Deep learning for population genetics

群体遗传学的深度学习

• 批准号: 9976348
• 负责人: ANDREW D KERN
• 金额: $ 52.92万
• 依托单位: UNIVERSITY OF OREGON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-21 至 2024-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9976348
• 关键词: Algorithms; Area; Biological; Biology; Classification; Code; Communities; Computer Vision Systems; Computer software; DNA Sequence; Data; Development; Ensure; Floods; Genetic; Genetic Recombination; Genome; Genomics; Genotype; Goals; Image; Lead; Learning; Left; Machine Learning; Measures; Medicine; Methodology; Methods; Modeling; Modernization; Natural Language Processing; Natural Selections; Nature; Performance; Population; Population Explosions; Population Genetics; Process; Program Development; Publishing; Research Personnel; Sequence Alignment; Software Tools; Techniques; Technology; Training; Trees; Uncertainty; Ursidae Family; Work; base; computational chemistry; convolutional neural network; deep learning; deep neural network; design; empowered; flexibility; genetic information; genome sequencing; genomic data; infancy; innovation; learning classifier; learning strategy; machine learning algorithm; machine learning method; network architecture; neural network; neural network architecture; next generation; novel; open source; random forest; recurrent neural network; research and development; speech recognition; statistics; stem; success; supervised learning; tool; tool development; user friendly software

Project Summary The revolution in genome sequencing technologies over the past 15 years has created an explosion of population genomic data but has left in its wake a gap in our ability to make sense of data at this scale. In particular, whereas population genetics as a field has been traditionally data-limited, the massive volume of current sequencing means that previously unanswerable questions may now be within reach. To capitalize on this flood of information we need new methods and modes of analysis. In the past 5 years the world of machine learning has been revolutionized by the rise of deep neural networks. These so-called deep learning methods offer incredible flexibility as well as astounding improvements in performance for a wide array of machine learning tasks, including computer vision, speech recognition, and natural language processing. This proposal aims to harness the great potential of deep learning for population genetic inference. In recent years our group has made great strides in using supervised machine learning for population genomic analysis (reviewed in Schrider and Kern 2018). However, this work has focused primarily on using more traditional machine learning methods such as random forests. As we argue in this proposal, DNA sequence data are particularly well suited for modern deep learning techniques, and we demonstrate that the application of these methods can rapidly lead to state-of-the-art performance in very difficult population genetic tasks such as estimating rates of recombination. The power of these methods for handling genetic data stems in part from their ability to automatically learn to extract as much useful information as possible from an alignment of DNA sequences in order to solve the task at hand, rather than relying on one or more predefined summary statistics which are generally problem-specific and may omit information present in the raw data. In this proposal we lay out a systematic approach for both empowering the field with these tools and understanding their shortcomings. In particular, we propose to design deep neural networks for solving population genetic problems, and incorporate successful networks into user-friendly software tools that will be shared with the community. We will also investigate a variety of methods for estimating the uncertainty of predictions produced by deep learning methods; this area is understudied in machine learning but of great importance to biological researchers who require an accurate measure of the degree of uncertainty surrounding an estimate. Finally, we will explore the impact of training data misspecification—wherein the data used to train a machine learning method differ systematically from the data to which it will be applied in practice. We will devise techniques to mitigate the impact of such misspecification in order to ensure that our tools will be robust to the complications inherent in analyzing real genomic data sets. Together, these advances have the potential to transform the methodological landscape of population genetic inference.

项目摘要 过去15年来，基因组测序技术的革命创造了一个爆炸式的增长， 人口基因组数据，但在我们理解这种规模的数据的能力方面留下了差距。在 特别是，而人口遗传学作为一个领域一直是传统的数据有限，大量的 目前的排序意味着，以前无法回答的问题现在可能触手可及。为了利用 面对信息的洪流，我们需要新的分析方法和模式。 在过去的5年里，机器学习的世界已经被深度神经网络的兴起所彻底改变。 网络.这些所谓的深度学习方法提供了令人难以置信的灵活性， 提高各种机器学习任务的性能，包括计算机视觉、语音 识别和自然语言处理。该提案旨在利用深海的巨大潜力， 学习群体遗传推断。 近年来，我们的团队在使用监督机器学习进行人口统计方面取得了很大进展。 基因组分析（在Schrider和克恩2018中综述）。然而，这项工作主要集中在使用 更传统的机器学习方法，例如随机森林。正如我们在这份提案中所说，DNA 序列数据特别适合现代深度学习技术，我们证明了 这些方法的应用可以迅速导致在非常困难的群体遗传学中的最先进的性能。 任务，如估计重组率。这些处理基因数据的方法的力量源于 部分原因是它们能够自动学习从 为了解决手头的任务，DNA序列的比对，而不是依赖于一个或多个预定义的 汇总统计数据，通常是特定问题的，可能会忽略原始数据中存在的信息。 在这份提案中，我们提出了一种系统的方法，既可以用这些工具来增强该领域的能力， 了解他们的缺点。特别是，我们建议设计深度神经网络来解决 人口遗传问题，并将成功的网络纳入用户友好的软件工具， 与社区分享。我们还将研究各种方法来估计的不确定性 由深度学习方法产生的预测;这一领域在机器学习中研究不足，但 对于需要精确测量不确定性程度的生物研究人员来说， 围绕一个估计。最后，我们将探讨训练数据错误指定的影响-其中数据 用于训练机器学习方法的数据系统地不同于它将被应用于 实践我们将设计技术来减轻这种错误说明的影响，以确保我们的 工具对于分析真实的基因组数据集所固有的复杂性将是鲁棒的。所有这些 这些进展有可能改变群体遗传推断的方法论格局。