High quality data is key to quality results. Considerable effort has been made to enhance quality assurance at every step. You can now easily generate a number of genotype statistics, view cluster plots of allele intensities, check gender and marker concordance, perform variance analysis on log ratios, filter poor quality markers and samples, and more. HelixTree also offers a powerful principal component analysis (PCA) approach for both SNP and CNV data to both detect and correct for batch effects, genomic waves, and population stratification.
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View results from a whole new perspective with richly interactive visualization and unprecedented whole-genome navigation. Apply data transformations in real-time or customize plots for more enhanced presentation. You can combine multiple data series, such as SNP, CNV, and haplotype results or compare graphs side-by-side as with two or more LD plots from differing populations. When you finalize the desired view save to a number of publication quality formats, including scalable vector graphics.
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Automate workflows, incorporate custom methods, or interoperate with other programs. These are just a few examples of how you can enhance the utility of HelixTree and other SVS 7 modules with a fully programmatic Python scripting interface. New in SVS 7 is an integrated Python editor that makes it easy to read and write scripts helping even novice users realize the power of scripting.
The core architecture of HelixTree has been completely reinvented to efficiently handle datasets of virtually any size and type on a desktop computer. Supported dependent variables include case-control and quantitative traits. Predictors can be binary, continuous, ordinal, categorical, nominal, and genetic (bi- and multi-allelic SNPs, log ratios, CNVs, microsatellites, etc.). Further, direct support for most common file formats streamlines data import, ensuring you spend most of your time on the more important aspects of analysis.
Find more associations with the most extensive collection of genetic association tests including allele, genotype, haplotype, CNV, ROH, LD, and advanced regression-based testing. Within a streamlined interface you can test against either cases vs. controls or quantitative traits using a variety of statistical measures under any one of several genetic model assumptions. Tests can be run individually or simultaneously while also correcting for stratification and applying a number of multiple testing corrections, including permutation testing.
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Interactively explore LD and haplotype analysis in an innovative and powerful new interface. You can view LD plots from one or more populations and explore them side-by-side with association results. For haplotype analysis it is easy to define and modify haplotype blocks from an LD plot or spreadsheet, compute haplotype and diplotype frequency tables, and perform a number of haplotype association tests, including per-block and per-haplotype methods.
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Real-time spreadsheet manipulation, data editing, and enrichment help eliminate the hassles of working with large-scale, complex data. Easily combine multiple sample sets and data of different types, from different arrays, or even platforms. Quickly recode genotypes based on a specified genetic model, flip DNA strands, transcode from AB to AGCT formats and more. Further, an integrated spreadsheet editor facilitates data editing and transformations on a grand scale.
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