ArrayStar


Software for Gene Expression and Variant Analysis

 

ArrayStar is a powerful application within Lasergene Genomics Suite that supports the analysis of a wide range of workflows from evaluating global gene expression experiments, to comparing sequence variation across large groups of genomic samples, to analyzing RNA-Seq, ChIP-Seq, CNV, and miRNA projects assembled by SeqMan NGen. Analyze variants using assembly projects and/or SNP data from SeqMan NGen and SeqMan Pro, or import oligonucleotide microarray and spotted array data to use ArrayStar's microarray functionality. ArrayStar offers statistical tools in conjunction with graphically rich visualizations to isolate gene or variant sets of interest and identify their biological significance.






“We just got some exciting results with ArrayStar over the weekend-I cannot tell how important that program is to our research!”

– Brenda Oppert, USDA ARS

Features & Highlights

 


Comprehensive Analysis for Wide Range of Next-Gen Workflows


  • RNA-Seq analysis to measure gene expression following de novo transcriptome assembly
  • ChIP-Seq peak detection to discover binding sites of DNA-associated proteins and determine how they interact with the DNA to affect expression in nearby genes.
  • Copy number variation (CNV) reporting and analysis for multiple samples
  • miRNA discovery and quantification of miRNA sequence data against known miRNA templates
  • Large-scale variant comparison across individuals and groups



 

 



SNP Analysis


  • Utilize DNASTAR's human variant annotation database, which bundles together data from dbNSFP as well as 1000 Genomes and ESP's Exome Variant Server, making variant analysis — including functional analysis of mutations, reviewing genotype and allele frequencies and genotype information — more convenient than ever.
  • Explore gene ontology to discover how SNPs and small indels may impact gene function, or to identify relationships between genes with particular biological functions.
  • Quickly locate variants of interest based on different experimental assumptions and confidence levels.
  • Determine the relative importance of genes in specific processes using statistical comparisons.