed J. Clinical applications of bioinformatics, genomics, and pharmacogenomics. Methods Mol Biol. 2006;316:159–177.

    PubMed  Google Scholar 

  28. Reeves GA, Thornton JM. BioSapiens Network of Excellence. Integrating biological data through the genome. Hum Mol Genet. 2006;15(Special No. 1):R81–R87.

    Article  PubMed  CAS  Google Scholar 

  29. Waggoner A. Fluorescent labels for proteomics and genomics. Curr Opin Chem Biol. 2006;10:62–66.

    Article  PubMed  CAS  Google Scholar 

  30. Ritchie MD. Bioinformatics approaches for detecting gene-gene and gene-environment interactions in studies of human disease. Neurosurg Focus. 2005;19:E2.

    Article  PubMed  Google Scholar 

  31. Hanai T, Hamada H, Okamoto M. Application of bioinformatics for DNA microarray data to bioscience, bioengineering and medical fields. J Biosci Bioeng. 2006; 101:377–384.

    Article  PubMed  CAS  Google Scholar 

  32. Goodman N. Biological data becomes computer literate: new advances in bioinformatics. Curr Opin Biotechnol. 2002;13:68–71.

    Article  PubMed  CAS  Google Scholar 

  33. Ness SA. Basic microarray analysis: strategies for successful experiments. Methods Mol Biol. 2006;316:13–33.

    PubMed  Google Scholar 

  34. Perco P, Rapberger R, Siehs C, et al. Transforming omics data into context: bioinformatics on genomics and proteomics raw data. Electrophoresis. 2006;27:2659–2675.

    Article  PubMed  CAS  Google Scholar 

  35. Haoudi A, Bensmail H. Bioinformatics and data mining in proteomics. Expert Rev Proteomics. 2006;3:333–343.

    Article  PubMed  CAS  Google Scholar 

  36. Ivanov AS, Veselovsky AV, Dubanov AV, Skvortsov VS. Bioinformatics platform development: from gene to lead compound. Methods Mol Biol. 2006;316:389–431.

    PubMed  Google Scholar 

  37. Teufel A, Krupp M, Weinmann A, Galle PR. Current bioinformatics tools in genomic biomedical research (review). Int J Mol Med. 2006;17:967–973.

    PubMed  CAS  Google Scholar 

  38. Regnstrom K, Burgess DJ. Pharmacogenomics and its potential impact on drug and formulation development. Crit Rev Ther Drug Carrier Syst. 2005;22:465–492.

    PubMed  CAS  Google Scholar 

  39. Willard HF, Angrist M, Ginsburg GS. Genomic medicine: genetic variation and its impact on the future of health care. Philos Trans R Soc Lond B Biol Sci. 2005;360:1543–1550.

    Article  PubMed  CAS  Google Scholar 

  40. Garraway LA, Seller WR. From integrated genomics to tumor lineage dependency. Cancer Res. 2006;66:2506–2508.

    Article  PubMed  CAS  Google Scholar 

  41. McDunn JE, Chung TP, Laramie JM, Townsend RR, Cobb JP. Physiologic genomics. Surgery. 2006;139:133–139.

    Article  PubMed  Google Scholar 

  42. Tost J, Gut IG. Genotyping single nucleotide polymorphisms by mass spectrometry. Mass Spectrom Rev. 2002;21:388–418.

    Article  PubMed  CAS  Google Scholar 

  43. Thomas DC, Haile RW, Duggan D. Recent developments in genomewide association scans: a workshop summary and review. Am J Hum Genet. 2005;77:337–345.

    Article  PubMed  CAS  Google Scholar 

  44. Bernig T, Chanock SJ. Challenges of SNP genotyping and genetic variation: its future role in diagnosis and treatment of cancer. Expert Rev Mol Diagn. 2006;6:319–331.

    Article  PubMed  CAS  Google Scholar 

  45. Anderson S, Bankier AT, Barrell BG, et al. Sequence and organization of the human mitochondrial genome. Nature. 1981;290:457–465.

    Article  PubMed  CAS  Google Scholar 

  46. Mundy C. The human genome project: a historical perspective. Pharmacogenomics. 2001;2:37–49.

    Article  PubMed  CAS  Google Scholar 

  47. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature. 2004;431:931–945.

    Article  CAS  Google Scholar 

  48. Baxevanis AD. Using genomic databases for sequence-based biological discovery. Mol Med. 2003;9:185–192.

    PubMed  CAS  Google Scholar 

  49. The International HapMap Consortium. The International HapMap Project. Nature. 2003;426:789–796.

    Article  CAS  Google Scholar 

  50. Thorisson GA, Stein LD. The SNP Consortium website: past, present and future. Nucleic Acids Res. 2003;31:124–127.

    Article  PubMed  CAS  Google Scholar 

  51. Liu T, Johnson JA, Casella G, Wu R. Sequencing complex diseases with HapMap. Genetics. 2004;168:503–511.

    Article  PubMed  CAS  Google Scholar 

  52. Riva A, Kohane IS. A SNP-centric database for the investigation of the human genome. BMC Bioinform. 2004;5:33.

    Article  Google Scholar 

  53. Kong X, Matise TC. MAP-O-MAT: internet-based linkage mapping. Bioinformatics. 2005;21:557–559.

    Article  PubMed  CAS  Google Scholar 

  54. Brandon MC, Lott MT, Nguyen KC, et al. MITOMAP: a human mitochondrial genome database-2004 update. Nucleic Acids Res. 2005;33:D611–D613.

    Article  PubMed  CAS  Google Scholar 

  55. Carulli JP, Artinger M, Swain PM, et al. High throughput analysis of differential gene expression. J Cell Biochem Suppl. 1998;30–31:286–296.

    Article  PubMed  Google Scholar 

  56. Scheel J, Von Brevern MC, Horlein A, et al. Yellow pages to the transcriptome. Pharmacogenomics. 2002;3:791–807.

    Article  PubMed  CAS  Google Scholar 

  57. Hedge PS, White IR, Debouck C. Interplay of transcriptomics and proteomics. Curr Opin Biotechnol. 2003;14:647–651.

    Article  CAS  Google Scholar 

  58. Suzuki M, Hayashizaki Y. Mouse-centric comparative transcriptomics of protein coding and non-coding RNAs. Bioessays. 2004;26:833–843.

    Article  PubMed  CAS  Google Scholar 

  59. Breitling R, Herzyk P. Biological master games: using biologists’ reasoning to guide algorithm development for integrated functional genomics. OMICS. 2005;9:225–232.

    Article  PubMed  CAS  Google Scholar 

  60. Storck T, von Brevern MC, Behrens CK, Scheel J, Bach A. Transcriptomics in predictive toxicology. Curr Opin Drug Discov Dev. 2002;5:90–97.

    CAS  Google Scholar 

  61. Hu YF, Kaplow J, He Y. From traditional biomarkers to transcriptome analysis in drug development. Curr Mol Med. 2005;5:29–38.

    Article  PubMed  CAS  Google Scholar 

  62. Kralj M, Kraljevic S, Sedic M, Kurjak A, Pavelic K. Global approach to perinatal medicine: functional genomics and proteomics. J Perinat Med. 2005;33:5–16.

    Article  PubMed  CAS  Google Scholar 

  63. Morgan KT, Jayyosi Z, Hower MA, et al. The hepatic transcriptome as a window on whole-body physiology and pathophysiology. Toxicol Pathol. 2005;33:136–145.

    Article  PubMed  CAS  Google Scholar 

  64. Jansen BJ, Schalkwijk J. Transcriptomics and proteomics of human skin. Brief Funct Genomic Proteomic. 2003;1:326–341.

    Article  PubMed  CAS  Google Scholar 

  65. Liang P, Zhu W, Zhang X, et al. Differential display using one-base anchored oligo-dT primers. Nucleic Acids Res. 1994;22:5763–5764.

    Article  PubMed  CAS  Google Scholar 

  66. Ahmed FE. Molecular techniques for studying gene expression in carcinogenesis. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev. 2002;20:77–116.

    PubMed  Google Scholar 

  67. Muller-Hagen G, Beinert T, Sommer A. Aspects of lung cancer gene expression profiling. Curr Opin Drug Discov Dev. 2004;7:290–303.

    Google Scholar 

  68. Anderson JS, Mann M. Functional genomics by mass spectrometry. FEBS Lett. 2000;480:25–31

    Article  Google Scholar 

  69. Liotta LA, Petricoin EF III. The promise of proteomics. Clin Adv Hematol Oncol. 2003;1:460–462.

    PubMed  Google Scholar 

  70. Jain KK. Role of oncoproteomics in the personalized management of cancer. Expert Rev Proteomics. 2004;1:49–55.

    Article  PubMed  CAS  Google Scholar 

  71. Hanash S. Disease proteomics. Nature. 2003;422:226–232.

    Article  PubMed  CAS  Google Scholar 

  72. Baggerman G, Vierstraete E, De Loof A, Schoofs L. Gel-based versus gel-free proteomics: a review. Comb Chem High Throughput Screen. 2005;8:669–677.

    Article  PubMed  CAS  Google Scholar 

  73. Calvo KR, Liotta LA, Petricoin EF. Clinical proteomics: from biomarker discovery and cell signaling profiles to individualized personal therapy. Biosci Rep. 2005;25:107–125.

    Article  PubMed  CAS  Google Scholar 

  74. Brown RE. Morphoproteomics: exposing protein circuitries in tumors to identify potential therapeutic targets in cancer patients. Expert Rev Proteomics. 2005;2:337–348.

    Article  PubMed  CAS  Google Scholar 

  75. Kalia A, Gupta RP. Proteomics: a paradigm shift. Crit Rev Biotechnol. 2005;25:173–198.

    Article  PubMed  CAS  Google Scholar 

  76. Scaros O, Fisler R. Biomarker technology roundup: from discovery to clinical applications, a broad set of tools is required to translate from the lab to the clinic. Biotechniques. 2005;Suppl:30-32.

    Google Scholar 

  77. Clarke W, Chan DW. ProteinChips: the essential tools for proteomic biomarker discovery and future clinical diagnostics. Clin Chem Lab Med. 2005;43:1279–1280.

    Article  PubMed  CAS  Google Scholar 

  78. Kolch W, Mischak H, Pitt AR. The molecular make-up of a tumour: proteomics in cancer research. Clin Sci (Lond). 2005;108:369–383.

    Article  CAS  Google Scholar 

  79. Patel PS, Telang SD, Rawal RM, Shah MH. A review of proteomics in cancer research. Asian Pac J Cancer Prev. 2005;6:113–117.

    PubMed  Google Scholar 

  80. Roboz J. Mass spectrometry in diagnostic oncoproteomics. Cancer Invest. 2005;23:465–478.

    PubMed  CAS  Google Scholar 

  81. Waldburg N, Kahne T, Reisenauer A, et al. Clinical proteomics in lung diseases. Pathol Res Pract. 2004;200:147–154.

    Article  PubMed  CAS  Google Scholar 

  82. Stroncek DF, Burns C, Martin BM, et al. Advancing cancer biotherapy with proteomics. J Immunother. 2005;28:183–192..

    Article  PubMed  CAS  Google Scholar 

  83. Fleming K, Kelley LA, Islam SA, et al. The proteome: structure, function and evolution. Philos Trans R Soc Lond B Biol Sci. 2006;361:441–451.

    Article  PubMed  CAS  Google Scholar 

  84. Domon B, Aebersold R. Mass spectrometry and protein analysis. Science. 2006;312:212–217.

    Article  PubMed  CAS  Google Scholar 

  85. Gulmann C, Sheehan KM, Kay EW, Liotta LA, Petricoin EF 3rd. Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer. J Pathol. 2006;208:595–606.

    Article  PubMed  CAS  Google Scholar 

  86. Kingsmore SF. Multiplexed protein measurement: technologies and applications of protein and antibody arrays. Nat Rev Drug Discov. 2006;5:310–320.

    Article  PubMed  CAS  Google Scholar 

  87. Davis CD, Milner J. Frontiers in nutrigenomics, proteomics, metabolomics and cancer prevention. Mutat Res. 2004;551:51–64.

    PubMed  CAS  Google Scholar 

  88. Griffin JL, Bollard ME. Metabonomics: its potential as a tool in toxicology for safety assessment and data integration. Curr Drug Metab. 2004;5:389–398.

    Article  PubMed  CAS  Google Scholar 

  89. Rochfort S. Metabolomics reviewed: a new “omics” platform technology for systems biology and implications for natural products research. J Nat Prod. 2005;68:1813–1820.

    Article  PubMed  CAS  Google Scholar 

  90. Griffin JL. The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball? Philos Trans R Soc Lond B Biol Sci. 2006;361:147–161.

    Article  PubMed  CAS  Google Scholar 

  91. Mahal LK. Glycomics: towards bioinformatics approaches to understanding glycosylation. Anticancer Agents Med Chem. 2008;8:37–51.

    Article  PubMed  CAS  Google Scholar 

  92. Packer NH, von der Lieth C-W, Aoki-Kinoshita KF, et al. Frontiers in glycomics: bioinformatics and biomarkers in disease. Proteomics. 2008;8:8–20.

    Article  PubMed  CAS  Google Scholar 

  93. Pilobello KT, Mahal LK. Deciphering the glycocode: the complexity and analytical challenge of glycomics. Curr Opin Chem Biol. 2007;11:300–305.

    Article  PubMed  CAS  Google Scholar 

  94. Bernas T, Gregori G, Asem EK, Robinson JP. Integrating cytomics and proteomics. Mol Cell Protemics. 2006;5:2–13.

    Article  CAS  Google Scholar 

  95. Gomase VS, Tagore S. Cytomics. Curr Drug Metab. 2008;9:263–266.

    Article  PubMed  CAS  Google Scholar 

  96. Tarnok A. Slide-based cytometry for cytomics: a minireview. Cytometry A. 2006;69A:555–562.

    Article  CAS  Google Scholar 

  97. Valet G. Cytomics as a new potential for drug discovery. Drug Discov Today. 2006;11:785–791.

    Article  PubMed  CAS  Google Scholar 

  98. Tarnok A, Bosci J, Brockhoff G. Cytomics: importance of multimodal analysis of cell function and proliferation in oncology. Cell Prolif. 2006;39:495–505.

    Article  PubMed  CAS  Google Scholar 

  99. Gomase VS, Tagore S. Physiomics. Curr Drug Metab. 2008;9:259–262.

    Article  PubMed  CAS  Google Scholar 

  100. Porterfield DM. Measuring metabolism and biophysical flux in the tissue, cellular and sub-cellular domains: recent developments in self-referencing amperometry for physiological sensing. Biosens Bioelectron. 2007;15:1186–1196.

    Article  CAS  Google Scholar 

  101. ul Haque A, Chatni MR, Li G, Porterfield DM. Biochips and other microtechnologies for physiomics. Expert Rev Proteomics. 2007;4:553–463.

    Article  PubMed  CAS  Google Scholar 

  102. Ramsay G. DNA chips: state-of-the art. Nat Biotechnol. 1997;16:40–44.

    Article  Google Scholar 

  103. Duggan DJ, Bittner M, Chen Y, Meltzer P, Trent JM. Expression profiling using cDNA microarrays. Nat Genet. 1999;21(suppl 1):10–14.

    Article  PubMed  CAS  Google Scholar 

  104. Chen l, Ren J. High-throughput DNA analysis by microchip electrophoresis. Comb Chem High Throughput Screen. 2004;7:29–43.

    PubMed  CAS  Google Scholar 

  105. Heller MJ. DNA microarray technology: devices, systems, and applications. Annu Rev Biomed Eng. 2002;4:129–153.

    Article  PubMed  CAS  Google Scholar 

  106. Obeid PJ, Christopoulos TK. Microfabricated systems for nucleic acid analysis.Crit Rev Clin Lab Sci. 2004;41:429–465.

    Article  PubMed  CAS  Google Scholar 

  107. Shi L, Tong W, Goodsaid F, et al. QA/QC: challenges and pitfalls facing the microarray community and regulatory agencies. Expert Rev Mol Diagn. 2004;4:761–777.

    Article  PubMed  Google Scholar 

  108. Zhumabayeva B, Chenchik A, Siebert PD, Herrler M. Disease profiling arrays: reverse format cDNA arrays complimentary to microarrays. Adv Biochem Eng Biotechnol. 2004; 86:191–213.

    PubMed  CAS  Google Scholar 

  109. Brentani RR, Carraro DM, Verjovski-Almeida S, et al. Gene expression arrays in cancer research: methods and applications. Crit Rev Oncol Hematol. 2005;54:95–105.

    Article  PubMed  Google Scholar 

  110. Diatchenko L, Lau YF, Campbell AP, et al. Suppression subtractive hybridization: a method for generating differentially regulated or tissue-specific cDNA probes and libraries. Proc Natl Acad Sci USA. 1996;93:6025–6030.

    Article  PubMed  CAS  Google Scholar 

  111. Wang X, Feuerstein GZ. Suppression subtractive hybridization: application in the discovery of novel pharmacological targets. Pharmacogenomics. 2000;1:101–108.

    Article  PubMed  Google Scholar 

  112. Velculescu VE, Vogelstein B, Kinzler KW. Analyzing uncharted transcriptomes with SAGE. Trends Genet. 2000;16:423–425.

    Article  PubMed  CAS  Google Scholar 

  113. Polyak K, Riggins GJ. Gene discovery using the serial analysis of gene expression technique: implications for cancer research. J Clin Oncol. 2001;19:2948–2958.

    PubMed  CAS  Google Scholar 

  114. Riggins GJ. Using serial analysis of gene expression to identify tumor markers and antigens. Dis Markers. 2001;17:41–48.

    PubMed  CAS  Google Scholar 

  115. Vihinen M. Bioinformatics in proteomics. Biomol Eng. 2001;18:241–248.

    Article  PubMed  CAS  Google Scholar 

  116. Rabilloud T. Two-dimensional gel electrophoresis in proteomics: old, old-fashioned, but it still climbs up the mountains. Proteomics. 2002;2:3–10.

    Article  PubMed  CAS  Google Scholar 

  117. Nicholson JK. Reviewers peering from under a pile of ‘omics’ data. Nature. 2006;440:992.

    Article  PubMed  CAS  Google Scholar 

  118. Belacel N, Wang Q, Cuperlovic-Culf M. Clustering methods for microarray gene expression data. OMICS. 2006;10:507–531.

    Article  PubMed  CAS  Google Scholar 

  119. Taylor CF. Progress in standards for reporting omics data. Curr Opin Drug Discov Dev. 2007;10:254–263.

    CAS  Google Scholar 

  120. Zhang X, Li L, Wei D, Yap Y, Chen F. Moving cancer diagnostics from bench to bedside. Trends Biotechnol. 2007;25:166–173.

    Article  PubMed  CAS  Google Scholar 

  121. Culhane AC, Howlin J. Molecular profiling of breast cancer: transcriptomic studies and beyond. Cel Mol Life Sci. 2007;64;3185–3200.

    Article  CAS  Google Scholar 

  122. Finn WG. Diagnostic pathology and laboratory medicine in the age of “omics”: a paper from the 2006 William Beaumont Hospital Symposium on Molecular Pathology. J Mol Diagn. 2007;9:431–436.

    Article  PubMed  CAS  Google Scholar 

  123. Wilkes T, Laux H, Foy CA. Microarray data quality-a review of current developments. OMICS. 2007;11:1–13.

    Article  PubMed  CAS  Google Scholar 

  124. Tanaka H. Bioinformatics and genomics for opening new perspective for personalized care. Stud Health Technol Inform. 2008;134:47–58.

    PubMed  Google Scholar 

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Allen, T.C., Cagle, P.T. (2009). Bioinformatics and Omics. In: Allen, T., Cagle, P.T. (eds) Basic Concepts of Molecular Pathology. Molecular Pathology Library, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-89626-7_6

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