Development of methods for transcript quantification and differential expression analysis using long-read sequencing technologies.

Pacbio和Oxford等第三代长读段测序（LRS）平台的快速发展 纳米孔技术（ONT）已经能够提高精确度和更高通量的测序， 成绩单长读段可以产生全长转录物序列，克服了转录过程中的许多不确定性。 短读方法来准确定义转录本，特别是那些具有选择性剪接的基因（更多 超过90%的人类基因），迄今为止，短读段测序已被证明是困难的。因此，LRS是 自然选择的转录变体的表达和替代异构体的作用， 疾病与发展。虽然长读技术的第一次迭代没有产生足够的读 为了量化超过最高表达的转录本，目前测序深度高达800万次读取 Sequel 2平台上的每个SMRT细胞的可定量性保证了更适度表达的基因的可靠可定量性。 还报道了纳米孔的显著产率增加。这表明LRS可能已经达到了 足够的通量以使得能够精确定量基因表达和差异表达分析。 然而，LRS转录组学数据具有其他转录组学中不存在的特定性质 技术，例如参考转录模型的部分匹配。因此， 需要进行量化和统计分析。在这个项目中，我们的目标是详细描述 长读数据中的数据分布，提出策略来处理其特定的读不确定性问题， 发展差异表达分析的新策略。总体目标是创建分析 充分利用LRS技术研究生物医学相关异构体动力学的框架 问题.

The rapid development of Third Generation, Long Read Sequencing (LRS) platforms such as Pacbio and Oxford Nanopore Technologies (ONT) have enabled increasing precision and higher-throughput sequencing of transcripts. Long reads can produce full-length transcript sequences, overcoming much of the uncertainty of short-read methods to accurately define transcripts, particularity for those genes with alternative splicing (more than 90% of human genes), for which short read sequencing has thus far proved difficult. LRS is therefore the natural choice for the study of the expression of transcript variants and of the role of alternative isoforms in disease and development. While the first iterations of the long-read technologies did not produce enough reads to quantify more than the highest expressed transcripts, the current sequencing depth of up to 8 million reads per SMRT cells on the Sequel 2 platforms promises reliable quantifiability for more modestly expressed genes. Also significant yield increases have been reported for Nanopore. This suggests that LRS may have reached sufficient throughput to enable accurate quantification of gene expression and differential expression analyses. LRS transcriptomics data have, however, specific properties that are absent in other transcriptomics technologies, such are partial matches of reference transcript models. Therefore specific methods for quantification and statistical analysis need to be developed. In this Project, we aim to characterize in detail the data distribution in long reads data, propose strategies to deal with their particular read uncertainty issues and develop new strategies for differential expression analysis. The overarching goal is to create the analytical framework to fully leverage LRS technologies for the study of isoform dynamics in relation of biomedical relevant questions.