Developing Ribolog: A toolbox for comprehensive analysis of ribosome profiling data
开发 Ribolog:核糖体分析数据综合分析的工具箱
基本信息
- 批准号:9760832
- 负责人:
- 金额:$ 7.22万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-08-01 至 2020-11-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAffectAlgorithmsAreaAutomobile DrivingBenchmarkingBinomial ModelBioconductorBiologicalBiological ProcessBiologyCell LineCellsClinicalCodon NucleotidesCommunitiesComplexCouplingDataData AnalysesData SetDetectionEmbryonic DevelopmentEpigenetic ProcessExperimental DesignsGene ExpressionGenesGenetic VariationGenomicsGenotypeGerm-Line MutationHumanImmuneIndividualInheritedLabelLogistic RegressionsMapsMeasuresMedicalMeta-AnalysisMetadataMethodsMicroRNAsModelingModificationMolecularNegative Binomial DistributionsNeoplasm MetastasisNonmetastaticOutputPatternPerformancePhenotypePlayPost-Transcriptional RegulationPrincipal Component AnalysisQuality ControlRNARNA-Binding ProteinsReproducibilityResearchRibosomesRoleSample SizeSamplingScientistSignal TransductionSiteSomatic MutationStandardizationTechnologyTest ResultTestingTissue-Specific Gene ExpressionTrainingTranscriptTransfer RNATranslatingTranslational RegulationTranslationsValidationWorkYeastsanalytical methodbasecarcinogenesiscareer developmentcomputer studiesdeep sequencingexperimental studyfollow-uphuman diseasehuman modelinsightmolecular dynamicsmultiple omicsnext generationnovelresponseribosome profilingstatisticstooltranscriptome sequencing
项目摘要
ABSTRACT
Ribosome profiling technology provides quantitative insights into translational regulation at a genomic scale, a
mechanism that plays a crucial role in several important biological processes from embryonic development to
carcinogenesis. Despite advances in ribosome profiling data analysis methods, a number of challenges remain
to be addressed including tests with small sample sizes and read count biases due to ribosome stalling. I
propose to develop a logistic-regression-based method called “Ribolog” to model ribosome profiling data in
which individual sequencing reads are units of observation and translation efficiency is calculated as the odds
of observing “RPF” vs. “RNA” reads. The logistic regression model has several distinct advantages over the
methods based on negative binomial modeling of RNA-seq and Ribo-seq read counts: (i) It neither assumes
equality of mean and variance nor does it require estimation of dispersion. (ii) It has much higher statistical
power than count-based methods because in this model, statistical sample size equals the number of reads,
not the number of replicates. (iii) It works with single sample per condition (unreplicated datasets); therefore, it
is applicable to clinical or single cell data. (iv) It is easily adaptable for experiments with synthetic spike-in
standards. (v) In replicated datasets, it enables empirical significance testing and calculation of novel
informative QC measures. (vi) It can accommodate complex experimental designs involving multiple samples
and covariates in one model; and is not limited to pairwise comparisons. Our preliminary results applying
Ribolog to a dataset comprising two non-metastatic and two corresponding metastatic cell lines indicate that
this method is indeed highly powerful and 80-90% reproducible among biological replicates. Additionally, we
provide modules for stalling bias correction, meta-analysis, model selection, experimental design and quality
control. Combining Ribolog with other analytical methods – some developed previously in our lab – we
construct a multiomic framework to integrate Ribo-seq data with RNA-seq, tRNA profiling, genetic variation,
miRNA, codon optimality etc. to identify the driving causes of translation dynamics and contribute to the next
generation of multi-layered genotype-to-phenotype maps. The method will be implemented in R and made
available to the scientific community as an open-access package. Given my expertise in statistics, my
continued training in experimental biology, access to state-of-the-art datasets, and support from multiple labs
with expertise in computational and experimental studies of translation and broader genomic topics and
technologies, I am uniquely situated to tackle this problem. In addition to providing novel insights into the
biology of translational control and benefiting the community, this project will enable me to extend my training
in a number of exciting areas that are most relevant to my career development as a successful and
independent academic research scientist.
摘要
核糖体分析技术提供了在基因组水平上对翻译调控的定量见解,
这种机制在从胚胎发育到
致癌作用尽管核糖体分析数据分析方法取得了进展,但仍存在许多挑战
包括小样本量的测试和由于核糖体停滞导致的读取计数偏差。我
我建议开发一种基于逻辑回归的方法,称为“Ribolog”,以模拟核糖体分析数据,
哪个单独的测序读数是观察单位,翻译效率计算为几率
“RPF”和“RNA”读数的对比。逻辑回归模型有几个明显的优势,
基于RNA-seq和Ribo-seq读段计数的负二项建模的方法:(i)它既不假设
平均值和方差相等,也不需要估计离差。(ii)它具有更高的统计学
因为在该模型中,统计样本大小等于读取的数量,
而不是重复的次数。(iii)它适用于每个条件(未复制的数据集)的单个样本;因此,它
适用于临床或单细胞数据。(iv)它很容易适用于合成加标实验
标准(v)在复制的数据集中,它使经验显著性测试和计算新的
信息性QC措施。(vi)它可以适应涉及多个样品的复杂实验设计
和协变量在一个模型中;并且不限于成对比较。我们的初步结果应用
Ribolog对包含两种非转移性细胞系和两种相应转移性细胞系的数据集的分析表明,
该方法确实非常有效,并且在生物学重复中具有80-90%的重现性。另外我们
提供失速偏倚校正、荟萃分析、模型选择、实验设计和质量模块
控制结合Ribolog与其他分析方法-一些在我们的实验室以前开发的-我们
构建多组学框架,将Ribo-seq数据与RNA-seq、tRNA谱、遗传变异
miRNA、密码子最优性等,以确定翻译动力学的驱动原因,并有助于下一个
多层基因型-表型图谱的生成。该方法将在R中实现并制作
作为一个开放获取的软件包提供给科学界。鉴于我在统计学方面的专业知识,
实验生物学的持续培训,获得最先进的数据集,以及来自多个实验室的支持
具有翻译和更广泛的基因组主题的计算和实验研究的专业知识,
技术,我是唯一的位置来解决这个问题。除了提供新的见解,
翻译控制的生物学和造福社会,这个项目将使我能够延长我的培训
作为一个成功的,
独立的学术研究科学家。
项目成果
期刊论文数量(0)
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Hosseinali Asgharian其他文献
Hosseinali Asgharian的其他文献
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{{ truncateString('Hosseinali Asgharian', 18)}}的其他基金
Developing Ribolog: A toolbox for comprehensive analysis of ribosome profiling data
开发 Ribolog:核糖体分析数据综合分析的工具箱
- 批准号:
10213543 - 财政年份:2019
- 资助金额:
$ 7.22万 - 项目类别:
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