Soledad Delgado1, Federico Morán1, Antonio Mora2, Juan Julián Merelo2, Carlos Briones2. 1. Department of Information Structure and Organization, Universidad Politécnica (UPM), Madrid 28031, Department of Biochemistry and Molecular Biology I, Universidad Complutense (UCM), Madrid 28040, Department of Computer Architecture and Computer Technology, Universidad de Granada (UGR), Granada 18071, Spain, CITIC, Campanillas, Malaga 29590, Spain, Department of Molecular Evolution, Centro de Astrobiología (CSIC-INTA), Torrejón de Ardoz, Madrid 28850 and Centro de Investigación Biomédica en Red de enfermedades hepáticas y digestivas (CIBERehd), Barcelona 08036, Spain. 2. Department of Information Structure and Organization, Universidad Politécnica (UPM), Madrid 28031, Department of Biochemistry and Molecular Biology I, Universidad Complutense (UCM), Madrid 28040, Department of Computer Architecture and Computer Technology, Universidad de Granada (UGR), Granada 18071, Spain, CITIC, Campanillas, Malaga 29590, Spain, Department of Molecular Evolution, Centro de Astrobiología (CSIC-INTA), Torrejón de Ardoz, Madrid 28850 and Centro de Investigación Biomédica en Red de enfermedades hepáticas y digestivas (CIBERehd), Barcelona 08036, Spain Department of Information Structure and Organization, Universidad Politécnica (UPM), Madrid 28031, Department of Biochemistry and Molecular Biology I, Universidad Complutense (UCM), Madrid 28040, Department of Computer Architecture and Computer Technology, Universidad de Granada (UGR), Granada 18071, Spain, CITIC, Campanillas, Malaga 29590, Spain, Department of Molecular Evolution, Centro de Astrobiología (CSIC-INTA), Torrejón de Ardoz, Madrid 28850 and Centro de Investigación Biomédica en Red de enfermedades hepáticas y digestivas (CIBERehd), Barcelona 08036, Spain.
Abstract
MOTIVATION: Self-organizing maps (SOMs) are readily available bioinformatics methods for clustering and visualizing high-dimensional data, provided that such biological information is previously transformed to fixed-size, metric-based vectors. To increase the usefulness of SOM-based approaches for the analysis of genomic sequence data, novel representation methods are required that automatically and objectively transform aligned nucleotide sequences into numeric vectors, dealing with both nucleotide ambiguity and gaps derived from sequence alignment. RESULTS: Six different codification variants based on Euclidean space, just like SOM processing, have been tested using two SOM models: the classical Kohonen's SOM and growing cell structures. They have been applied to two different sets of sequences: 32 sequences of small sub-unit ribosomal RNA from organisms belonging to the three domains of life, and 44 sequences of the reverse transcriptase region of the pol gene of human immunodeficiency virus type 1 belonging to different groups and sub-types. Our results show that the most important factor affecting the accuracy of sequence clustering is the assignment of an extra weight to the presence of alignment-derived gaps. Although each of the codification variants shows a different level of taxonomic consistency, the results are in agreement with sequence-based phylogenetic reconstructions and anticipate a broad applicability of this codification method.
MOTIVATION: Self-organizing maps (SOMs) are readily available bioinformatics methods for clustering and visualizing high-dimensional data, provided that such biological information is previously transformed to fixed-size, metric-based vectors. To increase the usefulness of SOM-based approaches for the analysis of genomic sequence data, novel representation methods are required that automatically and objectively transform aligned nucleotide sequences into numeric vectors, dealing with both nucleotide ambiguity and gaps derived from sequence alignment. RESULTS: Six different codification variants based on Euclidean space, just like SOM processing, have been tested using two SOM models: the classical Kohonen's SOM and growing cell structures. They have been applied to two different sets of sequences: 32 sequences of small sub-unit ribosomal RNA from organisms belonging to the three domains of life, and 44 sequences of the reverse transcriptase region of the pol gene of human immunodeficiency virus type 1 belonging to different groups and sub-types. Our results show that the most important factor affecting the accuracy of sequence clustering is the assignment of an extra weight to the presence of alignment-derived gaps. Although each of the codification variants shows a different level of taxonomic consistency, the results are in agreement with sequence-based phylogenetic reconstructions and anticipate a broad applicability of this codification method.
Authors: Brenda Martínez-González; María Eugenia Soria; Lucía Vázquez-Sirvent; Cristina Ferrer-Orta; Rebeca Lobo-Vega; Pablo Mínguez; Lorena de la Fuente; Carlos Llorens; Beatriz Soriano; Ricardo Ramos-Ruíz; Marta Cortón; Rosario López-Rodríguez; Carlos García-Crespo; Pilar Somovilla; Antoni Durán-Pastor; Isabel Gallego; Ana Isabel de Ávila; Soledad Delgado; Federico Morán; Cecilio López-Galíndez; Jordi Gómez; Luis Enjuanes; Llanos Salar-Vidal; Mario Esteban-Muñoz; Jaime Esteban; Ricardo Fernández-Roblas; Ignacio Gadea; Carmen Ayuso; Javier Ruíz-Hornillos; Nuria Verdaguer; Esteban Domingo; Celia Perales Journal: Pathogens Date: 2022-06-08