BASiNET-BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification
Abstract With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant inf...
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| Published in | Nucleic acids research Vol. 46; no. 16; p. e96 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
19.09.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0305-1048 1362-4962 1362-4954 1362-4962 |
| DOI | 10.1093/nar/gky462 |
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| Abstract | Abstract
With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET. |
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| AbstractList | Abstract
With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET. With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET. With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET.With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET. |
| Author | Pereira, Luiz Filipe Protasio Ito, Eric Augusto Lopes, Fabrício Martins Katahira, Isaque Vicente, Fábio Fernandes da Rocha |
| AuthorAffiliation | 2 Empresa Brasileira de Pesquisa Agropecuária, Embrapa Café, Brasília, DF 70770-901, Brazil 1 Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology – Paraná, Cornélio Procópio, PR 86300-000, Brazil |
| AuthorAffiliation_xml | – name: 1 Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology – Paraná, Cornélio Procópio, PR 86300-000, Brazil – name: 2 Empresa Brasileira de Pesquisa Agropecuária, Embrapa Café, Brasília, DF 70770-901, Brazil |
| Author_xml | – sequence: 1 givenname: Eric Augusto surname: Ito fullname: Ito, Eric Augusto organization: Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology - Paraná, Cornélio Procópio, PR 86300-000, Brazil – sequence: 2 givenname: Isaque surname: Katahira fullname: Katahira, Isaque organization: Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology - Paraná, Cornélio Procópio, PR 86300-000, Brazil – sequence: 3 givenname: Fábio Fernandes da Rocha surname: Vicente fullname: Vicente, Fábio Fernandes da Rocha organization: Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology - Paraná, Cornélio Procópio, PR 86300-000, Brazil – sequence: 4 givenname: Luiz Filipe Protasio surname: Pereira fullname: Pereira, Luiz Filipe Protasio organization: Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology - Paraná, Cornélio Procópio, PR 86300-000, Brazil – sequence: 5 givenname: Fabrício Martins orcidid: 0000-0002-8786-3313 surname: Lopes fullname: Lopes, Fabrício Martins email: fabricio@utfpr.edu.br organization: Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology - Paraná, Cornélio Procópio, PR 86300-000, Brazil |
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With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly... With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at... |
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| Title | BASiNET-BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification |
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