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Biblioteca(s): |
Embrapa Pecuária Sul. |
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Data corrente: |
30/11/2020 |
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Data da última atualização: |
30/11/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
FERREIRA, L. M.; SÁFADI, T.; FERREIRA, J. L. |
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Afiliação: |
Leila Maria Ferreira, UFLA; Thelma Sáfadi, UFLA; JULIANO LINO FERREIRA, CPPSUL. |
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Título: |
Evaluation of genome similarities using a wavelet-domain approach. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Revista da Sociedade Brasileira de Medicina Tropical, v. 53, e20190470, May 2020. |
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Idioma: |
Inglês |
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Conteúdo: |
Introduction: Tuberculosis is listed among the top 10 causes of deaths worldwide. The resistant strains causing this disease have been considered to be responsible for public health emergencies and health security threats. As stated by the World Health Organization (WHO), around 558,000 different cases coupled with resistance to rifampicin (the most operative first-line drug) have been estimated to date. Therefore, in order to detect the resistant strains using the genomes of Mycobacterium tuberculosis (MTB), we propose a new methodology for the analysis of genomic similarities that associate the different levels of decomposition of the genome (discrete non-decimated wavelet transform) and the Hurst exponent. Methods: The signals corresponding to the ten analyzed sequences were obtained by assessing GC content, and then these signals were decomposed using the discrete non-decimated wavelet transform along with the Daubechies wavelet with four null moments at five levels of decomposition. The Hurst exponent was calculated at each decomposition level using five different methods. The cluster analysis was performed using the results obtained for the Hurst exponent. Results: The aggregated variance, differenced aggregated variance, and aggregated absolute value methods presented the formation of three groups, whereas the Peng and R/S methods presented the formation of two groups. The aggregated variance method exhibited the best results with respect to the group formation between similar strains. Conclusion: The evaluation of Hurst exponent associated with discrete non-decimated wavelet transform can be used as a measure of similarity between genome sequences, thus leading to a refinement in the analysis. Keywords: GC content. Hurst exponent. Mycobacterium tuberculosis. Discrete non-decimated wavelet transform. Grouping. MenosIntroduction: Tuberculosis is listed among the top 10 causes of deaths worldwide. The resistant strains causing this disease have been considered to be responsible for public health emergencies and health security threats. As stated by the World Health Organization (WHO), around 558,000 different cases coupled with resistance to rifampicin (the most operative first-line drug) have been estimated to date. Therefore, in order to detect the resistant strains using the genomes of Mycobacterium tuberculosis (MTB), we propose a new methodology for the analysis of genomic similarities that associate the different levels of decomposition of the genome (discrete non-decimated wavelet transform) and the Hurst exponent. Methods: The signals corresponding to the ten analyzed sequences were obtained by assessing GC content, and then these signals were decomposed using the discrete non-decimated wavelet transform along with the Daubechies wavelet with four null moments at five levels of decomposition. The Hurst exponent was calculated at each decomposition level using five different methods. The cluster analysis was performed using the results obtained for the Hurst exponent. Results: The aggregated variance, differenced aggregated variance, and aggregated absolute value methods presented the formation of three groups, whereas the Peng and R/S methods presented the formation of two groups. The aggregated variance method exhibited the best results with respect to the group formation betwee... Mostrar Tudo |
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Thesagro: |
Genoma; Tuberculose. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02355naa a2200169 a 4500
001 2127291
005 2020-11-30
008 2020 bl uuuu u00u1 u #d
100 1 $aFERREIRA, L. M.
245 $aEvaluation of genome similarities using a wavelet-domain approach.$h[electronic resource]
260 $c2020
520 $aIntroduction: Tuberculosis is listed among the top 10 causes of deaths worldwide. The resistant strains causing this disease have been considered to be responsible for public health emergencies and health security threats. As stated by the World Health Organization (WHO), around 558,000 different cases coupled with resistance to rifampicin (the most operative first-line drug) have been estimated to date. Therefore, in order to detect the resistant strains using the genomes of Mycobacterium tuberculosis (MTB), we propose a new methodology for the analysis of genomic similarities that associate the different levels of decomposition of the genome (discrete non-decimated wavelet transform) and the Hurst exponent. Methods: The signals corresponding to the ten analyzed sequences were obtained by assessing GC content, and then these signals were decomposed using the discrete non-decimated wavelet transform along with the Daubechies wavelet with four null moments at five levels of decomposition. The Hurst exponent was calculated at each decomposition level using five different methods. The cluster analysis was performed using the results obtained for the Hurst exponent. Results: The aggregated variance, differenced aggregated variance, and aggregated absolute value methods presented the formation of three groups, whereas the Peng and R/S methods presented the formation of two groups. The aggregated variance method exhibited the best results with respect to the group formation between similar strains. Conclusion: The evaluation of Hurst exponent associated with discrete non-decimated wavelet transform can be used as a measure of similarity between genome sequences, thus leading to a refinement in the analysis. Keywords: GC content. Hurst exponent. Mycobacterium tuberculosis. Discrete non-decimated wavelet transform. Grouping.
650 $aGenoma
650 $aTuberculose
700 1 $aSÁFADI, T.
700 1 $aFERREIRA, J. L.
773 $tRevista da Sociedade Brasileira de Medicina Tropical$gv. 53, e20190470, May 2020.
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