RESUMEN

High-throughput molecular methods are currently exploited to characterize the complex and highly individual intestinal microbiota in health and disease. Definition of the human intestinal core microbiota, i.e. the number and the identity of bacteria that are shared among different individuals, is currently one of the main research questions. Here we apply a high-throughput phylogenetic microarray, for a comprehensive and high-resolution microbiota analysis, and a novel computational approach in a quantitative study of the core microbiota in over 100 individuals. In the approach presented we study how the criteria for the phylotype abundance or prevalence influence the resulting core in parallel with biological variables, such as the number and health status of the study subjects. We observed that the core size is highly conditional, mostly depending on the depth of the analysis and the required prevalence of the core taxa. Moreover, the core size is also affected by biological variables, of which the health status had a larger impact than the number of studied subjects. We also introduce a computational method that estimates the expected size of the core, given the varying prevalence and abundance criteria. The approach is directly applicable to sequencing data derived from intestinal and other host-associated microbial communities, and can be modified to include more informative definitions of core microbiota. Hence, we anticipate its utilization will facilitate the conceptual definition of the core microbiota and its consequent characterization so that future studies yield conclusive views on the intestinal core microbiota, eliminating the current controversy.

Asunto(s)

Biota , Tracto Gastrointestinal/microbiología , Estado de Salud , Metagenoma , Adulto , Procesamiento Automatizado de Datos/métodos , Femenino , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Masculino , Metagenómica/métodos , Análisis por Micromatrices/métodos , Filogenia

RESUMEN

This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the self-organizing map (SOM) algorithm. As the feature vectors for the documents statistical representations of their vocabularies are used. The main goal in our work has been to scale up the SOM algorithm to be able to deal with large amounts of high-dimensional data. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240-node SOM. As the feature vectors we used 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms.