Evidence map›Paper›PMID 32428175›Full record

ArticleRevista da Sociedade Brasileira de Medicina Tropical2020

Evaluation of genome similarities using a wavelet-domain approach.

Leila Maria Ferreira, Thelma Sáfadi, Juliano Lino Ferreira

Open access · diamondAbstract read
In one paragraph

Article in Revista da Sociedade Brasileira de Medicina Tropical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 47% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Leila Maria FerreiraPrograma de Pós-Graduação Stricto Sensu em Estatística e Experimentação Agropecuária, Universidade Federal de Lavras, Lavras, MG, Brasil.
Thelma SáfadiDepartamento de Estatística, Universidade Federal de Lavras, Lavras, MG, Brasil.
Juliano Lino FerreiraEmbrapa Pecuária Sul, Empresa Brasileira de Pesquisa Agropecuária, Bagé, RS, Brasil.
Universidade Federal de Lavras · BRBrazilian Agricultural Research Corporation · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTuberculosis 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.

methodsThe 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.

resultsThe 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.

conclusionThe 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.

Indexed as

Models, GeneticWavelet AnalysisGenome, BacterialHumansMycobacterium tuberculosis

Identifiers

PMID32428175
PMCPMC7269520
OpenAlexW3027064707

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.