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Updated: Feb 9, 2026

Identification of Coding and Non-coding RNA Classes Expressed in Swine Whole Blood
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BASiNET-BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification.

Eric Augusto Ito1, Isaque Katahira1, Fábio Fernandes da Rocha Vicente1

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BASiNET, a new alignment-free computational tool, accurately classifies biological sequences using complex network analysis. This method outperforms existing tools for coding and non-coding RNA identification across multiple species.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next Generation Sequencing (NGS) generates vast amounts of biological sequence data.
  • Efficient computational tools are crucial for analyzing this data to understand organism function.
  • De novo sequencing produces large datasets requiring advanced analytical methods.

Purpose of the Study:

  • Introduce BASiNET, an alignment-free tool for biological sequence classification.
  • Evaluate BASiNET's performance against established classification methods.
  • Demonstrate the robustness and accuracy of BASiNET across diverse species.

Main Methods:

  • Represent biological sequences as complex networks.
  • Extract topological network measures to create feature vectors.
  • Utilize feature vectors for alignment-free sequence classification.

Main Results:

  • BASiNET achieved superior accuracy in classifying coding and non-coding RNAs compared to CNCI, PLEK, and CPC2.
  • The tool demonstrated high accuracy and low standard deviation across 13 species.
  • Results indicate BASiNET is robust and organism-independent.

Conclusions:

  • BASiNET offers a powerful and reliable alignment-free approach for biological sequence classification.
  • The method's performance highlights the utility of complex network measurements in bioinformatics.
  • BASiNET is available as open-source software in R.