Abstract
In this paper, we introduce a new graph dataset based on the representation of RNA. The RNA dataset includes 3178 RNA chains which are labelled in 8 classes according to their reported biological functions. The goal of this database is to provide a platform for investigating the classification of RNA using graph-based methods. The molecules are represented by graphs representing the sequence and base-pairs of the RNA, with a number of labelling schemes using base labels and local shape. We report the results of a number of state-of-the-art graph based methods on this dataset as a baseline comparison and investigate how these methods can be used to categorise RNA molecules on their type and functions. The methods applied are Weisfeiler Lehman and optimal assignment kernels, shortest paths kernel and the all paths and cycle methods. We also compare to the standard Needleman-Wunsch algorithm used in bioinformatics for DNA and RNA comparison, and demonstrate the superiority of graph kernels even on a string representation. The highest classification rate is obtained by the WL-OA algorithm using base labels and base-pair connections.
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References
Shabash, B., Wiese, K.C.: RNA visualization: relevance and the current state-of-the-art focusing on pseudoknots. IEEE/ACM Trans. Comput. Biol. Bioinformatics 14(3), 696–712 (2017). https://doi.org/10.1109/TCBB.2016.2522421
Wilson, R.C., Algul, E.: Categorization of RNA molecules using graph methods. In: Bai, X., Hancock, E.R., Ho, T.K., Wilson, R.C., Biggio, B., Robles-Kelly, A. (eds.) S+SSPR 2018. LNCS, vol. 11004, pp. 439–448. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97785-0_42
Huang, J., Li, K., Gribskov, M.: Accurate classification of RNA structures using topological fingerprints. PLoS ONE 11, e0164726 (2016)
Chen, L., Calin, G.A., Zhang, S.: Novel insights of structure-based modeling for RNA-targeted drug discovery. J. Chem. Inf. Model. 52(10), 2741–2753 (2012). https://doi.org/10.1021/ci300320t. pMID: 22947071
Miao, Z., Westhof, E.: RNA structure: advances and assessment of 3D structure prediction. Annu. Rev. Biophys. 46(1), 483–503 (2017). https://doi.org/10.1146/annurev-biophys-070816-034125. pMID: 28375730
Rybarczyk, A., et al.: New in silico approach to assessing RNA secondary structures with non-canonical base pairs. BMC Bioinformatics 16, 276–288 (2015). https://doi.org/10.1186/s12859-015-0718-6
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 43(3), 443–453 (1970)
Shervashidze, N., Schweitzer, P., van Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-Lehman graph kernels. J. Mach. Learn. Res. 12, 2539–2561 (2011). http://dl.acm.org/citation.cfm?id=2078187
Vert, J.-P.: The optimal assignment kernel is not positive definite (2008). ArXiv e-prints http://adsabs.harvard.edu/abs/2008arXiv0801.4061V
Lodhi, H.: Computational biology perspective: kernel methods and deep learning. Wiley Interdisc. Rev. Comput. Stat. 4(5), 455–465. https://doi.org/10.1002/wics.1223
What is fasta format? https://zhanglab.ccmb.med.umich.edu/FASTA/
Shelton, J.M., Brown, S.J.: Fasta-O-Matic: a tool to sanity check and if needed reformat fasta files (2015). bioRxiv https://www.biorxiv.org/content/early/2015/08/21/024448
Protein data bank contents guide: atomic coordinate entry format description. Wwpdb.org. http://www.wwpdb.org/documentation/file-format-content/format33/v3.3.html
Protein data bank Japan. Pdbj.org. https://pdbj.org
Nucleic acid database (NDB). Ndbserver.rutgers.edu. http://ndbserver.rutgers.edu/
RCSB PDB. Rcsb.org. https://www.rcsb.org
Klosterman, P., Tamura, M., Holbrook, S., Brenner, S.: SCOR: a structural classification of RNA database. Nucleic Acids Res. 30, 392–394 (2002)
Chojnowski, G., Walen, T., Bujnicki, J.M.: RNA bricks - a database of RNA 3D motifs and their interactions. Nucleic Acids Res. 42(D1), D123–D131 (2014). http://dx.doi.org/10.1093/nar/gkt1084
Ray, S.S., Halder, S., Kaypee, S., Bhattacharyya, D.: HD-RNAS: an automated hierarchical database of RNA structures. Front. Genet. 3, 59 (2012). https://www.frontiersin.org/article/10.3389/fgene.2012.00059
York RNA Graph Dataset. https://www.cs.york.ac.uk/cvpr/RNA.html
Antczak, M., et al.: RNApdbee 2.0: multifunctional tool for RNA structure annotation. Nucleic. Acids Res. 46(W1), W30–W35 (2018). https://doi.org/10.1093/nar/gky314
3DNA: a suite of software programs for the analysis, rebuilding and visualization of 3-dimensional nucleic acid structures. x3dna.org. http://x3dna.org/
Duin, R.P.W., Pękalska, E., Harol, A., Lee, W.J., Bunke, H.: On euclidean corrections for non-euclidean dissimilarities. In: da Vitoria, L.N., et al. (eds.) SSPR/SPR 2008. LNCS, vol. 5342, pp. 551–561. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89689-0_59
Kriege, N.M., Giscard, P.-L., Wilson, R.C.: On valid optimal assignment kernels and applications to graph classification. In: Advances in Neural Information Processing Systems, pp. 1615–1623 (2016)
Borgwardt, K.M., Kriegel, H.: Shortest-path kernels on graphs. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27–30 November 2005, Houston, pp. 74–81 (2005). https://doi.org/10.1109/ICDM.2005.132
Giscard, P.-L., Wilson, R.C.: The all-paths and cycles graph kernel. arXiv preprint arXiv:1708.01410 (2017)
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Algul, E., Wilson, R.C. (2019). A Database and Evaluation for Classification of RNA Molecules Using Graph Methods. In: Conte, D., Ramel, JY., Foggia, P. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2019. Lecture Notes in Computer Science(), vol 11510. Springer, Cham. https://doi.org/10.1007/978-3-030-20081-7_8
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