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
• 负责人: Hosseinali Asgharian
• 金额: $ 2.53万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10213543
• 关键词: Address; Affect; Algorithms; Area; Automobile Driving; Benchmarking; Binomial Model; Bioconductor; Biological; Biological Process; Biology; Cell Line; Cells; Clinical; Codon Nucleotides; Communities; Complex; Coupling; Data; Data Analyses; Data Set; Detection; Embryonic Development; Epigenetic Process; Experimental Designs; Gene Expression; Genes; Genetic Variation; Genomics; Genotype; Germ-Line Mutation; Human; Immune; Individual; Inherited; Label; Logistic Regressions; Maps; Measures; Medical; Meta-Analysis; Metadata; Methods; MicroRNAs; Modeling; Modification; Molecular; Negative Binomial Distributions; Neoplasm Metastasis; Nonmetastatic; Output; Pattern; Performance; Phenotype; Play; Post-Transcriptional Regulation; Principal Component Analysis; Quality Control; RNA; RNA-Binding Proteins; Reproducibility; Research; Ribosomes; Role; Sample Size; Sampling; Scientist; Signal Transduction; Site; Somatic Mutation; Standardization; Technology; Test Result; Testing; Tissue-Specific Gene Expression; Training; Transcript; Transfer RNA; Translating; Translational Regulation; Translations; Validation; Work; Yeasts; analytical method; base; carcinogenesis; career development; computer studies; deep sequencing; experimental study; follow-up; human disease; human model; insight; molecular dynamics; multiple omics; next generation; novel; response; ribosome profiling; statistics; tool; transcriptome sequencing

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.

抽象的 核糖体分析技术提供了对基因组规模转化调节的定量见解， 从胚胎发育到几个重要的生物学过程中起着至关重要的作用的机制 致癌作用。尽管核糖体分析数据分析方法取得了进步，但仍有许多挑战 被解决，包括样本量较小的测试，并由于核糖体停滞而引起的计数偏见。我 提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 哪些单独的测序读数是观察和翻译效率的单位，计算为几率 观察“ RPF”与“ RNA”的内容。逻辑回归模型比 基于RNA-seq和Ribo-Seq读取计数的负二项式建模的方法：（i）它都不假设 平均值和差异的平等也不需要估计分散体。 （ii）它具有更高的统计 比基于计数的方法的功率，因为​​在此模型中，统计样本大小等于读数的数量， （iii）每个条件的单个样本（未复制的数据集）；因此，它 适用于临床或单细胞数据。 （iv）它很容易适应合成尖峰的实验 标准。 （v）在复制的数据集中，它可以实现新颖的经验意义测试和计算 QC的信息措施。 （vi）它可以容纳涉及多个样品的复杂实验设计 和一个模型中的协变量；并且不限于成对比较。我们适用的初步结果 到一个数据集的结合，完成两个非反移和两个相应的转移细胞系表示，表明 这种方法确实具有非常强大的功能，并且在生物学重复中重复80-90％。另外，我们 提供用于分期偏置校正，荟萃分析，模型选择，实验设计和质量的模块 控制。结合结合方法与其他分析方法（一些先前在我们的实验室中开发的方法 - 我们） 构建一个多构框架，以将核糖数据与RNA-seq，tRNA分析，遗传变异， miRNA，密码子最佳等 生成多层基因型到表型图。该方法将在R中实现并进行 科学界可作为开放式包装套餐。考虑到我在统计方面的专业知识，我 继续培训实验生物学，访问最新数据集和多个实验室的支持 在翻译和更广泛的基因组主题的计算和实验研究方面具有专业知识 技术，我在解决这个问题方面非常独特。除了提供新颖的见解 转化控制和受益社区的生物学，该项目将使我扩大培训 在许多令人兴奋的领域，这些领域与我的职业发展最相关，作为一个成功的领域 独立的学术研究科学家。