CAREER: Algorithms and Tools for Allele-Specific Transcript Assembly

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。包括人类在内的许多生物都有两套染色体，一套来自母亲，一套来自父亲，它们以同源对的形式存在。通常观察到，两个不同的基因（或等位基因）在两条同源染色体的同一位置，可能产生不平衡的基因产物（即mrna）。这种现象被称为等位基因特异性表达（ASE）。众所周知，ASE与多种表型密切相关，并可能导致癌症易感性。酶提供了一个重要的生物标志物来源，可以潜在地用作表型生物标志物或疾病诊断。此外，ASE分析还可以作为一种强大的分析工具来确定表达数量性状位点（eQTL），并研究各种生物过程，如印迹、蛋白质截断变异和x染色体失活。最近建立的rna测序技术（RNA-seq）为定量测量ASE提供了一种准确有效的方法。然而，从目前的技术产生的测序读数不是全长。因此，需要计算方法来重建同源染色体上存在的两个不同等位基因表达的全长mrna，这个问题被称为等位基因特异性转录物组装。等位基因特异性转录本组装是非常困难的。等位基因特异性转录本组装是困难的，因为它需要同时连接突变和剪接连接，同时推断未知的全长转录本数量及其丰度。该项目旨在开发准确的等位基因特异性转录物组装方法，适用于短读、长读和单细胞RNA-seq数据。具体来说，这些研究人员首先解决了如何使用阶段性snp（即突变）来改善等位基因特异性组装。他们表明，阶段性snp可以等效地表示为所谓的变异拼接图中的不相容顶点对。然后用启发式方法求解包含不相容对的公式。远程信息将用于配对/多端RNA-seq数据，以改进等位基因特异性组装，算法将变体剪接图分解为路径，同时充分保留配对/多端约束。等位基因特异性组装将在存在结构变异的情况下解决，这是研究癌症的一个关键场景。一种新的数据结构将对snp、可选拼接和结构变化进行建模。此外，还提出了一种新的识别等位基因特异性结构变异的算法，这是一个独立的研究方向，但也导致了等位基因特异性组装的两步算法。协变量自适应多元假设检验将控制假阳性率。实现这些算法可以为各种类型的数据提供精确的等位基因特异性转录本汇编器，并为更广泛的应用提供新的ASE分析管道。拟议的研究与教育活动很好地结合在一起。高中课程将侧重于使用图结构——数学和计算机科学中的一个关键抽象概念——来模拟生物数据。高中教师将有机会进行与成绩单汇编相关的跨学科研究。开设新的本科课程，重点提高学生建模和解决现实问题的能力。还将努力让来自代表性不足群体的本科生和研究生参与研究机会。该项目的结果可以在PI的网站上找到：https://sites.psu.edu/mxs2589.This该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Many organisms, including humans, have two sets of chromosomes, one from the mother and one from the father, that exist as homologous pairs. It is commonly observed that, the two distinct genes (or alleles) at the same location of two homologous chromosomes, may produce imbalanced gene products (i.e., mRNAs). This phenomenon is called allele-specific expression (ASE). ASE has been known to be closely related to multiple phenotypes and can contribute to cancer susceptibility. ASEs offer an important source of biomarkers that could be potentially used as phenotypic biomarkers or for disease diagnosis. Additionally, ASE analysis serves as a powerful analytical tool to determine expression quantitative trait locus (eQTL) and to study a variety of biological processes such as imprinting, protein-truncating variants, and X-chromosome inactivation. The recently established RNA-sequencing technology (RNA-seq) provides an accurate and efficient way to quantitatively measure ASE. However, the sequencing reads generated from current technologies are not full-length. Hence, computational methods are needed to reconstruct the full-length mRNAs expressed from the two different alleles that exist on homologous chromosomes, a problem referred to as allele-specific transcript assembly. Allele-specific transcript assembly is exceedingly difficult. Allel-specific transcript assembly is difficult because it requires simultaneously threading mutations and splice junctions while inferring unknown number of full-length transcripts and their abundances. This project aims to develop accurate allele-specific transcript assembly methods that are applicable to short-reads, long-reads, and single-cell RNA-seq data. Specifically, these investigators first tackle how to use phased SNPs (i.e., mutations) to improve allele-specific assembly. they show that, phased SNPs can be equivalently represented as incompatible pairs of vertices in a so-called variant splice graph. Then heuristics are proposed to solve a formulation with incompatible pairs included. Long-range information will be used in paired-/multi-end RNA-seq data to improve allele-specific assembly, with an algorithm that decomposes the variant splice graph into paths while fully preserving the paired-/multi-end constraints. The allele-specific assembly will be solved in the presence of structure variations, a crucial scenario in studying cancer. A new data structure will model SNPs, alternative splicing, and structure variations all together. A new algorithms is also proposed to identify allele-specific structure variations, which is of independent interests but also leads to a two-step algorithm for allele-specific assembly. Covariate-adaptive multiple hypothesis testing will control false positive rates. Implementing these algorithms result in accurate allele-specific transcript assemblers for various types of data and a new ASE analysis pipeline for broader use. The proposed research is well integrated with educational activities. High-school curricula will be developed that focus on using graph structure--a key abstract in mathematics and computer science--to model biological data. High school teachers will be provided opportunities to conduct interdisciplinary research related to transcript assembly. New undergraduate course will be developed with a focus to enhance students’ ability in modeling and solving real-world problems. Efforts will also be made to engage undergraduates and graduate students from underrepresented groups in research opportunities. The results of the project can be found at the PI’s website: https://sites.psu.edu/mxs2589.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.