BASiNET

Abstract

With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of DNA and RNA data is quickly sequenced at relatively lower costs. In this sense, computational tools are increasingly needed to aid in the selection of meaningful information for understanding the functioning of organisms. Given this need, we developed the Biological Sequences Network (BASiNET), an extraction tool capable of selecting significant characteristics for classification of RNAs in coding and non-coding. In order to represent the selected sequences, networks were configured in order to show the connections between the nucleotides and remove the less connected edges to generate subnets. Subsequently, each subnet was submitted to metrics: assortativity, degree, maximum degree, minimum degree, intermediation, clustering coefficient, mean minimum path, standard deviation and motifs, providing values for detecting distinctive patterns. Then, 10-fold cross-validation was performed.