Statistical Methods for MicroRNA-Seq Experiments

项目摘要/摘要 MicroRNAs(MiRNAs)是一类小的(18-24个核苷酸)RNA，是基因表达的基本调节因子， 它们作用于RNA诱导的沉默复合体(RISC)，结合mRNAs并抑制翻译。改建 在miRNA中的表达已经被证明扰乱了整个细胞通路，在很大程度上促进了 人类疾病。尽管经过了近25年的研究，miRNAs仍然很难测量，因为它们很短 长度、相对较小的数目、序列相似性以及与其他小RNA片段分离的缺陷。 虽然基于qPCR和微阵列的miRNA分析仍然被广泛使用，但最近的大多数研究使用的是小分子 RNA-seq(sRNA-seq)，因为它允许异构体(miRNA异构体)的量化，并有可能 识别新的miRNA。对从SRNA-SEQ数据生成的读取的处理在全局上区分 MiRNA读取和来自其他小RNA的那些，但不一定捕获miRNA的全部光谱 变种。处理后的srna-seq数据的后续统计分析仍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，但仍适用于mRNA-SEQ数据。 具体地说，用于mRNA-SEQ数据的方法假定特征计数之间近似独立；然而， 少数miRNAs的总数和少量非常高表达的miRNAs的存在导致 MiRNA之间缺乏独立性。此外，mrna-seq数据的归一化方法假定 要么整体转录水平在样本中是恒定的，要么是相同数量的特征已经结束-并且 在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。 解决sRNA-seq数据挑战的统计方法是miRNA研究的迫切需要。 我们的长期目标是通过开发量身定做的统计方法来推进miRNA研究 MiRNA表达数据的特殊复杂性。此应用程序的总体目标是改进 通过开发统计方法来分析srna-seq数据，说明srna-seq数据面临的挑战。 并且优于为mRNA-SEQ数据设计的方法。这解决了对统计方法的迫切需求 适当分析SRNA-SEQ数据，这些数据现在通常由TCGA等大财团生成 和FANTOM。这项拟议研究的基本原理是，明确解决 为了充分阐明miRNAs在许多人类中扮演的角色，测量miRNAs所固有的挑战是必要的 疾病过程。

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.