Exemplar Analytics

Category Genomics>Genetic Data Analysis/Tools

Abstract Exemplar Analytics is one of the leading tools for performing Genome Wide Association Studies. It is being used by leading researchers at many of the top 20 Pharmaceutical, Biotech and Non-profit research centers in the world.

With one of the most extensive sets of features covering both traditional association analysis and complex genetics, Exemplar can fulfill any researcher’s needs for analyzing Genotyping Data. With the future in mind, Exemplar was designed for scalability to ensure that it can process thousands of samples with 1 million + markers.

Products modules/features include:

Copy Number Module -- Exemplar for Copy Number is Sapio Sciences latest offering in a host of genotyping analysis solutions. Exemplar’s Copy Number Module is one of the first complete tools for discovering Copy Number Variations (CNV) for both Affymetrix and Illumina arrays. The Copy Number module has the following features:

1) Complete copy number solution for Affymetrix 10k, 100k, 500k.

2) Highly Scalable to 500K.

3) Seamless GeneChip Compatible import of summarized expression values (CHP) and CEL (cell intensity) files.

4) Creation of custom reference sets for all Illumina and Affymetrix arrays, as well as providing reference sets for Affymetrix 10K - 500K.

5) Computation of Single Point Analysis and Gaussian Smoothed copy number and significance values, plus Log2Ratio.

6) Annotations of related genes, positional info from built-in, auto-updated genomics database.

7) Advanced filtering of results on value ranges or Standard Deviation from the mean.

8) Automatic, real-time Gene Ontology (GO) annotations.

Exemplar for Copy Number also provides sort-able, hyper-linkable, annotated table views that can be filtered as well as the graphics, by data range or by # of standard deviations. The results can be exported as Excel spreadsheets.

Exemplar for Copy Number results can be filtered and displayed in many different combinations, include multi-sample, multi-metric (SPA_CN, GSA_CN, etc), multi-experiment, multi-platform (10K, 50K, 500K), and along with other analysis results, such as CNAG output. Full SNP and gene annotations and data point details are provided.

Association Statistics module -- The Association Statistics module leverages the fine-mapping power of high-density genotyping platforms to make statistical associations between genotype calls and the studied affectation.

The Association Statistics module is a core component of the Exemplar Analytics Suite and is fully integrated with the other analysis modules.

Because of this tight integration, scientists may view the results of statistical genetics analysis simultaneously with the results of our artificial intelligence analysis. The association study analyses performed by this module are standard statistics implemented in a clear and easy-to-use graphical user interface.

Statistics -- Exemplar's Association Study statistical analysis methods comprise an integrated computational tool that provides a host of traditional association statistics, such as:

1) Hardy-Weinberg;

2) Minor Allele Frequency;

3) Chi Square;

4) Odds Ratio;

3) Fishers Exact;

6) G-Test;

7) Relative Risk; and

8) Trend Test.

Statistics are computed by genotype, by allele, or for the whole single nucleotide polymorphism (SNP). These statistics pinpoint genomic regions of interest from any high density genotyping project.

Error Correction -- In addition, error corrections are performed on certain statistics, including:

1) Multiple Testing Permutation Correction;

2) False Discovery Rate; and

3) Bonferonni.

Results -- The results are displayed in graphical clarity to ease interpretation of results. The Association Statistics module also produces tabular reports with the results of the analyses.

The detailed displays provide interactive filtering and viewing of results to enable users to drill down to the significant elements in the study. All reports are exportable to either comma separated values (CSV) format or presentation quality Joint Photographic Experts Group's (JPG’s).

Genetic Algorithm Module -- The Genetic Algorithm method is an advanced machine learning algorithm for discovery of multi-locus models that are patterns of genotypes (or sequences) underlying phenotypes. Most diseases have complex genetics and Sapio’s genetic algorithm is one of the tools that allow researchers to discover these complex multi-locus interactions.

This approach to finding multi-locus models is a unique and advanced method that has proven its effectiveness in research that has been published in Nature Genetics.

Unlike traditional linkage methods, Exemplar's Genetic Algorithm Module discovers patterns of genetic material that produce medical affectations. To do this, Sapio developed sophisticated artificial intelligence (AI) algorithms, known as genetic algorithms (GA), to sift through datasets and learn what combinations of polymorphisms are most closely associated with the studied affectation.

Exemplar actually learns the genetics of disease by simulating evolutionary biology and evolving genetic models in-silico. This allows Exemplar to discern combinations of SNP's that together can characterize a given phenotype.

Exemplar’s patent pending genetic algorithms rapidly identify critical genomic regions responsible for disease. Rather than being caused by a single high-risk allele, most diseases are the result of the combined effects of several moderate risk alleles operating under the influence of certain environmental factors.

Exemplar was specifically designed to model the sets of moderate risk alleles at work in the disease process. Traditional statistical methods are unable to do this. Moreover, Exemplar can incorporate clinical and environmental data into the analysis so the interplay between genetics and environment can be modeled.

Results -- The results of the Genetic Algorithm experiment can be viewed as a graphical multi-locus tree. There is also a prediction table that shows the performance of each model. The SNP’s that appear in the multi-locus models can be displayed on the Chromosome Viewer (Genome Viewer) and hovered on to view additional annotations, such as related genes, position, cytoband, alternate SNP ID (if applicable), and the source experiment.

Loss of Heterozygosity --

Method Overview -- Sapio's Loss of Heterozygosity (LOH) experiments perform analysis on high density genotyping datasets.

LOH in Exemplar is calculated via a probability-based method. This method computes the probability that a string of homozygous genotypes would occur by chance alone. If a block of homozygous neighboring SNP’s in the analysis set has a very low probability of being homozygous in the reference set, then that block is shown to have a high probability of LOH.

LOH is calculated for each individual SNP in the input dataset, and then the results are ‘smoothed’ across user-specified blocks of SNP’s to reduce noise. The results are presented in a high quality graphical interface that includes interactive charts, tables, and visual mapping of the results onto a chromosome viewer.

The software allows the user to specify their own reference set to use against a set of samples to be analyzed for LOH. The results are computed across blocks of homozygous SNP's or optionally by using a user-defined block size.

Results -- Results are graphically displayed in both chart and tabular displays.

Gene Ontology (GO) Module --

Functional Genomics -- The Exemplar Analytics Suite additionally contains a Gene Ontology (GO) Module providing a high level view of genetic effects within a cell. The program works with Exemplar to identify the common biological process(es) abrogated in the studied affectation.

The GO Module maps the genetic markers (SNPs) modeled by Exemplar back to the genome and collects information about genes that are associated with those markers. While there may be many different genes responsible for an affectation, and while each gene might produce the affectation, it is likely that all the different genes participate in some common biological process(es).

By viewing the studied affectation at the level of the biological process, scientists can then target all the genes in that process for further investigation. The functional networks of ontological information constructed by the GO Module provide a good visual guide to identify common biological processes.

If the SNP-associated genes and gene products have been characterized by the Gene Ontology Consortium (GOC), then that information is used by the software to construct functional networks. The GO Module automatically generates graphics for each of the top three (3) GOC categories (Biological Process, Cellular Component, and Molecular Function).

Genes from modeled SNP’s are placed into their GOC assigned category and the entire hierarchy of ontologies above the modeled category is displayed by the module. The intersection point of modeled categories is a visual indication of the underlying biological process(es) that is disrupted in the affected condition.

EM algorithm for Haplotyping -- Haplotype analysis is becoming increasingly important in studying genetic diseases, especially in light of the recent increase in the abundance of high-density SNP data.

Haplotype information can provide valuable insight when researching the role of a group of candidate SNP's or genes in the origins of complex diseases. Exemplar employs the widely accepted method for deducing haplotypes from collections of genotypes in the absence of family data using the statistical method known as the Expectation-Maximization (EM) algorithm.

The EM algorithm is a maximum likelihood algorithm used in association-based studies to infer haplotypes from genotypic data and test their association with a phenotype of interest.

Exemplar performs haplotype identification within a configured experiment and then allows the user to walk through the results with the help of an interactive grid intensity display. The grid displays the intensity of the correlation between the SNP’s in a user-definable “window” on the chromosome. The data can also be displayed in tabular format and can be filtered in various ways.

Linkage Disequilibrium -- Overview -- Identification of genetic markers that manifest variations associated with risk of complex diseases remains one of the most challenging and important problems in human genetics. Linkage Disequilibrium mapping has proven a powerful tool for locating disease loci.

Two loci are in Linkage Equilibrium if genotype frequencies at one locus are independent of genotype frequencies at the second locus; otherwise the two loci are in Linkage Disequilibrium (LD). Linkage Disequilibrium can arise from physical linkage, genetic drift, and selection on multi-locus genotypes.

Exemplar computes Linkage Disequilibrium, D’ and Correlation Coefficient based on all SNP combinations from the input dataset that are on the same chromosome and within ‘n’ base pairs (bp’s) of each other, where 'n' is specified by the user. These calculations are done for cases and controls separately so comparisons can be made for divergence between the two (2) groups.

Results -- The results of the analysis can be viewed in either tabular form or as an intensity grid graphic.

Genome Viewer -- Exemplar's Genome Viewer tool is an integrated graphical display of the whole genome that has the capability to overlay results for multiple experiments and platform types simultaneously on a single graphic. The results are positionally accurate and allow the user to see at a glance what is potentially significant in the result set of an experiment or group of experiments.

The Genome Viewer provides additional information for each data point. Details are displayed on both hover with the mouse, which provides tool-tip information for what is selected, and on double-click which brings up pop-up displays with tables and result details, making it a fully interactive user tool.

The Genome Viewer is configured via a wizard that walks the user through the various display options for the selected experiments whose results will be displayed.

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G6G Abstract Number 20107R

G6G Manufacturer Number 102320