Quantifying RNA allelic ratios by microfluidic multiplex PCR and sequencing

Journal name:
Nature Methods
Year published:
Published online

We developed a targeted RNA sequencing method that couples microfluidics-based multiplex PCR and deep sequencing (mmPCR-seq) to uniformly and simultaneously amplify up to 960 loci in 48 samples independently of their gene expression levels and to accurately and cost-effectively measure allelic ratios even for low-quantity or low-quality RNA samples. We applied mmPCR-seq to RNA editing and allele-specific expression studies. mmPCR-seq complements RNA-seq for studying allelic variations in the transcriptome.

At a glance


  1. The development and performance of mmPCR-seq.
    Figure 1: The development and performance of mmPCR-seq.

    (a) Schematic of mmPCR-seq. (b) Uniformity of different amplicons. Read numbers from three technical replicates of mmPCR-seq on 240 RNA-editing loci amplified from 1 μg of HBRR cDNA were normalized to 0.8 million mapped reads per sample. Coverage of the same sites from RNA-seq data (the 300 million full set and the 50 million subset) is also shown. (c) Relationship between coverage at individual sites and gene expression levels reported in fragments per kilobase of transcript per million mapped reads (FPKM). For mmPCR-seq, the average depth of three technical replicates is shown. R2, Pearson correlation coefficient. Dotted line, 100 reads. (d) Relationship between the reproducibility of RNA-editing measurements and the amount of cDNA input (see Supplementary Fig. 3 for a comparison of all three technical replicates). (e) Comparison of editing levels measured by mmPCR-seq and RNA-seq for sites with at least 20 or 100 RNA-seq reads. (f) Comparison of expected allelic ratio and ratio measured by mmPCR-seq (average of six sites). Colors indicate the number of template copies per PCR reaction. Error bars, s.d. for 1,000 copies are shown as an example (see Supplementary Table 2 for a full description). (g) Reproducibility of editing-level measurement using preamplified cDNAs. Top, technical replicates using preamplified samples. Bottom, preamplified cDNA versus the unamplified 1,000 ng of cDNA. (h) Reproducibility of RNA editing–level measurement in technical duplicates using low-quality RNA samples, without (top) and with (bottom) preamplification. RNA integrity number (RIN) is indicated. 200 ng and 1,000 ng of cDNA were used for amplified and unamplified samples, respectively.

  2. Characterization of novel RNA-editing sites identified by mmPCR-seq.
    Figure 2: Characterization of novel RNA-editing sites identified by mmPCR-seq.

    (a) Relationship between the enrichment score and the minimum frequency of a variant nucleotide. The enrichment score was calculated as the number of detected A-to-G or T-to-C mismatches per 10 kb in RNA samples divided by the counterpart in DNA samples. The average value of two samples is shown. Sites with ≥1,000 reads were used. (b) Pairwise comparison of the editing level of each novel site and the nearest known site. (c) Cumulative distribution of RNA-editing levels for different groups of sites. For each site, the highest editing level among eight samples is shown. (d) Nucleotide composition in positions immediately upstream and downstream of the edited sites. The control is all A sites that are covered by mmPCR-seq reads and not edited in any samples tested.

  3. ASE analysis of mmPCR-seq data.
    Figure 3: ASE analysis of mmPCR-seq data.

    (a) Proportion of sites with ASE among all heterozygous sites using mmPCR-seq or RNA-seq for genes with various expression levels. The matched sites obtained from mmPCR-seq and RNA-seq data were used. FPKM, fragments per kilobase of transcript per million mapped reads. (b) Correlation of ASE of identical-by-descent siblings calculated from subsamplings of mmPCR-seq data. R2, Pearson correlation coefficient. Error bars, s.d. (c) The percentage and number of cis-acting expression quantitative trait loci (cis-eQTL) genes identified using different permutation p value thresholds. Only genes with mmPCR-seq sites were used in the analysis. Three models were used for the mapping. TReC, total read count, an association model using gene expression information only; TReCASE (RNA-seq), a joint model of TReC and ASE measured by RNA-seq; TReCASE (mmPCR-seq), a joint model of TReC and ASE measured by mmPCR-seq.

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Sequence Read Archive


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Author information


  1. Department of Genetics, Stanford University, Stanford, California, USA.

    • Rui Zhang,
    • Gokul Ramaswami,
    • Stephen B Montgomery &
    • Jin Billy Li
  2. Department of Pathology, Stanford University, Stanford, California, USA.

    • Xin Li,
    • Kevin S Smith &
    • Stephen B Montgomery
  3. McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada.

    • Gustavo Turecki


R.Z. developed and optimized the mmPCR-seq method with the help from G.R., K.S.S., S.B.M. and J.B.L. R.Z. and X.L. performed computational analyses with help from S.B.M. and J.B.L. G.T. provided the brain samples. R.Z., X.L., S.B.M. and J.B.L. wrote the paper.

Competing financial interests

The authors declare no competing financial interests.

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Supplementary information

PDF files

  1. Supplementary Text and Figures (2,246 KB)

    Supplementary Figures 1–17, Supplementary Tables 1–9 and Supplementary Notes 1–6

Excel files

  1. Supplementary Data 1 (59 KB)

    Targeted RNA editing sites and ASE sites

  2. Supplementary Data 2 (83 KB)

    Primer information

  3. Supplementary Data 3 (52 KB)

    Validation of A-to-I events

  4. Supplementary Data 4 (19 KB)

    Known and novel nonrepetitive recoding sites

Zip files

  1. Supplementary Software (3,470 KB)

    Perl script for multiplex PCR primer design

Additional data