The transcriptome-wide impact of biological perturbations
生物扰动对转录组的影响
基本信息
- 批准号:10672663
- 负责人:
- 金额:$ 3.56万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-05-01 至 2025-04-30
- 项目状态:未结题
- 来源:
- 关键词:AffectBiologicalBiological AssayBiologyBrainCell LineCell physiologyChromatinChronic Myeloid LeukemiaClustered Regularly Interspaced Short Palindromic RepeatsCollaborationsComputational BiologyComputer softwareDataData SetDiseaseFellowshipGene ClusterGene DeletionGene ExpressionGene Expression ProfileGenesGeneticGenetic VariationGenomeGoalsHumanHuman GeneticsIn VitroIndividualLiteratureMalignant NeoplasmsMapsMeasuresMentorsMentorshipMethodologyMethodsModelingModernizationMusMutationNeocortexNeurodevelopmental DisorderParameter EstimationPathogenicityPathologicPatternPhenotypeRepressionResearchScientistShapesSignal TransductionStatistical MethodsStratificationStructureTestingTrainingWorkbasechronic myeloid leukemia celldifferential expressionexperimental studyfunctional genomicsgene functiongenome-wideimprovedin vivoinsightinterestknock-downmethod developmentmodel buildingneocorticalnovelopen sourceresponserisk varianttooltool developmenttranscriptometranscriptome sequencingtranscriptomics
项目摘要
Abstract
An important goal in computational biology is to leverage data from high-throughput functional assays to infer
the biological consequences of genetic variation. This goal is frequently approached by pairing RNA sequencing
and differential expression analysis. Most differential expression methods seek to identify a small number of
genes and gene-sets that are affected by a genetic perturbation. However, some genes, such as chromatin
regulators, may impact thousands of genes across the transcriptome. These dispersed effects are not captured
by existing methods. We will address this methodological gap in the differential expression field by
developing a novel statistical tool, and will apply this tool to both normative and disease contexts. In
Aim 1, we propose the Transcriptome-wide Impact Model (TIM), a parametric likelihood-based estimator of the
overall effect that a perturbation has on the transcriptome. TIM builds on existing differential expression methods,
but estimates parameters of the distribution of differential expression effects, rather than individual per-gene
effect sizes. This model is also extended to estimate gene-set enrichments and correlation between differential
expression signatures. In Aim 2, we aim to apply TIM to a recent Perturb-Seq dataset that perturbs all expressed
genes in vitro in a massively parallel manner, enabling us to identify which genes and gene-sets induce the
greatest transcriptomic change in human chronic myeloid leukemia cell lines when knocked down. We will also
use TIM to identify modules of genes that have similar impact on the transcriptome, and use these modules to
annotate genic function. In Aim 3, we will apply TIM to an in vivo Perturb-Seq dataset of 35 neurodevelopmental
disorder genes in developing mouse neocortex. Through this Aim, we will stratify neurodevelopmental disorder
genes by degree of transcriptome-wide impact, testing the hypothesis that neurodevelopmental-disorder-
associated gene expression regulators exert highly dispersed effects on the transcriptome in brain. If true, this
finding would raise the intriguing question of whether small, dispersed expression effects can be pathogenic,
opening novel avenues for research into neurodevelopmental disorders, as well as many other diseases that are
associated with expression regulators (e.g. cancer). We will additionally use TIM to cluster neurodevelopmental
disorder genes by similarity of transcriptomic effects, to identify genes with putatively convergent mechanism.
Broadly, our model will allow conceptually novel insight to be extracted from differential expression experiments,
with applicability to any biological perturbation of interest.
摘要
计算生物学的一个重要目标是利用来自高通量功能测定的数据来推断
遗传变异的生物学后果。这一目标经常通过配对RNA测序来实现
和差异表达分析。大多数差异表达方法寻求鉴定少量的
受遗传扰动影响的基因和基因组。然而,一些基因,如染色质,
调节因子,可能会影响转录组中的数千个基因。这些分散的影响没有被捕捉到
现有的方法。我们将通过以下方式解决差异表达领域的方法学差距:
开发一种新的统计工具,并将这一工具应用于规范和疾病方面。在
目的1,我们提出了转录组范围影响模型(TIM),这是一个基于参数似然性的估计。
干扰对转录组的整体影响。TIM建立在现有的差异表达方法上,
但估计差异表达效应分布的参数,而不是单个基因
效果大小。该模型也被扩展到估计基因集富集和差异之间的相关性
表情签名在目标2中,我们的目标是将TIM应用于最近的Perturb-Seq数据集,该数据集扰动所有表达的
基因在体外以大规模平行的方式,使我们能够确定哪些基因和基因集诱导
当被敲低时,人类慢性髓性白血病细胞系中最大的转录组变化。我们还将
使用TIM来识别对转录组具有类似影响的基因模块,并使用这些模块来
注释基因功能。在目标3中,我们将TIM应用于35个神经发育的体内Perturb-Seq数据集,
发育中的小鼠新皮质中的失调基因。通过这个目标,我们将神经发育障碍
基因的转录组范围的影响程度,测试假设,神经发育障碍-
相关基因表达调节剂对脑中的转录组发挥高度分散的作用。如果这是真的,
这一发现将提出一个有趣的问题,即小的、分散的表达效应是否可能是致病的,
为研究神经发育障碍开辟了新的途径,以及许多其他疾病,
与表达调节因子相关(例如癌症)。我们还将使用TIM将神经发育
通过转录组学效应的相似性来识别具有聚合机制的基因。
从广义上讲,我们的模型将允许从差异表达实验中提取概念上新颖的见解,
其适用于任何感兴趣的生物扰动。
项目成果
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