• Email
    info@onearray.com
  • Call Us
    886.3.5781168
  • Submit an Inquiry
  • Genomic Services
    • MicroArray Services Overview
    • OneArray® Gene Expression Services
    • Gene Expression Service – Agilent
    • OneArray® microRNA Expression Services
    • Agilent microRNA Expression Services
    • qPCR Services
    • Next Generation Sequencing
    • Bioinformatics
    • Custom MicroArray Printing
    • RNA/DNA Extraction
  • Microarray Products
    • OneArray® Gene Microarrays
    • OneArray® miRNA Microarrays
    • OneArray® Chromosomal Microarrays
  • Clinical Services
    • aCGH-CytoOneArray®
    • CytoOnearray® Disease Database
  • Why Us?
  • Resources
    • Sample Submission Forms
    • All Published Research
    • Research Highlights
    • Frequently Asked Questions
    • Data Delivery System (DDS)
    • Phalanx Annotation Search System (PASS)
  • About
    • Company
    • Clients & Testimonials
    • Why Us?
    • News
    • Blog
    • Distributors
    • Submit an Inquiry
    • Contact Us

Gene Expression Analysis: RNA-Seq vs. Microarray

June 6, 2017

This infographic from Phalanx Biotech gives an overview of two widely used methods of gene expression analysis: RNA-Seq and Microarray.  See below for the pros and cons of each.

Microarrays have long been the method of choice for expression analysis. They are a proven method for affordable and reliable data acquisition. The microarray genomic platform remains a key player in high-throughput technology.

With the advent of next generation sequencing, transcriptome sequencing (or RNA-Seq) has also become a viable competitor for reliable expression profiling.

Microarray vs RNASeq

 

References

  1. Shi, L., et al., The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nature biotechnology, 2006. 24(9): p. 1151-1161.
  2. Trapnell, C., et al., Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature biotechnology, 2010. 28(5): p. 511-515.
  3. Richard, H., et al., Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments. Nucleic acids research, 2010. 38(10): p. e112-e112.
  4. Auer, P.L. and R. Doerge, Statistical design and analysis of RNA sequencing data. Genetics, 2010. 185(2): p. 405-416.
  5. Hansen, K.D., S.E. Brenner, and S. Dudoit, Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic acids research, 2010. 38(12): p. e131-e131.
  6. Bullard, J.H., et al., Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC bioinformatics, 2010. 11(1): p. 94.
  7. Li, J., H. Jiang, and W.H. Wong, Modeling non-uniformity in short-read rates in RNA-Seq data. Genome biology, 2010. 11(5): p. R50.
  8. Agarwal, A., et al., Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays. BMC genomics, 2010. 11(1): p. 383.
  9. Liu, S., et al., A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. Nucleic acids research, 2011. 39(2): p. 578-588.
  10. Bloom, J.S., et al., Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays. BMC genomics, 2009. 10(1): p. 221.
  11. Zhao, S., et al., Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PloS one, 2014. 9(1): p. e78644.
  12. Raghavachari, N., et al., A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease. BMC medical genomics, 2012. 5(1): p. 28.
  13. Malone, J.H. and B. Oliver, Microarrays, deep sequencing and the true measure of the transcriptome. BMC biology, 2011. 9(1): p. 34.
  14. Marioni, J.C., et al., RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research, 2008. 18(9): p. 1509-1517.
  15. Schurch, N.J., et al., How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA, 2016. 22(6): p. 839-851.
  16. Zhang, W., et al., Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome biology, 2015. 16(1): p. 133.
  17. Consortium, M., The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature biotechnology, 2010. 28(8): p. 827-838.
  18. Tan, P.K., et al., Evaluation of gene expression measurements from commercial microarray platforms. Nucleic acids research, 2003. 31(19): p. 5676-5684.
  19. Weis, B., Standardizing global gene expression analysis between laboratories and across platforms. Nature methods, 2005. 2(5): p. 351-356.
  20. McIntyre, L.M., et al., RNA-seq: technical variability and sampling. BMC genomics, 2011. 12(1): p. 293.
  21. Kukurba, K.R. and S.B. Montgomery, RNA sequencing and analysis. Cold Spring Harbor Protocols, 2015. 2015(11): p. pdb. top084970.
  22. Jiang, L., et al., Synthetic spike-in standards for RNA-seq experiments. Genome research, 2011. 21(9): p. 1543-1551.
  23. Li, S., et al., Detecting and correcting systematic variation in large-scale RNA sequencing data. Nature biotechnology, 2014. 32(9): p. 888-895.
  24. Consortium, S.M.-I., A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature biotechnology, 2014. 32(9): p. 903-914.

Filed Under: NGS

Categories

  • Microarray
  • News
  • NGS
  • qPCR
  • Uncategorized

Archives

  • January 2018
  • December 2017
  • July 2017
  • June 2017
  • April 2017
  • March 2017

Have a Question?

Send us a line.

Let us connect you with an expert to assist
with your project!

Genomic Services

  • MicroArray Services Overview
  • Gene Expression – OneArray®
  • Gene Expression Service – Agilent
  • miRNA Expression – OneArray®
  • Agilent microRNA Expression Services
  • qPCR Services
  • Next Generation Sequencing
  • Bioinformatics
  • Custom MicroArray Printing
  • RNA/DNA Extraction

Products

  • OneArray® Gene Microarrays
  • OneArray® miRNA Microarrays
  • Chromosomal Microarrays

Clinical Services

  • aCGH-CytoOneArray
  • CytoOnearray Disease Database

Resources

  • Sample Submission Forms
  • All Published Research
  • Research Highlights
  • Frequently Asked Questions
  • Data Delivery System (DDS)
  • Phalanx Annotation Search System (PASS)

About

  • Company
  • Clients & Testimonials
  • Why Us?
  • News
  • Blog
  • Distributors
  • Submit an Inquiry
  • Contact Us
  • Visit Our Blog

© 2023 Phalanx Biotech Group. All rights reserved. Design by TinyFrog Technologies. PRIVACY POLICY | TERMS OF USE