Development of methods for transcript quantification and differential expression analysis using long-read sequencing technologies.
使用长读长测序技术开发转录本定量和差异表达分析方法。
基本信息
- 批准号:10041221
- 负责人:
- 金额:$ 3.51万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-09-01 至 2021-05-06
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmsAlternative SplicingAtaxiaAwarenessBiological SciencesBrainCellsClassificationCodeComplementary DNAComplexComputer softwareDataData AnalysesData SetDescriptorDevelopmentDiseaseDropsEnsureEvaluationGene ExpressionGenerationsGenesGoalsGuanine + Cytosine CompositionHeartHigh-Throughput Nucleotide SequencingHumanInvestigationLengthLinear ModelsLungMachine LearningMalignant NeoplasmsMethodologyMethodsModelingMorphologic artifactsMusOrphanOutputPatternPerformancePlayPolyadenylationProcessPropertyProtein IsoformsQuality ControlRNA SplicingReportingReproducibilityRoleSamplingSolidStatistical Data InterpretationStatistical DistributionsTechnologyTestingTissuesTranscriptUncertaintyVariantcostdifferential expressionexperienceexperimental studyflexibilityhuman diseasehuman tissueimprovedleukemiamethod developmentnanoporenovelpreservationsequencing platformsoftware developmenttooltranscriptometranscriptome sequencingtranscriptomics
项目摘要
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.
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.
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Ana Victoria Conesa Cegarra其他文献
Ana Victoria Conesa Cegarra的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Ana Victoria Conesa Cegarra', 18)}}的其他基金
Development of methods for transcript quantification anddifferential expression analysis using long-read sequencing technologies
使用长读长测序技术开发转录本定量和差异表达分析方法
- 批准号:
10458139 - 财政年份:2020
- 资助金额:
$ 3.51万 - 项目类别:
Galaxy platform for integrative metabolomics and transcriptomics analysis
用于综合代谢组学和转录组学分析的 Galaxy 平台
- 批准号:
9433323 - 财政年份:2017
- 资助金额:
$ 3.51万 - 项目类别:
相似海外基金
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 3.51万 - 项目类别:
Research Grant