Trinity: Transcriptome assembly for genetic and functional analysis of cancer
Trinity:用于癌症遗传和功能分析的转录组组装
基本信息
- 批准号:10251056
- 负责人:
- 金额:$ 82.28万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-09-17 至 2023-08-31
- 项目状态:已结题
- 来源:
- 关键词:AlgorithmsAutomobile DrivingB cell clonalityBase SequenceCancer BiologyCatalogingCellsClinicalClinical ResearchCloud ComputingCommunitiesComputer AnalysisComputer softwareDataData AnalysesData SetDevelopmentDiseaseDocumentationEducational workshopEnsureExonsFundingGeneticGenetic IdentityGenetic TranscriptionGenomeGrowthImmunotherapyIndividualMalignant - descriptorMalignant NeoplasmsMapsMessenger RNAMetadataMethodsMicrobeModalityMutationMutation AnalysisMutation DetectionPatient CarePatientsPatternPersonsPoint MutationProtein IsoformsRNARNA SplicingReportingResearch PersonnelResolutionSamplingSoftware EngineeringSpecimenStructureT-LymphocyteTechnologyThe Cancer Genome AtlasTimeTrainingTraining SupportTranscriptUpdateVariantVisualizationWorkanticancer researchbasecancer cellcancer genomecohortcomputational platformcomputerized data processingcomputing resourcesdata resourcedisease diagnosisexomeexome sequencingexperienceflexibilitygenome-widehuman reference genomeindividual patientinsightliterature citationmicrobiomeneoantigensneoplastic cellnew therapeutic targetnovelnovel sequencing technologyopen sourcereconstructionreference genomesingle-cell RNA sequencingtooltranscriptometranscriptome sequencingtranscriptomicstumortumor heterogeneitytumor microenvironmenttumorigenesisuser-friendlyvirome
项目摘要
RNA-Seq studies indicate that the cancer transcriptome are shaped by genetic changes, variation in gene
transcription, mRNA processing, editing and stability, and the cancer microbiome. Deciphering this variation
and understanding its implications on tumorigenesis requires sophisticated computational analyses, and being
able to tackle analyses of bulk RNA-Seq as well as transcriptomes of individual tumor cells. Most RNA-Seq
analyses rely on methods that first map short reads to a reference genome, and then compare them to
annotated transcripts or assemble them. However, this strategy can be limited when the cancer genome is
substantially different than the reference or for detecting sequences from the cancer microbiome. `Assembly
first' (de novo) methods that combine reads into transcripts without any mapping are a compelling alternative.
The assembled transcriptome can then be used to identify mutations, fusion transcripts, splicing patterns,
expression levels, tumor-associated microbes, and – if collected from single cells – characterize tumor
heterogeneity. There is thus an enormous need for computationally efficient, accurate and user friendly tools
for transcriptome reconstruction and analysis in cancer. Trinity, first released in mid-2011 and freely available
as Open Source, is the leading software for de novo RNA-Seq assembly, executed millions of times by
thousands of rsearchers, over 4k literature citations, and now includes a host of modules for downstream
analyses, contributed by the Trinity development team or contributed by 3rd party developers. Here, we will
continue to enhance and maintain Trinity and further develop our Trinity Cancer Transcriptome Analysis Tookit
(CTAT) as leading tool suite for bulk and single-cell cancer transcriptomics. We will tailor analytic modules for
critical tasks in cancer biology, working with a network of cancer researchers on Driving Cancer Projects (Aim
1). We will continue to update the Trinity software to enhance the core algorithm, leveraging new sequencing
technologies and integrating genome data with genome-free assembly (Aim 2). We will integrate Trinity CTAT
into the NCI cloud computing platform via FireCloud for scalable cancer transcriptome data processing and
analyses (Aim 3). We will grow the Trinity cancer user community, using online and in person training and
support (Aim 4), to allow any cancer researcher to leverage it in diverse modalities.
RNA-Seq研究表明,癌症转录组是由遗传变化,基因变异,
转录,mRNA加工,编辑和稳定性,以及癌症微生物组。解读这种变异
理解其对肿瘤发生的影响需要复杂的计算分析,
能够处理批量RNA-Seq以及单个肿瘤细胞转录组的分析。大多数RNA-Seq
分析依赖于首先将短读段映射到参考基因组,然后将它们与
注释的抄本或汇编它们。然而,当癌症基因组是
在一些实施方案中,所述方法用于检测与参考基本上不同的序列或用于检测来自癌症微生物组的序列。大会
将读段联合收割机组合成转录物而不进行任何定位的“第一”(从头)方法是一种令人信服的替代方法。
然后,组装的转录组可用于鉴定突变、融合转录物、剪接模式,
表达水平,肿瘤相关微生物,以及-如果从单细胞收集-表征肿瘤
异质性因此,对计算高效、准确和用户友好的工具存在巨大需求
用于癌症的转录组重建和分析。Trinity,于2011年年中首次发布,免费提供
作为开源,是从头RNA测序组装的领先软件,由
成千上万的搜索者,超过4k的文献引用,现在包括下游的模块主机
由Trinity开发团队或第三方开发人员提供的分析。在这里,我们将
继续加强和维持Trinity,并进一步发展我们的Trinity癌症转录组分析工具
(CTAT)作为批量和单细胞癌症转录组学的领先工具套件。我们将定制分析模块,
癌症生物学的关键任务,与癌症研究人员网络合作推动癌症项目(Aim
1)。我们将继续更新Trinity软件,以增强核心算法,利用新的测序
技术和整合基因组数据与基因组自由组装(目标2)。我们将整合Trinity CTAT
通过FireCloud进入NCI云计算平台,进行可扩展的癌症转录组数据处理,
分析(目标3)。我们将发展Trinity癌症用户社区,使用在线和面对面培训,
支持(目标4),允许任何癌症研究人员以不同的方式利用它。
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)
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Brian Haas其他文献
Brian Haas的其他文献
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{{ truncateString('Brian Haas', 18)}}的其他基金
Open Software and Resource Development Leveraging Next-gen Sequencing Data to Facilitate Computational Analysis of Cancer Biology
开放软件和资源开发利用下一代测序数据促进癌症生物学的计算分析
- 批准号:
10001054 - 财政年份:2016
- 资助金额:
$ 82.28万 - 项目类别:
Open Software and Resource Development Leveraging Next-gen Sequencing Data to Facilitate Computational Analysis of Cancer Biology
开放软件和资源开发利用下一代测序数据促进癌症生物学的计算分析
- 批准号:
9759878 - 财政年份:2016
- 资助金额:
$ 82.28万 - 项目类别:
Open Software and Resource Development Leveraging Next-gen Sequencing Data to Facilitate Computational Analysis of Cancer Biology
开放软件和资源开发利用下一代测序数据促进癌症生物学的计算分析
- 批准号:
9352809 - 财政年份:2016
- 资助金额:
$ 82.28万 - 项目类别:
Trinity: Transcriptome assembly for genetic and functional analysis of cancer
Trinity:用于癌症遗传和功能分析的转录组组装
- 批准号:
10469550 - 财政年份:2013
- 资助金额:
$ 82.28万 - 项目类别:
A DIFFUSION TENSOR TRACTOGRAPHY STUDY OF YOUNG MALE CHILDREN WITH FRAGILE X
脆性X线幼童的弥散张量纤维描记术研究
- 批准号:
7722905 - 财政年份:2008
- 资助金额:
$ 82.28万 - 项目类别:
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