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dc.contributor.authorITO, E. A.eng
dc.contributor.authorKATAHIRA, I.eng
dc.contributor.authorVICENTE, F. F. da R.eng
dc.contributor.authorPEREIRA, L. F. P.eng
dc.contributor.authorLOPES, F. M.eng
dc.date.accessioned2019-05-07T00:49:48Z-
dc.date.available2019-05-07T00:49:48Z-
dc.date.created2019-05-06
dc.date.issued2018
dc.identifier.citationNucleic Acids Research, v. 46, n. 16, p. , 2018eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1108754-
dc.descriptionWith 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.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectRNA-seqeng
dc.titleBASiNET - Biological Sequences NETwork: a case study on coding and non-coding RNAs identification.eng
dc.typeArtigo de periódicoeng
dc.date.updated2019-05-07T00:49:48Z
dc.subject.nalthesaurusNeurodegenerative diseaseseng
dc.subject.nalthesaurusCardiovascular diseaseseng
dc.subject.nalthesaurusEpigeneticseng
dc.subject.nalthesaurusNucleotideseng
riaa.ainfo.id1108754eng
riaa.ainfo.lastupdate2019-05-06
dc.contributor.institutionEric Augusto Ito, Department of Computer Science, Bioinformatics Graduate Program/Federal University of Technology Paranáeng
dc.contributor.institutionIsaque Katahira, Department of Computer Science, Bioinformatics Graduate Program/Federal University of Technology – Paranáeng
dc.contributor.institutionFábio Fernandes da Rocha Vicente, Department of Computer Science, Bioinformatics Graduate Program/Federal University of Technology – Paranáeng
dc.contributor.institutionLUIZ FILIPE PROTASIO PEREIRA, CNPCaeng
dc.contributor.institutionFabrício Martins Lopes, Department of Computer Science, Bioinformatics Graduate Program/Federal University of Technology – Paraná.eng
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