Statistical Methods for MicroRNA-Seq Experiments

MicroRNA-Seq 实验的统计方法

• 批准号: 10092662
• 负责人: MATTHEW Nicholson MCCALL
• 金额: $ 40.58万
• 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/10092662
• 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.

项目概要/摘要 MicroRNA (miRNA) 是一类小（18-24 个核苷酸）RNA，是基因表达的重要调节因子， 它在 RNA 诱导沉默复合物 (RISC) 中发挥作用，结合 mRNA 并抑制翻译。改动 miRNA 表达中的 miRNA 已被证明会破坏整个细胞通路，从而极大地促进多种 的人类疾病。尽管经过近 25 年的研究，miRNA 仍然难以测量，因为它们的长度很短 长度、相对较少的数量、序列相似性以及难以与其他小RNA片段分离。 虽然基于 qPCR 和微阵列的 miRNA 检测仍被广泛使用，但最近的大多数研究都使用小样本 RNA-seq (sRNA-seq) 因为它可以对 isomiRs（miRNA 同工型）进行定量，并且可以 识别新的 miRNA。 sRNA-seq 数据生成的读取处理全局区分 miRNA 读取和来自其他小 RNA 的读取，但不一定捕获 miRNA 的全谱 变化。处理后的 sRNA-seq 数据的后续统计分析仍然使用开发的方法进行 尽管 sRNA-seq 数据违反了这些方法的几个假设，但对于 mRNA-seq 数据来说仍然如此。 具体来说，mRNA-seq 数据的方法假设特征计数之间近似独立；然而， miRNA 总数较少以及少量高表达 miRNA 的存在导致 miRNA 计数之间缺乏独立性。此外，mRNA-seq 数据的标准化方法假设 要么样本中转录的总体水平是恒定的，要么是相同数量的特征过度和 比较任何两个样本时表达不足，这两个样本都不适用于 sRNA-seq 数据。的发展 应对 sRNA-seq 数据挑战的统计方法代表了 miRNA 研究的迫切需求。 我们的长期目标是通过开发适合的统计方法来推进 miRNA 研究 miRNA 表达数据的特殊复杂性。该应用程序的总体目标是改善 通过开发解决 sRNA-seq 数据特有挑战的统计方法来分析 sRNA-seq 数据 且优于针对 mRNA-seq 数据设计的方法。这解决了对统计方法的迫切需求 正确分析 sRNA-seq 数据，这些数据现在通常由 TCGA 等大型联盟生成 和范托姆。拟议研究的基本原理是明确解决问题的方法 为了充分阐明 miRNA 在许多人类中所发挥的作用，测量 miRNA 所固有的挑战是必要的 疾病过程。