Table of Contents
Molecular Biology International
Volume 2011, Article ID 475718, 9 pages
http://dx.doi.org/10.4061/2011/475718
Review Article

Databases and Bioinformatics Tools for the Study of DNA Repair

1Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland
2Laboratory of Bioinformatics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland

Received 16 February 2011; Revised 28 April 2011; Accepted 22 May 2011

Academic Editor: Frédéric Coin

Copyright © 2011 Kaja Milanowska et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Jeggo and M. F. Lavin, “Cellular radiosensitivity: how much better do we understand it?” International Journal of Radiation Biology, vol. 85, no. 12, pp. 1061–1081, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  2. L. Maddukuri, D. Dudzińska, and B. Tudek, “Bacterial DNA repair genes and their eukaryotic homologues: 4. The role of nucleotide excision DNA repair (NER) system in mammalian cells,” Acta Biochimica Polonica, vol. 54, no. 3, pp. 469–482, 2007. View at Google Scholar · View at Scopus
  3. K. D. Arczewska and J. T. Kuśmierek, “Bacterial DNA repair genes and their eukaryotic homologues: 2. Role of bacterial mutator gene homologues in human disease. Overview of nucleotide pool sanitization and mismatch repair systems,” Acta Biochimica Polonica, vol. 54, no. 3, pp. 435–457, 2007. View at Google Scholar · View at Scopus
  4. N. C. Brissett and A. J. Doherty, “Repairing DNA double-strand breaks by the prokaryotic non-homologous end-joining pathway,” Biochemical Society Transactions, vol. 37, no. 3, pp. 539–545, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  5. A. Vaisman, A. R. Lehmann, and R. Woodgate, “DNA polymerases η and ι,” Advances in Protein Chemistry, vol. 69, pp. 205–228, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  6. A. B. Robertson, A. Klungland, T. Rognes, and I. Leiros, “DNA repair in mammalian cells: base excision repair: the long and short of it,” Cellular and Molecular Life Sciences, vol. 66, no. 6, pp. 981–993, 2009. View at Publisher · View at Google Scholar · View at PubMed
  7. J. Krwawicz, K. D. Arczewska, E. Speina, A. Maciejewska, and E. Grzesiuk, “Bacterial DNA repair genes and their eukaryotic homologues: 1. Mutations in genes involved in base excision repair (BER) and DNA-end processors and their implication in mutagenesis and human disease,” Acta Biochimica Polonica, vol. 54, no. 3, pp. 413–434, 2007. View at Google Scholar · View at Scopus
  8. R. Olinski, A. Siomek, R. Rozalski et al., “Oxidative damage to DNA and antioxidant status in aging and age-related diseases,” Acta Biochimica Polonica, vol. 54, no. 1, pp. 11–26, 2007. View at Google Scholar · View at Scopus
  9. B. Tudek, “Base excision repair modulation as a risk factor for human cancers,” Molecular Aspects of Medicine, vol. 28, no. 3-4, pp. 258–275, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  10. R. de Bont and N. van Larebeke, “Endogenous DNA damage in humans: a review of quantitative data,” Mutagenesis, vol. 19, no. 3, pp. 169–185, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Drabløs, E. Feyzi, P. A. Aas et al., “Alkylation damage in DNA and RNA—repair mechanisms and medical significance,” DNA Repair, vol. 3, no. 11, pp. 1389–1407, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  12. T. Lindahl, “Instability and decay of the primary structure of DNA,” Nature, vol. 362, no. 6422, pp. 709–715, 1993. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  13. E. C. Friedberg et al., “DNA repair and mutagenesis,” 2006. View at Google Scholar
  14. W. K. Hansen and M. R. Kelley, “Review of mammalian DNA repair and translational implications,” Journal of Pharmacology and Experimental Therapeutics, vol. 295, no. 1, pp. 1–9, 2000. View at Google Scholar · View at Scopus
  15. S. Raptis and B. Bapat, “Genetic instability in human tumors,” EXS, no. 96, pp. 303–320, 2006. View at Google Scholar · View at Scopus
  16. D. M. Wilson and D. Barsky, “The major human abasic endonuclease: formation, consequences and repair of abasic lesions in DNA,” Mutation Research, vol. 485, no. 4, pp. 283–307, 2001. View at Publisher · View at Google Scholar · View at Scopus
  17. R. D. Wood, M. Mitchell, and T. Lindahl, “Human DNA repair genes, 2005,” Mutation Research, vol. 577, no. 1-2, pp. 275–283, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  18. R. D. Wood, M. Mitchell, J. Sgouros, and T. Lindahl, “Human DNA repair genes,” Science, vol. 291, no. 5507, pp. 1284–1289, 2001. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  19. F. Chen, W. Q. Liu, A. Eisenstark, R. N. Johnston, G. R. Liu, and S. L. Liu, “Multiple genetic switches spontaneously modulating bacterial mutability,” BMC Evolutionary Biology, vol. 10, no. 1, article 277, 2010. View at Publisher · View at Google Scholar · View at PubMed
  20. R. D. Wood, M. Mitchell, and T. Lindahl, “Human DNA repair genes,” 2010. View at Google Scholar
  21. M. Kanehisa, M. Araki, S. Goto et al., “KEGG for linking genomes to life and the environment,” Nucleic Acids Research, vol. 36, no. 1, pp. D480–D484, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  22. K. Milanowska, J. Krwawicz, G. Papaj et al., “REPAIRtoire—a database of DNA repair pathways,” Nucleic Acids Research, vol. 39, supplement 1, pp. D788–D792, 2011. View at Publisher · View at Google Scholar · View at PubMed
  23. L. Wen and J. A. Feng, “Repair-FunMap: a functional database of proteins of the DNA repair systems,” Bioinformatics, vol. 20, no. 13, pp. 2135–2137, 2004. View at Publisher · View at Google Scholar · View at PubMed
  24. L. Matthews, G. Gopinath, M. Gillespie et al., “Reactome knowledgebase of human biological pathways and processes,” Nucleic Acids Research, vol. 37, no. 1, pp. D619–D622, 2009. View at Publisher · View at Google Scholar · View at PubMed
  25. E. C. Friedberg and L. B. Meira, “Database of mouse strains carrying targeted mutations in genes affecting biological responses to DNA damage Version 7,” DNA Repair, vol. 5, no. 2, pp. 189–209, 2006. View at Publisher · View at Google Scholar · View at PubMed
  26. R. Caspi, T. Altman, J. M. Dale et al., “The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases,” Nucleic Acids Research, vol. 38, no. 1, Article ID gkp875, pp. D473–D479, 2009. View at Publisher · View at Google Scholar · View at PubMed
  27. M. Scheer, A. Grote, A. Chang et al., “BRENDA, the enzyme information system in 2011,” Nucleic Acids Research, vol. 39, supplement 1, pp. D670–D676, 2011. View at Publisher · View at Google Scholar · View at PubMed
  28. E. G. Cerami, B. E. Gross, E. Demir et al., “Pathway Commons, a web resource for biological pathway data,” Nucleic Acids Research, vol. 39, supplement 1, pp. D685–D690, 2011. View at Publisher · View at Google Scholar · View at PubMed
  29. M. Hackenberg, G. Barturen, and J. L. Oliver, “NGSmethDB: a database for next-generation sequencing single-cytosine-resolution DNAmethylation data,” Nucleic Acids Research, vol. 39, supplement 1, pp. D75–D79, 2011. View at Publisher · View at Google Scholar · View at PubMed
  30. S. Cotterill and S. E. Kearsey, “DNAReplication: a database of information and resources for the eukaryotic DNA replication community,” Nucleic Acids Research, vol. 37, no. 1, pp. D837–D839, 2009. View at Publisher · View at Google Scholar · View at PubMed
  31. X. He, S. Chang, J. Zhang et al., “MethyCancer: the database of human DNA methylation and cancer,” Nucleic Acids Research, vol. 36, no. 1, pp. D836–D841, 2008. View at Publisher · View at Google Scholar · View at PubMed
  32. M. Ongenaert, L. Van Neste, T. de Meyer, G. Menschaert, S. Bekaert, and W. van Criekinge, “PubMeth: a cancer methylation database combining text-mining and expert annotation,” Nucleic Acids Research, vol. 36, no. 1, pp. D842–D846, 2008. View at Publisher · View at Google Scholar · View at PubMed
  33. C. Grunau, E. Renault, A. Rosenthal, and G. Roizes, “MethDB—a public database for DNA methylation data,” Nucleic Acids Research, vol. 29, no. 1, pp. 270–274, 2001. View at Google Scholar
  34. C. A. Nieduszynski, S. I. Hiraga, P. Ak, C. J. Benham, and A. D. Donaldson, “OriDB: a DNA replication origin database,” Nucleic Acids Research, vol. 35, no. 1, pp. D40–D46, 2007. View at Publisher · View at Google Scholar · View at PubMed
  35. R. J. Roberts, T. Vincze, J. Posfai, and D. Macelis, “REBASE-A database for DNA restriction and modification: enzymes, genes and genomes,” Nucleic Acids Research, vol. 38, no. 1, Article ID gkp874, pp. D234–D236, 2009. View at Publisher · View at Google Scholar · View at PubMed
  36. E. Paek, J. Park, and K. J. Lee, “Multi-layered representation for cell signaling pathways,” Molecular and Cellular Proteomics, vol. 3, no. 10, pp. 1009–1022, 2004. View at Publisher · View at Google Scholar · View at PubMed
  37. G. D. Bader, M. P. Cary, and C. Sander, “Pathguide: a pathway resource list,” Nucleic Acids Research, vol. 34, pp. D504–D506, 2006. View at Google Scholar
  38. A. Shipra, K. Chetan, and M. R. S. Rao, “CREMOFAC—a database of chromatin remodeling factors,” Bioinformatics, vol. 22, no. 23, pp. 2940–2944, 2006. View at Publisher · View at Google Scholar · View at PubMed
  39. A. L. Turinsky, B. Turner, R. C. Borja et al., “DAnCER: disease-annotated chromatin epigenetics resource,” Nucleic Acids Research, vol. 39, supplement 1, pp. D889–D894, 2011. View at Publisher · View at Google Scholar · View at PubMed
  40. J. D. Podlevsky, C. J. Bley, R. V. Omana, X. Qi, and J. L. Chen, “The Telomerase Database,” Nucleic Acids Research, vol. 36, no. 1, pp. D339–D343, 2008. View at Publisher · View at Google Scholar · View at PubMed
  41. N. Weddington, A. Stuy, I. Hiratani, T. Ryba, T. Yokochi, and D. M. Gilbert, “ReplicationDomain: a visualization tool and comparative database for genome-wide replication timing data,” BMC bioinformatics, vol. 9, p. 530, 2008. View at Publisher · View at Google Scholar · View at PubMed
  42. M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at Publisher · View at Google Scholar · View at PubMed
  43. M. Safran, I. Dalah, J. Alexander et al., “GeneCards version 3: the human gene integrator,” Database: The Journal of Biological Databases and Curation, vol. 2010, Article ID baq020, 2010. View at Google Scholar
  44. E. W. Sayers, T. Barrett, D. A. Benson et al., “Database resources of the National Center for Biotechnology Information,” Nucleic Acids Research, vol. 37, no. 1, pp. D5–D15, 2009. View at Publisher · View at Google Scholar · View at PubMed
  45. L. D. Stein, “Using the Reactome database,” Current Protocols in Bioinformatics, chapter 8, p. Unit 8.7, 2004. View at Google Scholar
  46. W. Ladiges, J. Wiley, and A. MacAuley, “Polymorphisms in the DNA repair gene XRCC1 and age-related disease,” Mechanisms of Ageing and Development, vol. 124, no. 1, pp. 27–32, 2003. View at Publisher · View at Google Scholar
  47. I. M. Keseler, C. Bonavides-Martínez, J. Collado-Vides et al., “EcoCyc: a comprehensive view of Escherichia coli biology,” Nucleic Acids Research, vol. 37, no. 1, pp. D464–D470, 2009. View at Publisher · View at Google Scholar · View at PubMed
  48. L. Cabusora, E. Sutton, A. Fulmer, and C. V. Forst, “Differential network expression during drug and stress response,” Bioinformatics, vol. 21, no. 12, pp. 2898–2905, 2005. View at Publisher · View at Google Scholar · View at PubMed
  49. J. Díez, D. Walter, C. Mũoz-Pinedo, and T. Gabaldón, “Editorial: DeathBase: a database on structure, evolution and function of proteins involved in apoptosis and other forms of cell death,” Cell Death and Differentiation, vol. 17, no. 5, pp. 735–736, 2010. View at Publisher · View at Google Scholar · View at PubMed
  50. J. Kosinski, I. Hinrichsen, J. M. Bujnicki, P. Friedhoff, and G. Plotz, “Identification of Lynch syndrome mutations in the MLH1-PMS2 interface that disturb dimerization and mismatch repair,” Human Mutation, vol. 31, no. 8, pp. 975–982, 2010. View at Publisher · View at Google Scholar · View at PubMed
  51. S. Shi et al., “Analysis of CASP8 targets, predictions and assessment methods,” Database, vol. 2009, Article ID bap003, 2009. View at Google Scholar
  52. K. Arnold, L. Bordoli, J. Kopp, and T. Schwede, “The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling,” Bioinformatics, vol. 22, no. 2, pp. 195–201, 2006. View at Publisher · View at Google Scholar · View at PubMed
  53. S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, “Basic local alignment search tool,” Journal of Molecular Biology, vol. 215, no. 3, pp. 403–410, 1990. View at Publisher · View at Google Scholar · View at PubMed
  54. J. Söding, “Protein homology detection by HMM-HMM comparison,” Bioinformatics, vol. 21, no. 7, pp. 951–960, 2005. View at Publisher · View at Google Scholar · View at PubMed
  55. M. A. Kurowski and J. M. Bujnicki, “GeneSilico protein structure prediction meta-server,” Nucleic Acids Research, vol. 31, no. 13, pp. 3305–3307, 2003. View at Publisher · View at Google Scholar
  56. J. Kosinski, I. A. Cymerman, M. Feder, M. A. Kurowski, J. M. Sasin, and J. M. Bujnicki, “A “FRankenstein's Monster” approach to comparative modeling: merging the finest fragments of Fold-Recognition models and iterative model refinement aided by 3D structure evaluation,” Proteins: Structure, Function and Genetics, vol. 53, no. 6, pp. 369–379, 2003. View at Publisher · View at Google Scholar · View at PubMed
  57. J. Kosinski, M. J. Gajda, I. A. Cymerman et al., “FRankenstein becomes a cyborg: the automatic recombination and realignment of fold recognition models in CASP6,” Proteins: Structure, Function and Genetics, vol. 61, no. 7, pp. 106–113, 2005. View at Publisher · View at Google Scholar · View at PubMed
  58. Y. Xiong, J. Liu, and D. Q. Wei, “An accurate feature-based method for identifying DNA-binding residues on protein surfaces,” Proteins: Structure, Function and Bioformatics, vol. 79, no. 2, pp. 509–517, 2011. View at Publisher · View at Google Scholar · View at PubMed
  59. L. Wang, C. Huang, M. Q. Yang, and J. Y. Yang, “BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features,” BMC Systems Biology, vol. 4, no. 1, article S3, 2010. View at Publisher · View at Google Scholar · View at PubMed
  60. Y. Ofran, V. Mysore, and B. Rost, “Prediction of DNA-binding residues from sequence,” Bioinformatics, vol. 23, no. 13, pp. i347–i353, 2007. View at Publisher · View at Google Scholar · View at PubMed
  61. C. Yan, M. Terribilini, F. Wu, R. L. Jernigan, D. Dobbs, and V. Honavar, “Predicting DNA-binding sites of proteins from amino acid sequence,” BMC Bioinformatics, vol. 7, article 262, 2006. View at Publisher · View at Google Scholar · View at PubMed
  62. S. J. de Vries, M. van Dijk, and A. M. Bonvin, “The HADDOCK web server for data-driven biomolecular docking,” Nature Protocols, vol. 5, no. 5, pp. 883–897, 2010. View at Publisher · View at Google Scholar · View at PubMed
  63. M. van Dijk and A. M. J. J. Bonvin, “Pushing the limits of what is achievable in protein-DNA docking: benchmarking HADDOCK's performance,” Nucleic Acids Research, vol. 38, no. 17, Article ID gkq222, pp. 5634–5647, 2010. View at Publisher · View at Google Scholar · View at PubMed
  64. M. J. Gajda, I. Tuszynska, M. Kaczor, A. Y. Bakulina, and J. M. Bujnicki, “FILTREST3D: discrimination of structural models using restraints from experimental data,” Bioinformatics, vol. 26, no. 23, Article ID btq582, pp. 2986–2987, 2010. View at Publisher · View at Google Scholar · View at PubMed
  65. P. Yue, E. Melamud, and J. Moult, “SNPs3D: candidate gene and SNP selection for association studies,” BMC Bioinformatics, vol. 7, article 166, 2006. View at Publisher · View at Google Scholar · View at PubMed
  66. V. Parthiban, M. M. Gromiha, and D. Schomburg, “CUPSAT: prediction of protein stability upon point mutations,” Nucleic Acids Research, vol. 34, pp. W239–W242, 2006. View at Publisher · View at Google Scholar · View at PubMed
  67. D. Gilis and M. Rooman, “PoPMuSiC, an algorithm for predicting protein mutant stability changes. Application to prion proteins,” Protein Engineering, vol. 13, no. 12, pp. 849–856, 2000. View at Google Scholar
  68. E. Capriotti, P. Fariselli, and R. Casadio, “I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure,” Nucleic Acids Research, vol. 33, no. 2, pp. W306–W310, 2005. View at Publisher · View at Google Scholar · View at PubMed
  69. J. Cheng, A. Randall, and P. Baldi, “Prediction of protein stability changes for single-site mutations using support vector machines,” Proteins: Structure, Function and Genetics, vol. 62, no. 4, pp. 1125–1132, 2006. View at Publisher · View at Google Scholar · View at PubMed
  70. I. A. Adzhubei, S. Schmidt, L. Peshkin et al., “A method and server for predicting damaging missense mutations,” Nature Methods, vol. 7, no. 4, pp. 248–249, 2010. View at Publisher · View at Google Scholar · View at PubMed
  71. P. C. Ng and S. Henikoff, “Predicting deleterious amino acid substitutions,” Genome Research, vol. 11, no. 5, pp. 863–874, 2001. View at Publisher · View at Google Scholar · View at PubMed
  72. B. Li, V. G. Krishnan, M. E. Mort et al., “Automated inference of molecular mechanisms of disease from amino acid substitutions,” Bioinformatics, vol. 25, no. 21, pp. 2744–2750, 2009. View at Publisher · View at Google Scholar · View at PubMed
  73. E. Capriotti, R. Calabrese, and R. Casadio, “Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information,” Bioinformatics, vol. 22, no. 22, pp. 2729–2734, 2006. View at Publisher · View at Google Scholar · View at PubMed