Benchmarking Differential Gene Expression Tools

In a recent study, Schurch et al., 2015 closely examine 9 differential gene expression (DGE) tools (baySeq , cuffdiff , DESeq , edgeR , limma , NOISeq , PoissonSeq , SAMSeq, DEGSeq) and rate their performance as a function of replicates in an RNA-Seq experiment. The group highlights edgeR and DESeq as the most widely used tools in the field and conclude that they along with limma perform the best in studies with high and low numbers of biological replicates. The study goes further, making the specific recommendation that experiments with greater than 12 replicates should use DESeq, while those with fewer than 12 replicates should use edgeR. As for the number of replicates needed, Schurch et al recommend at least 6 replicates/condition in an RNA-seq experiment, and up to 12 in studies where identifying the majority of differentially expressed genes is critical. 

With each technical replicate having only 0.8-2.8M reads, this paper and others (Rapaport et al., 2013) continue to suggest that more replicates in an RNA-seq experiment are preferred over simply increasing the number of sequencing reads. Several other papers, including differential expression profiling recommendations in our Sequencing Coverage Guide recommend at least 10M reads per sample, but do not make recommendations on the numbers of replicates needed. The read/sample number disparity is related to the relatively small and well annotated S. cerevisiae genome in this study and the more complex, multiple transcript isoforms in mammalian tissue. By highlighting studies that carefully examine the number of replicates that should be used, we hope to improve RNA-seq experimental design on Genohub

So why don’t researchers use an adequate number of replicates? 1) Sequencing cost, 2) Inexperience in differential gene expression analysis. We compare the costs between 6 and 12 replicates in yeast and human RNA-Seq experiments using 1 and 10M reads/sample to show that in many cases adding more replicates in an experiment can be affordable. 


6 replicates

12 replicates

Human (10M reads/sample)



Yeast (1M reads/sample)



*Prices are in USD and are inclusive of both sequencing and library prep cost. Click on prices in the table to see more project specific detail.

The table shows that the main factor in the price difference is related to library preparation costs. Sequencing on the Illumina Miseq or Hiseq at the listed sequencing depths do not play as significant a role in cost, due to the sequencing capacity of those instruments. 

To accurately determine the sequencing output required for your RNA-seq study, simply change the number of reads/sample in our interactive Project Page



Evaluation of tools for differential gene expression analysis by RNA-seq on a 48 biological replicate experiment. Nicholas J. Schurch, Pieta Schofield, Marek Gierliński, Christian Cole, Alexander Sherstnev, Vijender Singh, Nicola Wrobel, Karim Gharbi, Gordon G. Simpson, Tom Owen-Hughes, Mark Blaxter, Geoffrey J. Barton

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Franck Rapaport, Raya Khanin, Yupu Liang, Mono Pirun, Azra Krek, Paul Zumbo,Christopher E Mason, Nicholas D Socci and Doron Betel

2 thoughts on “Benchmarking Differential Gene Expression Tools

  1. Its a decent paper highlighting the tools and the replicate counts that perform better. But I’m more disappointed always, by the common genes that each of these tools give when compared amongst each other. That issue also needs to be addressed. A few genes different is ok, but most of these tools give a bigger dissimilarity than similarity, specially edgeR, cuffdiff and DESeq.


  2. Ultra-cheap library prep to make as many biological duplicates as you would like.

    Simultaneous generation of many RNA-seq libraries in a single reaction.

    Alexander A Shishkin, Georgia Giannoukos, Alper Kucukural, Dawn Ciulla, Michele Busby, Christine Surka, Jenny Chen, Roby P Bhattacharyya, Robert F Rudy, Milesh M Patel, Nathaniel Novod, Deborah T Hung, Andreas Gnirke, Manuel Garber, Mitchell Guttman & Jonathan Livny


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