Dataset Information

SEB genotyping: SmartAmp-Eprimer binary code genotyping for complex, highly variable targets applied to HBV.

ABSTRACT:

Background

SmartAmp-Eprimer Binary code (SEB) Genotyping is a novel isothermal amplification method for rapid genotyping of any variable target of interest.

Methods

After in silico alignment of a large number of sequences and computational analysis to determine the smallest number of regions to be targeted by SEB Genotyping, SmartAmp primer sets were designed to obtain a binary code of On/Off fluorescence signals, each code corresponding to a unique genotype.

Results

Applied to HBV, we selected 4 targets for which fluorescence amplification signals produce a specific binary code unique to each of the 8 main genotypes (A-H) found in patients worldwide.

Conclusions

We present here the proof of concept of a new genotyping method specifically designed for complex and highly variable targets. Applied here to HBV, SEB Genotyping can be adapted to any other pathogen or disease carrying multiple known mutations. Using simple preparation steps, SEB Genotyping provides accurate results quickly and will enable physicians to choose the best adapted treatment for each of their patients.

SUBMITTER: Delobel D

PROVIDER: S-EPMC9164387 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

SEB genotyping: SmartAmp-Eprimer binary code genotyping for complex, highly variable targets applied to HBV.

Delobel Diane D Furutani Yutaka Y Nagoshi Sumiko S Tsubota Akihito A Miyasaka Akio A Watashi Koichi K Wakita Takaji T Matsuura Tomokazu T Usui Kengo K

BMC infectious diseases 20220603 1

<h4>Background</h4>SmartAmp-Eprimer Binary code (SEB) Genotyping is a novel isothermal amplification method for rapid genotyping of any variable target of interest.<h4>Methods</h4>After in silico alignment of a large number of sequences and computational analysis to determine the smallest number of regions to be targeted by SEB Genotyping, SmartAmp primer sets were designed to obtain a binary code of On/Off fluorescence signals, each code corresponding to a unique genotype.<h4>Results</h4>Applie ...[more]

PMID: 35659601

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Project description:BackgroundMetabolomics datasets are often high-dimensional though only a limited number of variables are expected to be informative given a specific research question. The important task of selecting informative variables can therefore become complex. In this paper we look at discriminating between two groups. Two tasks need to be performed: (i) finding variables which differ between the two groups; and (ii) determining how the selected variables can be used to classify new subjects. We introduce an approach using minimum classification error rates as test statistics to find discriminatory and therefore informative variables. The thresholds resulting in the minimum error rates can be used to classify new subjects. This approach transforms error rates into p-values and is referred to as ERp.ResultsWe show that non-parametric hypothesis testing, based on minimum classification error rates as test statistics, can find statistically significantly shifted variables. The discriminatory ability of variables becomes more apparent when error rates are evaluated based on their corresponding p-values, as relatively high error rates can still be statistically significant. ERp can handle unequal and small group sizes, as well as account for the cost of misclassification. ERp retains (if known) or reveals (if unknown) the shift direction, aiding in biological interpretation. The threshold resulting in the minimum error rate can immediately be used to classify new subjects. We use NMR generated metabolomics data to illustrate how ERp is able to discriminate subjects diagnosed with Mycobacterium tuberculosis infected meningitis from a control group. The list of discriminatory variables produced by ERp contains all biologically relevant variables with appropriate shift directions discussed in the original paper from which this data is taken.ConclusionsERp performs variable selection and classification, is non-parametric and aids biological interpretation while handling unequal group sizes and misclassification costs. All this is achieved by a single approach which is easy to perform and interpret. ERp has the potential to address many other characteristics of metabolomics data. Future research aims to extend ERp to account for a large proportion of observations below the detection limit, as well as expand on interactions between variables.

Project description:In this study, our aims were to comparatively analyze the power of variable number tandem repeat (VNTR) typing to discriminate isolates within subspecies and to identify a potential genetic marker for better molecular typing of Mycobacterium abscessus complex (MABC) strains. A total of 103 clinical MABC isolates were collected from a nationwide cross-sectional study in China. Eighteen VNTR loci were chosen to genotype the MABC isolates. Of the 103 clinical MABC isolates, there were 76 (73.8%) M. abscessus subsp. abscessus (MAA) and 27 (26.2%) M. abscessus subsp. massiliense (MAM) isolates. Among the patients with MAA lung diseases, the percentage of patients older than 45 years (67.1%) was significantly higher than that of patients with MAM lung diseases [33.3%, adjusted odds ratio (aOR) = 0.36, 95% CI = 0.13-0.98, p = 0.046]. Fifteen VNTR loci were designated as being "highly discriminant" in our sample, except for TR109. The total of 103 MABC isolates were fully discriminated into 103 unique patterns by an 18-locus VNTR set [Hunter-Gaston Discriminatory Index (HGDI) = 1.000], of which the inclusion of the top 12 loci yielded a comparative HGDI value (HGDI = 0.9998). Remarkably, the order of the diversity of the VNTR loci showed significant difference between the MAA and MAM isolates. TR137 and TR2, two loci with high diversity indices for the MAA isolates, only yielded poor discriminatory power for the MAM isolates; the allelic diversity (h) values were 0.0000 and 0.2621, respectively. A detailed analysis of TR137 in combination with the other 17 VNTR loci showed that the combination of TR137-TR2 could fully distinguish MAA from MAM isolates. In conclusion, our data revealed that MAA is more prone to affect elderly patients. Additionally, the population structure of the MABC isolates circulating in China has high diversity. The combined use of the TR137 and TR2 loci provides a simple criterion for the precise identification of MABC to the subspecies level.

Dataset Information

SEB genotyping: SmartAmp-Eprimer binary code genotyping for complex, highly variable targets applied to HBV.

Background

Methods

Results

Conclusions

Publications

SEB genotyping: SmartAmp-Eprimer binary code genotyping for complex, highly variable targets applied to HBV.

Similar Datasets

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