Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.
Proc Natl Acad Sci U S A. 2012 Jan 24;109(4):1347-52. doi: 10.1073/pnas.1118018109. Epub 2012 Jan 9.
RNA sequencing (RNA-Seq) is a powerful tool for transcriptome profiling, but is hampered by sequence-dependent bias and inaccuracy at low copy numbers intrinsic to exponential PCR amplification. We developed a simple strategy for mitigating these complications, allowing truly digital RNA-Seq. Following reverse transcription, a large set of barcode sequences is added in excess, and nearly every cDNA molecule is uniquely labeled by random attachment of barcode sequences to both ends. After PCR, we applied paired-end deep sequencing to read the two barcodes and cDNA sequences. Rather than counting the number of reads, RNA abundance is measured based on the number of unique barcode sequences observed for a given cDNA sequence. We optimized the barcodes to be unambiguously identifiable, even in the presence of multiple sequencing errors. This method allows counting with single-copy resolution despite sequence-dependent bias and PCR-amplification noise, and is analogous to digital PCR but amendable to quantifying a whole transcriptome. We demonstrated transcriptome profiling of Escherichia coli with more accurate and reproducible quantification than conventional RNA-Seq.
RNA 测序（RNA-Seq）是一种强大的转录组分析工具，但受到序列依赖性偏差和指数 PCR 扩增中低拷贝数固有不准确性的限制。我们开发了一种简单的策略来缓解这些复杂问题，从而实现真正的数字 RNA-Seq。反转录后，过量添加了一组大量的条形码序列，并且几乎每个 cDNA 分子都通过条形码序列随机连接到两端而被唯一标记。PCR 后，我们应用配对末端深度测序来读取两个条形码和 cDNA 序列。不是基于读取的数量，而是基于给定 cDNA 序列观察到的独特条形码序列的数量来测量 RNA 丰度。我们优化了条形码，即使在存在多个测序错误的情况下也能进行明确的识别。该方法允许在存在序列依赖性偏差和 PCR 扩增噪声的情况下进行单拷贝分辨率的计数，类似于数字 PCR，但适用于整个转录组的定量。我们通过比传统 RNA-Seq 更准确和可重复的定量证明了大肠杆菌的转录组分析。
