Generalized lattice graphs for 2D-visualization of biological information |
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Authors: | H González-Díaz LG Pérez-Montoto E Paniagua R Vilas F Bolas-Fernández J Dorado FM Ubeira |
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Institution: | a Department of Microbiology and Parasitology, and Department of Organic Chemistry, Faculty of Pharmacy and Department of Special Public Law, Financial and Tributary Law Area, Faculty of Law, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain b Department of Genetics, Faculty of Veterinary, USC, 27002 Lugo, Spain c Department of Chemistry, Biochemistry and Molecular Biology, Faculty of Experimental and Health Sciences, University Cardenal Herrera, 46113 Moncada, Valencia, Spain d Department of Parasitology, Faculty of Pharmacy, Complutense University, 28040 Madrid, Spain e Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain f Fundación Pública Galega de Medicina Xenómica, Hospital Clínico Universitario de Santiago, E-15706 Santiago de Compostela, Spain |
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Abstract: | Several graph representations have been introduced for different data in theoretical biology. For instance, complex networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning in space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. However, despite the proved efficacy of new lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. This work proposes a generalized type of lattice and illustrates how to use it in order to represent and compare biological data from different sources. We exemplify the following cases: protein sequence; mass spectra (MS) of protein peptide mass fingerprints (PMF); molecular dynamic trajectory (MDTs) from structural studies; mRNA microarray data; single nucleotide polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein polymorphisms and protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of Graph theory in theoretical biology and other areas of biomedical sciences. |
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Keywords: | Graph theory Complex networks Proteomics Mass spectrometry Leishmaniosis 2D-electrophoresis Parasite population polymorphism Single nucleotide polymorphism Schizophrenia Microarray Cancer Patents and copyright studies |
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