Statistical Methods for MicroRNA-Seq Experiments

MicroRNA-Seq 实验的统计方法

• 批准号: 10261580
• 负责人: MATTHEW Nicholson MCCALL
• 金额: $ 39.23万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-11 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10261580
• 关键词: Address; Base Sequence; Binding; Bioconductor; Biological; Biological Assay; Case Study; Cations; Communities; Complex; Computer software; Data; Dependence; Development; Gene Expression; Gene Expression Alteration; Genetic Transcription; Goals; Length; Libraries; Maps; Measures; Messenger RNA; Methodology; Methods; MicroRNAs; Modeling; Nucleotides; Pathway interactions; Play; Preparation; Probability; Process; Protein Isoforms; RNA; RNA-Induced Silencing Complex; Regulator Genes; Research; Role; Sampling; Small RNA; Statistical Data Interpretation; Statistical Methods; The Cancer Genome Atlas; Transcript; Translations; Uncertainty; Variant; Work; base; data modeling; design; experimental study; human disease; improved; insight; lectures; mRNA sequencing; novel; open source; tool; transcriptome sequencing

Project Summary/Abstract MicroRNAs (miRNAs) are a class of small (18-24 nucleotide) RNAs that are essential regulators of gene expression, which act within the RNA-induced silencing complex (RISC) to bind mRNAs and suppress translation. Alterations in miRNA expression have been shown to disrupt entire cellular pathways, substantially contributing to a variety of human diseases. Despite nearly 25 years of research, miRNAs remain dicult to measure due to their short length, relatively small number, sequence similarity, and diculty to isolate from other small RNA fragments. While qPCR- and microarray-based miRNA assays are still widely used, the majority of recent studies use small RNA-seq (sRNA-seq) because it allows for the quanti cation of isomiRs (miRNA isoforms) and the possibility of identifying novel miRNAs. The processing of reads generated from sRNA-seq data globally distinguish between miRNA reads and those from other small RNAs, but do not necessarily capture the full spectrum of miRNA variation. Subsequent statistical analyses of processed sRNA-seq data are still performed using methods developed for mRNA-seq data despite the fact that sRNA-seq data violate several of the assumptions of these methods. Speci cally, methods for mRNA-seq data assume approximate independence between feature counts; however, the small total number of miRNAs and presence of a small number of very highly expressed miRNAs result in a lack of independence between miRNA counts. Additionally, normalization methods for mRNA-seq data assume either the overall level of transcription is constant across samples or an equal number of features are over- and under-expressed when comparing any two samples, neither of which hold for sRNA-seq data. The development of statistical methods that address the challenges of sRNA-seq data represents a critical need for miRNA research. Our long-term goal is to advance miRNA research by developing statistical methods that are tailored to the speci c complexities of miRNA expression data. The overall objective of this application is to improve the analysis of sRNA-seq data by developing statistical methods that account for challenges speci c to sRNA-seq data and outperform methods designed for mRNA-seq data. This addresses an urgent need for statistical methods to appropriately analyze sRNA-seq data, which are now routinely generated by large consortia such as TCGA and FANTOM. The rationale that underlies the proposed research is that methods that explicitly address the challenges inherent in measuring miRNAs are necessary to fully elucidate the role miRNAs play in many human disease processes.

项目摘要/摘要 microRNA（miRNA）是一类小（18-24个核苷酸）RNA，是基因表达的必不可少的调节剂， 在RNA诱导的沉默复合物（RISC）内作用以结合mRNA并抑制翻译。改变 在miRNA中，已经显示出破坏整个细胞途径，从而实质上有助于一种多样性 人类疾病。尽管进行了近25年的研究，但由于它们的短暂，miRNA仍然可以衡量 长度，相对较小的数字，序列相似性和与其他小RNA片段分离的文化。 虽然基于QPCR和微阵列的miRNA分析仍被广泛使用，但大多数最近的研究都使用了小型 RNA-Seq（SRNA-Seq）允许量化异构体（miRNA同工型），并且可能性的可能性 识别新颖的miRNA。从SRNA-Seq数据生成的读取的处理在全球区分 mirna读取和其他小RNA的读取物，但不一定捕获完整的miRNA 变化。随后使用开发的方法对处理后的SRNA-SEQ数据进行了统计分析 对于mRNA-seq数据，尽管SRNA-Seq数据违反了这些方法的几个假设。 特定的mRNA-seq数据方法假设特征数量之间的独立性近似；然而， 少数miRNA数量和少数非常表达的miRNA的存在导致 miRNA计数之间缺乏独立性。另外，mRNA-seq数据的归一化方法假设 整体转录水平在样本之间是恒定的，或者相等数量的特征是超出和 比较任何两个样品时表达不足，而两个样本都不适用于SRNA-SEQ数据。发展的发展 解决SRNA-SEQ数据挑战的统计方法代表了miRNA研究的关键需求。 我们的长期目标是通过开发量身定制的统计方法来推进miRNA研究 miRNA表达数据的特定复杂性。该应用程序的总体目的是改善 通过开发统计方法来分析SRNA-seq数据，该方法构成了挑战的挑战。 和胜过为mRNA-seq数据设计的方法。这解决了迫切需要统计方法 适当分析SRNA-SEQ数据，这些数据现在是由TCGA等大型财团常规生成的 和幻想。拟议研究的基础的理由是明确解决该方法的方法 测量miRNA所固有的挑战对于完全阐明miRNA在许多人中的作用是必要的 疾病过程。