I-Corps: Software for the Next Generation Sequence Analysis for Homogeneous Populations

I-Corps项目更广泛的影响/商业潜力是通过预测、监测和预防传染病的爆发来改善公共卫生系统。该测序数据分析软件系统的更广泛应用还将包括追踪癌症发展，分析其生长、突变和抵抗治疗的潜力。提出的软件系统将集成到实验室的管道中，用于聚类和组装测序读取到完整的基因组序列。所提出的统计算法在不增加测序深度的前提下，能够更准确地分析测序数据，提高测序质量，从而进一步降低测序成本。该软件系统将通过更准确的分析和降低高通量测序的成本来吸引流行病学和医学实验室。这个I-Corps项目处理对分析高通量测序数据的强大计算工具日益增长的需求。现有的软件工具可以成功地从同一样本中提取不同的序列，但在许多应用中（如传染病爆发调查、癌症研究和全基因组关联研究），有必要区分多个不同但密切相关的基因组序列。这类研究的主要障碍是高通量测序技术产生的高测序错误率和短reads长度。所提出的软件系统可以通过统计算法来克服这些障碍，该算法能够分类测序错误，提供完整的基因组序列，并准确识别区分密切相关序列的点突变。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is improving public health systems by predicting, monitoring, and preventing outbreaks of infectious diseases. The broader applications of the proposed software system for analysis of sequencing data will also include tracing cancer development and analyzing its potential to grow, mutate, and resist treatment. The proposed software system will be integrated into laboratories' pipelines for clustering and assembling sequencing reads into complete genomic sequences. The proposed statistical algorithms will analyze the sequencing data more accurately which improves sequencing quality without an increase in sequencing depth thus further reducing sequencing cost. The proposed software system will attract epidemiological and medical laboratories by more accurate analysis and cost reduction of high-throughput sequencing.This I-Corps project deals with growing demand for powerful computational tools for analyzing high-throughput sequencing data. Existing software tools can successfully extract different sequences form the same sample but, in many applications, (such as an investigation of outbreaks of infectious disease, cancer research, and genome-wide association studies) it is necessary to distinguish multiple distinct yet closely related genomic sequences. The main obstacle for such studies is a high sequencing error rate and short length of reads produced by high-throughput sequencing technologies. The proposed software system can overcome these obstacles by using statistical algorithms capable of sorting out the sequencing errors delivering complete genomic sequences and accurately identifying point mutations distinguishing closely related sequences.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.