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ScreenClust: Advanced statistical software for supervised and unsupervised high resolution melting (HRM) analysis
Abstract
Background:
High resolution melting (HRM) is an emerging new method for interrogating and characterizing DNA samples. An important aspect of this technology is data analysis. Traditional HRM curves can be difficult to interpret and the method has been criticized for lack of statistical interrogation and arbitrary interpretation of results.
Methods:
Here we report the basic principles and first applications of a new statistical approach to HRM analysis addressing these concerns. Our method allows automated genotyping of unknown samples coupled with formal statistical information on the likelihood, if an unknown sample is of a known genotype (by discriminant analysis or "supervised learning"). It can also determine the assortment of alleles present (by cluster analysis or "unsupervised learning") without a priori knowledge of the genotypes present.
Conclusion:
The new algorithms provide highly sensitive and specific auto-calling of genotypes from HRM data in both supervised an unsupervised analysis mode. The method is based on pure statistical interrogation of the data set with a high degree of standardization. The hypothesis-free unsupervised mode offers various possibilities for de novo HRM applications such as mutation discovery.
... Considering that HRM only requires two short primers, the cost of HRM is likely to be a little bit lower than that of KASP. Advanced statistical methods for HRM analysis are available to improve variant calling [27], and using HRM for plant genotyping may be a feasible approach. ...
... To provide a robust method to call variants for HRM analysis, we established a free and open source R-based pipeline to execute variant calling with PCA, based on the rationale of a previous study [27]. This study claimed regular HRM software uses the shape of melting curves that are not supported by statistics, and suggested automated statistical methods such as PCA was more appropriate [27]. ...
... To provide a robust method to call variants for HRM analysis, we established a free and open source R-based pipeline to execute variant calling with PCA, based on the rationale of a previous study [27]. This study claimed regular HRM software uses the shape of melting curves that are not supported by statistics, and suggested automated statistical methods such as PCA was more appropriate [27]. In our simplified version, the normalization of the raw fluorescence data for HRM analysis was carried out using the optimized lower limit (pre-melt region) and upper limit (post-melt region) temperatures of the melt curve analysis for each primer pair ( Table 2). ...
The chilling requirement (CR) is the main factor controlling the peach floral bud break and subsequent reproductive growth. To date, several peach CR quantitative trait loci (QTLs) have been identified. To improve the accessibility and convenience of this genetic information for peach breeders, the aim of this study was to establish an easy-to-use genotype screening system using peach CR molecular markers as a toolkit for marker-assisted selection. Here, we integrated 22 CR-associated markers from three published QTLs and positioned them on the Prunus persica physical map. Then, we built a PCR-based genotyping platform by using high-resolution melting (HRM) analysis with specific primers and trained this platform with 27 peach cultivars. Due to ambiguous variant calls from a commercial HRM software, we developed an R-based pipeline using principal component analysis (PCA) to accurately differentiate genotypes. Based on the PCA results, this toolkit was able to determine the genotypes at the CR-related single nucleotide polymorphisms (SNPs) in all tested peach cultivars. In this study, we showed that this HRM-PCA pipeline served as a low-cost, high-throughput, and non-gel genotyping solution. This system has great potential to accelerate CR-focused peach breeding.
... Next, we applied linear discriminant analysis (LDA) to the HRM data to obtain statistical confidence for the assignment of isolates to chemotype groups. LDA is a dimensionality reduction and supervised classification technique which has been employed for analyzing HRM data in previous studies [54][55][56] . Each chemotype class yielded a well-separated cluster in the LDA plot (Fig. 4a). ...
... Automated genotype assignment of HRM data can provide statistical confidence, reduce bias, and further improve throughput 66 . A supervised machine learning-based dimensionality reduction and classification method, Linear Discriminant Analysis (LDA), has been used to automate HRM analysis on other systems 54,55 including blue crab genotyping where it achieved > 90% accuracy differentiating between five species 56 . In our case, LDA analysis generated four distinct chemotype clusters and predicted the chemotypes with 99.68% accuracy. ...
Fusarium graminearum is a primary cause of Fusarium head blight (FHB) on wheat and barley. The fungus produces trichothecene mycotoxins that render grain unsuitable for food, feed, or malt. Isolates of F. graminearum can differ in trichothecene production phenotypes (chemotypes), with individuals producing predominantly one of four toxins: 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol, nivalenol, or NX-2. Molecular tools to diagnose chemotypes remain inefficient. This study aimed to develop a single-tube, multiplex molecular assay that can predict the four F. graminearum chemotypes. Conserved functional regions of three trichothecene biosynthetic genes (TRI1, TRI8, and TRI13) were targeted to develop a high-resolution melting (HRM) assay. Multiplex HRM analysis produced unique melting profiles for each chemotype, and was validated on a panel of 80 isolates. We applied machine learning-based linear discriminant analysis (LDA) to automate the classification of chemotypes from the HRM data, achieving a prediction accuracy of over 99%. The assay is sensitive, with a limit of detection below 0.02 ng of fungal DNA. The HRM analysis also differentiated chemotypes from a small sample of F. gerlachii, F. asiaticum, and F. vorosii isolates. Together, our results demonstrate that this simple, rapid, and accurate assay can be applied to F. graminearum molecular diagnostics and population surveillance programs.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-024-81131-5.
... We could demonstrate that using k-means clustering with both HRM and PSQ data is a powerful strategy to overcome the bottleneck of evaluating genotypes manually, e.g., when methods are applied in large cohort studies or clinical routine analysis. The drawback of the software package ScreenClust (Qiagen, Hilden, Germany) is that it does not allow analysis of HRM data from multiple runs [51], hampering appropriate clustering of rare variants. When HRM data from multiple runs were evaluated by cluster analysis, the use of difference curves was most appropriate. ...
... HRM data reduction by PCA prior to cluster analysis, as suggested for dimensionality reduction in some studies [51,53], was not found to be advantageous in this study. Even when PCAs explained almost 100% of the variance, clustering following PCA was less appropriate than direct clustering. ...
Due to its cost-efficiency, high resolution melting (HRM) analysis plays an important role in genotyping of candidate single nucleotide polymorphisms (SNPs). Studies indicate that HRM analysis is not only suitable for genotyping individual SNPs, but also allows genotyping of multiple SNPs in one and the same amplicon, although with limited discrimination power. By targeting the three C>T SNPs rs527559815, rs547832288, and rs16906252, located in the promoter of the O6-methylguanine-DNA methyltransferase (MGMT) gene within a distance of 45 bp, we investigated whether the discrimination power can be increased by coupling HRM analysis with pyrosequencing (PSQ). After optimizing polymerase chain reaction (PCR) conditions, PCR products subjected to HRM analysis could directly be used for PSQ. By analyzing oligodeoxynucleotide controls, representing the 36 theoretically possible variant combinations for diploid human cells (8 triple-homozygous, 12 double-homozygous, 12 double-heterozygous and 4 triple-heterozygous combinations), 34 out of the 36 variant combinations could be genotyped unambiguously by combined analysis of HRM and PSQ data, compared to 22 variant combinations by HRM analysis and 16 variant combinations by PSQ. Our approach was successfully applied to genotype stable cell lines of different origin, primary human tumor cell lines from glioma patients, and breast tissue samples.
... Several data mining methods have also been used to analyze the HRM curves. ScreenClust is the first HRM data analysis method which is able to genotype unknown samples using supervised, unsupervised, and statistical methods [15]. It was first applied to SNP genotyping and mutation discovery [15] and then it was applied to a variety of areas. ...
... ScreenClust is the first HRM data analysis method which is able to genotype unknown samples using supervised, unsupervised, and statistical methods [15]. It was first applied to SNP genotyping and mutation discovery [15] and then it was applied to a variety of areas. Sonmez and Ozdemir used unsupervised methods found in ScreenClust to detect polymorphisms in GDF5 and EPF8 genes [16]. ...
High resolution melting (HRM) curve analysis is an efficient, correct, and rapid technique for analyzing real-time polymerase chain reaction (PCR) results. HRM curves are formed based on increasing temperature and decreasing amount of fluorescent dye in real-time PCR process. The shapes of them are unique for each species due to the sequence, length, and GC content of species' DNA. In the literature, the classification of HRM curves is usually conducted through visual inspection and a limited number of data mining methods have been used to classify these curves. However, it becomes challenging as the number of species and their samples and the number of closely related species increase. In this study, a hybrid classification model, which is based on convolutional neural network (CNN) and long short-term memory (LSTM) models, is proposed to classify HRM curves, efficiently. In the proposed CNN-LSTM model, CNN model was used for feature extraction, and LSTM model was used for classification. It takes both the HRM curves and derivative curves as inputs and gives the predicted species of HRM curves as outputs. The performance of the proposed CNN-LSTM model was compared with that of CNN and support vector machines (SVM) approaches. The results show that the proposed CNN-LSTM model outperforms other models. The accuracy, macro-average of F1, specificity, precision, and recall values of the proposed model were 0.96±0.02,0.95±0.02,1±0,0.96±0.02, and 0.96±0.02, respectively.
... We used a dimension reduction method as a novel strategy for molecular subtyping by clustering and analysis of melting profiles which has previously been described by Reja et al. [15]. Principal component analysis (PCA) was used for clustering and differentiation of data sets by SPSS software version 23.0 (SPSS, Inc., Chicago, USA). ...
... Principal component analysis (PCA) was used for clustering and differentiation of data sets by SPSS software version 23.0 (SPSS, Inc., Chicago, USA). Those principal components (PCs) with Eigen value (Ev) more than 1 were retained as the different groups of data set for classification and differentiation of HRM difference curves as previously described by Reja et al. [15]. Sensitivity and specificity of the methods have been evaluated as previously described by Lalkhen et al. (2008) [16]. ...
Objective
Species identification of Shigella isolates are so prominent for epidemiological studies and infection prevention strategies. We developed and evaluated RAPD and ERIC-PCR coupled with HRM for differentiation of non-dysenteriae Shigella species as potential alternative methods. After isolation of eighteen Shigella strains from faecal specimens collected from children under 2 years of age with diarrhea (n = 143), the species of the isolates were identified by slide agglutination assay. Also, species were identified using developed RAPD-PCR-HRM and ERIC-PCR-HRM techniques. Differentiation of the data sets was measured by principal component analysis as a dimension reduction method. Then, sensitivity and specificity of the methods were evaluated.
Results
We found RAPD-PCR-HRM method with high sensitivity and specificity (100 and 85% respectively) to identify non-dysenteriae Shigella species in clinical specimens. However, sensitivity and specificity of ERIC-PCR-HRM were evaluated 33 and 46% respectively and significantly lower than that of RAPD-PCR-HRM assay. Regardless of inherent poor reproducibility of DNA fingerprinting-based methods, RAPD-PCR-HRM assay can be considered as a potential alternative method to identify non-dysenteriae species of Shigella in clinical specimens. As we observed in the current study, HRM technique is more rapid, inexpensive, and sensitive than gel electrophoresis method to characterize PCR amplicons.
... The use of multiple amplicons or barcodes (multi Bar-HRM) in a multiplexed reaction is also possible, and successfully discriminated ∼30 distinct plant families (Ballin et al., 2019). Combining the power of HRM with clustering statistics or supervised machine learning approaches allows further discrimination of highly similar sequences in a probabilistic framework (Reja et al., 2010;Winder et al., 2011;Bowman et al., 2017). HRM is beginning to be used for identification of marine species in the seafood industry (Fitzcharles, 2012;Jin et al., 2015;Fernandes et al., 2018;Verrez-Bagnis et al., 2018). ...
... Next, we employed a machine learning classification approach, linear discriminant analysis (LDA) from the R package MASS (Venables and Ripley, 2002), to classify species based on the change in multivariate fluorescence values across temperatures (melt curve data). LDA is frequently used as a dimensionality reduction technique in machine learning and classification and has been explored for analysis of HRM data in previous studies (e.g., Reja et al., 2010;Athamanolap et al., 2014). LDA models were created using class (species) information for all samples based on 12S rRNA Sanger sequence-based identification (C. ...
The blue crab Callinectes sapidus is one of the most widely studied marine crustaceans due to its high economic value and ecological significance. Despite extensive research on the blue crab in North America, many questions remain about the distribution and abundance of the species in the subtropics and tropics. In many places, C. sapidus is sympatric with morphologically similar Callinectes spp., which has implications for seafood mislabeling. To enable rapid identification of the species, we designed and tested two PCR-based assays targeting the 12S rRNA mitochondrial gene. The first assay discriminates C. sapidus from other Callinectes spp. via post-PCR restriction digestion (PCR-RFLP) and the second assay discriminates among multiple Callinectes spp. through High Resolution Melting (HRM) analysis and supervised machine learning analyses. A total of 58 DNA samples from five Callinectes spp. (validated via 12S gene sequencing) were used for assay testing. The PCR RFLP assay was 100% accurate identifying C. sapidus from other Callinectes spp. HRM analysis of amplicons showed good discrimination among species, with distinct clusters formed between species with higher sequence homology. Linear discriminant analysis (LDA) classification of HRM curves was quite successful given the small dataset available, producing ∼90–91% mean accuracy in classification over all species with 100-fold cross validation. Much of the error came from misclassifications between C. similis and C. danae, which are ∼99% similar in sequence for the amplicon; collapsing them into a single class increased overall classification success to 94%. Error also arose from C. bocourti classifications, which had a reference set containing only three samples. Classification accuracy of C. sapidus alone via HRM was 97.5%. Overall, these assays show great promise as rapid and inexpensive methods to identify Callinectes spp. and have application for both ecological research and seafood identification or labeling.
... Build 9) and Rotor-Gene ScreenClust HRM software (version 1.10.1.3) [29] in supervised mode. Principal component analysis (PCA) was used to better differentiate the HRM profiles based on the sequence variations in the parent viruses (A20 and Serva). ...
... Principal component analysis (PCA) was used to better differentiate the HRM profiles based on the sequence variations in the parent viruses (A20 and Serva). The ScreenClust software normalizes the pre-and post-melting fluorescence levels, generates residual plots, identifies two or three principal components (PC) in the normalized data, defines clusters based on the control samples, assigns unknown/test samples into these defined clusters, and reports the probabilities and typicalities of unknown samples belonging to known clusters based on posterior class probabilities [29]. When all of the unknown samples are classified, the covariance of each cluster is determined (represented by ellipsoids in Fig. 2b). ...
Live attenuated vaccines against infectious laryngotracheitis virus (ILTV) are widely used in the poultry industry to control disease and help prevent economic losses. Molecular epidemiological studies of currently circulating strains of ILTV within poultry flocks in Australia have demonstrated the presence of highly virulent viruses generated by genomic recombination events between vaccine strains. In this study, high-resolution melting (HRM) analysis was used to develop a tool to classify ILTV isolates and to investigate ILTV recombination. The assay was applied to plaque-purified progeny viruses generated after co-infection of chicken embryo kidney (CEK) monolayers with the A20 and Serva ILT vaccine strains and also to viruses isolated from field samples. The results showed that the HRM analysis is a suitable tool for the classification of ILTV isolates and can be used to detect recombination between ILTV vaccine strains in vitro. This method can be used to classify a broad range of ILTV strains to facilitate the classification and genotyping of ILTV and help to further understand recombination in these viruses.
Electronic supplementary material
The online version of this article (10.1007/s00705-018-4086-1) contains supplementary material, which is available to authorized users.
... Runs from each marker were analyzed in the Rotor Gene ScreenClust HRM software (v1.10.1.2; Qiagen) (Reja et al. 2010). The raw melt data were analyzed to confirm the amplification of only a single product. ...
... The raw melt data were analyzed to confirm the amplification of only a single product. An "unsupervised" analysis was used to partition the data into clusters to identify polymorphisms in the amplified region (Reja et al. 2010). For each marker, the SSR loci of representative isolates of each cluster were reamplified and sequenced to identify whether variation was due to differences in the number of motif repeats within the SSR loci. ...
Tan spot, caused by Didymella tanaceti, is one of the most important foliar diseases affecting pyrethrum in Tasmania, Australia. Population dynamics including mating-type ratios and genetic diversity of D. tanaceti was characterized within four geographically separated fields in both late winter and spring in 2012. A set of ten microsatellite markers were developed and used to genotype 774 D. tanaceti isolates. Isolates were genotypically diverse with 123 multilocus genotypes (MLGs) identified across the four fields. Fifty-eight MLGs contained single isolates and Psex analysis estimated that within many of the recurrent MLGs there were multiple clonal lineages derived from recombination. Isolates of both mating-types were at a 1:1 ratio following clone-correction in each field at each sampling period which was suggestive of sexual recombination. No evidence of genetic divergence of isolates of each mating-type was identified indicating admixture within the population. Linkage equilibrium in two of the four field populations sampled in late winter could not be discounted following clone-correction. Evaluation of temporal changes in gene and genotypic diversity identified that they were both similar for the two sampling periods despite an increased D. tanaceti isolation frequency in spring. Genetic differentiation was similar in populations sampled between the two sampling periods within fields or between fields. These results indicated that sexual reproduction may have contributed to tan spot epidemics within Australian pyrethrum fields and has contributed to a genetically diverse D. tanaceti population.
... All data were processed and analyzed using ScreenClust HRM software version 1.10.1.2 (Qiagen Inc., Germantown, MD) (Reja et al., 2010). Briefly, samples were normalized, residual plots from composite means were constructed, principal component analysis was used to identify data vectors which account for the most variation, "known" control samples were clustered using supervised discriminant analysis, "unknown" samples were allocated to "known" clusters using linear discriminant analysis, the probability of "unknowns" belonging to "known" clusters was calculated using posterior class probabilities. ...
... Briefly, samples were normalized, residual plots from composite means were constructed, principal component analysis was used to identify data vectors which account for the most variation, "known" control samples were clustered using supervised discriminant analysis, "unknown" samples were allocated to "known" clusters using linear discriminant analysis, the probability of "unknowns" belonging to "known" clusters was calculated using posterior class probabilities. See Reja et al. (2010) for additional details. ...
The current study evaluates the potential of using high resolution DNA melting assays to discriminate species in the genus Isaria. The study utilizes a previously identified 103 base pair PCR amplicon, which was reported to be selective for Isaria fumosorosea. Our study finds the amplicon selective for Isaria javanica and Isaria poprawskii when assayed against all members of the genus. In addition, the high resolution melting profile of this amplicon can be used to discriminate between I. javanica, I. poprawskii and a 1:1 mixture of the two species. The practical application of this technique was confirmed using a bioassay on whitefly nymphs (Bemisia tabaci biotype B) inoculated with I. javanica, I. poprawskii or a 1:1 mixture of the two species. This assay provides a simple assay to identify these two species of entomopathogenic fungi.
... The supervised mode of the software grouped the unknowns into known groups. Probabilities and typicalities provided statistical information that indicates the likelihood that an unknown belongs to a known cluster (Reja et al., 2010). According to the Screen-Clust HRM software, probabilities below 0.7 and typicalities below 0.05 should be treated with caution. ...
... (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (Reja et al., 2010). The typicality was 0.11 for the pine nut S4 a and 0.07 for S4 c . ...
... Furthermore, if so desired, users can visualize genotype clusters in two dimensions whereby each melt curve is plotted as a point and the positioning of each point or sample has a physical meaning. Although a method for determining the likelihood of each possible genotype derived from cluster plots has been reported [12], the methods and assumptions made in implementing its approach are different from ours and the location of each plotted point lacks physical meaning with respect to a fixed coordinate system. ...
... Supervised classification of HRM curves have been done before by a few researchers using some form of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) [12], or Supervised Vector Machine (SVM) learning [24], however the parameters derived from melt curves and methods are different from what we describe. ...
Introduction:
High Resolution Melting (HRM) following PCR has been used to identify DNA genotypes. Fluorescent dyes bounded to double strand DNA lose their fluorescence with increasing temperature, yielding different signatures for different genotypes. Recent software tools have been made available to aid in the distinction of different genotypes, but they are not fully automated, used only for research purposes, or require some level of interaction or confirmation from an analyst.
Materials and methods:
We describe a fully automated machine learning software algorithm that classifies unknown genotypes. Dynamic melt curves are transformed to multidimensional clusters of points whereby a training set is used to establish the distribution of genotype clusters. Subsequently, probabilistic and statistical methods were used to classify the genotypes of unknown DNA samples on 4 different assays (40 VKORC1, CYP2C9*2, CYP2C9*3 samples in triplicate, and 49 MTHFR c.665C>T samples in triplicate) run on the Roche LC480. Melt curves of each of the triplicates were genotyped separately.
Results:
Automated genotyping called 100% of VKORC1, CYP2C9*3 and MTHFR c.665C>T samples correctly. 97.5% of CYP2C9*2 melt curves were genotyped correctly with the remaining 2.5% given a no call due to the inability to decipher 3 melt curves in close proximity as either homozygous mutant or wild-type with greater than 99.5% posterior probability.
Conclusions:
We demonstrate the ability to fully automate DNA genotyping from HRM curves systematically and accurately without requiring any user interpretation or interaction with the data. Visualization of genotype clusters and quantification of the expected misclassification rate is also available to provide feedback to assay scientists and engineers as changes are made to the assay or instrument.
... The unsupervised mode was also employed to analyze Leptospira DNA fluorescence data obtained with human samples, and the results from the control strains were employed as pseudo-unknowns. The data were analyzed by ScreenClust using the principal component analysis statistical method [41], which enabled the maximum separation of genotypes. ...
... Although the performance measurements of our typing method [41] were excellent (reproducibility 100%, typeability 100%, discriminatory index 0.98), not all the strains (37/48, 77.1%) could be discriminated at the serovar level. The identification of Leptospira serovars by serological methods relies on surface-exposed lipopolysaccharides (LPS) and there is a poor [40,50] and HRM profiles but are distinguishable by PFGE [51]. ...
Leptospirosis is a worldwide zoonosis that is endemic in tropical areas, such as Reunion Island. The species Leptospira interrogans is the primary agent in human infections, but other pathogenic species, such as L. kirschner and L. borgpetersenii, are also associated with human leptospirosis.
In this study, a melting curve analysis of the products that were amplified with the primer pairs lfb1 F/R and G1/G2 facilitated an accurate species classification of Leptospira reference strains. Next, we combined an unsupervised high resolution melting (HRM) method with a new statistical approach using primers to amplify a two variable-number tandem-repeat (VNTR) for typing at the subspecies level. The HRM analysis, which was performed with ScreenClust Software, enabled the identification of genotypes at the serovar level with high resolution power (Hunter-Gaston index 0.984). This method was also applied to Leptospira DNA from blood samples that were obtained from Reunion Island after 1998. We were able to identify a unique genotype that is identical to that of the L. interrogans serovars Copenhageni and Icterohaemorrhagiae, suggesting that this genotype is the major cause of leptospirosis on Reunion Island.
Our simple, rapid, and robust genotyping method enables the identification of Leptospira strains at the species and subspecies levels and supports the direct genotyping of Leptospira in biological samples without requiring cultures.
... Similar to the LightCycler 480, the assay performed on the Bio-Rad CFX Duet unequivocally differentiated the NX-2 amplicon from the non-NX-2 amplicons (Fig 3A). To improve data visualization and add statistical confidence to HRM genotyping, we performed PCA of the HRM data (Lee et al. 2020;Reja et al. 2010). In the PCA plot, a separate cluster of NX-2 isolates was observed from the non-NX-2 isolates (Fig. 3B). ...
Fusarium head blight causes significant yield losses in wheat and other cereals and contaminates grain products with trichothecene mycotoxins. F. graminearum isolates are classified into different chemotypes depending on the type of mycotoxin produced, including the type B trichothecenes 3-acetyl deoxynivalenol (3-ADON), 15-acetyl deoxynivalenol (15-ADON), nivalenol (NIV), and the recently identified type A trichothecene NX-2. Molecular tools to differentiate NX-2 producers from other chemotypes have remained relatively laborious and time consuming. In this study, we developed and validated a high-resolution melting (HRM) assay that can identify NX-2 producers quickly and cost-effectively. By analyzing TRI1 coding sequences from 183 geographically diverse isolates representing all four F. graminearum chemotypes, we selected a 75-base pair region containing four non-synonymous single nucleotide polymorphisms (SNPs) that are specific to the NX-2 genotypes. The amplicon generated two HRM profiles, one of which was specific for only NX-2. We confirmed that the assay is robust across qPCR platforms and unambiguously differentiates NX-2 from other chemotypes using a panel of 72 diverse isolates previously collected from North America. The HRM assay was also successful in identifying NX-2 producers directly from DNA extracted from infected wheat spikes with varying levels of disease severity and fungal DNA. The assay can detect as little as 0.01 ng of fungal DNA in a background of 50 ng of plant DNA. This new diagnostic assay can be used for high-throughput molecular detection of the NX-2 chemotype of F. graminearum from infected plant samples and culture collections, thus making it a valuable tool for surveys of contemporary and historical FHB pathogen populations.
... Unfortunately, none of these studies reported exploration of statistical softwarebased models, which could help improve accuracy and remove subjectivity from melt curve analysis. The Qiagen Rotor-Gene ® ScreenClust HRM ® software (Qiagen) incorporates principle component analysis (PCA) as a way to group like-samples using melt curve data [12,19,20]. PCA is a correlational technique which transforms data into its main elements; from this transformation and reduction in dimension, a linear combination of variables can create new data. ...
Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen’s principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.
... The fluorescence signal is collected at a suitable temperature and a melt curve graph is generated. Real-time analysis allows the identification of a single sequence base change between samples [21,22]. The advantages of real-time melt curve analysis provide amplification of nucleic acids directly with real-time visualization, the absence of post-processing steps, and rapidity [15]. ...
... Successful implementation of MCA is influenced by the analytical process used to extract information from MCs [42]. The signal processing pipeline has seen gradual improvement over the years, and generally includes several steps, including: background fluorescence subtraction and normalization; curve overlay, a "temperature shifting" of curves that allows to correct for minor temperature errors between samples and runs; variant clustering, using hierarchical clustering algorithms; computation of difference plots, the fluorescence in each variant cluster being subtracted from the average fluorescence of a reference cluster; computation of negative first derivative plots of normalized melting data using SavitzkyeGolay polynomial estimation ( Fig. 2B) [43,44]. Signal processing techniques can be used to cluster MCs related to identical amplicons together, but for high-throughput labelling of large datasets of unknown samples, which implies matching each MC against a database of previously identified ones, methods beyond visual inspection or the clustering function included in the instrument software are needed. ...
Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including single-well techniques, using target-specific fluorescent oligonucleotide probes, and spatial multiplexing, where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) - two of the most standard bio-signals emitted during qPCR - for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR. Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent work converging towards the development of an end-to-end framework where knowledge-based and data-driven software solutions are integrated to streamline assay design, and increase the accuracy of target detection and quantification in the multiplex setting. We envision that concerted efforts by academic and industry scientists will help advance these technologies, to a point where they become mature and robust enough to bring about major improvements in the detection of nucleic acids across many fields.
... A standard DNA with a known sequence was included in all HRM analyses to obtain a reference HRM profile. The obtained temperature-fluorescence intensity raw data were analyzed using the Biospeedy® HRM Analysis Software (Bioeksen R&D Technologies Ltd. Şti., Turkiye) which is based on the statistical approach defined by Reja et al. (7). The profiles obtained as a result of HRM analysis were created using the Minitab 17 software program (Minitab, UK) and dendrograms were created by comparison with themselves. ...
Objective: In our study, we analyzed gut microbiota in allo-HSCT patients and aimed to evaluate the relationship of gut microbio-ta with transplant complications, mainly GVHD. Materials and Methods: A total of 25 adult recipients and donors who underwent allo-HSCT at Istanbul Anadolu Medical Center were included in the study. Stool samples were collected twice, before chemotherapy regimen and after allo-HSCT. Samples were analyzed by High Melting (HRM) Analysis and Next Generation Sequencing (NGS) methods after nucleic acid isolation. Sequencing was done with Illumina MiSeq. Bacteria Silva database was used for taxonomic classification and QIIME 2 programs were used for analysis. Statistical analyses were carried out with the R statistical programming language. Results: Twenty-five allo-HKHN recipients were included in the study. The mean age was 46.24±14.86 years in recipients and 43.40±13.20 years in donors. Gender distribution was M/F: 15/10 in patients and M/F: 17/8 in donors. Recipient and donor sib-ling HLA match was 10/10. The rate of GVHD associated with Allo-HSCT was 16%, and the relapse rate was 16%. It was observed that the Firmicutes and Proteobacteria phyla changed significantly before and after transplantation. The number of Entereccocus species was found to be higher in patients who developed GVHD and died. The loss of diversity was found to be statistically significant in the pre-transplant and post-engraft-ment samples of the patients. ÖZET Amaç: Çalışmamızda allo-hematopoetik kök hücre nakli (al-lo-HKHN) uygulanmış hastaların mikrobiyota analizleri yapılmış-tır. Nakile ve tedavilere bağlı olarak değişen mikrobiyota florası-nın engrafman ve Graft-Versus-Host Hastalığı (GVHH) gelişimi ile ilişkisinin gösterilmesi amaçlanmıştır. Gereç ve Yöntem: İstanbul Anadolu Sağlık Merkezi'nde al-lo-HKHN uygulanan toplam 25 yetişkin alıcı ve vericileri çalış-maya dahil edildi. Dışkı örnekleri, Hazırlık Rejimi (HR) öncesi ve allo-HKHN sonrası toplamda 2 kez alınmıştır. Örnekler, nükleik asit izolasyonu yapıldıktan sonra, Çözünürlüklü Erime Analizi (HRM) ve Yeni Nesil Dizileme (YND) yöntemi ile analiz edilmiştir. Dizileme işlemi, Illumina MiSeq cihazı ile yapılmıştır. Taksonomik sınıflandırma için Bacteria Silva veri bankası ve analiz için QIIME 2 programları kullanılmıştır. İstatistiksel analizler ise R istatistiksel programlama dili ile gerçekleştirilmiştir. Bulgular: Çalışmaya dahil edilen alıcılarda yaş ortalaması 46,24±14,86 (18-71) yıl, vericilerde 43,40±13,20 yıl (11-61) olarak saptandı. Hastalarda cinsiyet dağılımı; E/K: 15/10 vericilerde E/K: 17/8 idi. Alıcı ve verici kardeş HLA uyumu 10/10 idi. Allo-HKHN ile ilişkili GVHH oranı %16, relaps oranı ise %16 bulundu. Na-kil öncesi ve sonrası Firmicutes ve Proteobacteria filumlarının önemli ölçüde değiştiği gözlendi. GVHH geliştiren ve ex olan hastalarda Entereccocus türlerinin sayısı daha fazla bulundu. Hastaların nakil öncesi ve engrafman sonrası örneklerinde çeşit-lilik kaybının istatistiksel olarak anlamlı olduğu saptandı.
... A standard DNA with a known sequence was included in all HRM analyses to obtain a reference HRM profile. The obtained temperature-fluorescence intensity raw data were analyzed using the Biospeedy® HRM Analysis Software (Bioeksen R&D Technologies Ltd. Şti., Turkiye) which is based on the statistical approach defined by Reja et al. (7). The profiles obtained as a result of HRM analysis were created using the Minitab 17 software program (Minitab, UK) and dendrograms were created by comparison with themselves. ...
... Build 9) and the Rotor Gene ScreenClust HRM software (version 1.10.1.3) (Reja et al., 2010). ...
Mycoplasmas are important animal pathogens, but the functions and roles of many of their genes in pathogenesis remain unclear, in large part because of the limited tools available for targeted mutagenesis in these bacteria. In this study we used the Mycoplasma gallisepticum CRISPR/Cas system to target a nuclease gene, MGA_0637 (mnuA), which is predicted to play a role in survival and virulence. Our strategy used simultaneous targeting of the ksgA kasugamycin resistance gene, as a mutation in this gene would not interfere with replication but would confer a readily detectable and selectable phenotype in transformants. A guide RNA plasmid, pKM-CRISPR, was constructed, with spacers targeting the ksgA and mnuA genes transcribed under the control of the vlhA1.1 promoter in a backbone plasmid carrying the oriC of M. imitans, and this plasmid was introduced into electrocompetent M. gallisepticum strain S6 cells. PCR assays targeting the ksgA gene, followed by Sanger sequence analyses of the phenotypically resistant transformants, detected polymorphisms within the targeted region of ksgA, confirming the activity of the endogenous CRISPR/Cas system. The nuclease activity of the kasugamycin resistant colonies was then assessed using zymogram assays. The complete or partial loss of nuclease activity in the majority of kasugamycin resistant isolates transformed with the CRISPR plasmid confirmed that the endogenous CRISPR/Cas system had effectively interfered with the function of both ksgA and mnuA genes. Sanger sequencing and RT-qPCR analyses of the mnuA gene suggested that the M. gallisepticum CRISPR/Cas system can be programmed to cleave both DNA and RNA.
... The high-resolution melting or HRM was first developed in the 1990s (Ririe et al. 1997) for detecting genetic variation including SNPs, mutations, indels and methylated DNA by measuring changes in the dissociation ('melting') of a DNA duplex (Reja et al. 2010). HRM is analysed using melting behaviour of the nucleic acids through the detection of the signal of the double-stranded DNA (dsDNA) intercalating dye (Erali et al. 2008;Montgomery et al. 2010). ...
Identification of plant variety and cultivar is pivotal in the agricultural sector due to the abundance of plant varieties and cultivars developed in many crop species. However, plant variety and cultivar identification via basic morphological features is problematic and challenging when differentiating closely related species not only due to their limited differences but also due to technical limitations of the process being time-consuming, labour-intensive and costly, and statistically imprecise information being available due to phenotypic plasticity. Therefore, it is imperative to have rapid and highly efficient techniques to mitigate these limitations. This review provides an overview and summarization of the development and application of molecular markers such as Random Amplified Polymorphic DNA (RAPD), Restriction Fragment Length Polymorphism (RFLP), Simple Sequence Repeats (SSR), Inter-simple sequence repeats (ISSR), Amplified Fragment Length Polymorphism (AFLP), Single nucleotide polymorphism (SNP) and DNA barcoding, High-resolution melting (HRM) and biosensor technology as potential tools in the identification of plant variety and cultivar.
... Thus, the software considers not only Tm values but also the melting shapes of the amplicons. This provides the software with better discrimination ability (Reja et al. 2010, Reed & Wittwer 2004. After the normalisation of the melting shapes on the software, HRM analysis distinguished 32 cultivars of the 35 for ITS1, and 35 for ITS2. ...
Fast, accurate and affordable identification of food products is important to ensure authenticity and safety. There are various apricot (Prunus armeniaca L.) cultivars are being produced in Turkey. Each cultivar differs in quality and purpose of use. In this paper, we aimed to develop an easy and reliable method, Barcode High-Resolution Melting (Bar-HRM), to distinguish apricot cultivars. We designed and tested novel Bar-HRM primer sets HRM-ITS1 and HRM-ITS2, targeting the most popular barcoding region ITS1 and ITS2, specific to apricot cultivars. According to the results, HRM analysis distinguished 31 cultivars of 35 for ITS1, and 35 for ITS2. We recommend using ITS2 barcode region, amplified with using HRM-ITS2 primer set, for Bar-HRM analysis of different apricot cultivars.
... The software includes 3 main processes; the first step is to prepare the dataset, which is normalization and producing residual plots, the second step is to reduce the size of the melting curves using principal component analysis (PCA), and the third step is to cluster HRM data using k-means or to classify HRM data using linear discriminant analysis (LDA). The software is applied to analyze SNP genotyping and mutation discovery [43] . Chau et al. applied ScreenClust to analyze the chilling requirement [15] . ...
Background and Objective: High resolution melting (HRM) analysis is a rapid and correct method for identification of species, such as, microorganism, bacteria, yeast, virus, etc. HRM data are produced using real-time polymerase chain reaction (PCR) and unique for each species. Analysis of the HRM data is important for several applications, such as, for detection of diseases (e.g., influenza, zika virus, SARS-Cov-2 and Covid-19 diseases) in health, for identification of spoiled foods in food industry, for analysis of crime scene evidence in forensic investigation, etc. However, the characteristics of the HRM data can change due to the experimental conditions or instrumental settings. In addition, it becomes laborious and time-consuming process as the number of samples increases. Because of these reasons, the analysis and classification of the HRM data become challenging for species which have similar characteristics.
Methods: To improve the classification accuracy of HRM data, we propose to use image (visual) representation of HRM data, which we call HRM images, that are generated using recurrence plots, and propose convolutional neural network (CNN) based models for classifying HRM images. In this study, two different types of recurrence plots are generated, which are black-white recurrence plots (BW-RP) and gray scale recurrence plots (GS-RP) and four different CNN models are proposed for classifying HRM data.
Results: The classification performance of the proposed methods are evaluated based on average classification accuracy and F1 score, specificity, recall, and precision values for each yeast species. When BW-RP representation of HRM data is used as input to the CNN models, the best classification accuracy of 95.2% is obtained. The classification accuracies of CNN models for melting curve and GS-RP data representations of HRM data are 90.13% and 86.13%, respectively. The classification accuracy of support vector machines (SVM) model that take melting curve representation of HRM data is 86.53%. Moreover, when BW-RP representation of HRM data is used as input to the CNN models, the F1 score, specificity, recall and precision values are the highest for almost all of species.
Conclusions: Experimental results show that using BW-RP representation of HRM data improved the classification accuracy of HRM data and CNN models that take these images as input outperformed CNN models that take melting curve and GS-RP representations of HRM data as inputs and SVM model that take melting curve representation of HRM data as input.
... After amplification, the melting analysis was immediately performed. Based on the normalized Tm curves, the samples were clustered according to the principal component analysis (Reja et al., 2010) in the unsupervised mode using the Rotor-Gene ScreenClust HRM Software program in order to determine differences between the samples. ...
... Using custom-designed primer pairs (Supplementary table 2 The PCR protocol comprised polymerase activation at 95°C for 5 min, followed by 50 cycles of denaturation at 95°C for 10 s, annealing at 60°C for 25 s, and extension at 72°C for 15 s. Melting curves were recorded between 70°C and 98°C with a 1% temperature ramp, and were aligned and normalised using the HRM version 2.0.1 software (Applied Biosystems), followed by clustering analysis as described by Reja et al. [14]. ...
All children, who were born in 2004 and had undergone surgical treatment for recurrent acute tonsillitis and/or acute otitis media at the ear, nose and throat clinic (ENT) between 2004 and 2010, were called on dental examination and blood sampling. Out of 441 invitees, 113 children and their parents/legal guardians agreed to participate. The following data from this group of subjects are presented: the presence of clinical signs of molar–incisor hypomineralisation (MIH), the distribution of human leukocyte antigen (HLA) alleles DQ2 and DQ8 and eight single nucleotide polymorphisms (SNPs) located in amelogenesis-related genes (rs3796704 in the ENAM gene, rs546778141 in the AMBN gene, rs2106416 in the AMELX gene, rs7660807 and rs35286445 in the AMTN gene, rs4870723 in the COL14A1 gene, rs2245803 in the MMP20 gene, and rs3828054 in the TUFT1 gene).
Data on clinical signs of MIH were collected in accordance with the recommendation and on the proposed MIH clinical data recording sheet [1], and with appropriate preliminary training and calibration. Data on HLA DQ2 and DQ8 haplotypes and on SNPs of amelogenesis-related genes were obtained using DNA isolated from blood samples taken from subjects. The HLA DQ2 and DQ8 alleles were determined using the EliGene® Coeliac RT Kits (90048-RT; Elisabeth Pharmacon spol. s.r.o., Brno-Židenice, Czech Republic) on a 7500 Fast RT-PCR System (Applied Biosystems, Waltham, MA, USA). The distributions of SNPs in the amelogenesis-related genes were determined using high resolution melting (HRM) using the Type-IT HRM Master Mix (Qiagen), TaqMan genotyping assays (ID: C__25766207_10; Thermo Fisher Scientific, Waltham, MA, USA) with the TaqMan Universal Master Mix II, or Sanger sequencing using sequencing master mix BigDye® Terminator v3.1 (Applied Biosystems) and ABI 3500 Genetic Analyser (Applied Biosystems).
L. Hočevar, J. Kovač, K. Trebušak Podkrajšek, S. Battelino, A. Pavlič, 2020. The possible influence of genetic aetiological factors on molar–incisor hypomineralisation, Arch. Oral. Biol. 118, 104848. https://doi.org/10.1016/j.archoralbio.2020.104848.
... The polymerase chain reaction protocol comprised polymerase activation at 95 °C for 5 min, followed by 50 cycles of denaturation at 95 °C for 10 s, annealing at 60 °C for 25 s, and extension at 72 °C for 15 s. Melting curves were recorded between 70 °C and 98 °C with a 1 % temperature ramp, and were aligned and normalised using the HRM version 2.0.1 software (Applied Biosystems), followed by clustering analysis as described by Reja et al. (2010). We used specific TaqMan genotyping assays (ID: C__25766207_10; Thermo Fisher Scientific, Waltham, MA, USA) and the 7500 Fast RT-PCR System (Applied Biosystems) to determine the rs3796704 allele distribution in the analysed population. ...
Objective
The present study searched for evidence of possible associations between some genetic factors that could affect the development of molar–incisor hypomineralisation (MIH).
Methods
In 113 patients who were surgically treated at an Otorhinolaryngology and Cervicofacial Surgery Clinic (ORL) during early childhood, human leukocyte antigen (HLA) DQ2 and DQ8 haplotypes and single nucleotide polymorphisms (SNP) of eight amelogenesis-related genes were searched in genomic DNA. Genotypes were determined by high resolution melting (HRM), TaqMan genotyping assays, and Sanger sequencing. Association between MIH and the HLA DQ2 and DQ8 alleles was tested using a univariate logistic regression. The significance of genetic variants was analysed using the Cochran–Armitage tests for trend and the Fisher exact tests.
Results
We identified MIH in 22 (19.5 %) of the 113 children. Among the evaluated genetic variants, SNP rs2245803 in the MMP20 gene in a homozygous form in a recessive model was associated with MIH development (OR, 2.796; 95 %CI, 1.075 − 4.783; p = 0.0496) with the genotype distribution of TT(3), TG(6) or GG(13) in children with MIH and distribution of TT(18), TG(42) or GG(31) in children without MIH.
Conclusions
While the aetiology of MIH remains unclear, our findings suggest that variants of genes associated with amelogenesis may play important roles in susceptibility to MIH.
... Samples with Ct > 35 cycles or without sigmoidal amplification curves were called negative or invalid, respectively. Melt curves of valid amplifications were analyzed and clustering performed using ScreenClust software with unsupervised cluster analysis [55]. Table 13. ...
A dramatic increase in global antimicrobial resistance (AMR) has been well documented. Of particular concern is the dearth of information regarding the spectrum and prevalence of AMR within Category A Select Agents. Here, we performed a survey of horizontally and vertically transferred AMR determinants among Category A agents and their near neighbors. Microarrays provided broad spectrum screening of 127 Francisella spp., Yersinia spp., and Bacillus spp. strains for the presence/absence of 500+ AMR genes (or families of genes). Detecting a broad variety of AMR genes in each genus, microarray analysis also picked up the presence of an engineered plasmid in a Y. pestis strain. High resolution melt analysis (HRMA) was also used to assess the presence of quinolone resistance-associated mutations in 100 of these strains. Though HRMA was able to detect resistance-causing point mutations in B. anthracis strains, it was not capable of discriminating these point mutations from other nucleotide substitutions (e.g., arising from sequence differences in near neighbors). Though these technologies are well-established, to our knowledge, this is the largest survey of Category A agents and their near-neighbor species for genes covering multiple mechanisms of AMR.
... Within each run, three independent isolates with each known mutation were included as replicate positive controls. Data analysis of each run was undertaken in the Rotor Gene ScreenClust HRM software [72]. For analysis of each run, data was normalised within left and right boundaries of 1˚C in width spanning a 10-20˚C window over which melting occurred. ...
Failures in control of tan spot of pyrethrum, caused by Didymella tanaceti, has been associated with decreased sensitivity within the pathogen population to the succinate dehydrogenase inhibitor (SDHI) fungicide boscalid. Sequencing the SdhB, SdhC, and SdhD subunits of isolates with resistant and sensitive phenotypes identified 15 mutations, resulting in three amino acid substitutions in the SdhB (H277Y/R, I279V), six in the SdhC (S73P, G79R, H134R, H134Q, S135R and combined H134Q/S135R), and two in the SdhD (D112E, H122R). In vitro testing of their boscalid response and estimation of resistance factors (RF) identified isolates with wild-type (WT) Sdh genotypes were sensitive to boscalid. Isolates with SdhB-I279V, SdhC-H134Q and SdhD-D112E exhibited moderate resistance phenotypes (10 ≥ RF < 100) and isolates with SdhC-H134R exhibited very high resistance phenotypes (RF ≥ 1000). All other substitutions were associated with high resistance phenotypes (100 ≥ RF < 1000). High-resolution melt assays were designed and used to estimate the frequencies of substitutions in four field populations (n = 774) collected in August (pre-boscalid application) and November (post-boscalid application) 2012. The SdhB-H277Y, SdhC-H134R and SdhB-H277R genotypes were most frequently observed across populations at 56.7, 19.0, and 10.3%, respectively. In August 92.9% of D. tanaceti contained a substitution associated with decreased sensitivity. Following boscalid application, this increased to 98.9%, with no WT isolates detected in three fields. Overlaying previously obtained microsatellite and mating-type data revealed that all ten recurrent substitutions were associated with multiple genotypes. Thus, boscalid insensitivity in D. tanaceti appears widespread and not associated with clonal spread of a limited pool of individuals.
... Bar-HRM is a post-PCR process that requires a fluorescent dye to intercalate with the double-stranded DNA during PCR amplification. HRM enables rapid, highthroughput identification of variations in target DNA regions without sequencing (Reja et al. 2010). This method detects the decrease in fluorescence that occurs when the fluorescent dye is released upon dissociation of the double-stranded DNA as the temperature increases in the thermal cycler (Ririe et al. 1997). ...
In Thailand, there are three species of Bacopa, namely, B. monnieri, B. caroliniana, and B. floribunda. Among these species of Bacopa, B. monnieri is the only medicinal species, used for the treatment of cognitive impairment and improvement of cognitive abilities because of its bioactive constituents, bacoside A and B. However, because of the similar characteristics of these species, it is difficult to differentiate among related species, resulting in confusion during identification. For this reason, and to ensure therapeutic quality for consumers, authentication is important. In this study, the three abovementioned species of Bacopa were evaluated using barcoding coupled with high-resolution melting (Bar-HRM) analysis based on primers designed for the trnL-F sequences of the three species. The melting profiles of the trnL-F amplicons of B. caroliniana and B. floribunda were clearly different from the melting profile of the trnL-F amplicon from B. monnieri; thus, the species could be discriminated by Bar-HRM analysis. Bar-HRM was then used to authenticate commercial products in various forms. The melting curves of the six commercial samples indicated that all the tested products contained genuine B. monnieri species. This method provides an efficient and reliable authentication system for future commercial herbal products and offers a reference system for quality control.
... High resolution melting (HRM) is an emerging method for monitoring DNA dissociation ("melting") kinetics, and is a powerful technique for the detection of point mutations, indels, and methylated DNA. [19,20] . By using HRM, single base changes between samples can be readily detected and identified. ...
DNA barcoding coupled high resolution melting (Bar-HRM) is an emerging method for species discrimination based on DNA dissociation kinetics. The aim of this work was to evaluate the suitability of different primer sets, derived from selected DNA regions, for Bar-HRM analysis of species in Kaempferia (Zingiberaceae). Four primer pairs were evaluated (rbcL, rpoC, trnL and ITS1). It was observed that the ITS1 barcode was the most useful DNA barcoding region overall for species discrimination out of all of the regions and primers assessed. Thus, the primer pair derived from the ITS1 region was the single most effective region for the identification of the tested species, whereas the rbcL primer pair gave the lowest resolution. Our Bar-HRM developed here would not only be useful for identification of Kaempferia plant specimens lacking essential parts for morphological identification but will be useful for authenticating products in powdered form of a high value medicinal species Kaempferia parviflora, in particular.
... In developing countries including Thailand where sequencing facilities may not be widely equipped in laboratories, molecular studies that require sequencing service from outsources such as genotyping and DNA barcoding could have a number complications ranging from time-consuming, economical inefficiency, limitation of routine operation, etc. High resolution melting (HRM) is an emerging method for monitoring DNA dissociation ("melting") kinetics, and through this methods, even a single base change between samples can be readily detected and identified [14][15][16]. HRM involves tracking of change in light intensity emitted from double-stranded DNA intercalating fluorescent dye as the temperature gradually increases in 'high resolution' increment of at most 0.2°C or lower to denature DNA fragment [17]. The denaturation thermodynamics of individual double-stranded DNA to single strands are based on the binding affinities of individual nucleotide pairs, and melting pattern will vary due to variations in product sizes, GC contents and nucleotide composition. ...
Background
A variety of plants in Acanthaceae have long been used in traditional Thai ailment and commercialised with significant economic value. Nowadays medicinal plants are sold in processed forms and thus morphological authentication is almost impossible. Full identification requires comparison of the specimen with some authoritative sources, such as a full and accurate description and verification of the species deposited in herbarium. Intake of wrong herbals can cause adverse effects. Identification of both raw materials and end products is therefore needed.
Methods
Here, the potential of a DNA-based identification method, called Bar-HRM (DNA barcoding coupled with High Resolution Melting analysis), in raw material species identification is investigated. DNA barcode sequences from five regions (matK, rbcL, trnH-psbA spacer region, trnL and ITS2) of Acanthaceae species were retrieved for in silico analysis. Then the specific primer pairs were used in HRM assay to generate unique melting profiles for each plants species.
Results
The method allows identification of samples lacking necessary morphological parts. In silico analyses of all five selected regions suggested that ITS2 is the most suitable marker for Bar-HRM in this study. The HRM analysis on dried samples of 16 Acanthaceae medicinal species was then performed using primer pair derived from ITS2 region. 100% discrimination of the tested samples at both genus and species level was observed. However, two samples documented as Clinacanthus nutans and Clinacanthus siamensis were recognised as the same species from the HRM analysis. Further investigation reveals that C. siamensis is now accepted as C. nutans.
Conclusions
The results here proved that Bar-HRM is a promising technique in species identification of the studied medicinal plants in Acanthaceae. In addition, molecular biological data is currently used in plant taxonomy and increasingly popular in recent years. Here, DNA barcode sequence data should be incorporated with morphological characters in the species identification.
... Fungal identification using DTW distances between ITS melt curves limit reproducibility and complicate comparison of melt curves [46]. This variability is commonly dealt with in a limited way by temperature shifting, the alignment of similar curves based on input from the investigator [3] or statistical grouping of similar curves [54]. An alternative approach has been the incorporation of internal standards with melting points that flank the curves of interest and against which the test curves can be adjusted [3]. ...
Fungal infections are a global problem imposing considerable disease burden. One of the unmet needs in addressing these infections is rapid, sensitive diagnostics. A promising molecular diagnostic approach is high-resolution melt analysis (HRM). However, there has been little effort in leveraging HRM data for automated, objective identification of fungal species. The purpose of these studies was to assess the utility of distance methods developed for comparison of time series data to classify HRM curves as a means of fungal species identification. Dynamic time warping (DTW), first introduced in the context of speech recognition to identify temporal distortion of similar sounds, is an elastic distance measure that has been successfully applied to a wide range of time series data. Comparison of HRM curves of the rDNA internal transcribed spacer (ITS) region from 51 strains of 18 fungal species using DTW distances allowed accurate classification and clustering of all 51 strains. The utility of DTW distances for species identification was demonstrated by matching HRM curves from 243 previously identified clinical isolates against a database of curves from standard reference strains. The results revealed a number of prior misclassifications, discriminated species that are not resolved by routine phenotypic tests, and accurately identified all 243 test strains. In addition to DTW, several other distance functions, Edit Distance on Real sequence (EDR) and Shape-based Distance (SBD), showed promise. It is concluded that DTW-based distances provide a useful metric for the automated identification of fungi based on HRM curves of the ITS region and that this provides the foundation for a robust and automatable method applicable to the clinical setting.
... HRM sensitivity to subtle differences in experimental conditions such as inconsistencies in instrumental operation and pipetting often causes run-to-run melt curves variability. The current HRM data analysis, performed with the accompanying instrument software or a commercially available one such as ScreenClust 19 , is not capable of compensating for these fluctuations and therefore diminish the HRM assay's discriminatory power. To address this issue, we have developed an adaptive Naïve Bayes algorithm which has the capability to 1) enable automated melt curve classification with trained tolerance for variations in experimental conditions, 2) use a database of melt curves from reference bacterial organisms for subsequent curve-matching analysis of unknown samples, 3) discover unanticipated organisms when no match is found in the melt curve database, and 4) provide statistical interpretation. ...
There is still an ongoing demand for a simple broad-spectrum molecular diagnostic assay for pathogenic bacteria. For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) region as the phylogenetic marker for HRM, we observed complex melt curve signatures as compared to 16S rDNA amplicons with enhanced interspecies discrimination. We also developed a novel Naïve Bayes curve classification algorithm with statistical interpretation and achieved 95% accuracy in differentiating 89 bacterial species in our library using leave-one-out cross-validation. Pilot clinical validation of our method correctly identified the etiologic organisms at the species-level in 59 culture-positive mono-bacterial blood culture samples with 90% accuracy. Our findings suggest that broad bacterial sequences may be simply, reliably and automatically profiled by ITS HRM assay for clinical adoption.
... However the use of a composite median reference curve has also been found in a platform specific workflow such as performed in [14] and [15]. ...
High resolution melting curve analysis (HRM) is an emerging new method for interrogating and characterizing DNA samples. It has been used as a powerful tool for gene mutation and single-nucleotide polymorphism (SNP) detection with high throughput and low cost. Commercially available HRM analysis systems are mostly proprietary and expensive. It lacks the flexibility for the end users and researchers to incorporate new analysis algorithms into the existing system. This paper presents the development of a MATLAB-based open software program for high resolution melting curve analysis. Key analysis functions, such as to obtain the first derivative curve using Savitzky-Golay filter, to identify the melt region, subtraction of background fluorescence and curve normalization, are introduced, followed by case studies of HRM analysis using the developed software program.
... However, when using intercalating dyes, we must authenticate the target species, due to the non-specific fluorescent signal. In addition, the results were affected by the sequence of the sam-ple, generating heteroduplex from random concentration of Mg 2+ and other chemical variable (Reja et al., 2010). ...
Animal species identification has received growing attention, regarding genetic diversity and food traceability. The objective of this study is to apply a universal primer of part of the mitochondrial 16S rRNA gene analysis using the PCR-RFLP and HRM methods for identification of species origin in cattle, chicken, horse, sheep, pig, buffalo, and goat. PCR product size was 512 bp. The PCR product of 16S rRNA was digested with two restriction enzymes (BclI and MseI); sufficient to easily generate analyzable species-specific restriction profiles that could distinguish the unambigu-ity of all targeted species. The HRM method successfully identified all species by shape of melting temperature, and proved to be of higher resolution, and a more cost effective, alternative method compared with other identification techniques.
... HRM is an emerging method for monitoring DNA dissociation ("melting") kinetics, and is a powerful technique for the detection of point mutations, indels, and methylated DNA (Reja et al., 2010). The denaturation thermodynamics of individual DNA double strands to single strands are based on the binding affinities of individual nucleotide pairs, and the melting pattern varies due to indels, mutations, and methylations. ...
DNA barcoding, which was developed about a decade ago, relies on short, standardized regions of the genome to identify plant and animal species. This method can be used to not only identify known species but also to discover novel ones. Numerous sequences are stored in online databases worldwide. One of the ways to save cost and time (by omitting the sequencing step) in species identification is to use available barcode data to design optimized primers for further analysis, such as high-resolution melting analysis (HRM). This study aimed to determine the effectiveness of the hybrid method Bar-HRM (DNA barcoding combined with HRM) to identify species that share similar external morphological features, rather than conduct traditional taxonomic identification that require major parts (leaf, flower, fruit) of the specimens. The specimens used for testing were those, which could not be identified at the species level and could either be Uvaria longipes or Uvaria wrayias, indicated by morphological identification. Primer pairs derived from chloroplast regions (matK, psbA-trnH, rbcL, and trnL) were used in the Bar-HRM. The results obtained from psbA-trnH primers were good enough to help in identifying the specimen while the rest were not. Bar-HRM analysis was proven to be a fast and cost-effective method for plant species identification.
... High resolution melting (HRM) is an emerging method for monitoring DNA dissociation ("melting") kinetics, and is a powerful technique for the detection of point mutations, indels, and methylated DNA (Reja et al., 2010). In addition to standard PCR equipment and reagents, HRM requires a generic DNA intercalation fluorescent dye. ...
... Unauthenticated Download Date | 1/10/16 2:34 PM the sample, generating heteroduplex from random, concentration of Mg 2+ and other chemical variable Reja et al 2010 . In other studies, Ganopoulos, et al 2013 used specific mitochondrial DNA regions with high resolution melting (HRM) to detect bovine, ovine, and caprine species; through an attempt to authenticate Greek PDO Feta cheese. ...
Animal species identification has received growing attention, regarding genetic diversity and food traceability. The objective of this study is to apply a universal primer of part of the mitochondrial 16S rRNA gene analysis using the PCR-RFLP and HRM methods for identification of species origin in cattle, chicken, horse, sheep, pig, buffalo, and goat. PCR product size was 512 bp. The PCR product of 16S rRNA was digested with two restriction enzymes BclI and MseI; sufficient to easily generate analyzable species-specific restriction profiles that could distinguish the unambiguity of all targeted species. The HRM method successfully identified all species by shape of meting temperature, and proved to be of higher resolution, and a more cost effective, alternative method compared with other identification techniques.
... Developing and validating sequencing-free methods that are reliable, yet faster and more economical than DNA barcoding is challenging, but will be beneficial for the advancement of herbal product identification routines in developing countries. High resolution melting (HRM) is an emerging method for monitoring DNA dissociation ("melting") kinetics, and is a powerful technique for the detection of point mutations, indels, and methylated DNA [30,31]. In addition to standard PCR equipment and reagents, HRM requires a generic DNA intercalation fluorescent dye. ...
DNA barcoding coupled high resolution melting (Bar-HRM) is an emerging method for species discrimination based on DNA dissociation kinetics. The aim of this work was to evaluate the suitability of different primer sets, derived from selected DNA regions, for Bar-HRM analysis of species in Croton (Euphorbiaceae), one of the largest genera of plants with over 1,200 species. Seven primer pairs were evaluated (matK, rbcL1, rbcL2, rbcL3, rpoC, trnL and ITS1) from four plastid regions, matK, rbcL, rpoC, and trnL, and the nuclear ribosomal marker ITS1. The primer pair derived from the ITS1 region was the single most effective region for the identification of the tested species, whereas the rbcL1 primer pair gave the lowest resolution. It was observed that the ITS1 barcode was the most useful DNA barcoding region overall for species discrimination out of all of the regions and primers assessed. Our Bar-HRM results here also provide further support for the hypothesis that both sequence and base composition affect DNA duplex stability.
... A new set of primers were designed (Table 2) to amplify short PCR products, which will be used for qPCR-HRM analysis. Using the control genotyped samples qPCR-HRM method was developed to screen SNPs using a Rotor-Gene® Q PCR machine and ScreenClust software (Qiagen -CA 94588, USA) (Reja et al., 2010). Briefly, as follows, HRM primers were used for routine PCR with the control samples and the genotypes were confirmed by restriction digest, melt-curve analysis for single peak followed by a qPCR-HRM step. ...
Stress is clearly associated with the quality of life and many diseases, including mental
disorders, with cortisol being a recognized biomarker for stress. Polymorphisms of the
serotonin transporter gene (5-HTT), which results in long and short forms, have been
reported to be associated with depression among major depressive disorder (MDD) patients.
We have previously shown that 5-HTTLPR and waking cortisol do not predict depression in
a general population sample, however, psychological resilience is a defence against
depression. Reelin is an emerging biomarker for psychological resilience that plays an active
role in neuronal migration. It is responsible for cytoarchitechtonic pattern formation in brain
and modulates the migration of newly generated postmitotic neurons from the ventricular
zone. In mice, overexpression of reelin in the hippocampus has anti-depressant activity by
increasing neurogenesis and improving learning. A number of single nucleotide
polymorphisms (SNPs), methylation of the promoter and coding region of the reelin (RELN)
gene have been identified which affect the level of RELN mRNA and protein expression.
Thus RELN is a potential candidate as a biomarker of psychological resilience and we have
developed a rapid high-resolution melting (HRM) PCR analysis technique for the RELN
SNPs and loci using gDNA isolated from buccal cells to test this hypothesis.
... Bar-HRM is an emerging method that combines DNA barcoding with HRM [81]. The denaturation thermodynamics of individual double-stranded DNA to single strands are based on the binding affinities of individual nucleotide pairs, and the melting pattern will vary due to indels, mutations and methylation. ...
Pharmacovigilance of herbal medicines relies on the product label information regarding the ingredients and the adherence to good manufacturing practices along the commercialisation chain. Several studies have shown that substitution of plant species occurs in herbal medicines, and this in turn poses a challenge to herbal pharmacovigilance as adverse reactions might be due to adulterated or added ingredients. Authentication of constituents in herbal medicines using analytical chemistry methods can help detect contaminants and toxins, but are often limited or incapable of detecting the source of the contamination. Recent developments in molecular plant identification using DNA sequence data enable accurate identification of plant species from herbal medicines using defined DNA markers. Identification of multiple constituent species from compound herbal medicines using amplicon metabarcoding enables verification of labelled ingredients and detection of substituted, adulterated and added species. DNA barcoding is proving to be a powerful method to assess species composition in herbal medicines and has the potential to be used as a standard method in herbal pharmacovigilance research of adverse reactions to specific products.
Background and aims:
Sickle cell disease (SCD) is a chronic hemolytic anemia that may be life-threatening due to multisystemic effects. Identification of the factors which affect the pathophysiology of the disease is important in reducing mortality and morbidity. This study aimed to determine gut microbial diversity in children and adolescents with SCA compared with healthy volunteers and to evaluate the clinical impact of microbiota.
Materials and methods:
The study included 34 children and young adolescents with SCD and 41 healthy volunteer participants. The microbiome was assessed by 16S rRNA sequencing in stool samples. Laboratory parameters of all participants, such as complete blood count and C-reactive protein values and clinical characteristics of SCD patients, were determined and compared, as well as clinical conditions of the patients, such as vascular occlusive crisis and/or acute chest syndrome, frequency of transfusions, intake of penicillin, hydroxyurea, and chelation therapy were recorded.
Results:
White blood cell count, hemoglobin, immature granulocyte and C-reactive protein levels were significantly higher in the patient group (P<0.05). Microbiota analysis revealed 3 different clusters among subjects; controls and 2 clusters in the SCD patients (patient G1 and G2 groups). Bacteroides spp. were more prevalent, while Dialester spp. and Prevotella spp. were less prevalent in SCD compared with controls (t=2.142, P<0.05). Patient G2 (n=9) had a higher prevalence of Bacteroides and a lower prevalence of Prevotella than patient G1 (n=25).
Conclusion:
In our study, there was a difference between SCD patients and the control group, while 2 different microbiota profiles were encountered in SCD patients. This difference between the microbiota of the patients was not found to affect the clinical picture (such as vascular occlusive crisis, acute chest syndrome).
High‐resolution melting (HRM) analysis has been improved and applied for the first time to quantitative analysis of enzymatic reactions. By using the relative ratios of peak intensities of substrates and products, the quantitativity of conventional HRM analysis has been improved to allow detailed kinetic analysis. As an example, the ligation of sticky ends through the action of T4 DNA ligase has been kinetically analyzed, with comprehensive data on substrate specificity and other properties having been obtained. For the first time, the kinetic parameters (kobs and apparent Km) of sticky‐end ligation were obtained for both fully matched and mismatched sticky ends. The effect of ATP concentration on sticky‐end ligation was also investigated. The improved HRM method should also be applicable to versatile DNA‐transforming enzymes, because the only requirement is that the products have Tm values different enough from the substrates.
In recent years, species identification in herbs has attracted considerable attention due to several cases of fraud; hence inexpensive high-throughput authentication methods are highly welcomed. Species authentication is often performed through DNA analysis and several specific regions (barcodes) are considered suitable. Each barcode (Bar) possesses different qualities in terms of universality and discrimination power. A multiplexed format where information can be extracted simultaneously from several barcode regions is seemingly appropriate to ensure the power of both universality and discrimination. In this approach, we amplified DNA from five different barcode regions in a multiplexed PCR format followed by high-resolution melting (HRM). This multiplexed Bar-HRM approach was first applied to plants spanning the plant kingdom and then gradually narrowing down the genetic variability within the Lamiaceae and the Solanaceae families to finally reach closely related cultivars. Universality was demonstrated through distinct melting profiles obtained for species originating from 29 different families spanning the angiosperms, gymnosperm, mosses, and liverwort (Marchantiophyta). Discrimination power was retained for species, sub-species, and a few cultivars through the application of multivariate statistics to the high-resolution melting profiles. This preliminary investigation has shown the potential to discriminate a vast amount of species within the whole plant kingdom. It requires no a priori knowledge of the species' DNA sequence and occurs in a closed system within 2.5 h at a reduced cost per sample compared to other DNA based approaches.
High resolution DNA melting of PCR products is a simple technique for sequence variant detection and analysis. However, sensitivity and specificity vary and depend on many factors that continue to be defined. We introduce the area between normalized melting curves as a metric to quantify genotype discrimination. The effects of amplicon size (51-547 bp), melting rate (0.01-0.64 °C/s) and analysis method (curve shape by overlay vs absolute temperature differences) were qualitatively and quantitatively analyzed. To limit experimental variance, we studied a single nucleotide variant with identical predicted wild type and homozygous variant stabilities by nearest neighbor thermodynamic theory. Heterozygotes were easier to detect in smaller amplicons, at faster melting rates, and after curve overlay (superimposition), with some p-values <10(-20). As heterozygote melting rates increase, the relative magnitude of heteroduplex contributions to melting curves increases, apparently the result of non-equilibrium processes. In contrast to heterozygotes, the interplay between curve overlay, PCR product size, and analysis method is complicated for homozygote genotype discrimination and is difficult to predict. Similar to temperature cycling in PCR, if the temperature control and temperature homogeneity of the solution are adequate, faster rates improve melting analysis, just like faster rates improve PCR.
Species identification is an important facet of forensic investigation. In this study, human and non-human species (cow, chicken, pig, sheep, cat, dog, rabbit, fox, kangaroo and wombat) were assayed on the ViiA 7 Real-Time PCR System (Thermo Fisher Scientific) to rapidly screen for their species of origin using the high resolution melt (HRM) analysis targeting the 16S rRNA gene. Classification of HRM difference profiles using the onboard ViiA 7 software resulted in a classification accuracy of <20%. Derivative profiles (temperature versus negative first derivative of fluorescence, –dF/dT) were classified using random forest algorithms supplemented by bagging and boosting, with either a randomly partitioned test set or a variety of folds of cross-classification, in addition to a range of trees and variables. Random forest classification with bagging conditions (constructed over 500 trees) was found to considerably outperform the ViiA 7 software for species differentiation with 100% classification accuracy for biological material from humans, domestic pets (cat and dog) and consumable meats (chicken and sheep) with an average classification accuracy of 70% across all species.
High-resolution DNA melting is a simple, inexpensive, and homogeneous solution for genotyping of known variants and scanning for unknown variants within polymerase chain reaction products. We begin with a brief history of DNA melting along with the dye and instrument requirements for high-resolution melting and aspects of subsequent data analysis. This is followed by a description of genotyping methods, including small amplicon, unlabeled probe, and snapback primer genotyping. Next, variant scanning for single-nucleotide variants and small insertions and deletions are reviewed. Specific examples of genotyping and variant scanning for molecular diagnostics are presented and discussed. Additional applications of high-resolution melting are described including methylation analysis, rare allele detection, copy number analysis, and establishing sequence identity. Finally, tools for melting curve prediction and high-resolution melting assay design are presented.
Within a polyembryonic mango seedling tree population, the genetic background of individuals should be identical because vigorous plants for cultivation are expected to develop from nucellar embryos representing maternal clones. Due to the fact that the mango cultivar 'Hôi' is assigned to the polyembryonic ecotype, an intra-cultivar variability of ethylene receptor genes was unexpected. Ethylene receptors in plants are conserved, but the number of receptors or receptor isoforms is variable regarding different plant species. However, it is shown here that the ethylene receptor MiETR1 is present in various isoforms within the mango cultivar 'Hôi'. The investigation of single nucleotide polymorphisms revealed that different MiETR1 isoforms can not be discriminated simply by individual single nucleotide exchanges but by the specific arrangement of single nucleotide polymorphisms at certain positions in the exons of MiETR1. Furthermore, an MiETR1 isoform devoid of introns in the genomic sequence was identified. The investigation demonstrates some limitations of high resolution melting and ScreenClust analysis and points out the necessity of sequencing to identify individual isoforms and to determine the variability within the tree population.
Butyrylcholinesterase (BChE) deficiency is characterized by prolonged apnea after the use of muscle relaxants (suxamethonium or mivacurium). Although many acquired conditions may affect BChE activity, BChE deficiency is mainly due to mutations in the BCHE gene (MIM 177400). Though close to 70 natural mutations have been documented in human BCHE, the atypical variant (rs1799807) is the most frequently involved in prolonged apnea. We describe an HRM method for the detection of this variant. Thirty-four patients with known genotype [5 wild-type (U/U), 12 heterozygous (U/A), 17 homozygous (A/A) - A: atypical allele of BCHE, U: usual allele of BCHE -] were screened with the HRM analysis. Within and between-run precision were also evaluated. In silico prediction of HRM curves was performed in order to evaluate the potential impact of the other SNPs described within the PCR product on the HRM diagnostic accuracy. HRM analysis for the BCHE atypical variant genotyping is a simple, rapid, sensitive and low cost method.
High-resolution amplicon melting analysis was recently introduced as a closed-tube method for genotyping and mutation scanning (Gundry et al. Clin Chem 2003;49:396-406). The technique required a fluorescently labeled primer and was limited to the detection of mutations residing in the melting domain of the labeled primer. Our aim was to develop a closed-tube system for genotyping and mutation scanning that did not require labeled oligonucleotides.
We studied polymorphisms in the hydroxytryptamine receptor 2A (HTR2A) gene (T102C), beta-globin (hemoglobins S and C) gene, and cystic fibrosis (F508del, F508C, I507del) gene. PCR was performed in the presence of the double-stranded DNA dye LCGreen, and high-resolution amplicon melting curves were obtained. After fluorescence normalization, temperature adjustment, and/or difference analysis, sequence alterations were distinguished by curve shape and/or position. Heterozygous DNA was identified by the low-temperature melting of heteroduplexes not observed with other dyes commonly used in real-time PCR.
The six common beta-globin genotypes (AA, AS, AC, SS, CC, and SC) were all distinguished in a 110-bp amplicon. The HTR2A single-nucleotide polymorphism was genotyped in a 544-bp fragment that split into two melting domains. Because melting curve acquisition required only 1-2 min, amplification and analysis were achieved in 10-20 min with rapid cycling conditions.
High-resolution melting analysis of PCR products amplified in the presence of LCGreen can identify both heterozygous and homozygous sequence variants. The technique requires only the usual unlabeled primers and a generic double-stranded DNA dye added before PCR for amplicon genotyping, and is a promising method for mutation screening.
DNA melting analysis for genotyping and mutation scanning of PCR products by use of high-resolution instruments with special "saturation" dyes has recently been reported. The comparative performance of other instruments and dyes has not been evaluated.
A 110-bp fragment of the beta-globin gene including the sickle cell anemia locus (A17T) was amplified by PCR in the presence of either the saturating DNA dye, LCGreen Plus, or SYBR Green I. Amplicons of 3 different genotypes (wild-type, heterozygous, and homozygous mutants) were melted on 9 different instruments (ABI 7000 and 7900HT, Bio-Rad iCycler, Cepheid SmartCycler, Corbett Rotor-Gene 3000, Idaho Technology HR-1 and LightScanner, and the Roche LightCycler 1.2 and LightCycler 2.0) at a rate of 0.1 degrees C/s or as recommended by the manufacturer. The ability of each instrument/dye combination to genotype by melting temperature (Tm) and to scan for heterozygotes by curve shape was evaluated.
Resolution varied greatly among instruments with a 15-fold difference in Tm SD (0.018 to 0.274 degrees C) and a 19-fold (LCGreen Plus) or 33-fold (SYBR Green I) difference in the signal-to-noise ratio. These factors limit the ability of most instruments to accurately genotype single-nucleotide polymorphisms by amplicon melting. Plate instruments (96-well) showed the greatest variance with spatial differences across the plates. Either SYBR Green I or LCGreen Plus could be used for genotyping by T(m), but only LCGreen Plus was useful for heterozygote scanning. However, LCGreen Plus could not be used on instruments with an argon laser because of spectral mismatch. All instruments compatible with LCGreen Plus were able to detect heterozygotes by altered melting curve shape. However, instruments specifically designed for high-resolution melting displayed the least variation, suggesting better scanning sensitivity and specificity.
Different instruments and dyes vary widely in their ability to genotype homozygous variants and scan for heterozygotes by whole-amplicon melting analysis.
The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear Discriminant Analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called Uncorrelated Linear Discriminant Analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the Generalized Singular Value Decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets.
Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques it continues to be the subject of much research, ranging from new model- based approaches to algorithmic ideas from neural networks. It is extremely versatile with applications in many disciplines. The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition. Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra. Ian Jolliffe is
A microvolume fluorometer integrated with a thermal cycler was used to acquire DNA melting curves during polymerase chain reaction by fluorescence monitoring of the double-stranded DNA specific dye SYBR Green I. Plotting fluorescence as a function of temperature as the thermal cycler heats through the dissociation temperature of the product gives a DNA melting curve. The shape and position of this DNA melting curve are functions of the GC/AT ratio, length, and sequence and can be used to differentiate amplification products separated by less than 2°C in melting temperature. Desired products can be distinguished from undesired products, in many cases eliminating the need for gel electrophoresis. Analysis of melting curves can extend the dynamic range of initial template quantification when amplification is monitored with double-stranded DNA specific dyes. Complete amplification and analysis of products can be performed in less than 15 min.
Most algorithms for the least-squares estimation of non-linear parameters have centered about either of two approaches. On the one hand, the model may be expanded as a Taylor series and corrections to the several parameters calculated at each iteration on the assumption of local linearity. On the other hand, various modifications of the method of steepest-descent have been used. Both methods not infrequently run aground, the Taylor series method because of divergence of the successive iterates, the steepest-descent (or gradient) methods because of agonizingly slow convergence after the first few iterations. In this paper a maximum neighborhood method is developed which, in effect, performs an optimum interpolation between the Taylor series method and the gradient method, the interpolation being based upon the maximum neighborhood in which the truncated Taylor series gives an adequate representation of the nonlinear model. The results are extended to the problem of solving a set of nonlinear algebraic e
The dependence of DNA absorbance (for light at about 260 nm) on temperature is related to a specific DNA sequence structure in the vicinity of DNA thermal denaturation (the so-called DNA melting or coiling). A straightforward analysis of the experimental DNA melting curve allows us to determine the lengths, the A+T content, and the location in DNA of certain domains. In the case of a specific DNA fragmentation, the order of fragments in DNA can be learned from this analysis, nondestructively and quickly, without fractionating the fragments and other methods of fragmentation. If the DNA nucleotide sequence is known except for some sites and uncertain portions, the analysis determines these sites and the accuracy of the sequence at the portions. This information may complement exact methods of DNA sequencing. The proposed analysis is applied to bacteriophage phiX174, whose melting curve is known. The results are compared to and found to be in an excellent agreement with the known phiX174 nucleotide sequence.
We propose a method (the \Gap statistic") for estimating the numberof clusters (groups) in a set of data. The technique uses the outputof any clustering algorithm (e.g. k-means or hierarchical), comparingthe change in within cluster dispersion to that expected under an appropriatereference null distribution. Some theory is developed forthe proposal and a simulation study that shows that the Gap statisticusually outperforms other methods that have been proposed in the literature.We also...
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