Quality-Control of Next-Generation Sequencing Data

下一代DNA测序（NGS）可以快速（在几天内）对生物体的整个基因组进行测序，而且价格便宜（不到1万美元）。持续的技术进步正在减少时间和成本，很快这种方法将在常规商业应用中变得可行。NGS使研究人员能够了解进化史，改进和优化农业实践，更好地了解细胞的基本特性。未来，有创造力的企业家将开发出巧妙的新商业应用，利用具有成本效益的NGS。******但是现在每个样本都需要亲自分析。NGS的未来生产使用将需要自动化管道，以方便处理大量样品，并改进质量控制，以确保输出保持在可接受的公差范围内。正如在工业的其他领域一样，这可能涉及控制理论和统计过程控制领域的技术的应用和扩展。迄今为止，NGS实验的质量控制和优化研究尚不充分。******为了解决这些问题，我们开发了SeqControl，这是一个评估NGS数据质量的框架。在53个人类基因组中，SeqControl在早期识别低质量实验方面几乎是完美的，在它们产生重大成本之前。我们的长期目标是为NGS数据创建一个强大的质量控制系统。为了实现这一目标，我们有三个目标，重点关注NGS数据分析中的主要差距。******首先，我们将创建一个优化NGS数据分析的全球标准。我们将创建一个易于理解的“黄金标准”数据集，并将其发布给世界各地的团队进行“挑战”，并通过一个公开的排行榜来确定最佳方法。我们已经成功地将这种方法用于解决其他问题，吸引了数百名参与者。******我们的第二个目标是增强SeqControl统计模型。我们最初的公式采用了监督式机器学习。虽然成功，但这种方法不包含非数值信息（例如技术人员或协议），需要大量的训练数据集，并且计算强度很大。我们将创建在线机器学习技术，其中预测模型根据新信息不断更新。******最后，我们将开发新的方法来优化和合并不同的分析方法，使用“人群的智慧”——一组分析方法优于任何单一方法的想法。我们将首次在NGS分析中利用这一点。******迅速下降的成本使天然气更接近常规商业用途。迫切需要一个稳健的统计过程控制框架来预测质量、优化实验设计和快速识别低质量数据。我们将制定全球标准，以提高加拿大和全世界正在进行的价值数亿美元的NGS研究的质量和可靠性。

Next-generation DNA sequencing (NGS) allows the entire genome of an organism to be sequenced quickly (within days) and affordably (less than $10,000). Continued technological advances are reducing both time and cost, and soon this approach will become viable for routine commercial applications. NGS has allowed researchers to understand evolutionary history, improve and optimize agricultural practices and better understand fundamental properties of the cell. In the future, creative entrepreneurs will develop clever new commercial applications that leverage cost-effective NGS.******But today each individual sample requires hands-on analysis. Future production use of NGS will require automated pipelines to facilitate processing of large sample numbers and improved quality-control to ensure outputs remain within acceptable tolerances. As in other areas of industry, this will likely involve application and extension of techniques from the fields of control theory and statistical process control. To date the quality-control and optimization of NGS experiments is under-studied.******To address these issues we developed SeqControl, a framework for evaluating NGS data quality. Across 53 human genomes SeqControl was nearly perfect in identifying low-quality experiments early, before they incurred significant costs. Our long-term goal is to create a robust quality-control system for NGS data. To reach that goal we have three aims, focused on the major gaps in the NGS data analysis.******First, we will create a global standard for optimizing NGS data analysis. We will create a well-understood "gold standard" dataset and release it to groups around the world in a "Challenge", with an public leaderboard to identify the best methods. We have already successfully used this approach for other problems, attracting hundreds of participants.******Our second objective will enhance the SeqControl statistical model. Our initial formulation employs supervised machine-learning. While successful, this approach does not incorporate non-numerical information (e.g. technician or protocol), require large training datasets and are computationally intense. We will create on-line machine learning techniques, where predictive models are continually updated based on new information.******Finally we will develop new ways to optimize and merge different analysis methods using the "wisdom of the crowds" -- the idea that a group of analysis methods are superior to any single method. We will exploit this in NGS analysis for the first time.******Rapidly declining costs are bringing NGS closer to routine commercial use. A robust statistical process-control framework is urgently needed to predict quality, optimize experimental design and rapidly identify low-quality data. We will create global standards to improve the quality and reliability of hundreds of millions of dollars worth of NGS studies in-progress in Canada and world-wide.