Bioinformatics platform for Hybrid-Seq transcriptome data analysis

项目总结/摘要 虽然基于第二代测序（SGS）短读段的RNA-Seq实验已经实现了 尽管我们在分析转录组的能力上取得了显著的进步，但一些基本问题仍然没有解决。 这是由于基因组的高度复杂性和不能鉴定组合基因组事件。三 Generation Sequencing（TGS），包括PacBio测序和Oxford Nanopore Technologies（ONT）， 提供更长的读取（1- 100 kb），具有克服这些问题的潜力。但目前 仅使用PacBio数据高成本和费力的策略对于中等规模的实验室是不切实际的。杂交测序 （“Hybrid-Seq”），其整合了TGS和SGS数据，已经成为解决限制的方法。 与短SGS读数的分析和TGS读数的错误率相关。然而，分析混合动力的工具- Seq转录组数据目前还不可用，因为大多数方法学的发展已经 专注于杂交测序基因组数据。为了提高我们对转录组复杂性的理解，我们 将开发一个综合的Hybrid-Seq平台，采用新颖的统计和计算方法来分析 TGS长读段在SGS短读段的辅助下，并鉴定基因亚型、融合转录物和等位基因。 特异性表达（ASE）。拟议的研究建立在我们已发表和初步工作的基础上， 开发了TGS数据的误差校正和新基因异构体的检测方法，并将其应用于 到来自人类胚胎干细胞（hESC）的Hybrid-Seq转录组数据。在目标1中，我们将开发 计算和统计方法来鉴定和量化基因异构体。在目标2中，我们将开发 计算方法来发现融合转录本。在目标3中，我们将确定基因等位基因的单倍型 并使用Hybrid-Seq数据定量ASE。本提案中制定的方法将被纳入一个 用于分析杂交测序转录组数据的软件平台。这个用户友好的生物信息学平台将 通过为全面的转录组提供前所未有的机会， 分析，具有广泛的适用性和更高的分辨率。此外，这些工具将使更多的研究人员， 将Hybrid-Seq应用于他们的转录组研究。

PROJECT SUMMARY / ABSTRACT While RNA-Seq experiments based on Second Generation Sequencing (SGS) short reads have enabled remarkable advances in our ability to analyze the transcriptome, a few fundamental problems remain unsolved due to the high complexity of the genome and the inability to identify combinatorial genomic events. Third Generation Sequencing (TGS), including PacBio sequencing and Oxford Nanopore Technologies (ONT) which provide much longer reads (1-100kb), has the potential to overcome these problems. However, the current high-cost and laborious strategy of only using PacBio data is not practical for mid-size labs. Hybrid sequencing (“Hybrid-Seq”), which integrates TGS and SGS data, has emerged as an approach to address the limitations associated with analysis of short SGS reads and the error rate of TGS reads. However, tools to analyze Hybrid- Seq transcriptome data are not currently available because the majority of methodological developments have focused on Hybrid-Seq genomic data. In order to improve our understanding of transcriptome complexity, we will develop a comprehensive Hybrid-Seq platform of novel statistical and computational methods to analyze TGS long reads with the aid of SGS short reads, and to identify gene isoforms, fusion transcripts and allele- specific expression (ASE). The proposed studies build on our published and preliminary work where we developed methods for error correction for TGS data and detection of novel gene isoforms, which were applied to Hybrid-Seq transcriptome data from human embryonic stem cells (hESCs). In Aim 1, we will develop computational and statistical approaches to identify and quantify gene isoforms. In Aim 2, we will develop computational methods to discover fusion transcripts. In Aim 3, we will determine the haplotypes of gene alleles and quantify ASE using Hybrid-Seq data. The methods developed in this proposal will be integrated into a software platform for analysis of Hybrid-Seq transcriptome data. This user-friendly bioinformatics platform will have important positive impacts by providing an unprecedented opportunity for comprehensive transcriptome profiling, with broad applicability and higher resolution. In addition, these tools will enable more researchers to apply Hybrid-Seq to their transcriptome studies.