Open Access Highly Accessed Research article

3' tag digital gene expression profiling of human brain and universal reference RNA using Illumina Genome Analyzer

Yan W Asmann1, Eric W Klee1, E Aubrey Thompson2, Edith A Perez2, Sumit Middha1, Ann L Oberg1, Terry M Therneau1, David I Smith3, Gregory A Poland4, Eric D Wieben5 and Jean-Pierre A Kocher1*

Author Affiliations

1 Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA

2 Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Jacksonville, FL 32224, USA

3 Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA

4 Mayo Vaccine Research Group, the Program in Translational Immunovirology and Biodefense, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA

5 Advanced Genomics Technology Center DNA sequencing lab, Mayo Clinic College of Medicine, Mayo Clinic, Rochester, MN 55905, USA

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BMC Genomics 2009, 10:531  doi:10.1186/1471-2164-10-531

Published: 16 November 2009



Massive parallel sequencing has the potential to replace microarrays as the method for transcriptome profiling. Currently there are two protocols: full-length RNA sequencing (RNA-SEQ) and 3'-tag digital gene expression (DGE). In this preliminary effort, we evaluated the 3' DGE approach using two reference RNA samples from the MicroArray Quality Control Consortium (MAQC).


Using Brain RNA sample from multiple runs, we demonstrated that the transcript profiles from 3' DGE were highly reproducible between technical and biological replicates from libraries constructed by the same lab and even by different labs, and between two generations of Illumina's Genome Analyzers. Approximately 65% of all sequence reads mapped to mitochondrial genes, ribosomal RNAs, and canonical transcripts. The expression profiles of brain RNA and universal human reference RNA were compared which demonstrated that DGE was also highly quantitative with excellent correlation of differential expression with quantitative real-time PCR. Furthermore, one lane of 3' DGE sequencing, using the current sequencing chemistry and image processing software, had wider dynamic range for transcriptome profiling and was able to detect lower expressed genes which are normally below the detection threshold of microarrays.


3' tag DGE profiling with massive parallel sequencing achieved high sensitivity and reproducibility for transcriptome profiling. Although it lacks the ability of detecting alternative splicing events compared to RNA-SEQ, it is much more affordable and clearly out-performed microarrays (Affymetrix) in detecting lower abundant transcripts.