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Comparative Study
. 2010 Sep;7(9):709-15.
doi: 10.1038/nmeth.1491. Epub 2010 Aug 15.

"V体育安卓版" Comprehensive comparative analysis of strand-specific RNA sequencing methods

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Comparative Study

Comprehensive comparative analysis of strand-specific RNA sequencing methods

Joshua Z Levin et al. Nat Methods. 2010 Sep.

"VSports在线直播" Abstract

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 VSports手机版. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms. .

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Figures

Figure 1
Figure 1. Methods for strand-specific RNA-Seq
Salient details for seven protocols for strand-specific RNA-Seq, differential adaptor methods (a) and differential marking methods (b). mRNA is shown in grey, and cDNA in black. For differential adaptor methods, 5’ adaptors are shown in blue, and 3’ adaptors in red.
Figure 2
Figure 2. Key criteria for evaluation of strand-specific RNAseq libraries
Four categories of quality assessment. Double stranded genome (black parallel lines), with Gene ORF orientation (thick blue arrow) and UTRs (thin blue line), along with mapped reads (short black arrows – reads mapped to sense strand; red – reads mapped to antisense strand). (a) Complexity. (b) Strand Specificity. (c) Evenness of coverage. (d) Comparison to known transcript structure‥
Figure 3
Figure 3. Complexity of single- and paired-end libraries
Bar graphs comparing library complexity by the fraction 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). Libraries are ordered as in Figure 1. Full data for all library variations are presented in Supplementary Table 2.
Figure 4
Figure 4. Strand specificity and evenness of transcript coverage
(a) Strand specificity (% antisense) and evenness of coverage (average coefficient of variation (CV)). The average CV of the control (non strand-specific library) is shown by an orange line. Libraries are sorted as in Figure 1. Full data for all library variations are presented in Supplementary Table 2. (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. Full data for all library variations are presented in Supplementary Fig. 3. (c) 5’ and 3’ end coverage. Shown is the percentage of genes with 5’ and 3’ coverage (left and right bars, respectively; Online Methods) in each library. Full data for all library variations are presented in Supplementary Table 2.
Figure 5
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. Full data for all library variations are presented in Supplementary Table 2. (b–e) Fraction of bases not covered by reads for each gene (blue dot) in the genome, plotted against the fraction of total reads for that gene in the pooled library, for the dUTP method (c), the 3’ split adaptor method (d) and the SMART method (e). In each case, a Lowess fit is shown as a red curve, with fits from all libraries shown in (b). Full data for all library variations are presented in Supplementary Fig. 2.
Figure 6
Figure 6. Digital expression profiling using strand-specific RNA-Seq
(a, b) Pearson correlation coefficient (a) and RMSE (b) for each library when compared to a pooled reference (left bars), the control library (middle bars) and Agilent microarrays (right bars). Full data for all library variations are presented in Supplementary Table 2. (c, d) Scatter (left panel), Q-Q (middle panel) and MA (right panel) 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 crosses). The MA plot shows for each gene (dot) the difference in expression levels between the control and strand-specific libraries (Y axis) compared to their mean expression level (X axis). Red dashed lines – two fold difference in expression. Full data for all library variations are presented in Supplementary Fig. 4.

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