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SVS Plots


SNP & Variation Suite is an integrated collection of user-friendly, yet powerful analytic tools for managing, analyzing, and visualizing multifaceted genomic and phenotypic data. SVS was created specifically to empower biologists and other researchers to easily perform complex analyses and visualizations, eliminating the need to rely exclusively on bioinformatics experts or use incompatible freeware. With SVS you can focus on your research instead of learning to be a programmer or waiting in line for bioinformaticians.

SNP & Variation Suite 8 supports:

Genome-Wide Association Studies (GWAS)

SVS includes a broad range of analytic tools built to empower you to quickly and easily perform quality-assurance and statistical tests for genetic association studies.

Small Sample DNA-Sequencing Workflows

SVS gives users access to the latest annotation sources for filtering and annotating rare variants from secondary analysis pipelines to obtain a short list of potentially pathogenic variants.

Genomic Prediction

SVS provides the ability to perform genetic prediction including various means of defining the relationship between samples, the ability to validate models and visualize the results.

Large Sample DNA-Sequencing Analysis

SVS includes quality-assurance utilities, annotation of variants and collapsing methods for region-based association and other statistical frameworks for analyzing variant data associations.

Copy Number Analysis

SVS offers the ability to process CNV intensity data from various platforms, identify regions of copy number variability, perform statistical tests on the copy number results, and visualize normalized intensity data overlaid with the identified copy number regions.

RNA-Sequencing Analysis

SVS offers advanced analysis tools designed to detect differences in expression profiles of RNA-Sequencing data between groups. Read count normalization, QA, differential expression with DESeq, and other statistical tests can be performed. Visualize data and results with dendrograms, heatmaps, as well as p-values and other statistics in GenomeBrowse.

Optional Add-on:

Free Collaboration Tool:

PBAT Analysis

The SVS PBAT add-on delivers an exclusive array of advanced statistical routines for the design and analysis of family-based SNP and CNV association studies.

SVS Viewer

Not everyone needs the full analysis capabilities of SVS, but would still benefit from viewing a colleague's projects, results, and plots... for free!

Core Features of SVS:

Marker mapped spreadsheet

Data Management

  • Efficiently handle micro-array and whole-exome data for thousands of samples on a desktop computer
  • Scales to whole-genome and imputed datasets
  • Projects can be password protected and locked for security
Data Import

Supported Data Formats

  • Text Files
  • Excel XLS and XLSX
  • Affymetrix CEL, CHP, CNT and CHP.TXT files
  • Illumina Final Report Text Files, Matrix Text Files, iControlDB Data
  • Plink PED, TPED and BED Files with supporting files
  • Agilent Files
  • NimbleGen Data Summary Files
  • VCF Files version 4.0+
  • Impute2 GWAS Files
  • HapMap Format
  • MACH Output
  • And over 50 other formats
Manhattan Plot

GWAS Analysis Methods

  • Classify genetic data by models including allelic, genotypic, additive, dominant and recessive
  • Tests include Correlation/Trend, Chi-Squared, Odds Ratio, Fisher's Exact Test, Armitage Trend Test
  • Regression methods including: linear and logistic regression with optional covariates
  • Mixed model regression methods including: EMMAX and MLMM
  • Principal component analysis based on genetic model
  • Extensive Linkage Disequilibrium reports
  • Haplotype block detection
  • Haplotype association tests
  • Haplotype trend regression
  • Runs of homozygosity (ROH)
  • Genomic BLUP (GBLUP) for both estimation and prediction
Recessive Homozygous Variant

Filtering and Analysis Methods for DNA-Seq

  • Set genotypes to a missing value if the supporting quality metrics fail the specified thresholds
  • Runs of homozygosity (ROH) optimized for NGS data
  • Calculate Alt Read Ratio from GATK's Allelic Depth field from a VCF file
  • Variant classification to determine location within a gene and HGVS notation
  • Analysis using collapsing methods that include CMC and KBAC
  • Adjust for population stratification and relationships between samples with Mixed Model KBAC
  • Scoring methods for variants based on the expected inheritance pattern
OS Icons

Supported Operating Systems

  • Windows XP, Vista, 7, 8, Server 2008+
  • Mac OSX 10.6+
  • Linux Ubuntu Hardy 8.04+ and compatible versions
  • Red Hat Enterprise Linux (RHEL) and CentOS 5+

GenomeBrowse Genomic Visualization

  • Display p-value results, raw data and annotation sources all in the same view
  • Natural pan and zoom controls quickly allow you to zero in on a region of interest
  • A smart labeling system balances clarity with information density
  • Full font controls allow for editing titles of plots
  • Integrated search and location bar allow for jumping quickly to a region or gene of interest
  • A library of hosted annotation sources allows you to access the latest annotations without having to download gigabytes of files
  • Support for a variety of file formats including BAM, BED, FASTA, VCF, GTF, TSV, 2BIT and WIG
  • Visualize Linkage Disequilibrium and Heatmap Intensity data in genomic context.
Cluster plot

Visualization Tools

  • Histograms
  • XY scatter plots
  • Linkage Disequilibrium
  • Heatmaps
  • Side-by-side box plots
  • NxN scatter plots
  • Stacked histograms
  • Pie charts
  • Up to 5-way Venn diagrams
Log Ratio Plot

Numeric Analysis Methods

  • Principal component analysis for integer or quantitative data
  • Linear and logistic regression with optional covariates
  • Wave detection/correction
  • Derivative log ratio spread
  • Percentile-based Winsorizing
  • Segmentation of log-ratio data to detect copy number regions
  • Standard sample statistics to summarize columns or rows of data
  • Fisher's Exact Test for binary predictors and a binary dependent variable
  • Matched pairs T-Test
Volcano plot

Tools for RNA-Seq Analysis

  • Normalization and transformation of gene or transcript read counts
  • DESeq to normalize and analyze the difference in expression between the counts for two categories
  • Visualize results with a dendrogram and a heatmap

Support and Extensibility

  • Technical manual with methods fully documented and explained
  • Customer support available by phone and e-mail
  • Training available on live web demonstrations
  • Full archive of webcasts including applications and software overviews
  • On-site workshops and training sessions are available
  • Python scripting is available to quickly add needed features
  • Python scripting tools are available for users to write their own add-on scripts.

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