Power Seat

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Explore the full breadth and capability of SNP & Variation Suite (SVS) with the Power Seat - the complete collection of SVS features and functionality from all of the packages (except PBAT).

Whether conducting studies using DNA and RNA sequencing, or coupling SNP and CNV analysis, the Power Suite gives you an unprecedented level of control and flexibility in analyzing your genetic research data.

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The Power Seat includes:

All packages of SVS include extensive data management functionality, powerful genome browser visualization, and a Python scripting interface. The ability to import/export over 70 different file formats makes it easy to move datasets between software packages, subset data based on a variety of criteria, perform numerous data conversion and filtering tasks, manage marker maps and genomic annotations, and much more. SVS also comes with powerful plotting and genome browser visualizations enabling the exploration of many different data types simultaneously in heat maps, variant maps, Manhattan Plots, scatter plots, etc. as well as print publication quality graphics. Further, fully-programmatic access is available via a Python scripting interface.

SNP Analysis

SNP Analysis Package

With support for both case-control and quantitative traits, candidate genes or whole genome data, the SNP Analysis Package delivers a broad array of workflows for quickly and easily identifying SNPs associated with disease or other phenotypes.

Functionality encompasses the breadth of the SNP analysis pipeline from quality assurance and genotype association testing to linkage disequilibrium and haplotype analysis. More advanced analyses improve results even further, such as principal component analysis, linear/logistic regression, runs of homozygosity, and more. Any number and type of non-genetic covariates can also be used separately for analysis or in conjunction with genotype data to correct for confounding factors.

  CNV Analysis

CNV Analysis Package

The CNV Analysis Package offers a complete set of tools for identifying and assessing the significance of copy number variations from microarray or aCGH data.

Included is the ability to process raw intensities, identify break points in log ratio data, perform association or regression on a variety of covariates, and visually compare copy number variations between two or more sample groups in a variety of plots. To help ensure identified CNVs are true and meaningful, the CNV Analysis Package incorporates the most complete set of quality assurance methods available, including wave detection and correction, PCA analysis to remove batch effects and stratification, percentile based winsorizing, derivative log ratio spread, and much more.

DNA-Seq Analysis

DNA-Seq Analysis Package

The DNA-Seq Analysis Package delivers the latest tertiary analysis methods for rare and common variants enabling quick and easy identification of causal variants from next-generation sequencing data.

General workflows include the ability to rapidly filter massive datasets by any genomic region, annotation, or quality score, perform collapsing and association methods to assess rare variant burden, and run variant classification to understand potential downstream effects of variants. Genotypes and indels can also be visualized in a dynamic variant map, helpful for identifying differences in variants patterns among sample groups.

For family-based analyses, the DNA-Seq Analysis Package offers the ability to detect compound heterozygous inheritance as well as score recessively inherited variants.

The DNA-Seq Analysis Package also includes all functionality contained within the SNP Analysis Package.

RNA-Seq Analysis

RNA-Seq Analysis Package

The RNA-Seq Package offers advanced analysis tools designed to perform differential expression workflows for RNA expression profiling experiments. Regardless of the upstream secondary analysis tool used to align and quantify reads into weighted counts, SVS provides all the data normalization, differential expression, and visualization techniques needed to be able to conduct RNA sequencing analysis quickly and easily, giving you everything you might expect from expression micrarrays and more.

The DESeq tool is designed to estimate variance-mean dependence in count data and test for differential expression between types using a model based on the negative binomial distribution. While DESeq has a built in normalization method, you can also normalize your data as outlined by Bullard et al. Finally, advanced visualization can be used to interpret the analysis of your RNA-seq differential expression.

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