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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
06/05/2019 |
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Data da última atualização: |
06/05/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ITO, E. A.; KATAHIRA, I.; VICENTE, F. F. da R.; PEREIRA, L. F. P.; LOPES, F. M. |
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Afiliação: |
Eric Augusto Ito, Department of Computer Science, Bioinformatics Graduate Program/Federal University of Technology Paraná; Isaque Katahira, Department of Computer Science, Bioinformatics Graduate Program/Federal University of Technology – Paraná; Fábio Fernandes da Rocha Vicente, Department of Computer Science, Bioinformatics Graduate Program/Federal University of Technology – Paraná; LUIZ FILIPE PROTASIO PEREIRA, CNPCa; Fabrício Martins Lopes, Department of Computer Science, Bioinformatics Graduate Program/Federal University of Technology – Paraná. |
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Título: |
BASiNET - Biological Sequences NETwork: a case study on coding and non-coding RNAs identification. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Nucleic Acids Research, v. 46, n. 16, p. , 2018 |
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Idioma: |
Inglês |
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Conteúdo: |
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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Palavras-Chave: |
RNA-seq. |
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Thesaurus Nal: |
Cardiovascular diseases; Epigenetics; Neurodegenerative diseases; Nucleotides. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/196978/1/BASinet-Biological-sequences-NETwork.pdf
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Marc: |
LEADER 01912naa a2200229 a 4500
001 2108754
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008 2018 bl uuuu u00u1 u #d
100 1 $aITO, E. A.
245 $aBASiNET - Biological Sequences NETwork$ba case study on coding and non-coding RNAs identification.$h[electronic resource]
260 $c2018
520 $aWith 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.
650 $aCardiovascular diseases
650 $aEpigenetics
650 $aNeurodegenerative diseases
650 $aNucleotides
653 $aRNA-seq
700 1 $aKATAHIRA, I.
700 1 $aVICENTE, F. F. da R.
700 1 $aPEREIRA, L. F. P.
700 1 $aLOPES, F. M.
773 $tNucleic Acids Research$gv. 46, n. 16, p. , 2018
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