Tijana Milenković
ABSTRACT
Analogous to sequence alignment, network alignment (NA) can be used to transfer biological knowledge across species between conserved network regions. NA faces two algorithmic challenges: 1) Which cost function to use to capture "similarities" between nodes in different networks? 2) Which alignment strategy to use to rapidly identify "high-scoring" alignments from all possible alignments? We "break down" existing state-of-the-art methods that use both different cost functions and different alignment strategies to evaluate each combination of their cost functions and alignment strategies. We find that a combination of the cost function of one method and the alignment strategy of another method beats the existing methods. Hence, we propose this combination as a novel superior NA method. Then, since human aging is hard to study experimentally due to long lifespan, we use NA to transfer aging-related knowledge from well annotated model species to poorly annotated human between aligned network regions. By doing so, we produce novel aging-related information, which complements currently available information about aging that has been obtained mainly by sequence alignment, especially in human. To our knowledge, we are the first to use NA to learn more about aging.
- S. Altschul, W. Gish, W. Miller, and D. Lipman. Basic local alignment search tool. Journal of Molecular Biology, 215:403--410, 1990.Google Scholar
Cross Ref
- J. Berg and M. Lassig. Local graph alignment and motif search in biological networks. Proceedings of the National Academy of Sciences, 101:14689--14694, 2004.Google ScholarCross Ref
- J. Berg and M. Lassig. Cross-species analysis of biological networks by Bayesian alignment. Proceedings of the National Academy of Sciences, 103(29):10967--10972, 2006.Google ScholarCross Ref
- B. J. Breitkreutz et al. The BioGRID Interaction Database: 2008 update. Nucleic Acids Research, 36:D637--D640, 2008.Google ScholarCross Ref
- The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics, 25:25--29, 2000.Google ScholarCross Ref
- J. de Magalhães et al. The Human Ageing Genomic Resources: online databases and tools for biogerontologists. Aging Cell, 8(1):65--72, 2009.Google ScholarCross Ref
- F. E. Faisal and T. Milenković. Dynamic networks reveal key players in aging. arXiv:1307.3388 {cs.CE}, 2013.Google Scholar
- J. Flannick, A. Novak, S. Balaji, H. Harley, and S. Batzglou. Graemlin: General and robust alignment of multiple large interaction networks. Genome Research, 16(9):1169--1181, 2006.Google ScholarCross Ref
- J. Flannick, A. Novak, C. Do, B. Srinivasan, and S. Batzoglou. Automatic parameter learning for multiple network alignment. In Research in Computational Molecular Biology (RECOMB), pages 214--231, 2008. Google ScholarDigital Library
- D. Gautheret, F. Major, and R. Cedergren. Pattern searching/alignment with RNA primary and secondary structures: an effective descriptor for tRNA. Computer Applications in the Biosciences, 6(4):325--331, 1990.Google Scholar
- X. Guo and A. Hartemink. Domain-oriented edge-based alignment of protein interaction networks. Bioinformatics, 25(12):i240--1246, 2009. Google ScholarDigital Library
- H. Ho, T. Milenković, V. Memišević, J. Aruri, N. Pržulj, and A. Ganesan. Protein interaction network uncovers melanogenesis regulatory network components within functional genomics datasets. BMC Systems Biology, 4(84), 2010.Google Scholar
- D. W. Huang, B. T. Sherman, R. Stephens, M. W. Baseler, C. Lane, and R. A. Lempicki. DAVID gene ID conversion tool. Bioinformatics, 2(10):428--430, 2008.Google Scholar
- B. Kelley, Y. Bingbing, F. Lewitter, R. Sharan, B. Stockwell, and T. Ideker. PathBLAST: a tool for alignment of protein interaction networks. Nucleic Acids Research, 32:83--88, 2004.Google ScholarCross Ref
- G. Klau. A new graph-based method for pairwise global network alignment. BMC Bioinformatics, 10(Suppl 1):S59, 2009.Google ScholarCross Ref
- M. Koyuturk, Y. Kim, U. Topkara, S. Subramaniam, W. Szpankowski, and A. Grama. Pairwise alignment of protein interaction networks. Journal of Computational Biology, 13(2), 2006.Google ScholarCross Ref
- O. Kuchaiev, T. Milenković, V. Memišević, W. Hayes, and N. Pržulj. Topological network alignment uncovers biological function and phylogeny. Journal of the Royal Society Interface, 7:1341--1354, 2010.Google ScholarCross Ref
- O. Kuchaiev and N. Pržulj. Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics, 27(10):1390--1396, 2011. Google ScholarDigital Library
- Z. Liang, M. Xu, M. Teng, and L. Niu. NetAlign: a web-based tool for comparison of protein interaction networks. Bioinformatics, 22(17):2175--2177, 2006. Google ScholarDigital Library
- C. Liao, K. Lu, M. Baym, R. Singh, and B. Berger. IsoRankN: spectral methods for global alignment of multiple protein networks. Bioinformatics, 25(12):i253--258, 2009. Google ScholarDigital Library
- T. Lu et al. Gene regulation and DNA damage in the ageing human brain. Nature, 429(6994):883--891, 2004.Google ScholarCross Ref
- V. Memisević, T. Milenković, and N. Pržulj. Complementarity of network and sequence information in homologous proteins. Journal of Integrative Bioinformatics, 7(3):135, 2010.Google ScholarCross Ref
- V. Memišević and N. Pržulj. C-GRAAL: Common-neighbors-based global graph alignment of biological networks. Integrative Biology, 4(7):734--743, 2012.Google ScholarCross Ref
- T. Milenković, W. Ng, W. Hayes, and N. Pržulj. Optimal network alignment with graphlet degree vectors. Cancer Informatics, 9:121--137, 2010.Google ScholarCross Ref
- T. Milenković and N. Pržulj. Uncovering biological network function via graphlet degree signatures. Cancer Informatics, 6:257--273, 2008.Google ScholarCross Ref
- H. Motulsky. Intuitive Biostatistics. Oxford University Press, 1 edition, 1995.Google Scholar
- A. Narayanan, E. Shi, and B. Rubinstein. Link prediction by de-anonymization: How we won the Kaggle social network challenge. In Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), pages 1825--1834. IEEE, 2011.Google ScholarCross Ref
- R. Patro and C. Kingsford. Global network alignment using multiscale spectral signatures. Bioinformatics, 28(23):3105--3114, 2012. Google ScholarDigital Library
- N. Pržulj, D. G. Corneil, and I. Jurisica. Modeling interactome: Scale-free or geometric? Bioinformatics, 20(18):3508--3515, 2004. Google ScholarDigital Library
- R. Sharan and T. Ideker. Modeling cellular machinery through biological network comparison. Nature Biotechnology, 24(4):427--433, 2006.Google ScholarCross Ref
- R. Sharan et al. Conserved patterns of protein interaction in multiple species. Proceedings of the National Academy of Sciences, 102(6):1974--1979, 2005.Google ScholarCross Ref
- R. Sharan, I. Ulitsky, and R. Shamir. Network-based prediction of protein function. Molecular Systems Biology, 3(88):1--13, 2007.Google Scholar
- R. Singh, J. Xu, and B. Berger. Pairwise global alignment of protein interaction networks by matching neighborhood topology. In Research in Computational Molecular Biology (RECOMB), pages 16--31. Springer, 2007. Google ScholarDigital Library
- R. Singh, J. Xu, and B. Berger. Global alignment of multiple protein interaction networks. Proceedings of Pacific Symposium on Biocomputing, pages 303--314, 2008.Google Scholar
- R. Solava, R. Michaels, and T. Milenković. Graphlet-based edge clustering reveals pathogen-interacting proteins. Bioinformatics, 18(28):i480--i486, 2012. Also, in Proceedings of the 11th European Conference on Computational Biology (ECCB), Basel, Switzerland, September 9-12, 2012 (acceptance rate: 14%). Google ScholarDigital Library
- R. Solava and T. Milenković. Revealing missing parts of the interactome. arXiv:1307.3329 {q-bio.MN}, 2013.Google Scholar
- R. Tacutu, A. Budovsky, and V. Fraifeld. The netage database: a compendium of networks for longevity, age-related diseases and associated processes. Biogerontology, 11(4), 2010.Google Scholar
- K. Venkatesan et al. An empirical framework for binary interactome mapping. Nature Methods, 6(1):83--90, 2009.Google ScholarCross Ref
- D. West. Introduction to Graph Theory. Prentice Hall, Upper Saddle River, NJ, 2nd edition, 2001.Google Scholar
- D. Wieser, I. Papatheodorou, M. Ziehm, and J. Thornton. Computational biology for ageing. Philosophical Transactions of the Royal Society B: Biological Sciences, 366(1561):51--63, 2011.Google ScholarCross Ref
- M. Yandell and W. Majoros. Genomics and Natural Language Processing. Nature Reviews Genetics, 3(8):601--610, 2002.Google ScholarCross Ref
- M. Zaslavskiy, F. Bach, and J. P. Vert. Global alignment of protein-protein interaction networks by graph matching methods. Bioinformatics, 25(12):i259--i267, 2009. Google ScholarDigital Library
Index Terms
- Global Network Alignment In The Context Of Aging
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