Additional file 1: Table S1. of A lung cancer risk classifier comprising genome maintenance genes measured in normal bronchial epithelial cells

List of assays, SNP site, primer and internal standard sequences, demographics, and transcript abundance (target gene molecules/106 ACTB molecules). Bold: Selected features. (XLSX 103 kb

Targeted RNA-Sequencing with Competitive Multiplex-PCR Amplicon Libraries

<div><p>Whole transcriptome RNA-sequencing is a powerful tool, but is costly and yields complex data sets that limit its utility in molecular diagnostic testing. A targeted quantitative RNA-sequencing method that is reproducible and reduces the number of sequencing reads required to measure transcripts over the full range of expression would be better suited to diagnostic testing. Toward this goal, we developed a competitive multiplex PCR-based amplicon sequencing library preparation method that a) targets only the sequences of interest and b) controls for inter-target variation in PCR amplification during library preparation by measuring each transcript native template relative to a known number of synthetic competitive template internal standard copies. To determine the utility of this method, we intentionally selected PCR conditions that would cause transcript amplification products (amplicons) to converge toward equimolar concentrations (normalization) during library preparation. We then tested whether this approach would enable accurate and reproducible quantification of each transcript across multiple library preparations, and at the same time reduce (through normalization) total sequencing reads required for quantification of transcript targets across a large range of expression. We demonstrate excellent reproducibility (R² = 0.997) with 97% accuracy to detect 2-fold change using External RNA Controls Consortium (ERCC) reference materials; high inter-day, inter-site and inter-library concordance (R² = 0.97–0.99) using FDA Sequencing Quality Control (SEQC) reference materials; and cross-platform concordance with both TaqMan qPCR (R² = 0.96) and whole transcriptome RNA-sequencing following “traditional” library preparation using Illumina NGS kits (R² = 0.94). Using this method, sequencing reads required to accurately quantify more than 100 targeted transcripts expressed over a 10⁷-fold range was reduced more than 10,000-fold, from 2.3×10⁹ to 1.4×10⁵ sequencing reads. These studies demonstrate that the competitive multiplex-PCR amplicon library preparation method presented here provides the quality control, reproducibility, and reduced sequencing reads necessary for development and implementation of targeted quantitative RNA-sequencing biomarkers in molecular diagnostic testing.</p></div

Performance of competitive amplicon library preparation with ERCC Reference Materials.

<p><b>a)</b> Measured signal abundance of ERCC targets in samples A, B, C and D. X-axis units are derived from Ambion product literature for the known concentration of ERCC spike-in controls (n = 104). <b>b)</b> Difference plots of data in panel A ordered numerically by ERCC ID. Each ERCC target depicted was measured at least once in all four samples A–D. For purposes of clarity, ERCC-170 is highlighted orange in panels A and B (n = 104). <b>c)</b> Samples C and D represent a 3∶1 and 1∶3 mixture, respectively, of samples A and B. These ratios were used to calculate expected measurements for samples C and D (X-axis). Actual measurements of samples C and D are plotted on the Y-axis (n = 52). <b>d)</b> Coefficient of variation (CV) in measurements of ERCC targets in samples A-D, for those assays with at least two IS dilution points. Red line depicts expected CV based on a Poisson sampling (n = 95). <b>e)</b> ROC curves to detect fold change with corresponding area under the curve (AUC) with 95% confidence intervals. ROC curves are derived from the comparison of differential ratio subpools of ERCC targets in samples: A vs. B, A vs. C, A vs. D, B vs. C, B vs. D and C vs. D. Results for 1.1-fold change represent a range of differential ratio subpools [1.05–1.174] (controls n = 100, tests n = 96); 1.25-fold change [1.175–1.374] (controls n = 163, tests n = 163); 1.5-fold change [1.375–1.74] (controls n = 229, tests n = 227); 2.0-fold change [1.75–2.49] (controls n = 229, tests n = 223); ≥4.0-fold change [2.5–10.0] (controls n = 286, tests n = 290).</p

Schematic depiction of how competitive amplicon library preparation reduces oversampling.

<p><b>a)</b> Depicted are two native targets (NT) within a hypothetical cDNA sample. One NT is in high abundance, 10⁸ copies (“Abundant” NT), while another is in low abundance, 10² copies (“Rare” NT), representing a one million-fold difference in abundance between targets. This hypothetical cDNA sample is combined with a mixture of internal standards (IS) with a fixed relationship of concentrations at 10⁵ copies. <b>b)</b> Depicted is the competitive multiplex-PCR library preparation for panel A. The PCR amplification plots for both the “Abundant” and “Rare” NT are separated for purposes of clarity, but occur in the same reaction. During competitive multiplex-PCR, each NT competes equally with its respective competitive IS for dNTPs, polymerase and a limiting concentration of primers. Because the starting concentration of each target’s primer-pair is the same, each competitive reaction will plateau around the same end-point concentration (∼10⁹ copies). <b>c)</b> The equal competition between each NT and respective IS preserves the proportional relationship between NT in the original sample, allowing for measurement of native target abundance without signal compression (also see <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079120#pone.0079120.s012" target="_blank">Animation S1</a></b>). Yet, a 10⁶ fold range of templates is reduced to 10³ after competitive multiplex-PCR library preparation resulting in a 1,000-fold reduction in oversampling/sequencing of the high abundance target.</p

Cross-platform comparison of competitive amplicon library preparation with TaqMan qPCR and Illumina RNA-Sequencing.

<p><b>a</b>) Comparison of TaqMan qPCR with competitive amplicon library preparation (n = 146) for samples A and B without correction for systematic biases. Data is normalized to a median relative abundance. <b>b</b>) Comparison of Illumina RNA-Sequencing with competitive amplicon library preparation (n = 170) for samples A and B without correction for systematic biases. Data is normalized to a median relative abundance. For a) and b), Spearman’s rank correlation coefficient is noted (r_s). The average of differences for measurements of samples A and B between competitive amplicon library preparation and TaqMan qPCR (<b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079120#pone.0079120.s001" target="_blank">Figure S1</a></b>) or Illumina RNA-sequencing (<b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079120#pone.0079120.s002" target="_blank">Figure S2</a></b>) was determined for each endogenous target; and to illustrate the systematic bias away from the regression line, data points for MMP2 have been highlighted in orange. This difference was subtracted from TaqMan qPCR or Illumina RNA-sequencing measurements for samples C and D and plotted (X-axis). Competitive amplicon library preparation measurements of C and D are plotted on the Y-axis. <b>c)</b> Comparison of TaqMan qPCR with competitive amplicon library preparation (n = 146) for samples C and D with correction for platform and assay specific bias. <b>d)</b> Comparison of Illumina RNA-Sequencing with competitive amplicon library preparation (n = 170) for samples C and D with correction for platform and assay specific bias.</p

Performance of competitive amplicon library preparation with endogenous cDNA targets.

<p><b>a–d)</b> Absolute signal abundance of cDNA targets in sample A in units of copies per library preparation measured on separate days, at different sites (OU = Ohio University; UTMC = University of Toledo Medical Center), and between different reverse transcription preparations (RT1 and RT2). <b>a)</b> Inter-day effect (n = 88). <b>b</b>) Inter-day and Inter-site effect (n = 81). <b>c)</b> Inter-day and Inter-library effect (n = 92). <b>d)</b> Inter-day, Inter-site and Inter-library effect (n = 80). <b>e–f)</b> Samples C and D represent a 3∶1 and 1∶3 mixture, respectively, of total RNA from samples A and B. These ratios were used to calculate expected measurements for samples C and D (X-axis) from measurements of A and B. Plotted on the Y-axis are actual measurements of samples C (n = 86) and D (n = 90).</p

Additional file 1: of RNAseq analysis of bronchial epithelial cells to identify COPD-associated genes and SNPs

Tables S1, S2, S3, S4, S5 and S6. Table S1. This table provides: a) Gene-specific assay information including SNP sites, primer and internal standard sequences, b) Subject-specific demographic information, and c) assay- and subject-specific transcript abundance values (target gene molecules/106 ACTB molecules). Table S2. Population used for allele specific expression analysis: Summary demographic characteristics of the study population of allele specific expression (subject total n = 180). Table S3. Transcription factor-target inter-gene correlation in Control, COPD, or All subjects (p-value < 0.05). Table S4. Analysis of covariance (ANCOVA). Gene expression values (Independent Variables) significantly correlated (positively or negatively) with COPD subjects (Dependent Variable) after control for expression values of other genes (Covariates). Table S5. ERCC5 SNPs linked to rs17655 and rs873601 (D > 0.95) and with p < 0.05 in LHS and COPDgene NHW CB cohorts. COPD GWAS p-values, population-specific genotype frequencies, and epigenetic annotation information from Haploreg/Encode. Table S6. Haplotype structure between COPDgene NHW1 associated SNP rs4150275, putative functional cis-rSNP rs873601, and DAE2 SNP rs17655. (XLSX 109 kb