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Comparative analysis of RNA sequencing methods for degraded or low-input samples

Nature Methods volume 10, pages 623629 (2013) | Download Citation


  • A Corrigendum to this article was published on 30 January 2014

This article has been updated


RNA-seq is an effective method for studying the transcriptome, but it can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations or cadavers. Recent studies have proposed several methods for RNA-seq of low-quality and/or low-quantity samples, but the relative merits of these methods have not been systematically analyzed. Here we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and compared them against two control libraries. We found that the RNase H method performed best for chemically fragmented, low-quality RNA, and we confirmed this through analysis of actual degraded samples. RNase H can even effectively replace oligo(dT)-based methods for standard RNA-seq. SMART and NuGEN had distinct strengths for measuring low-quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development.

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Change history

  • 02 December 2013

    In the version of this article initially published, in the Online Methods "RNase H libraries" section, the sentence beginning with "We added 5 μl preheated RNase H...." should have read "We added 5 μl preheated RNase H reaction mix that contains 10 U of Hybridase Thermostable RNase H (Epicentre), 0.5 μmol Tris-HCl, pH 7.5, 1 μmol NaCl and 0.2 μmol MgCl2 to the RNA and DNA oligo mix, incubated this mixture at 45 °C for 30 min and then placed it on ice." The errors have been corrected in the HTML and PDF versions of this article.


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We thank D. Sinicropi for alerting us to his RNase H method, Broad Genomics Platform for sequencing work, C. Nusbaum, R. Johnson, M. Yassour and V. Savova for helpful discussions, and L. Gaffney for assistance in drafting figures. Work was supported by US National Institutes of Health Pioneer Award DP1-OD003958-01, US National Human Genome Research Institute (NHGRI) 1P01HG005062-01, NHGRI Center of Excellence in Genome Science Award 1P50HG006193-01, the Howard Hughes Medical Institute, the Merkin Foundation for Stem Cell Research and the Klarman Cell Observatory at the Broad Institute (to A.R.), and by NHGRI grant HG03067. N.P. was supported by a postdoctoral research fellowship of the Fund for Scientific Research–Flanders (FWO Vlaanderen).

Author information

Author notes

    • Xian Adiconis
    •  & Diego Borges-Rivera

    These authors contributed equally to this work.


  1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Xian Adiconis
    • , Diego Borges-Rivera
    • , Rahul Satija
    • , David S DeLuca
    • , Michele A Busby
    • , Aaron M Berlin
    • , Andrey Sivachenko
    • , Dawn Anne Thompson
    • , Alec Wysoker
    • , Timothy Fennell
    • , Andreas Gnirke
    • , Nathalie Pochet
    • , Aviv Regev
    •  & Joshua Z Levin
  2. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Aviv Regev
  3. Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Aviv Regev


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J.Z.L., X.A. and A.R. conceived the research. X.A. prepared the cDNA libraries. D.B.-R., R.S., N.P., M.A.B. and A.R. developed and performed computational analysis. D.S.D. contributed code. D.S.D., A.M.B., A.S., A.W. and T.F. helped with computational analysis. D.A.T., N.P., A.R. and J.Z.L. supervised the research. J.Z.L., X.A., D.B.-R. and A.R. wrote the paper. R.S., A.G. and D.S.D. assisted in editing the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Aviv Regev or Joshua Z Levin.

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    RNase H oligonucleotide sequences

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