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: 9760832
• 负责人: Hosseinali Asgharian
• 金额: $ 7.22万
• 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/9760832
• 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基因组规模翻译调控的定量见解， 机制在从胚胎发育到发育的几个重要生物过程中发挥着至关重要的作用 尽管核糖体分析数据分析方法取得了进展，但仍然存在许多挑战。 需要解决的问题包括小样本量的测试和由于核糖体停滞导致的读数计数偏差。 建议开发一种名为“Ribolog”的基于逻辑回归的方法来对核糖体分析数据进行建模 哪些单个测序读数是观察单位，翻译效率计算为赔率 观察“RPF”与“RNA”读数相比，逻辑回归模型有几个明显的优势。 基于 RNA-seq 和 Ribo-seq 读取计数负二项式建模的方法：(i) 它既不假设 (ii) 具有更高的统计数据 比基于计数的方法更有效，因为在该模型中，统计样本大小等于读取数， (iii) 它适用于每个条件的单个样本（未复制的数据集）； (iv) 它很容易适用于合成掺入实验 (v) 在复制数据集中，它可以进行经验显着性测试和计算新的 (vi) 可适应涉及多个样本的复杂实验设计 和一个模型中的协变量；并且不限于我们的初步结果应用。 对包含两个非转移细胞系和两个相应的转移细胞系的数据集进行 Ribolog 表明 这种方法确实非常强大，并且在生物复制中具有 80-90% 的可重复性。 提供失速偏差校正、荟萃分析、模型选择、实验设计和质量模块 我们将 Ribolog 与其他分析方法（其中一些方法是我们实验室之前开发的）相结合。 构建多组学框架，将 Ribo-seq 数据与 RNA-seq、tRNA 分析、遗传变异、 miRNA、密码子最优性等，以确定翻译动态的驱动因素，并为下一步做出贡献 该方法将在 R 中实现并制作多层基因型到表型图谱。 鉴于我在统计学方面的专业知识，我可以将其作为开放获取包提供给科学界。 继续接受实验生物学培训、获取最先进的数据集以及多个实验室的支持 拥有翻译和更广泛的基因组主题的计算和实验研究方面的专业知识， 除了提供有关该技术的新颖见解之外，我还具有独特的优势来解决这个问题。 翻译控制生物学并造福社区，这个项目将使我能够扩展我的培训 在许多与我作为成功和成功的职业发展最相关的令人兴奋的领域 独立学术研究科学家。