Easily Analyze and Compare a Variety of Genomic Data Using Partek Genomics Suite
Partek Genomics Suite (Partek GS) can import and analyze raw and processed data from all major chip platforms, and can seamlessly import data from .CEL, .CHP, .CNT (CNAT), .MSK, metaprobeset files, and plain text file formats from one color and two-color microarrays. Partek GS also automatically provides links to NetAffx, UCSC Genome Browser, IGB, and other internet databases. Try Partek Genomics Suite for your microarray data.
Specific Applications for Microarray in Partek Genomics Suite
Partek Genomics Suite supports a complete workflow including convenient data access tools, identification and annotation of important biomarkers, and construction and validation of predictive diagnostic classification systems. Now one tool can take you from start to finish for genomic data analysis. With Partek GS, you can easily import and analyze gene expression or other genomic data, reliably identify and annotate the genes or biomarkers of interest, and share findings with other researchers in your organization with information rich graphical displays.
Partek Genomics Suite provides rigorous and easy-to-use statistical tests for differential expression of genes or exons, and a flexible and powerful statistical test to detect alternative splicing based on a powerful mixed model analysis of variance. In addition, the Partek Genome Browser can display exon-level events such as alternative splicing and differential expression alternative splicing data with Partek GS. The Automated Model Selection tool in Partek GS will find an optimal set of predictive genes or exons, the best classifier, and optimal tuning parameters to obtain the best prediction possible from your data. Since simple pre-filtering to exclude low expressed probes or exons may ignore alternative splicing, Partek GS has been optimized to process and analyze all exons on the array at the probe level, exon level, or gene level -even on today's standard desktop and laptop computers.
Partek Genomics Suite's statistical and visual features make it easy to detect, display, and report on mapping sites of protein/DNA interaction in ChIP on Chip experiments. Seamlessly import from .CEL files, normalize to baselines samples, statistically detect regions of binding, map regions to genes, SNPs or sequences, and visualize genomic location of features. Partek GS can import all millions of probes allowing you to analyze the data in full and not in batches, eliminating potential errors and wasted time.
Partek Genomics Suite's statistical and visual features make it easy to detect, display, and report on regions of amplification or deletions on the genome. In addition, regions of interest that differ between drug responders and non-responders can be identified using proven statistical methods. After Partek GS identifies the regions of significant copy number change or LOH, gene lists can be created and exported, and corresponding gene or exon expression data can be seamlessly linked to the mapping array data. The Automated Model Selection tool in Partek GS will find an optimal set of predictive genes, SNPs, or exons, the best classifier, and optimal tuning parameters to obtain the best prediction possible from your data. Using Partek's Model Selection Tool, you can build accurate diagnostic or prognostic prediction models based on copy number, LOH, or raw SNP expression values.
Partek Genomics Suite supports single/multiple SNP association tests performed on allele, genotype, and dominant/recessive models. Confidently analyze your genotyping data using such statistical tests as Chi², Hardy-Weinberg Equilibrium, and Linkage Disequilibrium. Get accurate estimation of p-values in even the smallest data sets using Monte Carlo tests. Explore data with tabular analysis as well as visual analysis tools including frequency plots, biplots, and heat maps, which can be used to visualize linkage disequilibrium within the data. After identifying loci of interest, explore copy number and LOH data in the same region. Gene and SNP lists can be easily created and exported. Corresponding data can be seamlessly linked to and visualized with the mapping array data.