Bioinformatics platform for Hybrid-Seq transcriptome data analysis

用于 Hybrid-Seq 转录组数据分析的生物信息学平台

基本信息

批准号：
9976556
负责人：
Kin Fai Au
金额：
$ 36.51万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-09 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9976556
关键词：
Address Alleles Bayesian Analysis Bioinformatics Biological Sciences Biomedical Research Collaborations Computer software Computing Methodologies Consensus Data Data Analyses Data Set Detection Development Diploidy Embryo Event Exons Foundations Generations Genes Genome Genomics Goals Gold Haplotypes Human Hybrids Inosine Diphosphate Laboratory Research Length Maps Methodology Methods Modeling Pathologic Processes Phase Physiological Processes Positioning Attribute Protein Isoforms Publishing Recurrence Research Research Personnel Resolution Risk Sampling Scientist Seeds Site Solid Statistical Methods Structure Techniques Technology Third Generation Sequencing Transcript Update Work base combinatorial cost data standards experimental study fusion gene genetic variant genomic data genomic locus human data human embryonic stem cell improved malignant breast neoplasm nanopore novel public health relevance reconstruction reference genome stem cells tool transcriptome transcriptome sequencing user-friendly

项目摘要

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.

项目概要/摘要虽然基于第二代测序 (SGS) 短读长的 RNA-Seq 实验已启用我们分析转录组的能力取得了显着进步，但一些基本问题仍未解决由于基因组的高度复杂性和无法识别组合基因组事件。世代测序 (TGS)，包括 PacBio 测序和 Oxford Nanopore Technologies (ONT)，提供更长的读取（1-100kb），有可能克服当前的这些问题。仅使用 PacBio 数据的高成本且费力的策略对于中型实验室来说并不实用。（“Hybrid-Seq”）整合了 TGS 和 SGS 数据，已成为解决局限性的一种方法然而，与短 SGS 读取的分析和 TGS 读取的错误率相关的分析工具。 Seq 转录组数据目前无法获得，因为大多数方法学的发展已经为了提高我们对转录组复杂性的理解，我们专注于混合测序基因组数据。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借助 SGS 短读长进行 TGS 长读长，并识别基因亚型、融合转录本和等位基因特定表达（ASE）。拟议的研究建立在我们已发表的初步工作的基础上。开发了 TGS 数据纠错和新基因亚型检测的方法，并应用在目标 1 中，我们将开发来自人类胚胎干细胞 (hESC) 的 Hybrid-Seq 转录组数据。在目标 2 中，我们将开发识别和量化基因亚型的计算和统计方法。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 数据量化 ASE 本提案中开发的方法将被集成到一个模型中。用于分析 Hybrid-Seq 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。通过为综合转录组提供前所未有的机会来产生重要的积极影响此外，这些工具将使更多的研究人员能够进行分析。将 Hybrid-Seq 应用于他们的转录组研究。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Revealing tumor heterogeneity of breast cancer by utilizing the linkage between somatic and germline mutations.

DOI：
10.1093/bib/bby084
发表时间：
2019-11-27
期刊：
Briefings in bioinformatics
影响因子：
9.5
作者：
Zou M;Jin R;Au KF
通讯作者：
Au KF

A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data.

DOI：
10.1016/j.ymeth.2020.06.005
发表时间：
2021-05
期刊：
METHODS
影响因子：
4.8
作者：
Wang, Dingjie;Zou, Xiufen;Au, Kin Fai
通讯作者：
Au, Kin Fai

IDP-denovo: de novo transcriptome assembly and isoform annotation by hybrid sequencing.

DOI：
10.1093/bioinformatics/bty098
发表时间：
2018-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
Fu S;Ma Y;Yao H;Xu Z;Chen S;Song J;Au KF
通讯作者：
Au KF

Discovery of novel determinants of endothelial lineage using chimeric heterokaryons.

使用嵌合异核体发现内皮谱系的新决定因素。

DOI：
10.7554/elife.23588
发表时间：
2017
期刊：
eLife
影响因子：
7.7
作者：
Wong,WingTak;Matrone,Gianfranco;Tian,XiaoYu;Tomoiaga,SimionAlin;Au,KinFai;Meng,Shu;Yamazoe,Sayumi;Sieveking,Daniel;Chen,Kaifu;Burns,DavidM;Chen,JamesK;Blau,HelenM;Cooke,JohnP
通讯作者：
Cooke,JohnP

Single cell expression analysis of primate-specific retroviruses-derived HPAT lincRNAs in viable human blastocysts identifies embryonic cells co-expressing genetic markers of multiple lineages.

DOI：
10.1016/j.heliyon.2018.e00667
发表时间：
2018-06
期刊：
Heliyon
影响因子：
4
作者：
Glinsky G;Durruthy-Durruthy J;Wossidlo M;Grow EJ;Weirather JL;Au KF;Wysocka J;Sebastiano V
通讯作者：
Sebastiano V