YOUR DATA. YOUR ANALYSIS. YOUR DISCOVERY.
SNP & Variation Suite
is a powerful analytic tool created specifically to empower biologists and other researchers to easily perform complex analyses and visualizations on genomic and phenotypic data. With SVS you can focus on your research instead of learning to be a programmer or waiting in line for bioinformaticians.
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.
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.
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.
SVS includes quality-assurance utilities, annotation of variants and collapsing methods for region-based association and other statistical frameworks for analyzing variant data associations.
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.
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.
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.
Core Features of SVS:
- 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
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
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
Supported Operating Systems
- Windows 7+, Server 2008+
- Mac OSX 10.7+
- Linux Ubuntu Precise 12.04+ and compatible versions
- Red Hat Enterprise Linux (RHEL) and CentOS 6+
- XY scatter plots
- Pie charts
- Linkage Disequilibrium
- Side-by-side box plots
- NxN scatter plots
- Stacked histograms
- Up to 5-way Venn diagrams
Numeric Analysis Methods
- Principal component analysis for integer or quantitative data
- Linear and logistic regression with optional covariates
- Wave detection/correction
- Matched pairs T-Test
- Fisher's Exact Test for binary predictors and a binary dependent variable
- 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
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.
Case Study - Peter Gregersen, Feinstein Institute
"I have always thought that putting the analytic power in the hands of the biologists who are thinking about the disease is really important."
Webcast - Pharmacogenomic Prediction
Blog Post - Kinship Matrices & GBLUP on Very Large Sample Sets
We have big data in the field of genomics, yet all that crunching is not the hard part.
Case Study - Fielding Hejtmancik, NIH-NEI
“SVS is extremely user friendly. I can use it to teach my fellows who are for the most part, Ophthalmologists, not researchers."