RNA-Seq Analysis with Partek Software


This three-minute video demonstrates how to analyze an RNA-Seq data set using simple point-and-click actions in Partek Flow.  

The complete Partek RNA-Seq analysis solution takes your study from unaligned data to pathways with powerful statistical and visualization tools. With an intuitive user interface that doesn't sacrifice statistical power, sophisticated analysis can be performed by anyone.

  • Measure gene expression changes
  • Identify visual data patterns
  • Inspect alternative splicing events
  • Discover novel transcripts
  • Detect variants (gene fusion events)
  • Identify allele specific expression
  • Contextualize expression changes with pathway and gene ontology analysis
  • Integrate with small RNA, DNA, ChIP and methylation data
  • Compare RNA-Seq and microarray data

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RNA-Seq Tools


    The Sample Management tools in Partek Flow make it easy to keep track of all your samples and associated data files.

    • Sample information can be pulled from a file or added manually
    • Descriptions and attributes can be project specific or system-wide, allowing multiple users to apply the same attributes and laying the groundwork for easy study comparisons
    • Sample attributes can be categorical or numeric allowing optimal statistical algorithms to be selected for your downstream analysis
    • Controlled vocabulary can be used to standardize terminology and improve consistency across studies


  • Pre-Alignment Tools

    Use the Pre-alignment QA/QC tools in Partek to check the quality of your reads before moving forward with analysis.

    • View the overall project quality or drill down to individual samples for a detailed report
    • Use the flexible trimming options to trim residual adaptors, barcodes, or bases on either end to improve alignment
    • Plot ERCC spike-in controls
  • Alignment

    Choose among 9 popular publicly available aligners wrapped within an easy-to-use visual interface: Bowtie, Bowtie 2, BWA, GSNAP, Isaac 2, STAR, TMAP, TopHat, and TopHat 2.

    • Use the default aligner parameters or configure them; all the power of the command line, in an easy to use point-and-click interface
    • Save your preferred aligner options and re-apply them to save time and ensure consistency
    • Save time by subsampling data before alignment to test different aligners or aligner options
    • Merge alignments allows reads that aren’t aligned with your first aligner to be re-run using another aligner or reference genome and combined with previously aligned reads
    • Set up an email notification to alert you when the alignment is finished
  • Post-alignment QAQC

    The Post-alignment QA/QC tools let you evaluate alignment performance and filter aligned reads

    • View the overall alignment quality at a glance with the project-level summary or drill down to individual samples for a detailed report
    • View the percentage of aligned reads, number to aligned reads, coverage depth, genomic coverage, and more
    • Filter alignments based on alignment quality or genomic location
    • Visualize aligned reads using Chromosome view
  • Quantification Visualizations

    Use the quantification tools to calculate gene and transcript abundance.

    • Quantify aligned reads to the reference of your choice including RefSeq, Ensembl, Gencode, or any custom database
    • Get fast and accurate quantification using the Partek E/M algorithm or use Cufflinks to also identify novel transcripts
    • Visualize quantification results with transcript and gene-level reports
  • Normalization Visualizations

    Quickly and easily normalize your data using the point-and-click interface.

    • All standard methods for RNA-Seq data are available: RPKM, TMM, TPM, Total count, Quantile normalization, and more
    • Visual report with box plots and histograms showing the effects of normalization

    Use the interactive principal components analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) scatter plots to explore your gene expression data.

    • Find outliers, check for batch effects, and assess the effects of experimental factors on overall gene expression
    • Overlay sample information, gene expression values, clustering results, and more
  • Differential analysis visualizations

    Identify differentially expressed genes using Gene Specific Analysis (GSA), ANOVA, DESeq2, or Non-parametric ANOVA.

    • Incorporate multiple factors and interactions into your statistical model to answer any experimental question
    • Analyze complex experimental designs including repeated measures and numerical sample attributes
    • Powerful and flexible filtering options let you easily identify significant gene
    • Visualize statistical results using Volcano plots and view gene-level results with dot, box, or violin plots
    • Use the Venn Diagram tool to compare sets of differentially expressed genes

    Clustering is a powerful way to discover groups of similar samples or genes within a data set. Partek Flow makes it easy to use clustering algorithms including Hierarchical Clustering, K-means clustering, and Graph-based clustering methods.

    • View hierarchical clustering results in an interactive and customizable heat map
    • Overlay K-means and Graph-based clustering results on PCA or t-SNE plots
    • Find the optimal number of clusters for K-means clustering using Davies-Bouldin index
  • Go Enrichment

    Discover the biological significance of differentially expressed genes using pathway and GO enrichment analysis.

    • Find enriched GO terms from geneontology.org or test for enrichment of imported gene sets
    • Identify enriched KEGG pathways for any species in the KEGG database