Comprehensive comparative analysis of strand-specific RNA sequencing methods

Journal name:
Nature Methods
Year published:
Published online


Strand-specific, massively parallel cDNA sequencing (RNA-seq) is a powerful tool for transcript discovery, genome annotation and expression profiling. There are multiple published methods for strand-specific RNA-seq, but no consensus exists as to how to choose between them. Here we developed a comprehensive computational pipeline to compare library quality metrics from any RNA-seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library-construction protocols, including both published and our own methods. We found marked differences in strand specificity, library complexity, evenness and continuity of coverage, agreement with known annotations and accuracy for expression profiling. Weighing each method's performance and ease, we identified the dUTP second-strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms.

At a glance


  1. Methods for strand-specific RNA-seq.
    Figure 1: Methods for strand-specific RNA-seq.

    (a,b) Salient details for differential adaptor methods including RNA ligation29, SMART30 and NNSR priming31 (a) and differential marking methods (b). USER, uracil-specific excision reagent. mRNA is shown in gray and cDNA in black. For differential adaptor methods, 5′ adaptors are shown in blue, and 3′ adaptors are shown in red.

  2. Key criteria for evaluation of strand-specific RNA-seq libraries.
    Figure 2: Key criteria for evaluation of strand-specific RNA-seq libraries.

    (ad) Categories of quality assessment were complexity (a), strand specificity (b), evenness of coverage (c) and comparison to known transcript structure (d). Double-stranded genome with gene ORF orientation (blue arrows) and UTRs (blue lines) are shown along with mapped reads (black and red arrows, reads mapped to sense and antisense strands, respectively).

  3. Complexity of single- and paired-end libraries.
    Figure 3: Complexity of single- and paired-end libraries.

    (a,b) Percentage of unique reads mapping out of the total number of mapped reads, when considering only single-mapped reads (a; all libraries) or uniquely mapped pairs (b; only paired-end libraries).

  4. Strand specificity and evenness of transcript coverage.
    Figure 4: Strand specificity and evenness of transcript coverage.

    (a) Strand specificity (percentage antisense) and evenness of coverage (average coefficient of variation (CV)) for all libraries. (b) Relative gene coverage at each percentile of a gene's length, averaged across all genes in each library. The 5′ end is on the left. (c) Percentage of genes with 5′-end and 3′-end coverage in each library.

  5. Continuity of transcript coverage.
    Figure 5: Continuity of transcript coverage.

    (a) Average number of segments (separated by at least five bases of zero coverage) weighted by the average expression of each gene, in each library. (b) Lowess fit for each library. (ce) Plots for the dUTP method (c), the 3′ split adaptor method (d) and the SMART method (e). In ce, a Lowess fit is shown as a red curve, and each gene is represented by a blue dot.

  6. Digital expression profiling using strand-specific RNA-seq.
    Figure 6: Digital expression profiling using strand-specific RNA-seq.

    (a,b) Pearson correlation coefficient (a) and r.m.s. error (b) for each library when compared to a pooled reference, the control library and Agilent microarrays (right). (c,d) Scatter (left), Q-Q (middle) and MA (right) plots for the best performing (dUTP; c) and worst performing (NNSR; d) libraries, in comparison to the control library. The scatter plots show the fraction of total reads for each gene (blue dot) in the control library against a strand-specific library. The Q-Q plot shows the level at each quantile (rank) of expression in the control library against the strand-specific library. A slope = 1 line is shown for reference (red). The MA plot shows for each gene (dot) the difference in expression levels between the control and strand-specific libraries (M; y axis) compared to their mean expression level (A; x axis). Red and blue dashed lines indicate twofold and onefold difference in expression, respectively.

Accession codes

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Author information

  1. These authors contributed equally to this work.

    • Joshua Z Levin &
    • Moran Yassour


  1. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA.

    • Joshua Z Levin,
    • Moran Yassour,
    • Xian Adiconis,
    • Chad Nusbaum,
    • Dawn Anne Thompson,
    • Andreas Gnirke &
    • Aviv Regev
  2. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Moran Yassour &
    • Aviv Regev
  3. School of Engineering and Computer Science, Hebrew University, Jerusalem, Israel.

    • Moran Yassour &
    • Nir Friedman
  4. Alexander Silberman Institute of Life Sciences, Hebrew University, Jerusalem, Israel.

    • Nir Friedman
  5. Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Aviv Regev


J.Z.L., M.Y., X.A., D.A.T., N.F. and A.R. wrote the paper. J.Z.L., M.Y., X.A., C.N., D.A.T., N.F., A.G. and A.R. assisted in editing the paper. D.A.T. prepared the poly(A)+ RNA. J.Z.L. and X.A. prepared the cDNA libraries. M.Y., N.F. and A.R. developed and performed the computational analysis. J.Z.L., X.A., M.Y., N.F. and A.R. conceived the research.

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