RNA-seq data - now as easy as arrays

In recent years, transcriptomic profiling via next generation sequencing (RNA-seq) has emerged as both a technical and cost-effective alternative to arrays.

One of the most prominent advantages of RNA-seq compared to array-based techniques is that RNA-seq can be applied without extensive knowledge of the genomic sequence and the location of genes or other features of interest.

Both microarray data and RNA-seq data can be analyzed with Qlucore Omics Explorer. We have made an effort to make sure that it is equally easy to work with RNA-seq based analysis as with array based analysis. To start the analysis you need to have your data in aligned BAM files. These files can be directly imported and normalized and then all functionality from heatmaps to statistical filters and PCA plots are available.

For RNA-seq data a suite of new statistical methods has been launched that takes into consideration that data is count based. However, it is also shown that statistical methods combining a variance-stabilizing transformation with t-test perform very well under many different conditions and also seem to be more robust towards outliers. This means that all existing functionality in Qlucore Omics Explorer can be used directly. You get the same speed and interactivity analyzing digital gene expression as with arrays.

Read more in the White Paper 'Analyzing RNA-seq data with Qlucore Omics Explorer'
Watch the webinar 'Analyze RNA-seq data'
Test yourself. Download a free trial version