April 20, 2014
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RNA, the link from phenotype to genotype

RNA sequencing (RNA-seq), and differential expression analysis provide researchers with an intermediate step between the observed phenotype and genomic differences. Identifying relative differences in gene expression between controls and samples of interest is instructive in both identifying what type of further genetic studies should be pursued as well as narrowing the scope to a subset of genes or pathways implicated by RNA-seq.

Researchers interested in understanding phenotypic differences often employ comparative techniques including genome wide SNP studies, exome SNP studies, genome methylation studies, large genomic deletions/rearrangements detection studies, or microRNA marker studies to better understand the genomic basis of observed differences. While these approaches offer researchers a set of powerful tools, it can be a challenge to establish a direct cause/effect association between the genomic and phenotypic differences. The assay may not have captured the source of the variation, too many potential candidates were found, or worse, genomic differences end up falsely associated with the phenotype. RNA-sequencing and its analysis offer researchers the insight they need to firmly establish the link from phenotype to genotype.


We establish the optimal number of reads needed by measuring detection rate of genes as a function of reads sequenced.


We employ biological replicates and statistics as a measure of specificity to determine if an observed difference is statistically significant.


We impose an expression cutoff to separate the consistent expression signal from the low-level noise, better defining the signal:noise threshold.

Access your data in Cofactor’s ActiveSite

Cofactor’s ActiveSite Viewer is a web portal that hosts all of the information associated with a project. ActiveSite enables clients to examine, sort, and discover patterns in their data that may not be visible when working with just text files or in classical expression applications. Using the viewer, clients are also able to download the background files used to build their analysis.
  • Track your project.
  • Collaborate with your team.
  • Reach candidates faster.
  • Get on with discovery

End to End Service

  • Experimental Design

    Work with our Project Scientists to design the right experiment

  • Library Construction

    We've got it from here: Cofactor's scientists create a library. Don't worry, it doesn't move on without passing QC

  • Sequencing

    Y'know, As, Ts, Gs, and Cs

  • Analysis

    The good stuff. Our resident eggheads crunch the data

  • Discovery

    Use ActiveSite to sort, organize, share, and pick through your data. Take caution though, you may have too many choices of what to do next

Don’t settle. Learn why top researchers choose Cofactor.

  • Starting from
    $1,133Per Sample
    • Good for small samples or skim level sequencing
    • 12 samples
    • Poly-A library construction
    • qPCR
    • Molecular spike-ins
    • 10 million reads per sample
    • RNA-seq/comparative expression analysis and ActiveSite results viewer
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