AI/ML Ready appraoches for integrative RNA processing, splicing and spatial genomics

项目摘要/摘要来自母基金：细胞和生物体，从简单到复杂，都携带着相同的基因。 基因DNA序列组成的基因。多细胞真核生物将基因转录并加工成RNA 通过一个叫做选择性剪接的过程。选择性剪接是发育性的， 具体规定。它是如何解码高等生物基因组的基础。然而，关键的是， 关于其管理和产出功能的基本问题仍然没有答案。比如说， circRNA是一种普遍存在的选择性剪接产物，直到2012年才被发现，其调控和 功能仍然是个谜。circRNA的发现揭示了该领域在“什么， 基因选择性剪接的方式和原因RNA剪接变异体的表达，剪接是如何 调控，哪些剪接的RNA具有基本功能？回答这些问题对于 预测无数遗传变异中的哪一种会导致疾病以及它们为什么会这样做。答案也将使一个新的 数字核酸生物标志物的生成和疾病诊断，用于校正的药物靶点 （分别）异常调节的剪接和致病性蛋白质或非编码产物的鉴定以及 对真核生物基因组的进化和功能的基本科学见解。尽管大 尽管该领域有望在大规模单细胞RNAseq实验中发现剪接是如何调节的，但该领域仍处于探索阶段。 缺乏无偏见的精确方法来解决剪接分析的统计和计算挑战， scRNA-Seq.最先进的、可重复的、统计算法，以实现精确的剪接变体调用， 检测它们在细胞类型中的调节方式，以及在亚细胞中远远落后于单细胞RNA- 生成seq（scRNA-seq）数据，限制了ML/AI准备。在这里，我们将打开分析的可能性， 新的RNA调控生物学通过ML/AI就绪的软件和处理的数据，以一个巨大的社区， 生物医学研究人员，使新的基础和转化发现。

Project Summary/Abstract From parent grant: Cells and organisms, from simple to complex, carry the same genetic DNA sequence organized into genes. Multicellular eukaryotes transcribe and process genes into RNA isoforms through a process called alternative splicing. Alternative splicing is developmentally, and cell-type specifically regulated. It is foundational to how higher organisms’ genomes are decoded. Yet, critical, and fundamental questions regarding its regulation and the function of its output remain unanswered. For example, circRNA being a ubiquitous product of alternative splicing was only discovered in 2012, and its regulation and function remains enigmatic. circRNAs’ discovery revealed a larger critical knowledge gap in the field for “what, how and why” genes are alternatively spliced. What RNA splice variants are expressed, how splicing is regulated, and which spliced RNAs have essential functions? Answering these questions is critical for predicting which of myriad genetic variants cause disease and why they do so. Answers will also enable a new generation of digital nucleic acid biomarkers and diagnostics for disease, drug targets for correcting dysregulated splicing and identification of pathogenic protein- or non-coding products (respectively) as well as fundamental basic scientific insight into evolution and function of eukaryotic genomes.. Despite the great promise for discovering how splicing is regulated in massive single cell RNAseq experiments, the field is still lacking unbiased precise methods to address statistical and computational challenges of splicing analysis in scRNA-Seq. State-of-the-art, reproducible, statistical algorithms to achieve precise splice variant calls, detecting how they are regulated in cell types and subcellularly lag far behind the rate at which single cell RNA- seq (scRNA-seq) data is generated, limiting ML/AI readiness. Here, we will open the possibility of analyzing novel RNA regulatory biology through ML/AI-ready software and processed data to a huge community of biomedical researchers enabling new basic and translational discoveries.