Deep-Learning Algorithms for Evolutionary Inferences from Genomic and Ecological Data

进化生物学中最难以捉摸的问题之一是，适应在多大程度上塑造了现存物种和种群的基因组。暴露在新的环境条件下施加了选择压力，这导致了遗传适应和种群之间的分化(Quach，Quintana-Murci 2017)。因此，识别基因组中自然选择的特征具有双重重要性：(I)评估濒危物种应对气候变化的能力；(Ii)定位功能变异。由于目前检测选择特征的方法能力有限，我们仍然远未全面了解中性和选择性事件如何表征物种的进化及其基因组。人工智能或机器学习(ML)算法通过自动和迭代地调整其内部参数，同时对它们试图预测的现象保持相对无意识，从而最大化预测精度。最近引入的一类有监督的最大似然算法是Deel学习，这是一种基于人工神经网络的推理框架。深度学习是一个深入研究的课题，在模式识别(如语音)、计算机视觉、机器人学和生物信息学(如识别剪接位点)方面取得了令人印象深刻的成果。尽管深度学习算法具有预测能力，但其在进化基因组学中的应用仍处于初级阶段(Sheehan和Song，2016)。在种群遗传学中，种群内和种群间的基因组变异被用来推断历史事件，包括所关注物种的大小变化。由于RNA/DNA测序技术的进步，我们现在能够收集和分析大量的基因组数据。然而，人口遗传学数据本质上是噪声和多维的，其背后的模型也同样复杂，限制了新见解的揭示。因此，深度学习算法具有解决这些问题和解决该领域中一些长期存在的问题的潜力。该项目将探索深度学习算法，特别是卷积神经网络的适用性，以从现存和古代(如果有)样本的大规模基因组数据中推断进化参数，例如种群规模的历史变化或自然选择的目标位置。而类似的策略已被应用于推断二元参数(例如，如Chen等人所述，是否存在重组热点。2018)，我们将扩展这些方法，包括连续参数的多分类和估计的可能性，这是目前深度学习中具有挑战性的任务。在种群遗传学中引入深度学习算法是从生态基因组数据中提取有意义信息的关键。该项目有可能获得人工智能的好处，以了解物种如何进化和适应环境，这对保护战略具有明显的影响。鉴于我们在进化基因组学中的独特应用，我们预见了引入新的体系结构或神经层的前景，这些结构或神经层可以应用于其他领域。

One of the most elusive questions in evolutionary biology is to what extent adaptation has shaped genomes of extant species and populations. The exposure to novel environmental conditions imposed selective pressures, which led to genetic adaptations and differentiation between populations (Quach, Quintana-Murci 2017). The identification of signatures of natural selection in the genome has therefore the two-fold importance of (i) assessing the ability for endangered species to respond to climate change and (ii) localising functional variants. Due to the limited power of current methods to detect selection signatures, we are still far from a comprehensive view of how neutral and selective events have characterised species' evolution and their genomes. Artificial intelligence, or machine learning (ML), algorithms maximise the predictive accuracy by automatically and iteratively tuning their internal parameters while remaining relatively unconscious to the phenomenon they are trying to predict. A recently introduced class of supervised ML algorithms is deel learning, an inference framework based on artificial neural networks. Deep learning is a subject of intensive research and has provided impressive results in pattern (e.g. speech) recognition, computer vision, robotics and bioinformatics (e.g. identification of splice-sites). Despite their predictive powerfulness, application of deep learning algorithms in evolutionary genomics is still in its infancy (Sheehan and Song, 2016). In population genetics, variation of genomes within and between populations is used to infer historical events, including size changes, characteristic of the species of interest. Thanks to the technological advances of RNA/DNA sequencing, we are now able to collect and analyse a large amount of genomic data. However, population genetics data are inherently noisy and multidimensional and models underlying them are similarly complex, limiting the unveiling of novel insights. Therefore, deep learning algorithms have the potential to solve these problems and address some of the long-standing issues in this field. This project will explore the applicability of deep learning algorithms, specifically convolutional neural networks, to infer evolutionary paramters, such as historical changes of population size or sites targeted by natural selection, from large-scale genomic data of extant and ancient (when available) samples. While similar strategies have been applied to infer binary parameters (e.g. presense or not of recombination hotspots, as in Chen et al. 2018), we will expand these methods by including the possibility of multiclassification and estimation of continuous parameters, a task currently challenging in deep learning. The introduction of deep learning algorithms in population genetics is key for extracting meaningful information from eco-genomics data. The project has the potential to reap the benefits of artificial intelligence to understand how species evolved and adapted to their environments, with obvious implications for conservation strategies. Given our unique application in evolutionary genomics, we foresee the scope for introducing either novel architectures or neural layers which can be applied to other fields.