Golden Helix SNP & Variation Suite - Enhancemants

Category Genomics>Genetic Data Analysis/Tools and Cross-Omics>Next Generation Sequence Analysis/Tools

Abstract The Golden Helix SNP & Variation Suite is a comprehensive collection of standard and advanced statistical tools for population and family-based genetic association studies.

With over 30 new features, the fourth installment of SNP & Variation Suite 7 (SVS) empowers you to explore your data as never before. Identify rare causal variants with a new Sequence Analysis Module.

Improve your GWAS and CNV results with state-of-the-art workflows. Dramatically increase your productivity. And gain greater insights with the manufacturer's advanced genome browser.

Integrated Solution for DNA Next-Generation Sequencing Analysis --

SVS 7.4 brings you an entirely new Sequence Analysis Module with the latest advances in tertiary or “sense making” analysis methods for whole-genome and whole-exome DNA next-generation sequencing.

With this integrated desktop solution, you will be able to quickly analyze millions of common and rare variants from tens to thousands of samples to assess their impact on inherited traits.

1) Large Data. Unparalleled Performance - Targeted resequencing, whole-exome, or whole-genome. It doesn't matter. With SVS you can efficiently manage, analyze, and interactively explore millions of variants for thousands of samples.

2) Streamlined Data Import - SVS makes importing variant calls easy with streamlined import and mapping of the most common and standardized formats, such as variant call files (VCF), and SoapSNP from the Beijing Genome Institute.

3) Variant Filtering - Using genomic annotation tracks such as dbSNP, SIFT, 1000 Genomes, and more, SVS enables you to quickly and easily sift through millions of variants to filter out those that are common, benign, poorly covered, or don’t matter for your study.

You can also use case-control or familial data to identify variants that are unique to affected individuals only.

4) Rare Variant Analysis - SVS gives you the ability to assess the impact of rare variants on your trait of interest when traditional association techniques don't apply. Find genes or regions with an abundance of variants in your sample set.

Classify the rarity of variants when your sample size is too small to calculate in-sample minor allele frequency. Assess rare variant burden using advanced collapsing and association methods, including the Combined Multivariate Collapsing (CMC) method. And understand the contribution of rare variants with functional prediction.

New State-of-the-art GWAS and CNV Quality Assurance Methods --

In addition to the already comprehensive quality assurance procedures available in SVS, here's what's new in 7.4:

1) Identity by Descent and Inbreeding Coefficient - Related individuals wreak havoc on association tests where independence is assumed. Identity by descent (right) and inbreeding coefficient calculations help you control for unknown or cryptic relatedness in your samples.

2) LD Pruning - To obtain better results when running certain tests you can quickly filter (prune) correlated markers prior to analysis.

3) Autosome Heterozygosity - Identifying outliers in autosome heterozygosity helps detect contaminated DNA samples (and population stratification in some cases).

4) Gender Misidentification - Several new methods make it easy to verify that a sample’s reported gender is consistent with its inferred gender. These include X chromosome heterozygosity on genotypes, plotting X versus Y intensity values and averaging log ratio values of the X chromosome (especially helpful for identifying gender anomalies).

5) Quartile Summary Statistics - Calculating the inter-quartile range (IQR) of a numeric distribution is useful for determining outliers for many quality assurance measurements.

6) Multidimensional Outlier Detection - An extension of quartile summary statistics, you can use this feature to identify outliers on multiple dimensions, such as samples whose ethnicity does Not match that of your study population when examining two (2) or more principal components.

7) Derivative Log Ratio Spread - Derivative log ratio spread (DLRS) is a measurement of point-to-point consistency or noisiness in log ratio (LR) data. It correlates with low call rates and over/under abundance of identified copy number segments. Samples with higher values of DLRS tend to have poor signal-to-noise properties and are good candidates to exclude from analysis.

8) Chromosomal Aberration Screening - Detecting large chromosomal aberrations is both a quality assurance step and an analysis step. For example, by averaging log ratios across all autosomal chromosomes you can quickly detect cell line artifacts. But you may also be able to detect large aberrations that are instrumental in detecting disease causing loci.

9) Wave Detection and Correction - Genomic waves are ubiquitous in copy number data and can cause inaccuracies with any copy number detection algorithm.

SVS employs the Diskin, et. al., 2008 method (Diskin, et. al - Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms; Nucleic Acids Res. 2008 November; 36(19): e126.), to help you both detect and correct for genomic waves.

10) Percentile-Based Winsorizing - Percentile-based winsorizing can be used to prevent segmentation algorithms from being driven by outlier values, resulting in a more accurate determination of regions of copy number variation (CNV).

Significantly Faster and Improved Copy Number Analysis Workflows --

Major enhancements to key copy number analysis workflows help you get the most accurate and informative results significantly faster than before.

1) GPU Accelerated Copy Number Segmentation - By using your computer’s video graphics card, which acts like a mini compute cluster for a fraction of the price, CNV segmenting that used to take hours or days, can now be completed in minutes - without compromising accuracy.

Internal benchmark tests have shown 5-20x speed increases for univariate segmenting using a GPU over the CPU. Even more exciting is the 10-100x speed increase for multivariate segmenting.

2) Streamlined Import of Affymetrix CEL Files - The manufacturer has completely revamped Affymetrix CEL file import to be much more streamlined and versatile. You can now easily select all samples as the reference without building a reference spreadsheet. You can also choose to use pre-computed HapMap populations as references.

Based on the type of CEL files you're importing SVS will also automatically identify the proper marker map and annotation files you need. If you don't have them, it will automatically download them for you. And for downstream analysis you have more flexibility in choosing the type of data you can import.

3) Support for Affymetrix Cytogenetics Array - The Affymetrix Cytogenetic Whole Genome 2.7M Array is now fully supported, with enhanced CEL and CYCHP import, downloadable marker maps and library files, and access to pre-computed normalization data built on 485 samples so that you can normalize log ratios on a sample-by-sample basis.

4) Improved CNAM Output Analysis - A number of new methods and enhancements are also available once you segment your log ratio data. You can discretize your segment covariates and segment list spreadsheets to categorize segment means into two or three state models.

This helps magnify small, statistically significant differences between cases and controls and reduces the influence of outliers. You can also assess the over abundance of segments per sample. An unusually large number of segments are often indicative of data quality problems such as wave effects.

More Advanced Genome Browsing and Annotation Track Management --

After the successful launch of the SVS Genome Browser in v7.3, the manufacturer immediately began making it more advanced and flexible. You now have the ability to convert, create, and visualize any potential annotation information that can help you understand your data.

1) Custom Annotation Tracks (WGA) - You can now customize the genome browser with annotation tracks that matter to you. Support for 2Bit, Wiggle, FASTA, and tabular files enables you to import your own custom annotations or tables from popular online databases such as UCSC, RefSeq, and dbGaP.

You can also create any type of annotation from an SVS spreadsheet or download network annotation tracks from Golden Helix and store them locally for speed and efficiency.

2) New Network Annotation Tracks (WGA) - You now have immediate access to several new annotation tracks from our network server including probe tracks for dbSNP builds 129, 130, and 131, SNPs catalogued from the 1000 Genomes project, miRNA, and Affymetrix MIP and Cytogenetics array annotations.

For rare variant analysis a SIFT track is also available with predictions of how likely a mutation at a given loci is damaging.

3) Set Default Genome and Annotation Tracks (WGA) - Whether you're studying human genetics on newer or older builds, or one of many plant and animal species, you can now set the default genome so that you don't have to switch the build every time you open a plot. You can also set default annotation tracks to appear every time a genome browser is opened.

On-Demand, Advanced Feature Development with Improved Python --

1) Now included with Python in SVS are the mature statistical and numeric methods packages of NumPy and SciPy, giving SVS a broad base of standardized test statistics and linear algebra.

Combined with the advanced interactive features of SVS, Python scripts using these packages are first class features with polished interfaces, interactions and logging support. In fact, the Combined Multivariate and Collapsing method was entirely developed in Python!

Productivity Enhancers --

1) Several enhancements and new additions will make SVS easier to use and learn. Download complete projects to help learn new analysis tricks and plotting techniques. Access pre-processed public data such as the 1K Genomes and HapMap to use as references.

And easily download a full assortment of Affymetrix and Illumina marker maps. You'll also find a redesigned Regression Analysis window that makes it more intuitive as well as some handy dimension data at the top of every spreadsheet so you always know exactly how many sample and variables are represented without having to scroll.

Note: For the basic features/capabilities of this advanced product - (see G6G Abstract Number 20109R).

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Manufacturer Web Site Golden Helix SNP & Variation Suite - Enhancemants

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G6G Abstract Number 20109R74

G6G Manufacturer Number 101135