A multiple-tubes approach for accurate genotyping of very small DNA samples by using PCR: statistical considerations.

A multiple-tubes procedure is described for using PCR to determine the genotype of a very small DNA sample. The procedure involves dividing the sample among several tubes, then amplifying and typing the contents of each tube separately. The results are analyzed by a statistical procedure which determines whether a genotype can be conclusively assigned to the DNA sample. Simulation studies show that this procedure usually gives correct results even when the number of double-stranded fragments in the sample is as small as 30. The procedure remains effective even in the presence of small amounts of laboratory contamination. We find that the multiple-tubes procedure is superior to the standard one-tube procedure, either when the sample is small or when laboratory contamination is a potential problem; and we recommend its use in these situations. Because the procedure is statistical, it allows the degree of certainty in the result to be quantified and may be useful in other PCR applications as well.     

A reliable multiplex genotyping assay for HCV using a suspension bead array

The genotyping of the hepatitis C virus (HCV) plays an important role in the treatment of HCV because genotype determination has recently been incorporated into the treatment guidelines for HCV infections. Most current genotyping methods are unable to detect mixed genotypes from two or more HCV infections. We therefore developed a multiplex genotyping assay to determine HCV genotypes using a bead array. Synthetic plasmids, genotype panels and standards were used to verify the target‐specific primer (TSP) design in the assay, and the results indicated that discrimination efforts using 10 TSPs in a single reaction were extremely successful. Thirty‐five specimens were then tested to evaluate the assay performance, and the results were highly consistent with those of direct sequencing, supporting the reliability of the assay. Moreover, the results from samples with mixed HCV genotypes revealed that the method is capable of detecting two different genotypes within a sample. Furthermore, the specificity evaluation results suggested that the assay could correctly identify HCV in HCV/human immunodeficiency virus (HIV) co‐infected patients. This genotyping platform enables the simultaneous detection and identification of more than one genotype in a same sample and is able to test 96 samples simultaneously. It could therefore provide a rapid, efficient and reliable method of determining HCV genotypes in the future.     

Predicting the success of primer extension genotyping assays using statistical modeling

Using an empirical panel of more than 20 000 single base primer extension (SNP-IT) assays we have developed a set of statistical scores for evaluating and rank ordering various parameters of the SNP-IT reaction to facilitate high-throughput assay primer design with improved likelihood of success. Each score predicts either signal magnitude from primer extension or signal noise caused by mispriming of primers and structure of the PCR product. All scores have been shown to correlate with the success/ failure rate of the SNP-IT reaction, based on analysis of assay results. A logistic regression analysis was applied to combine all scored parameters into one measure predicting the overall success/failure rate of a given SNP marker. Three training sets for different types of SNP-IT reaction, each containing about 22000 SNP markers, were used to assign weights to each score and optimize the prediction of the combined measure. c-Statistics of 0.69, 0.77 and 0.72 were achieved for three training sets. This new statistical prediction can be used to improve primer design for the SNP-IT reaction and evaluate the probability of genotyping success for a given SNP based on analysis of the surrounding genomic sequence.     

A multivariate statistical approach to Centaurea classification using essential oil composition data of some species from Turkey

The composition of the essential oils of ten Centaurea species from Turkey, Centaurea aladaghensis, C. antiochia var. prealta, C. antitauri, C. babylonica, C. balsamita, C. cheirolepidoides, C. deflexa, C. iconiensis, C. lanigera, C. ptosimopappoides have been studied. Multivariate statistical analyses (Principal Component Analysis, Multidimensional Scaling, Hierarchical Cluster Analysis) applied to GC-MS data, seem to be very useful to investigate and establish the natural taxonomic delimitation of this very difficult genus. The groupings resulted independent from the ecological similarities (i.e. plants that live in the same habitats or share similar morphological characteristics), so it seems that the environment has no influence on the biosynthesis of the volatiles of these plants.     

Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds

The identification of individuals’ breed of origin has several practical applications in livestock and is useful in different biological contexts such as conservation genetics, breeding and authentication of animal products. In this paper, penalized multinomial regression was applied to identify the minimum number of single nucleotide polymorphisms (SNPs) from high-throughput genotyping data for individual assignment to dairy sheep breeds reared in Sicily. The combined use of penalized multinomial regression and stability selection reduced the number of SNPs required to 48. A final validation step on an independent population was carried out obtaining 100% correctly classified individuals. The results using independent analysis, such as admixture, F_st, principal component analysis and random forest, confirmed the ability of these methods in selecting distinctive markers. The identified SNPs may constitute a starting point for the development of a SNP based identification test as a tool for breed assignment and traceability of animal products.     

Detecting network cyber-attacks using an integrated statistical approach

Cluster Computing - Anomaly detection in the Internet of Things (IoT) is imperative to improve its reliability and safety. Detecting denial of service (DOS) and distributed DOS (DDOS) is one of the...     

A classification approach for genotyping viral sequences based on multidimensional scaling and linear discriminant analysis

Background  

Accurate classification into genotypes is critical in understanding evolution of divergent viruses. Here we report a new approach, MuLDAS, which classifies a query sequence based on the statistical genotype models learned from the known sequences. Thus, MuLDAS utilizes full spectra of well characterized sequences as references, typically of an order of hundreds, in order to estimate the significance of each genotype assignment.     

Primer design and marker clustering for multiplex SNP-IT primer extension genotyping assay using statistical modeling

MOTIVATION: The optimization of the primer design is critical for the development of high-throughput SNP genotyping methods. Recently developed statistical models of the SNP-IT primer extension genotyping reaction allow further improvement of primer quality for the assay. RESULTS: Here we describe how the statistical models can be used to improve primer design for the assay. We also show how to optimize clustering of the SNP markers into multiplex panels using statistical model for multiplex SNP-IT. The primer set failure probability calculated by a model is used as a minimization function for both primer selection and primers clustering. Three clustering algorithms for the multiplex genotyping SNP-IT assay are described and their relative performance is evaluated. We also describe the approaches to improve the speed of primer design and clustering calculations when using the statistical models. Our clustering decreases the average failure probability of the marker set by 7-25%. The experimental marker failure rate in the multiplex reaction was reduced dramatically and success rate can be achieved as high as 96%. AVAILABILITY: The primer design using statistical models is freely available from www.autoprimer.com.     

A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data

We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours.     

Sequence-guided approach to genotyping plant clones and species using polymorphic NB-ARC-related genes

Key message

Leveraging the heightened levels of polymorphism in NB-ARC-related protein encodinggenes in higher plants, a bioinformatic pipeline was created to identify regions in thisgene family from sequenced plant genomes that exhibit fragment length or singlenucleotide differences in different accessions of the same species. Testing this approachwith the aquatic plant Spirodela polyrhiza demonstrated its superior performance incomparison with currently available genotyping technologies based on PCR amplification.

Abstract

Rapid and economical genotyping tools that can reliably distinguish species and intraspecific variations in plants can be powerful tools for biogeographical and ecological studies. Clones of the cosmopolitan duckweed species, Spirodela polyrhiza, are difficult to distinguish morphologically due to their highly abbreviated architecture and inherently low levels of sequence variation. The use of plastidic markers and generic Amplification Fragment Length Polymorphism approaches have met with limited success in resolving clones of S. polyrhiza from diverse geographical locales. Using whole genome sequencing data from nine S. polyrhiza clones as a training set, we created an informatic pipeline to identify and rank polymorphic regions from nuclear-encoded NB-ARC-related genes to design markers for PCR, Sanger sequencing (barcoding), and fragment length analysis. With seven primer sets, we found 21 unique fingerprints from a set of 23 S. polyrhiza clones. However, three of these clones share the same fingerprint and are indistinguishable by these markers. These primer sets can also be used as interspecific barcoding tools to rapidly resolve S. polyrhiza from the closely related S. intermedia species without the need for DNA sequencing. Our work demonstrates a general approach of using hyper-polymorphic loci within genomes as a resource to produce facile tools that can have high resolving power for genotyping applications.

     

A statistical approach to the classification of biological cells from their diffraction patterns.

An effective method of graphically representing complex biological data is presented, and a statistical classification technique is outlined including an example involving exfoliated cervical cells.     

A new approach to the detection and statistical classification of Ca2+ sparks

The availability of high-speed, two-dimensional (2-D) confocal microscopes and the expanding armamentarium of fluorescent probes presents unprecedented opportunities and new challenges for studying the spatial and temporal dynamics of cellular processes. The need to remove subjectivity from the detection process, the difficulty of the human eye to detect subtle changes in fluorescence in these 2-D images, and the large volume of data produced by these confocal microscopes call for the need to develop algorithms to automatically mark the changes in fluorescence. These fluorescence signal changes are often subtle, so the statistical estimate of the likelihood that the detected signal is not noise is an integral part of the detection algorithm. This statistical estimation is fundamental to our new approach to detection; in earlier Ca(2+) spark detectors, this statistical assessment was incidental to detection. Importantly, the use of the statistical properties of the signal local to the spark, instead of over the whole image, reduces the false positive and false negative rates. We developed an automatic spark detection algorithm based on these principles and used it to detect sparks on an inhomogeneous background of transverse tubule-labeled rat ventricular cells. Because of the large region of the cell surveyed by the confocal microscope, we can detect a large enough number of sparks to measure the dynamic changes in spark frequency in individual cells. We also found, in contrast to earlier results, that cardiac sparks are spatially symmetric. This new approach puts the detection of fluorescent signals on a firm statistical foundation.     

A field based statistical approach for validating a remotely sensed mangrove forest classification scheme

Amongst the most threatened ecosystems on Earth, mangrove forests are also one of the more difficult to work in due to their growth in mud and open water coastal zones and their dense tangled stems, branches and prop roots. Consequently, there has been an impetus to employ remotely sensed imagery as a means for rapid inventory of these coastal wetlands. To date, the majority of mangrove maps derived from satellite imagery utilize a simple mangrove classification scheme which does not distinguish mangrove species and may not be useful for conservation and management purposes. Although more elaborate satellite based mangrove classification schemes are being developed, given their enhanced complexity they deserve additional justification for end users. The purpose of this study was to statistically examine the appropriateness of one such classification scheme based on an inventory of field data. In January of 2007 and May of 2008, 61 field sample plots were selected in a stratified random fashion based on a previous classification of a degraded mangrove forest of the Isla La Palma (Sinaloa, Mexico) using Landsat TM5 data. Unlike other previous Landsat TM based classifications of this region, which simply identified the mangrove forests as one class, the mangroves were classified (i.e. mapped) according to four conditions; healthy tall, healthy dwarf, poor condition, and dead mangroves. Within each sample plot, all mangroves of diameter of breast height (dbh) greater than 2.5 cm were identified and their height, condition and dbh recorded. An estimated Leaf Area Index (LAI) value also was obtained for each sample and the shortest distance from the center of each sample plot to open flowing water was determined using a geographic information system (GIS) overlay procedure. These data were then used to calculate mean values for the four classes as well as to determine stem densities, basal areas, and the Shannon–Wiener diversity index. In order to assess the appropriateness of this mangrove classification scheme a discriminant analysis approach was then applied to these field data. The results indicate this forest has undergone severe degradation, with decreasing mean tree heights, mean dbh and species diversity. In regards to the discriminant analysis procedure, further classification of these field plots and cross-validation based on these significant variables provided high classification accuracy thus validating the appropriateness of the satellite based image classification scheme. Moreover, the discriminant analysis indicated that the estimated LAI, mean height, and mean dbh are significant in the separation of the classification of mangrove forest condition along these field sample plots.     

Quality evaluation of Astragali Radix using a multivariate statistical approach

The quality of 43 Astragali Radix samples collected in China and Mongolia was evaluated using multivariate statistical analysis of data obtained from liquid chromatography-ion trap-time of flight (LC-IT-TOF) mass spectrometry. The samples were classified into four characteristic groups and most of the marker compounds were identified by elemental composition data and the results of MS/MS analysis. The approach provides useful information and gives an overview of the difference between crude drugs originating from different production environments and the genetic nature of the medicinal plants. In addition, the ease with which particular marker compounds could be identified and the effectiveness of the comparison by means of multivariate statistics, such as principal component analysis (PCA), indicates that this method could be utilized for the establishment of standardization and quality control procedures for crude drugs.     

Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data

MOTIVATION: Novel methods, both molecular and statistical, are urgently needed to take advantage of recent advances in biotechnology and the human genome project for disease diagnosis and prognosis. Mass spectrometry (MS) holds great promise for biomarker identification and genome-wide protein profiling. It has been demonstrated in the literature that biomarkers can be identified to distinguish normal individuals from cancer patients using MS data. Such progress is especially exciting for the detection of early-stage ovarian cancer patients. Although various statistical methods have been utilized to identify biomarkers from MS data, there has been no systematic comparison among these approaches in their relative ability to analyze MS data. RESULTS: We compare the performance of several classes of statistical methods for the classification of cancer based on MS spectra. These methods include: linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor classifier, bagging and boosting classification trees, support vector machine, and random forest (RF). The methods are applied to ovarian cancer and control serum samples from the National Ovarian Cancer Early Detection Program clinic at Northwestern University Hospital. We found that RF outperforms other methods in the analysis of MS data.     

Rapid genotyping of three polymorphisms in the LBP gene using a triplex pyrosequencing approach

We described a triplex polymerase chain reaction (PCR) and triplex pyrosequencing assay which allowed a simultaneous determination of three tag single nuleotide polymorphisms (tag SNPs) in the lipopolysaccharide-binding protein (LBP) gene: rs1780623, rs11536972 and rs2232618. This method enables a fast and cost-effective genotyping and a simultaneous determination of the three tag SNPs.     

High-throughput genotyping in citrus accessions using an SNP genotyping array

We developed a 384 multiplexed SNP array, named CitSGA-1, for the genotyping of Citrus cultivars, and evaluated the performance and reliability of the genotyping. SNPs were surveyed by direct sequence comparison of the sequence tagged site (STS) fragment amplified from genomic DNA of cultivars representing the genetic diversity of citrus breeding in Japan. Among 1497 SNPs candidates, 384 SNPs for a high-throughput genotyping array were selected based on physical parameters of Illumina’s bead array criteria. The assay using CitSGA-1 was applied to a hybrid population of 88 progeny and 103 citrus accessions for breeding in Japan, which resulted in 73,726 SNP calls. A total of 351 SNPs (91 %) could call different genotypes among the DNA samples, resulting in a success rate for the assay comparable to previously reported rates for other plant species. To confirm the reliability of SNP genotype calls, parentage analysis was applied, and it indicated that the number of reliable SNPs and corresponding STSs were 276 and 213, respectively. The multiplexed SNP genotyping array reported here will be useful for the efficient construction of linkage map, for the detection of markers for marker-assisted breeding, and for the identification of cultivars.     

Rapid classification of a protein fold family using a statistical analysis of dipolar couplings

MOTIVATION: One of the primary aims of the structural genomics initiative is the determination of representative structures from each protein fold family. Given this objective, it is important to rapidly identify proteins that belong to a family that is already well populated (so they can be eliminated from further studies), or more importantly identify proteins that represent new families of fold. RESULTS: A method for rapid classification to a fold family by the statistical analyses of unassigned the (15)N-(1)H residual dipolar couplings is presented. The required NMR data can be quickly acquired and analyzed. Using this method, structure determination efforts can be focused on more unique and interesting structures, and the overall efficiency in the construction of an information-rich library can be increased.